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essays on asset pricing anomalies, information flow and riskJesper Haga

ekonomi ocH samHälle economics and society

294

Jesper Haga

essays on asset pricing anomalies, information flow and risk

Asset pricing models provide investors with a rela-tion between risk and expected returns. Higher risk levels should be linked to higher expected returns. In addition, trading strategies that earn risk adjusted abnormally high or low returns are referred to as asset pricing anomalies. These asset pricing anomalies present an important chal-lenge for us researchers. Either our asset pricing models are incorrect or there exist frictions in the capital markets allowing such anomalies to persist. A better understan-ding of these anomalies can help in the development of asset pricing models. Knowledge about these anomalies is of course gained by studying them, which is where my thesis comes in.

This dissertation investigates three different topics in asset pricing literature. The first two papers study anoma-lies. In the first essay the momentum anomaly is investi-gated. In this respect, the momentum strategy consists of buying previous outperformers and selling previous underperformers. Moreover, this strategy generates ab-normal returns. More specifically, the first essay studies the robustness of intermediate-term momentum. The result suggests that the difference found between short-term and intermediate-term momentum is mainly driven by low cre-

dit risk firms and that the optimal momentum strategy can be dependent on firm characteristics.

In the second essay we investigate the credit risk puzzle. Previous studies have shown that firms with a high credit risk exhibit lower excepted returns than firms with a low credit risk. This phenomenon is referred to as the cre-dit risk puzzle. Contrary to previous findings, we suggest that the credit risk puzzle is only a temporary occurrence. Furthermore, the reason for this temporary mispricing of high credit risk firms could be the result of stronger limits to arbitrage during the subsample or possibly due to a sud-den increased power to the debtholders during the early subsample.

The third essay shows that a higher reporting frequen-cy can act as a stabilizing factor in times of market dist-ress. Firms that report quarterly instead of semi-annually experience lower stock price volatility during times of mar-ket distress. However, the important systematic volatility is higher for stock prices of firms that report quarterly. Ultimately, there exists a trade-off between higher firm specific systematic volatility on average and lower total volatility in times of market distress.

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Jesper Haga

Essays on Asset Pricing Anomalies, Information Flow and Risk

Helsinki 2016 <

Essays on Asset Pricing Anomalies, Information Flow and Risk

Key words: Asset Pricing, Market Anomalies, Momentum, Credit risk, Volatility

© Hanken School of Economics & Jesper Haga, 2016

Jesper Haga Hanken School of Economics Department of Finance and Statistics P.O.Box 287, 65101 Vaasa, Finland

Hanken School of Economics

ISBN 978-952-232-296-8 (printed) ISBN 978-952-232-297-5 (PDF) ISSN-L 0424-7256 ISSN 0424-7256 (printed) ISSN 2242-699X (PDF)

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Acknowledgements

Starting as a doctoral student I had only a vague idea of the academia way. Now years later I have learned some things about academia and conducting research, and a lot of things about myself. I have many people whom I would like to thank for standing by me during this journey.

I would like to start by thanking my thesis supervisor Professor Johan Knif for all his support this would not have been possible without you. I also would like to thank Professor Kenneth Högholm for his help with the thesis and administration tasks surrounding my studies. Johan and Kenneth have not only helped me during my doctoral studies, but through my bachelor and master studies as well. In addition, I want to thank Klaus Grobys who has co-authored one of the articles in my thesis. My external thesis examiners, Professor Gregory Koutmos and Professor Hossein Asgharian have provided me with valuable comments and suggestions. Moreover, during my years as a doctoral student I have developed my research skills by participating in good and challenging courses. For that I wish to thank Hanken School of Economics, Graduate School of Finance (GSF) and Nordic Finance Network (NFN).

I am also happy that I, as a doctoral student, had the opportunity to stay for an academic year at Charles F. Dolan School of Business at Fairfield University. I would like to thank Professor Gregory Koutmos, Professor Johan Knif and the supporting staff at Fairfield University for arranging the visit.

During my studies I had the pleasure to discuss research ideas and work with many colleagues and friends, thank you: Hilal Butt, Gustav Finne, David Gonzalez, Klaus Grobys, Benita Gullkvist, Fredrik Huhtamäki, Henri Högkulla, Henrik Höglund, Kim Ittonen, Christian Johansson, Nasib Nabulsi, Olugbenga Olufeagba, John Pettersson, Abu Shaker, Jimi Siekkinen, Dennis Sundvik, Nader Virk, Emilia Vähämaa, Tage Vest and Mo Zhang.

At Hanken I have not only conducted research, I have also had time to drink large amounts of coffee together with my colleagues Kim Ittonen, Fredrik Huhtamäki, Kenneth Högholm, Henrik Höglund, Jimi Siekkinen and Dennis Sundvik. Thank you, I would not have had this much fun without you.

I am very grateful for the financial support I received from Stiftelsen Svenska Handelshögskolan, WCEFIR, Marcus Wallenbergs stiftelse för företagsekonomisk forskning, Stiftelsen för främjandet av värdepappersmarknaden i Finland, Liikesivistysrahasto and Svensk-Österbottniska samfundet.

From my personal life, I would like to thank my friends, my siblings My, Melissa and Nicolas Haga and my parents Benny and Karina Haga for encouraging and believing in me. Finally, I would like to thank my supporting and loving fiancée Johanna Häggblom and our daughter Elsa.

February, 2016

Jesper Haga

ii

CONTENTS

I THEORY, BACKGROUND AND SUMMARY OF FINDINGS

1 INTRODUCTION....................................................................................... 1

2 A BRIEF OVERVIEW OF ASSET PRICING THEORY ............................ 6

2.1 Capital asset pricing model ................................................................................. 6

2.2 Efficient Market................................................................................................... 9

2.3 Consumption-based capital asset pricing models ............................................. 10

2.4 Anomalies .......................................................................................................... 12

2.4.1 Momentum............................................................................................ 13

2.4.2 Credit risk anomaly ............................................................................... 16

3 SUMMARY OF THE ESSAYS ................................................................. 18

3.1 Intermediate-term momentum and credit rating ............................................. 18

3.2 The market price of credit risk and economic states ........................................ 19

3.3 Individual stock volatility and reporting frequency ......................................... 20

REFERENCES ............................................................................................ 22

II THE ESSAYS

Essay 1 Haga, J., 2015. Intermediate-term momentum and credit rating, Finance Research

Letters, 15, 59—67.

Essay 2 Grobys, K., Haga, J., 2015. The market price of credit risk and economic states,

Empirical Economics, Forthcoming, 1—24.

Essay 3 Haga, J., 2015. Individual stock volatility and reporting frequency, Manuscript, Hanken

School of Economics, 1—27.

Part I

Background, Theory and Summary of Findings

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1 INTRODUCTION

One of the main benefits from a functioning asset market is that it provides people with the opportunity to choose between spending their money today, saving their money in the form of a risk-free asset or investing their money in the form of risky investments. In this dissertation, the focus is on the latter of those three. More specifically, this dissertation contributes to the literature focusing on the pricing procedure of risky assets. In finance literature, there is a plethora of different asset pricing models. Nonetheless, the most fundamental assumption in these asset pricing models is that riskier assets should be associated with higher expected returns. This assumption relies on a solid theoretical framework. However, there is a discussion on how to identify the risk that should be priced. In a fundamental consumption-based setting, asset risk is measured by the returns covariance of the asset with consumption growth. Assets with returns that have a negative covariance with consumption growth should have lower expected returns, while the opposite is true for assets with returns that have a positive covariance with consumption growth. The underlying reason is that in consumption-based asset pricing, investors are risk-averse and concerned about their consumption. The consumption risk can be lowered by investing in assets with returns that have a negative covariance with consumption growth. Another asset pricing model, and the most famous of these asset pricing models, is the so-called capital asset pricing model (CAPM), which was developed by Sharpe (1964) and Lintner (1965). CAPM was the first model with testable predictions for the relationship between risk and return. The CAPM model includes two important assets, the risk-free asset and the Markowitz (1959) mean-variance efficient market portfolio. In the CAPM framework, investors are concerned about the systematic risk of an asset. This systematic risk originates from the mean-variance efficient market portfolio, which is a value-weighted sum of all existing assets. The CAPM predicts that an asset’s expected return should stand in relation to the asset’s covariance with the market portfolio. An asset’s covariance with the market portfolio is often present in a standardized measure referred to as a beta. In the CAPM framework, investors should be compensated for holding systematic risk and systematic risk only. The expected return of an asset or portfolio should equal the risk-free rate plus the market risk premium multiplied by the asset’s or portfolio’s beta.

Both the consumption-based capital asset pricing model (CCAPM) and the CAPM have problems replicating the pattern observed in real return data. However, the CCAPM has more severe problems. One of these problems is the equity premium puzzle, a phrase coined by Mehra and Prescott (1985), who show that consumption growth has a low variance, which makes the covariance between the market and the consumption growth low. In addition, this predicts a low risk premium. However, the data reveals a different story.1 The main problem is that risk premiums are too high to be explained by the covariance with consumption growth. Cochrane (2005) states that consumption-based asset pricing models are a complete answer to the asset pricing question and that, moreover, the CAPM is a consumption-based asset pricing model

1 Mehra and Prescott (1985) find that the maximum risk premium could be 0.35% for reasonable parameter values in a simple consumption-based capital asset pricing model. They estimated the historical average risk premium to be 6.18 %. The difference between the model’s prediction and the reality is huge.

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with additional assumptions. Despite the fact that the CAPM performs empirically better than the CCAPM, the empirical evidence nevertheless suggests that cross-sectional return patterns exist that the CAPM cannot explain. One early critic is presented by Banz (1981). He shows that small stocks (stocks with low market value) have higher average returns than their beta estimates predict. On the other hand, large stocks (stocks with high market value) have too low average returns in comparison to the CAPM prediction. In addition, Roseberg, Ried, and Lanstein (1985) find a similar violation when investigating average returns for high and low book-to-market (book value of equity divided by market value of equity) stocks. They find that stocks with low book-to-market ratios have higher beta adjusted returns than stocks with high book-to-market ratios. This return pattern is often called the value effect. Moreover, in addition to the value and size effect, Basu (1977) shows anomalous high returns for stocks with a low price-to-earnings ratio (P/E). Overall, these studies find empirical violations to the CAPM. In the spirit of these findings, Fama and French (1992) conclude that the CAPM is not able to explain variations in average stock returns.

As a response to the CAPM failure, Fama and French (1993) proposed a three-factor model. The three factors included in the model were a market factor, a size factor and a value factor. This three-factor model has the ability to price many for the CAPM anomalous return patterns, such as the size, value and price-to-earnings anomaly. Since the original paper by Fama and French (1993), many additional factors have been proposed, the most prominent being the illiquidity factor (Pastor and Stambaugh, 2003), momentum factor (Carhart, 1997) and profitability factor (Novy-Marx, 2013). In addition to proposing new factors, new asset pricing models have been proposed as well. Novy-Marx (2013), for example, proposes a four-factor model and Fama and French (2015) propose a five-factor model; both these asset pricing models include a profitability factor. A major question for finance researchers, therefore, is to distinguish between the return patterns originating from factor exposure and those which are anomalous return patterns. The momentum phenomenon is the clearest example of this problem, since in most asset pricing models a momentum factor is included, even though there is no widely accepted theory suggesting that momentum is a systematic risk factor.

This dissertation investigates anomalies in respect to both the CAPM and the Fama and French (1993) three-factor model. Moreover, this dissertation has two essays: one essay investigates the momentum phenomenon and the other investigates the credit risk puzzle. Both these return patterns are anomalies with respect to the three-factor model. Fama and French (2008) state that anomalies are return patterns that the asset pricing model cannot explain. In line with this statement, both phenomena are anomalies, since they have positive excess returns even after adjusting for the risk factors in the three-factor model. Jegaadeesh and Titman (1993) were the first to report on evidence regarding the momentum phenomenon. Their finding was that it is a difference in future returns between firms that have previously underperformed and outperformed their peers. Firms that have outperformed have higher future returns than firms that have underperformed. Moreover, the momentum anomaly is one of the most robust anomalies with respect to the Fama and French (1993) three-factor model. The momentum phenomenon generates abnormal profits on international equity markets (Rouwenhorst, 1998; Asness, Moskowitz and Pedersen,

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2013), although Japan is one exception (Chui, Titman and Wei, 2010). More proof of the robustness of momentum is given by Geczy and Samonov (2013) when they show that momentum has generated abnormal profits in over 212 years. Moreover, Grinblatt, Titman and Wermers (1995) and Asness, Ilmanen, Israel and Moskowitz (2013) show that both mutual funds and hedge funds have returns that covariate with the momentum anomaly. Although these funds do not strictly trade a momentum strategy, per se, the returns of the funds do, nonetheless, covariate with the returns of the momentum phenomenon. Still, even though momentum is a robust empirical finding, no theoretical explanation to momentum has been definitely accepted. Another possible explanation for high momentum returns is that momentum crashes from time to time. A momentum investor experiencing a crash will have to wait decades to recover their losses.

The empirical evidence on the credit risk puzzle is more ambiguous than the empirical evidence regarding momentum. The credit risk puzzle is the empirical finding that firms with high credit risk have lower returns than their peers. The first issue when investigating the impact of credit risk on expected returns is to figure out how to measure credit risk. Avramov, Chordia, Jostova and Philipov (2009) proxy credit risk with Standard & Poor’s credit ratings. They show that firms with a higher credit risk have abnormally low average returns. Griffin and Lemmon (2002) and Dichev (1998) estimate credit risk with Ohlson’s O-score and Altman’s Z-model, respectively. Both studies show evidence of the credit rating puzzle. In addition, Avramov et al. (2009) and Griffin and Lemmon (2002) suggest that the credit risk puzzle is driven by a systematic mispricing of firms with high credit risk. In contrast to the negative relation between credit risk and expected returns, Vassalou and Xing (2004) with Merton’s (1974) measure of default distance show that higher credit risk is associated with higher expected returns. Furthermore, they argue that credit risk is a systematic risk factor and is related to both the size and value factor.

Finding anomalies or systematic derivations from the asset pricing models is a way to bring the finance literature forward by showing the types of return patterns that the asset pricing models are unable to explain. There are several additional anomalies with respect to the three-factor model than those mentioned above. Ritter (1991) reports that after an initial public offering, the firm abnormally underperforms its peers for several years. In addition, Sloan (1996) reports evidence suggesting that firms with high accruals earn abnormally low returns in comparison to firms with low accruals. Further, the explanation to this return pattern is that investors overestimate the persistence of the accrual when calculating expectations for future returns. Two more recently discovered anomalies are the asset growth anomaly (Cooper, Gulen and Schill, 2008) and the net operating assets anomaly (Hirshleifer, Hou, Teoh and Zhang, 2004). The asset growth anomaly suggests that firms which increase their assets more earn lower returns in the future. A behavioral explanation is proposed by Cooper et al. (2004) to this anomaly; investors initially overreact to the improvement in prospect signaled by the growth rate of total assets. This initial overreaction is followed by lower returns.

This dissertation contains three empirical essays, which all contribute with empirical evidence to the asset pricing debate. These three essays investigate the relationship between intermediate-term momentum and credit rating; the price of

4

credit risk and different economic states; and how reporting frequency has an impact on the return volatility of stocks. As expected returns and return volatility are two key concepts in asset pricing, understanding how the individual stock volatility changes when reporting frequency changes is of interest for both researchers and practitioners.

The first essay investigates the relationship between the intermediate-term momentum anomaly - recently discovered by Novy-Marx (2012) - and credit rating. Controversially, Novy-Marx showed that intermediate-term momentum is more robust and stronger than short-term momentum. The robustness of this finding has been tested in many recent papers. First, Yao (2012) suggests that intermediate-term momentum performs better than short-term momentum in January and that this is the key driver in the results of Novy-Marx (2012). Second, both Goyal and Wahal (2015) and Gong et al. (2015) argue that the finding by Novy-Marx (2012) arises because the short-term reversal was not appropriately accounted for in his study. Third, Goyal and Wahal (2015) find no robust evidence in any country to suggest that intermediate-term momentum outperforms short-term momentum, with one exception; the United States. This article tests the robustness of intermediate-term momentum when accounting for credit rating. Moreover, Avramov, Chordia, Jostova and Philipov (2007) have shown that short-term momentum only exists among low credit rated firms. Interestingly, this study finds that intermediate-term momentum is robust for all credit rating groups. Furthermore, many of the earlier explanations to the outperformance of intermediate-term momentum can be related to high credit rated firms. Firms with a good credit rating have a larger short-term reversal and higher returns in January. In addition, it is shown that intermediate-term momentum only outperforms short-term momentum among good credit rated firms. The difference between intermediate-term and short-term momentum that Novy-Marx (2012) found is driven by the good rated firms. A possible reason for why Goyal and Wahal (2015) did not find the difference in other countries could be because of the lower amount of high rated firms. Moreover, the optimal momentum strategy could actually depend on the characteristics of the firms.

The second article proposes a market-wide credit risk factor for the U.S. stock market and empirically investigates the behavior of this credit risk factor under different time periods and economic states. First, prior evidence on the pricing of credit risk (financial distress or default risk) is very contradictory. On one hand, Denis and Denis (1995) and Vassalou and Xing (2004) suggest that credit risk is a systematic risk. Moreover, Vassalou and Xing (2004) show that credit risk has a positive risk premium. On the other hand, Avramov et al. (2009) suggest the complete opposite, namely that credit risk is an idiosyncratic risk and that higher credit risk is associated with lower returns due to mispricing.2 Moreover, Garlappi, Shu and Yan (2008) agree with Avramov et al. (2009) on the negative relationship between credit risk and future expected returns, although they do suggest that high credit risk firms contain less market risk than average firms. This is because these firms should give investors lower expected returns. In this article we start by constructing a credit risk factor for the U.S. stock market based on S&P credit ratings. We find that firms with more credit risk have higher expected returns. However, neither the Carhart (1997) model nor the Novy-Marx (2013) model are able to capture this anomalous return pattern. Yet, when we

2 Furthermore, Avramov et al. (2009) argue that this mispricing is not corrected because of limits to arbitrage.

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further examine the credit risk impact on expected returns with a split-sample analysis, we find that this anomalous return pattern is only significant in the early sample. Furthermore, this anomalous pattern is driven by high returns in the short-side of the strategy. The conclusion drawn from this is that the credit risk puzzle existed only because of a temporary mispricing.

The third essay investigates the relationship between individual firm volatility and reporting frequency. This essay uses data from 14 member states of the European Union (EU). For this study, the EU market presents several advantages. First, there are variations in the domestic legislation regarding the reporting frequency. Second, financial and disclosure regulations are very similar across the countries with one exception, the reporting frequency. This essay examines three measures of volatility: systematic volatility, idiosyncratic volatility and total volatility. Further, to examine the reporting frequency's impact on these volatility measures we compare means, run difference-in-difference regressions and Fama-MacBeth regressions. The main findings are that higher reporting frequency decreases volatility during times of market distress and that higher reporting frequency slightly increases the average systematic risk per firm. The most important practical finding is that a higher reporting frequency is able to stabilize the markets during times of distress. Moreover, the finding that a higher reporting frequency increases a firm’s systematic risk is in line with a study by Savor and Wilson (2014). According to them, investors use announcements to update beliefs about both the announcing firm and the non-announcing firms. As an effect, the covariance between firm-specific and market news peaks around a firm’s announcement. In line with this, a firm’s systematic risk is expected to rise with more frequent reporting.

The rest of the introduction proceeds as follows: Section 2 contains a brief presentation of asset pricing theory; Section 3 presents the three essays in short and discusses their contribution to asset pricing literature.

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2 A BRIEF OVERVIEW OF ASSET PRICING THEORY

This chapter consists of a brief presentation of important asset pricing models. First, the capital asset pricing model is presented. Second, the efficient market hypothesis is presented. Third, the consumption-based capital asset pricing model is presented. Finally, we discuss two anomalies to these asset pricing models.

2.1 Capital asset pricing model

The capital asset pricing model (CAPM) has been one of the most important models in finance literature for the last 40 years. Furthermore, the CAPM was one of the first testable asset pricing models developed. The fundamentals behind the CAPM originate from the Markowitz mean-variance-efficiency model. In addition, the CAPM is a one period model with risk-averse investors. Moreover, in the CAPM world, investors only care about expected returns and risk, where risk is measured as the variance of an asset’s returns. In this framework, since investors are risk aversive, they seek to minimize their portfolio variance for each expected return and also maximize the expected return of their portfolio for each level of variance or risk. Under these conditions, Markowitz (1959) shows that all the possible and optimal portfolio combinations constitute the mean-variance-efficient frontier.

Sharpe (1964) and Lintner (1965) added two assumptions to the Markowitz (1959) framework to complete the CAPM model. First, investors need to agree on the joint distribution of the asset returns from t-1 to t. Second, all investors have the possibility to borrow or lend money at a risk-free rate, which is equal for all investors. With risk-free borrowing and lending, new mean-variance-efficient portfolios exist, and these portfolios are all combinations between the risk-free asset and the tangency portfolio. The tangency portfolio is located on the mean-efficient-frontier where a line from the risk-free rate tangents the frontier. These combinations between the risk-free asset and the tangency portfolio together create the capital-allocation-line. Tobin (1958) in his separation theorem showed how to combine the risk-free asset and the tangency portfolio.

Since all investors have the same view on the distribution of expected returns for all assets, each rational investor combines the tangency portfolio with risk-free lending or borrowing. By combining the risk-free asset and the tangency portfolio, an investor can reach a desired risk level for his portfolio. An investor can also increase the risk of his portfolio by borrowing (shorting the risk-free asset) capital to invest in the tangency portfolio. Moreover, since all investors only invest in one risky asset, i.e. the tangency portfolio, that portfolio has to contain all outstanding units of all risky assets. Since the tangency portfolio contains all risky assets, it can also be referred to as the market portfolio. Further, the market portfolio has to be a minimum variance portfolio. This implies that the minimum variance condition has to hold for the market portfolio. The minimum variance condition with N risky assets,

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( ) = ( ) + [ ( ) ( )] , = 1, … , . (1) Here, ( ) is the expected return on risky asset i and M indicates the market portfolio.

Further, the market beta is = ( , ) ( ). ( ) is the expected return

for an asset that has the market equal to zero. By subtracting ( ) from ( ) the risk premium per unit of beta is obtained. Sharpe (1964) and Lintner (1965) show that an asset with zero beta has to have the same return as a risk-free asset. With this they were able to solve the CAPM equation for an asset’s expected return. The CAPM shows that, ( ) = + ( ) , = 1, … , . (2) Each asset’s expected return can be expressed as the risk-free rate ( ) plus the risk premium ( ( ) ) multiplied by the asset’s market beta ( ). This is the result in the famous CAPM.

The CAPM model contains some very strict assumptions, which lead Black (1972) to derive a more general version of the model by relaxing the assumption that all investors can borrow and lend unlimited amounts at the same risk-free rate. However, Black (1972) instead allowed for unlimited short sales of risky assets. The main difference between the results from Black (1972) and the CAPM developed by Sharpe (1964) and Lintner (1965) is that, according to Black (1972), ( ) does not have to be the risk-free rate. ( ) only has to be less than the expected return for the market portfolio. Fama and French (2004) state that Black’s (1972) assumption about unrestricted short selling is as unrealistic as the original assumption by Sharpe (1964) and Lintner (1965) about free lending and borrowing. Moreover, many theoretical models contain unrealistic restrictions or assumptions. Because of this, the theoretical models need to be empirically tested with real data.

One shortcoming of the CAPM, due to its single-period framework, is that it indirectly implies constant expected returns over time. However, Fama and French (1989) documented that expected returns are higher in weak economic conditions and low in strong economic conditions. Although time-varying expected returns are a product of either time-varying risk or time-varying risk aversion, the CAPM allows neither.

Another critique to the CAPM is given by Banz (1981) and Basu (1977), who show that the CAPM is not able to price all assets correctly. According to the CAPM, the expected returns on small stocks, value stocks and stocks with low price-to-earnings are all too high. Fama and French (1992) investigated the cross-section return patterns against the CAPM and were forced to conclude that CAPM is not able to explain variations in average stock returns.

After Fama and French (1992) concluded that the CAPM is unable to explain variations in average stock returns, Fama and French (1993) proposed a new three-factor asset pricing model. In contrast to the single-factor CAPM, the three-factor model is multi-dimensional. The Fama and French (1993) three-factor model suggests that an assets excess return ( ( ) ) depends on the exposure to three factors. The first factor is the expected excess return of the market factor. The second factor is the difference between the return on a portfolio with small stocks and a portfolio with large stocks; this factor is the small-minus-big factor (SMB). The third and last factor is the

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difference in returns between a portfolio of high-book-to-market stocks and a portfolio of low-book-to-market stocks. This factor is the high-minus-low factor (HML). The expected excess return of an asset i is: ( ) = ( ) + ( ) + ( ). (3) In this equation ( ) , ( ) and ( ) are expected risk premiums and ,

and are asset i’s sensitivities to each of the factors. Theoretically, Fama and French (1993) support their model theoretically with the equilibrium asset pricing model by Merton (1973) (Intertemporal CAPM, ICAPM) and arbitrage pricing theory (APT) by Ross (1976). In the ICAPM all priced additional (in addition to the market factor) factors should be hedges for future changes in the investment opportunity set. In line with this, Fama and French (1995) argue that SMB and HML are proxies for systematic risk factors or state variables. Moreover, Fama and French (1996) suggest that the HML and the SMB factors could be possible proxies for systematic financial distress risk.

Not only did Fama and French (1993) present a three-factor asset pricing model, they also showed other researchers how to construct additional asset pricing factors. Since the paper by Fama and French (1993), many new asset pricing factors have been presented. These additional factors are motivated by multifactor asset pricing theory. In this theory, all additional factors capture systematic risk that were uncaptured by the market factor. Most prominent of these factors are the momentum factor (Carhart, 1997), the liquidity factor (Pastor and Stambaugh, 2003), the profitability factor (Novy-Marx, 2013), the betting-against-beta factor (Franzzini and Pedersen, 2014), the quality factor (Asness, Franzzini and Pedersen, 2013) and the investment factor (Aharoni, Grundy and Zeng, 2013). To qualify as an asset pricing factor, the ability to explain average cross-sectional returns is not enough, since there also has to be an economical explanation to the factor. Fama and French (2015) argue that since firm value is a function of investments and profitability, a new multifactor asset pricing model should include an investment and a profitability factor. In the case of the liquidity factor, Pastor and Stambough (2003) suggest that illiquid assets deserve an additional risk premium. In addition to new factors, new asset pricing models have been proposed, such as a recent four-factor model presented by Novy-Marx (2013). This model consists of four factors, which are a market factor, an industry adjusted value factor, an industry adjusted momentum factor and an industry adjusted profitability factor. According to the results in Novy-Marx (2013), this asset pricing model outperforms the original Fama and French (1993) three-factor model. In response, Fama and French (2015) present a new five-factor model, in which they include the investment factor and the Novy-Marx (2013) identified profitability factor, in addition to the three original factors. Fama and French (2015) also show that their new five-factor model outperforms the previous three-factor model. However, they find that the five-factor model has problems pricing small firms that invest a lot with low profitability.

Harvey, Liu and Zhu (2015) raise a concern regarding the ongoing attempt to explain the cross-section of expected returns. Due to the extensive search for new risk factors and the potential data mining issue, the traditional significance level may not be enough to ensure the validity of a factor. In terms of t-ratio, Harvey et al. (2015) suggest that we increase the threshold for the t-ratio from 2.0 to ensure the robustness of new factors. This issue also highlights the importance of economic explanation to the risk

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factors, since a factor chosen purely on empirical evidence is more prone to data mining.

2.2 Efficient Market

In investigations of the markets’ efficiency, the interest is on whether prices at a given point in time “fully reflect” all available relevant information. Moreover, what is the available relevant information is another key question in the efficient market framework. As an extension of the work by Samuelson (1965), Fama (1970) presents the efficient market hypothesis. He defines three different levels of market efficiency depending on the set of information available. The lowest level of efficiency is weak form efficiency. On a weak form efficient market, investors cannot use historical price data to predict future returns. In other words, historical prices on a weak form efficient market are “fully reflected” in today's prices. The next level of market efficiency is the semi-strong form of market efficiency, on which investors can use neither historical price data nor public information to predict future returns. A strong form efficient market is the most efficient market, and the returns are unpredictable with any information sets. In a formal way, the efficient market hypothesis can be described as follows: , = [1 + ( , | )] , , (4) where , represents the price of asset j at time t and , the price of the asset at time t+1. Further, , is the one-period percentage return for asset j. Finally, is the known set of information by investors. More specifically, this is the information set that Fama (1970) assumed to be “fully reflected” in the prices. Moreover, the tildes suggest that those variables are random variables at time t.

The key suggestion by the efficient market hypothesis is that investors are unable to consistently generate risk adjusted abnormal profits using the information available to them. An asset pricing model is needed to test the efficient market hypothesis. As asset pricing models, researchers usually use the CAPM or a multi-factor model. Since an asset pricing model is used to test the efficient market hypothesis, the test is automatically a joint test of the efficient market hypothesis and the asset pricing model. Furthermore, in cases when the joint hypothesis is rejected, the reason can be due to either the asset pricing model or the EMH.

The random walk hypothesis (RWH) is a special case of the EMH. In line with the RWH, the competition between investors to benefit from any return predictability makes the asset prices change completely randomly. The difference between EMH and RWH is that EMH allows for time variation in the expected risk premium and RWH assumes that the expected risk premium is constant over time.

Researchers have of course empirically investigated the level of market efficiency. In contrast to the expectations from the efficient market hypothesis, however, historical asset return data has return patterns that suggest that markets are not even weak form efficient. The strongest evidence against weak form efficient markets is the momentum phenomenon (Jegadeesh and Titman, 1993) and the idiosyncratic volatility anomaly (Ang, Hodrick, Xing and Zhang, 2006), in which investors can use past returns to develop a trading strategy that allows them to harvest abnormal returns. Investing in

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firms with high prior idiosyncratic volatility generates lower expected returns than what is predicted from the asset pricing models. However, the test of market efficiency is sensitive to the choice of asset pricing model, since miss-specified asset pricing models lead to the conclusion that markets are inefficient. Still, Chordia, Subrahmanyam and Tong (2011) find support for efficient markets. They show that most anomalies profits have decreased after the discovery of them. In line with Chordi et al. (2011), Grobys and Haga (2014) show that the credit risk anomaly was significant during a short period of time. Furthermore, Chordi et al. (2013) provide two possible explanations for this finding. First, researchers use data mining to find anomalies, and such behavior leads to weaker anomalies after the discovery of them. Second, transaction costs and illiquidity decrease over time, which enable investors to correct the mispricing generating the abnormal returns.

2.3 Consumption-based capital asset pricing models

The consumption-based capital asset pricing model (CCAPM) refers to the type of framework invented by Rubinstein (1976), Lucas (1978) and Breeden (1979), which has been the leading framework when considering multi-period asset pricing models. CCAPM’s connect macroeconomics with asset pricing. Further, a CCAPM is an extension of both the CAPM and the intertemporal capital asset pricing model (ICAPM). The traditional CAPM suggests that an asset’s returns are related to the asset’s market beta, while the CCAPM’s suggest that an asset’s returns are related to the asset’s consumption beta. Further, in the CCAPM the risk premium per consumption beta unit depends on the relative risk aversion of the representative investor. The difference between the CAPM and CCAPM arises when the market returns and the consumption growth rate are not perfectly correlated. Cochrane (2005:151) states that all factor models (such as CAPM and the Fama and French (1993) three-factor model) are only special cases of CCAPM, where assumptions are made which allow factors to be proxies of marginal utility. Moreover, CAPM receives criticism for not having a theoretical explanation as to why the market portfolio is risky. The CCAPM framework answers this question by suggesting that the real risk is the uncertainty regarding consumption. One of the most fundamental problems for an investor is to choose the ratio between saving and consumption. Investors exhibit a marginal utility loss from consuming less today and buying more assets. This loss in marginal utility has to equal the gain in marginal utility created from consuming the assets’ payoff in the future. In line with this, an investors’ first order condition is: ( ) = [ ( ) ]. (5) This equation states that the loss in utility ( ) from investing in one unit more has to be equal to the expected increase in utility in the future [ ( ) ]. In equation (5) is the price at time t and is the future pay off at time t+1, and represents how to discount future consumption (beta can also be referred to as the subjective discount factor). Further, ( ) and ( ) are the marginal utility at time t and t+1, respectively. A concave utility function generates risk aversion and then investors prefer a smooth consumption level. By modifying formula (5) the asset pricing formula can be written as:

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= [ ( )( ) ]. (6)

Moreover, CCAPM’s usually have a stochastic discount factor that determines the risk premium for the assets. The stochastic discount factor is: = [ ( )( ) ]. (7)

The stochastic discount factor, , can as well be referred to as the marginal rate of substitution or the pricing kennel (Cochrane, 2005). More specifically, the stochastic discount factor describes the increase in consumption at t+1 that investors demand to postpone their consumption at time t. Further, by setting the asset price to one, the following equation can be obtained, 1 = [ , ], (8)where , is the gross return for asset i and again refers to the stochastic discount factor. The asset pricing model is described in returns instead of prices, because returns are stationary, which is preferable for empirical testing. Additionally, equation (8) shows that the expected discounted returns should always equal the same value, in this case one. Moreover, by using , = ] [ , +[ , , ] we can show that , , = , [ , , ]. (9)

In equation (9) , = [ ], which is referred to as the risk-free rate. This can also be

referred to as the zero-beta rate, since an asset with zero covariance with the stochastic discount factor has an expected return ( , ) equal to the return of the risk-free asset ( , ). Moreover, equation (9) also shows that an asset’s expected excess return , , is proportional, in relation to the covariance between the asset’s return and the stochastic discount factor.

The main conclusion from the CCAPM framework is that investors prefer assets that negatively covariate with consumption growth, because assets that positively covariate with consumption growth increase the volatility of the consumption. Still, assets that positively covariate with consumption growth pay off in high consumption states when investors already feel wealthy, while the same assets do not pay off in low consumption states when investors feel poor. Since the investors mainly care about their consumption, assets that positively covariate with consumption growth should have a risk premium to compensate for the increase in consumption risk. Insurances are good examples of assets that pay off when the investor’s consumption would be low. Because of this, investors accept negative expected returns from insurances.

Even though the theoretical framework behind the CCAPM is robust, the model has problem empirically. Mehra and Prescott (1985) discovered that the CCAPM in a simple form is unable to explain the equity risk premium on the stock markets. The reason why CCAPM produces low risk premiums is because of the low covariance between the assets’ returns and consumption growth. Because of this, the representative investor has to be extremely risk averse to predict a similar risk premium. Moreover, the CCAPM has problems to explain the low risk-free rates and the high asset volatility.

The CCAPM’s also have a problem explaining the countercyclical time variation in expected returns. However, Campbell and Cochrane (1999), with a habit model, are

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able to predict some time variation in the expected returns. In their model individuals’ utility increases when their consumption exceeds the subsistence consumption level. However, the predictions from the CCAPM’s are still presently unable to explain the patterns in the return data.

2.4 Anomalies

This section presents the momentum phenomenon and the credit risk puzzle as well as other anomalies.3 The discussion of anomalies starts from the argumentation in the efficient market hypothesis (EMH). Assumptions that have to hold for an efficient market to exist are that markets are rational and friction free. Importantly, the assumption is not that all agents are fully rational. Instead the assumption is that the aggregation of all agents, the so-called market is rational. Since all agents are trying to profit from asset mispricing the prices of the assets are driven towards the “correct” value of the asset. In an EMH framework the reason why some assets or trading strategies yield predictably higher returns than other is that these assets or strategies are riskier. In this framework, a strategy with profits that are expected to be high or low and lack a risk based explanation is referred to as anomalies. Moreover, which return patterns are anomalies also depends on the choice of the asset pricing model used. This is because the asset pricing model states the risks that are classed as systematic or the risks that should be accounted for. For example, there is a size anomaly if the CAPM is used as the asset pricing model. But if the Fama and French (1993) three-factor model is used instead, then there is no size anomaly.

In contrast, behavioral finance believes that some abnormal profits are unexplainable by the purely risk based frameworks. These abnormal profits instead arise due to violations of the assumptions in the asset pricing models. One violation in the behavioral finance literature concerns limits to arbitrage. Limits to arbitrage implies that in some situations investors are unable to drive the price of the asset toward the value of the asset due to trading frictions. Trading frictions could be, for example, high trading costs, short sell constraints and low liquidity. Several papers have presented evidence with the conclusion that many anomalies only exist because of limits to arbitrage (Avramov et al., 2009; Lemmon and Griffin, 2002). The logic is that if an anomaly exists due to limits of arbitrage, investors are unable to profit from the anomaly. For example, the transaction costs for trading the strategy that generates the abnormal return can be high and cancel out the abnormal return. Another example can be a situation where the investor needs to short sell an asset to harvest the abnormal return, if the asset is short-sell constrained, the abnormal return is not harvestable. In both situations, the abnormal returns cannot be fully obtained by the investors; then

3 Finance literature reports several variables that are related to future returns in the cross-section as book-to-market value (Fama and French, 1992), accruals (Sloan, 1996), unexpected earnings (Ball and Brown, 1968), idiosyncratic volatility (Ang et al., 2006), capital investment (Titman et al., 2004) and asset growth (Cooper et al., 2004). All of these return patterns (except book-to-market value) are unexplained by the CAPM as well as the Fama and French (1993) three-factor model.

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the mispricing can be left uncorrected. As a result, anomalous returns can be discovered in the data.

In addition to limits of arbitrage explanations to market anomalies, behavioral finance literature also tries to explain market anomalies as being caused by irrational agents or agents with behavioral biases. Two early studies in this field were presented by Odean (1998) and Barber and Odean (1999), who find that: First, investors are reluctant to sell assets when they are selling at a loss. In contrast, investors are willing to sell assets when they are selling at a profit. Second, due to overconfidence, investors trade more than is considered optimal. Since these two early studies, research has tried to explain many anomalies which biases trading behavior among investors. For example, according to Daniel et al. (1998), momentum is caused by two psychological biases; investor overconfidence and self-attribution. Prospect theory, however, presents an alternative behavioral explanation. In prospect theory, Kaheneman and Tversky (1979) aim to present a model that explains the actual choices of individuals. The main findings in this theory are that individuals care more about gains and losses than about their level of wealth. Moreover, prospect theory predicts that individuals overweight low-probability events.

Another source for anomalous asset price behavior in behavioral finance is usually the misinterpretation or diffusion of information. For example, Cooper et al. (2008) suggest that the lower returns for firms with high asset growth are driven by investors’ initial overreactions to the improvement in prospect signaled by the growth rate of the total assets. This initial overreaction is later followed by lower returns. Another example of problems when interpreting information is presented by Da, Gurun and Warachka (2014). They show that investors underreact to information that arrives continuously in small amounts.

Asset pricing models are under constant development. The chain of events starts when anomalous return patterns are found with respect to the asset pricing model. These anomalous patterns are then examined in the search for an explanation. After an explanation is found, the asset pricing model is updated accordingly if possible.

2.4.1 Momentum

Momentum is a strong phenomenon on the security market. In this phenomenon, prior outperformers outperform prior underperformers. The basics of the momentum phenomenon is that stocks with high (low) cumulative returns over a 3 - 12 month time period continue to perform well (poor) over 3 - 12 months. Moreover, the empirical evidence for this phenomenon is strong. Since Jegadeesh and Titman’s (1993) discovery of the phenomenon, researchers have gone on to reveal its empirical robustness as well as provide possible explanations. In a recent paper, Geczy and Samonov (2013) show that momentum returns have actually existed for 212 years (from 1801 to 2012). In addition, since its discovery in 1993, the momentum has existed for over 20 years. Moreover, Jegadeesh and Titman (1993) report momentum on the U.S. equity market. However, after their initial findings, other researchers show that momentum also exists in 40 other countries (Rouwenhorst, 1998; Asness et al. 2013). Nonetheless, the momentum phenomenon does not generate abnormal returns in Japan. Furthermore, momentum has been proven to exist in many asset classes

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(Asness et al., 2013). However, Daniel and Moskowitz (2012) show that momentum has a dark-side, since momentum crashes from time to time. Moreover, a momentum’s return distribution has excess kurtosis and a negative skewness.

Even though momentum is well-established empirically, researchers still debate the explanation for this phenomenon, since the debate contains rational and behavioral explanations. Furthermore, the literature contains a larger plethora of behavioral explanations than rational explanations. Moreover, the models presented usually try to explain both the momentum phenomenon and long-term reversal. More specifically, many behavioral models (e.g. Hong and Stein 1999) mainly build on an underreaction in the short run that change to an overreaction, which is corrected, in the long run. Still, Jegadeesh and Titman already wrote in 1993 that the momentum finding is "... consistent with delayed price reactions to firm-specific information".

Hong and Stein (1999) present a framework which can explain the momentum anomaly and the long term reversal anomaly. In their framework, momentum exists because of gradual diffusion of information among investors. More specifically, the model accounts for two types of investors, news-watchers and momentum traders. The news-watchers receive private information about the firms’ fundamentals and they trade on that information. However, each news-watcher only receives a part of the total information about the fundamentals. In a market with only news-watchers, the prices would slowly adjust to new information. This underreaction leads to the momentum phenomenon. In addition to the news-watchers, Hong and Stein (1999) suggest that some investors are momentum traders. These momentum traders have a simple strategy, they only base their trades on the asset’s return in a previous time interval. The logic behind the momentum traders is that they try to profit from the underreaction caused by the news-watcher. This leads to the following: first there is an initial underreaction to news, which both news-watchers and momentum traders correct. Later, an overreaction to the news occurs due to the momentum traders. The overreaction is correct in the long run, which leads to the long-term reversal. In support of this model, Hong and Stein (1999) show that momentum is stronger among small firms and firms with less analyst coverage. Among those firms, the information is spread slower which increases the strength of the momentum phenomenon. Da et al. (2014) present another momentum explanation that also builds on investors’ inability to interpret information correctly. They suggest that investors underreact to information that arrives frequently and in small amounts, while investors react more correctly to information arriving infrequently. According to Da et al. (2014), the underlying reason for this behavior is that information that arrives frequently attracts less attention. In short, this behavior generates an underreaction to information in the short-run. The momentum phenomenon then arises when this underreaction is corrected.

Moreover, Daniel et al. (1998) use two psychological biases to create a model which can predict market overreaction to news. These two biases are overconfidence and self-attribution. Investors are human and humans have several psychological biases that can lead to irrational behavior. One such bias is overconfidence. Psychological research has found humans to be especially overconfident regarding their own ability. Moreover, DeBondt and Thaler (1995) argue that overconfidence is one of the most robust psychological biases. In addition, Einhorn (1980) reports that the

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overconfidence bias increases for diffuse tasks; pricing a company can be seen as such a task. Daniel et al. (1998) suggest that the overconfident investor is overconfident about his private information signal, but not about public information signals. Due to the investors’ overconfidence, they overreact to new private information. This overreaction is later corrected. However, a market with overconfident investors usually has a negative autocorrelation in its returns. Overconfidence can explain long-run reversals (negative long-lag autocorrelation), but not short-term momentum (positive short-lag autocorrelation). By introducing the second psychological bias self-attribution, Daniel et al. (1998) can explain short-term momentum as well as long-term reversal. Investors with self-attribution bias usually ascribe their success to their own abilities, but ascribe failures to external factors. In situations when investors’ initial overreaction to private information is confirmed with new public information, investors’ overconfidence increases, which leads to an even larger overreaction. Einhorn (1980) reports that these biases are stronger when the task is more diffuse, and that, in line with this momentum, these biases should be stronger among firms that are harder to value. Moreover, this is actually in line with a finding by Daniel and Titman (1998), who find that momentum is stronger among growth firms (firms with a higher market-to-book value).

Johnson (2002) has developed one of the more promising rational models available to date. By allowing stochastic expected growth rates, he is able to produce a model with a positive correlation between past realized returns and expected returns. One of the key features of the model is that growth rate risk increases with the growth rate. In line with rational models, assets with more growth rate risk should have higher expected returns. This is correct as long as the expected growth rate is a priced risk. In this framework, firms with high recent past returns have probably experienced a positive shock to their growth rate, and vice versa for firms with low recent past returns. These shocks in growth rates affect the expected returns for the firms. As such, firms with high past returns will have higher expected returns as well, due to the increase in growth rate risk from the positive shock on growth rate, while the opposite is expected to occur for firms with low past returns. All this leads to rational momentum. Empirical support for this model is shown by Nyberg and Pöyry (2014), who show a robust relationship between firm-level asset expansion and momentum. However, there are other possible rational explanations to momentum. Sagi and Seasholes (2007), for example, suggest that firm-specific characteristics drive momentum. Granted, firm-specific characteristics are especially important determinants of the return autocorrelation. Further, four important characteristics for the dynamics of the return autocorrelation are: revenues, costs, growth options and shutdown options. In this model, firms with more growth options are expected to have a stronger return autocorrelation. The reasoning behind this is that firms with a good recent past performance have larger opportunities to use their growth options. Furthermore, when firms activate their growth options, the riskiness of the firms increases; again, riskier firms should have higher expected returns in a rational world.

In a recent article, Vayanos and Woolley (2013) theoretically show how fund flows can create both momentum and reversal patterns in return series. Their theory starts with a positive/negative fundamental shock to a firm, which results in a positive/negative return for the firm. A negative return naturally has a negative impact

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on the return of a fund investing in the firm. And since fund investors are constantly comparing previous returns - because that is the best information on the fund managers’ ability - funds with good/poor returns are going to experience in/out flows of capital. In case of an out flow, fund managers are forced to sell assets. The momentum pattern arises since the funds are selling the firms with negative fundamental shocks. Due to this, a mispricing occurs. Later the mispricing is corrected, which leads to the reversal.

There are also anomalies that are closely related to the momentum anomaly. One such anomaly was discovered in a recent paper by Moskowitz, Ooi and Pedersen (2012). They report on an abnormal return pattern that they refer to as time series momentum. Their finding was that past 12-month excess return is a positive predictor of future returns. This return pattern has also been referred to as absolute momentum. Although time series momentum is related to the phenomenon known as momentum, momentum profits originate from the difference in future performance between the prior outperformers and prior underperformers, while time series momentum profits originate from buying previous winners independently of the relative performance of their peers.

Another momentum related phenomenon is intermediate-term momentum. Novy-Marx (2012) discovered that creating momentum portfolios from a sorting procedure on intermediate-term past returns instead of short-term past returns increases the momentum robustness and profitability. However, this finding is very hard to reconcile with previous theoretical momentum explanations. Because of the controversiality of these findings, researchers have investigated the robustness of them. First, Yao (2012) presents evidence suggesting that the outperformance of intermediate-term momentum mainly originates from higher returns in January. Second, Goyal and Wahal (2015) suggest that outperformance of intermediate-term momentum only exists in the United States. Finally, in the first essay in this dissertation, Haga (2015) shows that the outperformance of intermediate-term momentum is driven by stocks with a low credit risk.

DeBondt and Thaler (1985) present one of the earliest anomalies, the long-term reversal anomaly. When firms are sorted according to three- to five-years past cumulative returns, outperformers tend to be future underperformers, and vice versa. This return pattern is usually explained by an overreaction to news from the investors in the short run, which is later corrected in the long run. As earlier mentioned, alternative asset pricing models often try to explain the momentum and the long-term reversal anomaly jointly.

2.4.2 Credit risk anomaly

As mentioned earlier, one of the fundamentals in finance is that riskier assets should have higher expected returns. Default or credit risk refers to the risk that a firm with debts is unable to pay the contract stated interest or principal to meet its debt obligation. This credit risk, if it is a systematic risk, should be associated with a risk premium. In contrast, Dichev (1998), Griffin and Lemmon (2002), Campbell et al. (2008) and Avramov et al. (2009) find a negative cross-sectional correlation between credit risk and future stock returns. Their finding is the so-called credit risk puzzle.

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Neither a systematic risk credit risk factor nor a non-systematic credit risk factor predicts a negative correlation between credit risk and future stock returns. On one hand, if credit risk is systematic risk, investors should demand higher returns for stocks with more credit risk. On the other hand, if credit risk is non-systematic, investors should not demand a return premium, because non-systematic risk is diversifiable. However, there is some mixed evidence regarding the credit risk puzzle. Vassalou and Xing (2004) construct default probabilities by using a Merton (1974) model. Furthermore, by studying how the returns depend on the default probabilities, they find that default risk is a systematic risk factor and has a positive risk premium. Even though there exists some mixed evidence regarding the impact from credit risk on expected returns, the majority of the articles find the anomalous pattern that firms with more credit risk have lower returns.

Avramov et al. (2009) present evidence suggesting that firms in financial distress have lower expected returns. Moreover, they conclude that the low returns among financially distressed firms arise due to mispricing caused by individual investors. Additionally, the sophisticated investors are unable to correct this mispricing because of limits to arbitrage. Especially, high illiquidity and strong short sale constraints limit the sophisticated investor’s ability to trade the mispriced firms. Campbell et al. (2008) find the same anomalous stock return pattern, where high default risk firms have low expected returns. According to them, this pattern can occur because of increased power to debtholders or increased equity ownership by institutions with a preference for safe stocks. Furthermore, Grobys and Haga (2015) present evidence to suggest that the credit risk puzzle disappeared after 1999. Possibly, the limits to arbitrage decreased after 1999 or that the unexpected developments mentioned by Lemmon and Griffin (2002) occurred before 1999.

In contrast to Avramov et al. (2009) and Campbell et al. (2008), Garlappi et al. (2008) propose a theoretical model answering the question as to why financially distressed firms do not have a risk premium. The model predicts that in situations where shareholders have high bargaining power in case of default, credit risk should be associated with lower expected returns, and vice versa in situations where shareholders have low bargaining power. In case of default in firms where shareholders have high bargaining power, the default can actually improve the situation for the shareholders, for example through a reduction of debt. Their empirical results show that firms where shareholders have high bargaining power in case of default, also have lower market betas.

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3 SUMMARY OF THE ESSAYS

This doctoral thesis, Essays on Asset Pricing Anomalies, Information Flow and Risk, consists of three essays. This section briefly discusses how each essay contributes to the asset pricing literature.

3.1 Intermediate-term momentum and credit rating

There exist cross-sectional stock return patterns that neither the CAPM nor the Fama and French (1993) three-factor model can explain. One of these patterns is momentum. A momentum trader buys past outperformers and sells past underperformers. This zero-cost trading strategy generates risk-adjusted positive returns in the future. In this essay the focus is on the relationship between a special form of momentum, the intermediate-term momentum and credit rating. It is of interest to study intermediate-term momentum, since Novy-Marx (2012) shows that intermediate-term momentum is more profitable than short-term momentum on the U.S. equity market. However, since his findings, the robustness of the results has been questioned. For example, Goyal and Wahal (2015) find only one country (the U.S.) where intermediate-term momentum significantly outperforms short-term momentum. Moreover, Yao (2012) reports that intermediate-term momentum performs better than short-term momentum in January. In addition, the outperformance in January is the driving factor for the difference in profitability between the two trading strategies.

Why study the relationship between intermediate-term momentum and credit ratings? Momentum literature points out several firm characteristics that increase the momentum returns. Such characteristics are: low market capitalization (Hong, Lim and Stein, 2000), low analyst coverage (Hong et al., 2000), high analyst forecast dispersion (Zhang, 2006), high market-to-book ratio (Daniel and Titman, 1998) and high credit risk (Avramov et al., 2007). A very strong relation between momentum and credit rating is found by Avramov et al. (2007). They show that momentum only exists among low credit-grade stocks. According to Avramov et al. (2007), the connection between momentum and credit risk dominates the connection between momentum and the other characteristics, such as size, high market-to-book ratio and analyst coverage. Moreover, Avramov et al. (2013) show that most anomalies’ abnormal profits disappear after excluding financially distressed firms from the sample. In the wake of these findings, it is of interest to investigate whether the relation between intermediate-term momentum and credit risk is similar to the short-term momentum and credit risk relation.

In line with Avramov et al. (2007; 2013), Standard & Poor’s credit ratings are used as a proxy for credit risk. Firms are divided into groups depending on their credit risk, and intermediate-term momentum profits are calculated for each group. A parametric test is also used to investigate how intermediate-term momentum, short-term momentum and short-term reversal are affected by credit risk. The finding in this essay is that intermediate-term momentum, in contrast to short-term momentum, is significantly independent of credit ratings. With this finding, it is possible to argue that intermediate-term momentum is more robust than short-term momentum. Still, it is

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worth mentioning that there exists an economically significant difference in profitability between intermediate-term momentum for high-grade firms and low-grade firms; intermediate-term momentum for low-grade firms is twice as profitable as intermediate-term momentum for high-grade firms. Moreover, intermediate-term momentum has smaller crashes and less extreme skewness than short-term momentum. Both these results suggest that intermediate-term momentum is more stable than short-term momentum. Further, the results show that the difference between intermediate-term and short-term momentum, shown by Novy-Marx (2012), mainly exists among high-grade firms. Furthermore, it is possible to connect this finding with Yao’s (2012) finding that intermediate-term momentum is superior to short-term momentum due to the returns in January and with Goyal and Wahal’s (2015) finding that intermediate-term momentum’s superiority is due to a longer short-term reversal than Novy-Marx (2012) originally accounted for. The results show that both these critiques of the robustness of superiority of intermediate-term momentum are related to the finding that the outperformance of intermediate-term momentum is driven by high-grade firms. Possibly, the reason why Goyal and Wahal (2015) only find intermediate-term momentum to robustly outperform short-term momentum on the U.S. stock market is due to the fact that other markets contain less low credit risk firms.

3.2 The market price of credit risk and economic states

This essay contributes to the literature by showing that the credit risk puzzle originates from a mispricing of high credit risk stocks between March 1987 and June 1999. It starts by developing a market-wide credit risk factor. To construct the credit risk factor, we follow Fama and French (1996) by conducting a three-way sort. First, firms are sorted according to previous returns; by doing so we control for momentum. After sorting on momentum, firms are sorted by size (market capitalization), and finally firms are sorted into credit rating groups. This procedure enables us to control for both momentum and size, which is important since Avramov et al. (2007) show a strong link between momentum and credit risk and Vassalou and Xing (2004) and Fama and French (1996) both suggest a relationship between size and credit risk. This three-way sort generates 15 portfolios. Our proxy for credit risk is the Standard & Poor’s credit rating.

After creating the credit risk factor, we run multivariate asset pricing models to test whether they are able to price the 15 three-way sorted portfolios. The asset pricing models used are the Carhart (1997) model and the recent model by Novy-Marx (2013). As an additional test, we use the GRS test developed by Gibbons, Ross and Shanken (1989) to test whether the asset pricing models are jointly able to price the 15 portfolios. These tests show that the asset pricing models tested do not have the ability to correctly price the 15 zero-cost credit risk portfolios. In addition, these tests suggest an anomalous negative market price of credit risk.

Further, we use single firms to test if our credit risk factor is able to improve the asset pricing models. The findings from these tests suggest that our credit risk factor significantly improves the original model. In addition, we use Fama-MacBeth regressions to investigate the marginal ability of the credit risk factor to predict returns. Here, the results show an insignificant credit risk factor premium. In summary, the

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ability to use credit risk factors in asset pricing models is disputable, due to the disagreement between the cross-sectional and the time-series evidence.

Several other studies have shown the same anomalous negative market price of credit risk. To further investigate the robustness of our results, we include interactions with a recession dummy and use a sample-split analysis. First, the anomalous negative market price of credit risk only exists in the earlier subsample. Second, the significance originates from the short positions in the strategy. Low rated firms have generated significantly higher returns during the first subsample.

The contribution from this essay is that the credit risk puzzle was only temporary. The reason for this temporary mispricing of the high credit risk firms could be due to stronger limits to arbitrage during the subsample or possibly due to a sudden increased power to the debtholders during the early subsample.

3.3 Individual stock volatility and reporting frequency

One of the key concepts in asset pricing is volatility. The volatility of an asset’s return is often referred to as a particular asset’s risk. Investors are expected to demand higher expected returns for riskier assets. This essay investigates how reporting frequency affects average individual stock volatility. In other words, the interest is on the impact that more information has on average individual stock volatility. This study examines 14 member states of the European Union (the EU). The EU capital market has several advantages for this study. First, there are domestic differences in legislations regarding reporting frequency. This enables an examination of the impact from a more continuous information flow. Second, the EU capital market is very harmonized with only one exception, which is the reporting frequency.

Three volatility measures are investigated; total volatility, systematic volatility and idiosyncratic volatility. A systematic risk model is used to estimate the systematic and idiosyncratic volatility. This systemic risk model contains a global market factor, a domestic market factor, a regional size factor and a regional value factor. Systematic risk is expected to increase with more frequent reporting. This hypothesis is in line with Savor and Wilson (2014), who claim that investors use earnings announcements to update expectations about both the reporting firm and the non-reporting firms. As a consequence, the covariance between firm-specific fundamental news and market news peaks around the firm’s earnings report. In line with this logic, systematic risk is expected to increase with more frequent reporting.

The investigation of how reporting frequency affects volatility is conducted using three different methodologies. First, mean and median volatility is compared between quarterly reporting firms and propensity-score matched non-quarterly reporting firms. Second, difference-in-difference regressions are used to investigate the impact on firms’ stock price volatility for firms which have increased the reporting frequency. Third, Fama-MacBeth regressions are used to investigate the relationship between average stock price volatility and reporting frequency.

Generally, there is no clear impact from a higher reporting frequency on the total stock price volatility. However, during recessions stock price volatility decreases with a higher reporting frequency. This is in line with the argumentation by Teoh, Yang and Zhang (2009) that more information disclosure leads to lower volatility, since investors

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have less unknown information to disagree about. Moreover, the uncertainty or unknown information, especially in times of high market distress, is one of the main reasons behind the result.

In addition, the study gives support to Savor and Wilson (2014) predictions that systematic risk increases with more frequent reporting. Fama-MacBeth regressions show that firms with quarterly reporting have significantly higher systematic risk on average than firms without quarterly reporting.

22

REFERENCES

Ang, A., Hodrick, R., Xing, Y., Zhang, X., 2006. The cross-section of volatility and expected returns, Journal of Finance, 61, 259—299.

Asness, C., Ilmanen, A., Israel, R., Moskowitz, T., 2013. Investing with style, Journal of Investment Management, 13, 27—63.

Asness, C., Moskowitz, T., Pedersen, L., 2013. Value and momentum everywhere, Journal of Finance, 68, 929—985.

Avramov, D., Chordia, T., Jostova, G., Philipov, A., 2007. Momentum and credit rating, Journal of Finance, 62, 2503—2520.

Avramov, D., Chordia, T., Jostova, G., Philipov, A., 2009. Credit ratings and the cross-section of stock returns, Journal of Financial Markets, 12, 469—499.

Avramov, D., Chordia, T., Jostova, G., Philipov, A., 2013. Anomalies and financial distress, Journal of Financial Economics, 108, 139—159.

Banz, R., 1981. The relationship between return and the market value of common stocks, Journal of Financial Quantitative Analysis, 14, 421—441.

Barber, B., Odean, T., 1999. The courage of misguided convictions: The trading behavior of individual investors, Financial Analyst Journal, November/December, 41—55.

Basu, S., 1977. The investment performance of common stocks in relation to their price-earnings ratios: a test of the efficient market hypothesis, Journal of Finance, 32, 663—682.

Black, F., 1972. Capital market equilibrium with restricted borrowing, Journal of Business, 45, 444—454.

Breeden, D., 1979. An intertemporal asset pricing model with stochastic consumption and investment opportunities, Journal of Financial Economics, 7, 265—296.

Campbell, J., Cochrane, J., 1999. By force of habit: A consumption-based explanation of aggregate stock market behavior, Journal of Political Economy, 107, 205—251.

Campbell, J., Hilscher, J., Szilagyi J., 2008. In search of distress risk, Journal of Finance, 63, 2899—2939.

23

Carhart, M., 1997. On persistence in mutual fund performance, Journal of Finance, 52, 57—82.

Chordia, T., Subrahmanyam , A., Tong, Q., 2011. Trends in capital market anomalies, Working paper. Emory University.

Chui, A., Titman S., Wei, J., 2010. Individualism and momentum around the world, Journal of Finance, 65, 361—392.

Cochrane, J., 2005. Asset pricing, Princeton, New Jersey: Princeton University Press.

Cooper, M., Gulen, H., Schill M., 2008. Asset growth and the cross-section of stock returns, Journal of Finance, 63, 1609—1652.

Da, Z., Gurun, U., Warachka, M., 2014. Frog in the pan: Continuous information and momentum, Review of Financial Studies, 27, 2171—2218.

Daniel, K., Moskowitz, T., 2012. Momentum crashes, Swiss Finance Institute Research Paper No. 13—61; Columbia Business School Research Paper. Available at SSRN:http://ssrn.com/abstract=1825934.

Daniel, K., Hirshleifer, D., Subrahamanyam, A., 1998. A theory of overconfidence, self-attribution, and security market under and over-reaction, Journal of Finance, 53, 1839-1885.

Daniel, K., Titman, T., 1998. Characteristics or covariances? Journal of Portfolio Management, 24, 24—33.

Denis, D., Denis, D., 1995. Performance changes following top management dismissals, Journal of Finance, 50, 1029—1057.

DeBondt, W., Thaler, R., 1985. Does the stock market overreact? Journal of Finance, 40, 793—805.

Dichev, I., 1998. Is the risk of bankruptcy a systematic risk? Journal of Finance, 53, 1131—1147.

Einhorn, H., 1980. Overconfidence in judgment, In: R, Schweder, Fallible judgment in behavioural research, San Francisco: Josse-Bass.

Fama, E., 1970. Efficient capital markets: A review of theory and empirical work, Journal of Finance, 25, 383—417.

24

Fama, E., French, K., 1989. Business conditions and expected returns on stocks and bonds, Journal of Financial Economics, 25, 23—49.

Fama, E., French, K., 1992. The cross-section of expected stock returns, Journal of Finance, 47, 427—465.

Fama, E., French, K., 1993. Common risk factors in the returns on stocks and bonds, Journal of Financial Economics, 33, 3—56.

Fama, E., French, K., 1995. Size and book-to-market factors in earnings and returns, Journal of Finance, 50, 131—155.

Fama, E., French, K., 1996. Multifactor explanations of asset pricing anomalies, Journal of Finance, 51, 55—84.

Fama, E., French, K., 2004. The capital asset pricing model: Theory and evidence, Journal of Economic Perspectives, 18, 25—46.

Fama, E., French, K., 2008. Dissecting anomalies, Journal of Finance, 63, 1653—1678.

Fama, E., French, K., 2015. A five-factor asset pricing model, Journal of Financial Economics, 1, 1—22.

Garlappi, L., Shu, T., Yan, H., 2008. Default risk, shareholder advantage, and stock returns, Review of Financial Studies, 21, 2743—2778.

Geczy, C., Samonov M., 2013. 212 years of price momentum, Working paper. Available at SSRN: http://papers.ssrn.com/ abstract =2292544.

Gibbons, M., Ross, S., Shanken, J., 1989. A test of the efficiency of a given portfolio, Econometrica, 57, 1121—1152.

Gong, Q., Liu, M., Liu, Q., 2015. Momentum is really short-term momentum, Journal of Banking & Finance, 50, 169—182.

Goyal, A., Wahal, S., 2015. Is momentum an echo? Journal of Financial and Quantitative Analysis, Forthcoming.

Griffin, J., Lemmon, M., 2002. Book-to-market equity, distress risk, and stock returns, Journal of Finance, 57, 2317—2336.

25

Grinblatt, M., Titman S., Wermers R., 1995. Momentum investment strategies, portfolio performance, and herding: A study of mutual fund behavior, American Economic Review, 85, 1088—1105.

Grobys, K., Haga, J., 2015. The market price of credit risk and economic states, Empirical Economics, Forthcoming.

Haga, J., 2015. Intermediate-term momentum and credit rating, Finance Research Letters, Forthcoming.

Harvey, C., Liu, Y., Zhu, H., 2015. ... and the Cross-section of expected returns, Review of Financial Studies, Forthcoming.

Hirshleifer, D., Hou, K., Teoh, S., Zhang, Y., 2004. Do investors overvalue firms with bloated balance sheets? Journal of Accounting and Economics, 38, 297—331.

Hong, H., Stein, J., 1999. A unified theory of underreaction, momentum trading, and overreaction in asset markets, Journal of Finance, 54, 2143—2184.

Hong, H., Lim, T., Stein, J., 2000, Bad news travels slowly: Size, analyst coverage, and the profitability of momentum strategies, Journal of Finance, 55, 265—295.

Jegadeesh, N., Titman S., 1993. Returns to buying winners and selling losers: Implications for stock market efficiency, Journal of Finance, 48, 65—91.

Johnson, T., 2002. Rational momentum effects, Journal of Finance, 57, 585—608.

Kahneman, D., Tversky, A., 1979. Prospect theory: An analysis of decisions under risk, Econometrica, 47, 263—291.

Lintner, J., 1965. The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets, Review of Economics and Statistics, 47, 13—37.

Lucas, R., 1978. Asset prices in an exchange economy, Econometrica, 46, 1429—1446.

Markowitz, H., 1959. Portfolio Selection: Efficient Diversification of Investments. Cowles Foundation Monograph No. 16. New York: John Wiley & Sons, Inc.

Mehra, R., Perscott E., 1985. The equity premium puzzle, Journal of Monetary Economics, 15, 145—161.

26

Merton, R., 1973. An intertemporal capital asset pricing model, Economcetrica, 41, 867—887.

Merton, R., 1974. On the pricing of corporate debt: The risk structure of interest rates, Journal of Finance, 29, 449—470.

Moskowitz, T., Ooi, Y., Pedersen, L., 2012. Time series momentum, Journal of Financial Economics, 104, 228—250.

Novy-Marx, R., 2012. Is momentum really momentum? Journal of Financial Economics, 103, 429—453.

Novy-Marx, R., 2013. The other side of value: The gross profitability premium, Journal of Financial Economics, 108, 1—23.

Nyberg, P., Pöyry, S., 2014. Firm expansion and stock price momentum, Review of Finance, 18, 1465—1505.

Odean, T., 1998. Are investors reluctant to realize their losses? Journal of Finance, 53, 1775—1798.

Pastor, L., Stambaugh R., 2003. Liquidity risk and expected returns, Journal of Political Economy, 111,642—685.

Ritter, J., 1991. The long-run performance of initial public offerings, Journal of Finance, 46, 3—27.

Rosenberg, B., Ried, K., Lanstein. R., 1985. Persuasive evidence of market inefficiency, Journal of Portfolio Management, 11, 9—17.

Ross, S., 1976. The arbitrage theory of capital asset pricing, Journal of Economic Theory, 13, 341—360.

Rouwenhorst, K., 1998. International momentum strategies, Journal of Finance, 53, 267—284.

Rubinstein, M., 1976. The valuation of uncertain income streams and the pricing of options, Bell Journal of Finance, 7, 407—425.

Samuelson, P., 1965. Proof that properly anticipated prices fluctuate randomly, Industrial Management Review, 6:2, 41—49.

27

Sagi, J., Seasholes, M., 2007. Firm-specific attributes and the cross-section of momentum, Journal of Financial Economics, 84, 389—434.

Savor, P., and M. Wilson 2014, Earnings announcements and systematic risk, Journal of Finance, Forthcoming.

Sharpe, W., 1964. Capital asset prices: A theory of market equilibrium under conditions of risk, Journal of Finance, 19, 425—442.

Shleifer, A., Vishny, R., 1997. The limits of arbitrage, Journal of Finance, 52, 35—55.

Sloan, R., 1996. Do stock prices fully reflect information in accruals and cash flows about future earnings? Accounting Review, 71, 281—316.

Teoh, S., Yang , Y., Zhang, Y., 2009, R-square and market efficiency, Working paper. Available at SSRN: http://papers.ssrn.com/abstract=926948.

Vassalou, M., Xing, Y., 2004. Default risk in equity returns, Journal of Finance, 59, 831—868.

Vayanos, D., Woolley, P., 2013. An institutional theory of momentum and reversal, Review of Financial Studies, 26, 1087—1145.

Yao, Y., 2012. Momentum, contrarian, and the January seasonality, Journal of Banking & Finance, 36, 2757—2769.

Zhang, X., 2006. Information uncertainty and stock returns, Journal of Finance, 61, 105—137.

Part II The Essays

Finance Research Letters 15 (2015) 59–67

Contents lists available at ScienceDirect

Finance Research Letters

journal homepage: www.elsevier.com/locate/frl

Intermediate-term momentum and credit rating�

Jesper Haga∗

Hanken School of Economics, Handelsesplanden 2, Vaasa 65100, Finland

a r t i c l e i n f o

Article history:

Received 5 May 2015

Accepted 10 August 2015

Available online 17 August 2015

JEL classification:

G11

G12

G14

G24

Keywords:

Intermediate-term momentum

Credit risk

Credit rating

a b s t r a c t

This study examines the relationship between intermediate-term

momentum and credit risk. Credit risk is approximated with Stan-

dard & Poor’s (S&P’s) credit ratings. With a sample of S&P credit rated

firms, I show that intermediate-term momentum is profitable inde-

pendent of firms’ credit rating. Further, I show that the difference

found in U.S. between intermediate-term and short-termmomentum

is mainly driven by high-grade firms.

© 2015 Elsevier B.V. All rights reserved.

1. Introduction

Momentum literature begins with the discovery by Jegadeesh and Titman (1993) that investors can

earn economically and statistically significant abnormal profits by buying past outperformers and sell-

ing past underperformers.1 Further, in a recent paper Novy-Marx (2012) suggests that buying/selling

intermediate-term (months t-12 to t-7) outperformers/underperformers also create abnormal profits.

� I would like to thank an anonymous referee, Søren Hvidkjaer, Johan Knif, Kenneth Högholm, Klaus Grobys, Jimi Siekkinen, Nader

Virk, and Hilal Butt, and seminar participants at the 2014 Eastern Finance Association, and the 2014 Arne Ryde workshop, for helpful

comments. The paper was finalized during my stay at Fairfield University. All errors are my own.∗ Tel.: +358 44 3797918.

E-mail address: jesper.haga@hanken.fi1 Studies have found robust momentum: in time series (Moskowitz et al., 2012), in industries (Moskowitz and Grinblatt, 1999),

in most asset classes (Asness et al., 2013), on international equity markets (Rouwenhorst, 1998; Griffin et al., 2003) and under

economically distressed periods (Arsahanapalli et al., 2006). Even though, the robustness of momentum is empirically proven the

explanation for the phenomenon is still debatable. In this debate there are both rational (e.g. Johnson, 2002; Sagi and Seasholes,

2007) and behavioral (e.g. Daniel et al., 1998; Hong and Stein, 1999) explanations to the momentum profits.

http://dx.doi.org/10.1016/j.frl.2015.08.004

1544-6123/© 2015 Elsevier B.V. All rights reserved.

60 J. Haga / Finance Research Letters 15 (2015) 59–67

Moreover, according to his finding the profits from the intermediate-term strategy are even larger than

the profits from buying/selling short-term (months t-6 to t-2) outperformers/underperformers. Because

of the controversial finding by Novy-Marx (2012) several papers have examined the robustness of the

difference discovered between intermediate-term and short-term momentum. First, Yao (2012) shows

that intermediate-term momentum performs better in January than short-term momentum further she

suggests that is the reason for the finding by Novy-Marx (2012). Second, in an international study Goyal

and Wahal (2013) find no robust evidence from any country suggesting that intermediate-term momen-

tum is superior to short-term momentum, with one exception the U.S. Third, Gong et al. (2015) argue

that the short-term reversal is longer than accounted for in the study by Novy-Marx (2012) when this

longer short-term reversal is accounted for the difference between intermediate-term momentum and

short-term momentum disappears.

Since the original momentum finding by Jegadeesh and Titman (1993), the robustness and driving

forces of momentum have been of interest among researchers. A stream of literature has shown that mo-

mentum returns are higher for firms with specific characteristics. Characteristics which increase momen-

tum profits are: low market capitalization (Hong et al., 2000), low analyst coverage (Hong et al., 2000),

high analyst forecast dispersion (Zhang, 2006), high market-to-book ratio (Daniel and Titman, 1999) and

high credit risk (Avramov et al., 2007). My interest for the impact from credit risk originates from Avramov

et al. (2007), who show that the credit risk effect absorbs the size, analyst coverage and dispersion effect

in capturingmomentum returns. In addition, credit risk and size are positively correlated further Avramov

et al. (2009) suggest a potential link between credit rating and analyst forecast dispersion. Possibly, credit

risk is the common factor in all these momentum return increasing characteristics. Furthermore, in the

asset pricing anomaly literature, Avramov et al. (2013) point out that many anomalies exist only among

high credit risk firms. Further, they suggest that anomalies only existing among high credit risk firms

can be hard for investors to profit from due to limits to arbitrage. A limitation to arbitrage can be that

high credit risk firms are costly to trade for investors since these firms are illiquid and more likely to be

short-sale constrained.

As mentioned, Avramov et al. (2007) show a strong link between credit ratings and short-term mo-

mentum. According to them only the high credit risk firms have significant short-term momentum prof-

its. Because of the strong relationship between anomalies and credit ratings, especially the link between

short-term momentum and credit ratings, it is important to investigate if a similar relationship exists be-

tween intermediate-term momentum and credit ratings. Moreover, understanding the relationship can

improve the understanding of the puzzling evidence that intermediate-term outperforms short-termmo-

mentum.

In this paper I investigate the relationship between intermediate-term momentum and credit ratings.

For this analysis, I have sampled 4447 Standard & Poor (S&P) credit rated stocks over the time period De-

cember 1984 to December 2011. As in Avramov et al. (2007), Avramov et al. (2009), Avramov et al. (2013),

I use S&P’s credit ratings as an approximation of credit risk. This paper gives two contributions to the ex-

isting momentum literature. First, intermediate-term momentum is significant for a wide range of firms

independent of the firms’ credit rating. This result supports Novy-Marx (2012) view that intermediate-

term momentum is more robust than short-term momentum.

Second, I show that the difference between intermediate-term and short-term momentum only ex-

ist among high and medium credit rated firms. Goyal and Wahal (2013) and Gong et al. (2015) argue

that the return difference between short-term and intermediate-term momentum found by Novy-Marx

(2012) occurs because of a longer short-term reversal than what Novy-Marx (2012) accounted for further

when accounting for the longer reversal the return difference disappears. Regarding this issue, I find that

high-grade firms have a strong short-term reversal, but the difference between intermediate-term and

short-term momentum is still significant for high-grade firms after controlling for the short-term rever-

sal. My findings suggest that intermediate-term momentum is profitable for a wider range of firms than

short-termmomentum. Further, I show that the difference that Novy-Marx (2012) found between the two

momentum strategies is driven by firms with a high credit rating.

This is prima facie evidence that the optimal momentum strategy for each firm depends on the

firm’s credit rating. In a recent study, Antoniuo et al. (2013) connect the momentum phenomenon

with cognitive dissonance and sentiment. Possibly, investors underreact to news more persistently

when they receive negative news regarding low credit risk firms due to cognitive dissonance. The

J. Haga / Finance Research Letters 15 (2015) 59–67 61

more persistent underreaction among high-grade firms decreases the short-term momentum, but the

correction of the underreaction increases the intermediate-term momentum. Further, I believe that the

difference in size between momentum profits for high-grade and low-grade firms are driven by stronger

limits to arbitrage for high credit risk firms.

The paper is organized as follows. Section 2 presents data and raw returns for intermediate-term mo-

mentum. In Section 3, I present the results from the investigation on intermediate-term momentum’s

relation with credit ratings. The conclusion is in Section 4.

2. Data

My sample contains S&P credit rated stocks fromNYSE, AMEX and NASDAQ during the time period De-

cember 1984 to December 2011. The data has been collected from the CRSP database. The dataset contains

325 monthly observations, which gives 313 observations for the momentum strategy based on prior re-

turns 12–7 months ago. Further, 4447 companies are included in our sample. This gives a total of 681,510

firm-month returns and in average 2097 returns per month. Standard & Poor’s credit ratings are used as a

measure for credit risk. Credit rating data is collected from COMPUSTAT.

Before 1998 Standard & Poor’s credit rating was depending on the firms most senior public traded

debt, after 1998 the credit ratings are based on companies’ total outstanding debt both private and public.

Formally, Standard & Poor has defined the credit rating as their current opinion about the borrower’s

capability to repay their debts. On Standard & Poor’s scale, AAA is the best possible credit rating and D

is the worst. In this paper, as in Avramov et al. (2007), every credit rating is given a numerical value.2

Furthermore, Avramov et al. (2007) argue that a sample with only S&P credit rated firms is representative

for all firms.3

Table 14 reports monthly average raw return, CAPM-alpha, FF3-alpha, and mean credit rating for ten

portfolios sorted according to stock returns from month t-12 to t-7. Further, in Table 1, P1 contains prior

underperformers and P10 contains prior outperformers, the intermediate-term momentum strategy has

a long position in P10 and a short position in P1. For each month t, all stocks are ranked as in Novy-

Marx (2012) according to their cumulative return from month t-12 to t-7 and assigned to one of the

10 portfolios depending on their cumulative return. Further, the holding period for this strategy is one

month. All portfolio returns are calculated as an equally weighted average of the returns from the stocks

in that portfolio. The evidence in Table 1 suggests that intermediate-term momentum strategy generates

significant positive returns. The average raw monthly return is 1.51%, which is economically significant

since 1.51% per month is equal to an annual return of 19.7%. In addition, both CAPM-alpha and FF3-alpha

are significantly positive. Further, the negative alphas for P1 and positive alphas for P10 suggest that both

legs contribute to the abnormal return of the intermediate-term momentum strategy. Table 1 represents

themean credit rating for each portfolio. Column 4 shows that the tail portfolios containmore firmswith a

low credit rating (high numerical credit rating). Further, the tail with the underperformers contains more

low rated firms than the tail with the outperformers. One reason why the tail portfolios have more low-

grade firms is probably that these firms are riskier and because of that have a higher volatility. Logically,

firms with higher volatility are more likely to be placed in the tail portfolios. Indeed, P1 has the highest

mean numerical credit ratingwith 12.7which is equal to a credit rating of BB− (13). Furthermore, Portfolio

10 has a mean of 11.1 (BB), P5 and P6 have a mean of BBB (8). The numerical mean for the mid portfolios

is slightly higher than found by Avramov et al. (2007), although the tail portfolios have a similar average

numerical credit rating. This difference is maybe due to the fact that there is more low rated firms after

2003 (Avramov et al. (2007)sample ends in 2003).

2 The numerical values are: AAA = 1, AA+ = 2, AA = 3, AA− = 4, A+ = 5, A = 6, A− = 7, BBB+ = 8, BBB = 9, BBB− = 10, BB+ = 11,

BB = 12, BB− = 13, B+ = 14, B = 15, B− = 16, CCC+ = 17, CCC = 18, CCC− = 19, CC = 20, C = 21, and D = 22.3 Companies can also be classified into investment grade (IG) and non-investment grade (NIG) companies. The limit is between

BBB− and BB+ rated firms, firms with a rating of BBB− (10) and above are IG firms and as follows firms with a rating of BB+ (11) and

below are NIG firms. During this sample the mean percentage of firms rated as IG firms was 59.5%. Highest percentage of IG credit

rated firms is found in July 1991, when 69.1% of the firms were rated as IG. In contrast, lowest was the proportion of IG firms in June

2010, with only 53.2% of the firms rated as IG firms. In short, the proportion of IG firms has decreased over time.4 The data needed to estimate Fama and French (1993) three-factor model (FF3) is downloaded from Kenneth French’s homepage.

62 J. Haga / Finance Research Letters 15 (2015) 59–67

Table 1

Performance of portfolios sorted on intermediate-term past returns.

Portfolio Raw return CAPM-alpha FF3-alpha Mean credit rating

P1 −0.04 −0.86 −1.09 12.7

(−0.08) (−2.43)a (−3.69)aP2 0.68 0.03 −0.20 10.3

(1.86) (0.13) (−1.02)P3 0.84 0.25 0.03 9.3

(2.58)a (1.12) (0.17)

P4 0.80 0.23 0.02 8.8

(2.64)a (1.19) (0.19)

P5 1.02 0.47 0.28 8.6

(3.51)a (2.51)a (2.44)a

P6 1.03 0.51 0.34 8.4

(3.79)a (3.31)a (3.73)a

P7 1.12 0.59 0.43 8.5

(4.05)a (4.00)a (4.63)a

P8 1.23 0.68 0.54 8.8

(3.40)a (4.86)a (5.85)a

P9 1.35 0.76 0.65 9.4

(4.41)a (5.40)a (6.07)a

P10 1.48 0.76 0.65 11.1

(3.89)a (3.66)a (4.05)a

P10-P1 1.51 1.62 1.74

(4.22)a (4.05)a (4.63)a

Max Min Skewness Kurtosis

P10-P1 20.55 −34.70 −1.28 9.39

This table reports monthly average equal-weighted raw return, CAPM-alpha, FF3-alpha, and mean credit rating for portfolios sorted

on intermediate-term past returns. Further, the bottom of the table presents raw return, CAPM-alpha, FF3-alpha, and descriptive

statistics for intermediate-termmomentum. The S&P credit rating is represented by a numerical value 1 = AAA, 2 = AA+, … , 21 = C,

and 22 = D. T-statistics are presented in the parenthesis. All regression alphas have Newey-West corrected standard errors. The

sample covers December 1984 to December 2011.a Statistically significant on a 5% significance level.

The last row of Table 1 reports themax, min, skewness, and kurtosis of intermediate-termmomentum.

In a recent paper, Daniel and Moskowitz (2013) show that there is a negative aspect of momentum strate-

gies, which is that momentum strategies produce large, negative returns from time to time. The results

regarding the distribution of the intermediate-term momentum returns are consistent with current mo-

mentum literature, in this sample the largest crash is−34.70%, the skewness is negative (−1.29), and thereis excess kurtosis (9.39). However, these results are less extreme than those for short-termmomentum. In

this sample short-term momentum created from a sortation of cumulative returns from month t-6 to t-2

has a crash of−58.63%, skewness of−2.77, and kurtosis of 22.03. This difference in return distribution be-tween intermediate-term and short-term momentum suggests that intermediate-termmomentum has a

smaller crash risk than short-term momentum.

3. Results

In this section, I start by investigating if there is a link between intermediate-term momentum

and credit ratings, later in the section I investigate if credit ratings can help us understand the differ-

ence in performance between intermediate-term and short-term momentum. In Table 2, to investigate

how credit ratings affect intermediate-term momentum I create subsamples depending on credit rating

and investigate the intermediate-term momentum for those subsamples. I start by dividing the sam-

ple into three credit risk groups. The groups are created in the following way: the first group contains

the 30% of firms those with the best credit rating, the second group contains the 40% of firms those

with medium credit rating and the third group contains the 30% of firms those with the worst credit

rating. Table 2 shows the mean credit rating for each group, the mean credit rating for the low risk

J. Haga / Finance Research Letters 15 (2015) 59–67 63

Table 2

Intermediate-term momentum by three credit risk groups.

Panel A: Full sample Panel B: No credit rating changes

Risk group Risk group

(1 = Low risk, 3 = High risk) (1 = Low risk, 3 = High risk)

1 2 3 1 2 3

Average A BBB− BB− A BBB− BB−Rating 5.5 9.4 13.9 5.3 9.1 13.6

Overall P10-P1 0.67 0.81 2.20 0.81 1.09 1.87

(2.41)a (2.78)a (4.90)a (2.74)a (3.61)a (3.79)a

P1 0.85 0.74 −0.75 0.68 0.55 −0.27(2.39)a (1.83) (−1.20) (1.80) (1.32) (−0.42)

P10 1.52 1.55 1.45 1.48 1.63 1.60

(5.16)a (4.39)a (3.14)a (5.04)a (4.78)a (3.45)a

FF3-alpha 0.88 0.96 2.39 1.05 1.26 2.07

(3.31)a (3.40)a (5.57)a (3.68)a (4.28)a (4.46)a

Non-January P10-P1 0.66 0.62 2.54 0.82 1.26 2.21

(2.32)a (3.22)a (5.64)a (2.76)a (4.22)a (4.34)a

January P10-P1 0.77 −0.67 −1.64 0.62 −0.88 −1.88(0.67) (−0.51) (−0.83) (0.46) (−0.61) (−1.06)

Early subsample P10-P1 0.75 1.21 2.90 0.90 1.32 2.73

(2.83)a (4.09)a (6.27)a (2.89)a (3.89)a (5.57)a

FF3-alpha 0.63 1.18 2.87 0.81 1.33 2.55

(2.22)a (3.85)a (6.80)a (2.54)a (4.17)a (5.74)a

Late subsample P10-P1 0.59 0.40 1.50 0.71 0.85 1.01

(1.21) (0.81) (1.96) (1.42) (1.71) (1.19)

FF3-alpha 0.76 0.57 1.67 0.94 1.04 1.04

(1.71) (1.21) (2.31)a (2.02)a (2.18)a (1.29)

Each month, three groups are created. Firms are assigned to a group depending on the firm’s S&P credit rating. The table consists of

two panels: Panel A includes all firms and in Panel B firms with credit rating changes in their intermediate-term past are excluded.

For all credit rating groups, I calculate intermediate-term momentum returns by deducting the return for P1 from the return for

P10. The table reports monthly average raw returns for P1, P10, and intermediate-term momentum. The S&P credit rating is repre-

sented by a numerical value 1 = AAA, 2 = AA+, …, 21 = C, and 22 = D. T-statistics are presented in parenthesis. All regression alphas

have Newey-West corrected standard errors. The sample covers December 1984 to December 2011. The early subsample runs from

December 1984 to May 1999 and the late subsample runs from June 1999 to December 2011.a Statistically significant on a 5% significance level.

group is 5.5 (A), for the medium credit risk group 9.4 (BBB−), and for the high credit risk group 13.9

(BB−). For each credit rating group, Table 2 reports raw returns and alphas for P1, P10 and P10-P1. More-

over, Table 2 consists of two panels in Panel A the full sample is used, but in Panel B all firms with a credit

rating change in their intermediate past time horizon are removed.

Furthermore, Panel A of Table 2 reports positive and significant intermediate-term momentum for

all three credit risk groups. Even though intermediate-term momentum is positive for all credit risk

groups there exists an economically significant difference between the monthly average raw returns.

Overall, intermediate-term momentum formed with low credit rated stocks has a monthly return of

2.20% while intermediate-term momentum formed with high credit rated stocks has a monthly re-

turn of 0.67%. In contrast, low credit risk intermediate-term momentum has positive returns in Jan-

uary while high credit risk intermediate-term momentum has negative returns in January. Interestingly,

Avramov et al. (2007) show negative short-term momentum returns in January for low credit risk as

well as high credit risk firms. As earlier mentioned, Yao (2012) suggests that the difference between

intermediate-term momentum and short-term momentum is in the January returns. Those two stud-

ies together with the results from Table 2 suggest that the difference between intermediate-term and

short-term momentum is mainly driven by high-grade firms. In addition, Table 2 reports returns and

alphas for an early and a late subsample. The early subsample has robust intermediate-term momen-

tum returns. In contrast, the later subsample has mostly positive insignificant intermediate-term mo-

mentum returns for all risk groups. This is due to the momentum crashes that occurred in the later

subsample. Avramov et al. (2013) show that most anomalies are non-profitable after removing firms

64 J. Haga / Finance Research Letters 15 (2015) 59–67

in financial distress from the sample. As in Avramov et al. (2013), I suggest that financial distress firms

are firms for which the credit rating has been lowered. Moreover, Parnes (2007) finds positive autocorre-

lation in downgrading probabilities, this could possible be driving both intermediate-term and short-term

momentum. In this sample there are on average 31.5 credit rating changes per month of those are 19.5

per month credit rating downgrades. To investigate if the financially distressed firms drive the results in

Panel A, Panel B of Table 2 contains only firms without credit rating change in the intermediate-term past

horizon, further the same tests as in Panel A are done. Panel B of Table 2 suggests that intermediate-term

momentum is significant even after removing firms with credit rating changes. Overall, the results after

removing the financial distressed firms are slightly stronger for the low and medium risk group, while

slightly weaker for the high risk group.

Table 3 reports the intermediate-term momentum’s average raw returns and FF3-alphas for differ-

ent subsamples. These subsamples are created by progressively dropping the lowest rated firms. In

addition, column 6 and 7 in Table 3 report time series averages of percentage of firms and percent-

age of market capitalization. In line with previously shown results, Table 3 shows that intermediate-

term momentum has significant positive returns and alphas for all subsamples. Even though, the re-

turns are significant they are decreasing with the removal of high credit risk firms. For example

the subsample containing AAA-D rated firms has an average monthly intermediate-term momentum

return of 1.09% in comparison to that a subsample containing AAA-BBB+ rated firms has an aver-

age intermediate-term momentum return of 0.57%. Interestingly, intermediate-term momentum re-

turns increase slightly again when the subsample only contain high-grade firms. This increase oc-

curs for 8.4% of all firms those with the highest credit rating, without overstating this result it is at

least another sign of the robustness of intermediate-term momentum. In contrast to the results pre-

sented in Table 3, Avramov et al. (2007) show that short-term momentum is not significantly posi-

tive for stocks with a rating better than BB−. The results from Table 3 in comparison with the re-

sult from Avramov et al. (2007) suggest that intermediate-term momentum exists for a wider range of

stocks than short-term momentum. Further, Tables 2 and 3 suggest that intermediate-term momentum

Table 3

Intermediate-term momentum over different credit risk subsamples.

Subsample Raw return T-statistics FF3-alpha T-statistics Percentage of firms Percentage of market cap

AAA-D 1.09 (3.99)a 1.29 (4.47)a 100 100

AAA-C 1.03 (3.78)a 1.23 (4.29)a 99.5 99.97

AAA-CC 1.03 (3.78)a 1.23 (4.29)a 99.5 99.97

AAA-CCC− 1.00 (3.70)a 1.20 (4.18)a 99.3 99.96

AAA-CCC 0.97 (3.60)a 1.17 (4.10)a 99.1 99.96

AAA-CCC+ 0.92 (3.45)a 1.11 (3.93)a 98.6 99.92

AAA-B− 0.88 (3.38)a 1.06 (3.88)a 97.6 99.84

AAA-B 0.84 (3.40)a 1.02 (3.97)a 95.2 99.59

AAA-B+ 0.81 (3.42)a 0.98 (3.95)a 90.6 98.97

AAA-BB− 0.69 (3.08)a 0.85 (3.69)a 81.6 97.78

AAA-BB 0.66 (3.01)a 0.81 (3.70)a 72.9 96.07

AAA-BB+ 0.62 (2.91)a 0.77 (3.52)a 65.9 94.05

AAA-BBB− 0.61 (2.95)a 0.75 (3.49)a 60.2 91.68

AAA-BBB 0.56 (2.68)a 0.72 (3.38)a 51.7 86.99

AAA-BBB+ 0.57 (2.59)a 0.75 (3.48)a 40.8 79.53

AAA-A− 0.62 (2.72)a 0.81 (3.75)a 32.1 71.26

AAA-A 0.59 (2.60)a 0.80 (3.59)a 24.1 62.03

AAA-A+ 0.59 (2.57)a 0.80 (3.24)a 14.9 46.01

AAA-AA- 0.78 (3.23)a 0.94 (3.45)a 8.9 32.90

AAA-AA 0.73 (2.45)a 0.90 (3.10)a 5.4 24.27

AAA-AA+ 1.38 (2.91)a 1.69 (3.76)a 2.3 13.27

AAA 1.60 (2.62)a 1.95 (3.46)a 1.6 10.74

In this table, each subsample is created by monotonically removing the lowest S&P credit rated firms. For each subsample

intermediate-term momentum returns are calculated, these returns arise from selling P1 and buying P5. This table presents in-

termediate momentum monthly average raw return and FF3-alpha. T-statistics are presented in parenthesis. All regression alphas

have Newey-West corrected standard errors. The sample covers December 1984 to December 2011.a Statistically significant on a 5% significance level.

J. Haga / Finance Research Letters 15 (2015) 59–67 65

exists independent of credit ratings. I continue by investigating if credit ratings can shed light on the

difference between intermediate-term and short-term momentum.

I use parametric tests to examine how the relationship between short-term and intermediate-term

momentum differs for different credit rating groups. Table 4 presents results from Fama–MacBeth

regressions where returns are regressed on intermediate-term past returns (r12,7) and control vari-

ables. These control variables are short-term past returns (r6,2), last month’s returns (r1,0) and size.

In the Panel B and Panel C of Table 4 I use different specifications for intermediate-term past re-

turns (r11,7) and short-term past returns (r6,3) to investigate the robustness of my results. The Fama–

MacBeth results are presented for the full sample and for three groups where firms are placed de-

pending on their credit rating. The second column in Table 4 reports the result for the entire sample,

while columns three to five reports the results for the three credit risk groups. In line with the results

from Novy-Marx (2012), Table 4 shows that the coefficient for intermediate-term past returns is twice

Table 4

Fama–MacBeth regressions results.

Risk group (1 = Low risk, 3 = High risk)

1 2 3

Average Overall A BBB− BB−Rating 5.5 9.4 13.9

Panel A

r1,0 −0.40 −4.43 −1.93 0.07

(−0.59) (−4.80)a (−2.42)a (0.10)

r6,2 0.60 −0.79 −0.17 1.52

(1.32) (−1.48) (−0.34) (3.41)a

r12,7 1.32 1.08 0.85 1.39

(2.74)a (2.07)a (1.90) (2.86)a

log(Size) 0.04 0.06 0.05 0.04

(2.22)a (3.14)a (2.37)a (1.45)

r12,7 − r6,2 0.71 1.87 1.01 −0.13(1.53) (3.65)a (2.21)a (−0.31)

Panel B

r2,0 0.18 −3.26 −1.10 0.81

(0.31) (−4.40)a (−1.66) (1.44)

r6,3 0.73 −0.26 0.09 1.58

(1.56) (−0.49) (0.20) (3.29)a

r12,7 1.13 1.08 0.89 1.41

(2.86)a (2.27)a (2.04)a (2.96)a

log(Size) 0.04 0.05 0.05 0.04

(2.22)a (2.97)a (2.39)a (1.44)

r12,7 − r6,3 0.61 1.39 0.79 −0.18(1.22) (2.39)a (1.67) (−0.40)

Panel C

r2,0 0.24 −3.13 −1.05 0.87

(0.42) (−4.19)a (−1.57)a (1.55)

r6,3 0.78 −0.25 0.13 1.66

(1.68) (−0.47) (0.27) (3.49)a

r11,7 1.34 1.09 0.91 1.39

(2.71)a (2.13)a (1.90) (2.74)a

log(Size) 0.05 0.06 0.05 0.04

(2.32)a (3.08)a (2.44)a (1.51)

r11,7 − r6,3 0.56 1.33 0.78 −0.27(1.16) (2.30)a (1.63) (−0.65)

Panel A of this table reports Fama and MacBeth (1973) regressions coefficients and Newey-West corrected T-statistics. In these

regressions firms’ returns are regressed on past intermediate-term returns (r12,7), past short-term returns (r6,2), prior month’s return

(r1,0) and size. This table also reports the difference between the coefficients from intermediate-term and short-term past returns.

Panel B and Panel C present the results for variations of the specifications for intermediate-term past returns and short-term past

returns.a Statistically significant on a 5% significance level.

66 J. Haga / Finance Research Letters 15 (2015) 59–67

the size of the coefficient for short-term past returns. In contrast to Novy-Marx (2012) findings, the dif-

ference between the coefficients for intermediate-term and short-term past returns is not statistically

significant. Further, Table 4 reports two interesting results when examining the three credit risk groups.

First, as Avramov et al. (2007) suggested the low and medium credit risk groups have insignificant co-

efficients for short-term past returns. This supports their result that short-term momentum only exists

among stocks with high credit risk. Second, when looking at the differences in coefficients between short-

term and intermediate-term past returns a pattern emerges: the difference increases when credit risk

decreases. The coefficient difference between short-term and intermediate-term past returns is negative

for low-grade firms, but significantly positive for high-grade and medium-grade firms. These results sug-

gest that the difference that Novy-Marx (2012) shows is driven by the low and medium credit risk firms.

Further, there is an interesting pattern in the short-term reversal (r1,0), which is that the magnitude of the

short-term reversal increases when credit risk decreases. Both Goyal and Wahal (2013) and Gong et al.

(2015) argue that a short-term reversal longer than one month is one of the reasons for the difference be-

tween intermediate-term and short-termmomentum. As shown in Table 4, the strong short-term reversal

only exists among high credit rated firms. Further, to address the issue raised by Goyal and Wahal (2013)

and Gong et al. (2015) I change the specification of short-term past returns from r6,2 to r6,3. Panel B in

Table 4 reports the results after the specification change. Here, the difference between the coefficients

for r12,7 and r6,3 decreased for all subsamples, but the difference is still positive and significant for low

credit risk firms. In Panel C I change the specification for intermediate-term past returns from r12,7 to

r11,7. This is done to ensure that annual seasonality in stock returns that Heston and Sadka (2008) discov-

ered does not drive my results. Panel C in Table 4 shows that the difference between the coefficients for

intermediate-term and short-term only exists for low credit risk firms. In summary of the result section,

I find that intermediate-term momentum exists independent of credit ratings. Moreover, I show that the

puzzling difference between intermediate-term and short-termmomentum exists only among firmswith

a high credit rating.

4. Conclusion

In this paper I provide evidence on the relationship between intermediate-termmomentum and credit

ratings. Further, this paper shows how the difference between intermediate-term and short-term mo-

mentum changes depending on credit ratings.

This paper contributes to the literature in two ways. First, I show that intermediate-term momen-

tum exists for low-grade firms as well as for high-grade firms. This is interesting, because Avramov

et al. (2007) show that short-term momentum only exists for low-grade firms. Moreover, this result

supports the finding by Novy-Marx (2012) that intermediate-term momentum is profitable for a wider

range of firms than short-term momentum. Further, I believe that the reason why intermediate-term

momentum but not short-term momentum is significant among low credit risk firms is due to an in-

vestors bias referred to as cognitive dissonance. Investors are slower to update their negative believes

about high-grade firms because of this bias. In addition, Avramov et al. (2013) argue that anomalies

which are only profitable for low-grade firms are hard to exploit for investors because of the trad-

ing frictions. According to them high credit risk firms are more short-sale constrained and less liquid,

due to this anomaly returns generated from high credit risk firms may disappear when accounting for

transaction costs. However, this study shows that intermediate-term momentum is profitable indepen-

dent of credit ratings. This suggests that intermediate-term momentum not only exists due to limits to

arbitrage.

Second, my results suggest that the difference between intermediate-term and short-term mo-

mentum that Novy-Marx (2012) discovered originates from high-grade firms. There is a possible

link between this finding and the finding by Yao (2012). She shows that when the calendar month

January is excluded the difference between intermediate-term and short-term momentum disap-

pears. I show that intermediate-term momentum is actually positive for high-grade firms in Jan-

uary, but negative for low-grade firms. It is possible that the positive returns from high credit

rated firms in January are one of the reasons behind the conclusions by Yao (2012). Further,

J. Haga / Finance Research Letters 15 (2015) 59–67 67

Goyal and Wahal (2013) show that only in one of 38 countries do intermediate-term momentum out-

perform short-term momentum. Furthermore, the U.S. is the only country where intermediate-term mo-

mentum outperforms. According to the authors firms in the U.S. have a longer short-term reversal and

this causes the outperformance of intermediate-term momentum. I find that the short-term reversal is

both stronger and longer for high credit rated firms than for low credit rated firms. Possibly, the reason

why intermediate-termmomentum does not outperform short-termmomentum in the other countries is

because those countries’ have a low proportion of low credit risk firms. It is interesting that intermediate-

term momentum is significant independent of credit ratings, although short-term momentum is not. I

believe that the optimal momentum strategy can vary with different firm characteristics.

References

Antoniuo, C., Doukas, J., Subrahmanyam, A., 2013. Cognitive dissonance, sentiment and momentum. J. Financ. Quant. Anal. 48, 245–275.

Arsahanapalli, B., Fabozzi, F., Nelson, W., 2006. The value, size and momentum spread during distressed economic periods. FinanceRes. Lett. 3, 244–252.

Asness, C., Moskowitz, T., Pedersen, L., 2013. Value and momentum everywhere. J. Finance 68, 929–985.Avramov, D., Chordia, T., Jostova, G., Philipov, A., 2007. Momentum and credit rating. J. Finance 62, 2503–2520.

Avramov, D., Chordia, T., Jostova, G., Philipov, A., 2009. Dispersionin analysts’ earnings forecasts and credit rating. J. Financ. Econ. 91,

83–101.Avramov, D., Chordia, T., Jostova, G., Philipov, A., 2013. Anomalies and financial distress. J. Financ. Econ. 108, 139–159.

Daniel, K., Hirshleifer, D., Subrahmanyam, A., 1998. Investor psychology and security market under- and overreactions. J. Finance 53,1839–1885.

Daniel, K., Moskowitz, T., 2013. Momentum Crashes. Working Paper.Daniel, K., Titman, S., 1999. Market efficiency in an irrational world. Financial Anal. J. 55, 28–40.

Fama, E., French, K., 1993. Common risk factors in the returns on stocks and bonds. J. Financ. Econ. 33, 3–56.

Fama, E., MacBeth, J., 1973. Risk, return, and equilibrium: empirical tests. J. Polit. Econ. 81, 607–636.Gong, Q., Liu, M., Liu, Q., 2015. Momentum is really short-term momentum. J. Banking Finance 50, 169–182.

Goyal, A., Wahal, S., 2013. Is momentum an echo? J. Financ. Quant. Anal. (forthcoming).Griffin, J., Ji, X., Martin, S., 2003. Momentum investing and business cycle risk: evidence from pole to pole. J. Finance 58, 2515–2547.

Heston, S., Sadka, R., 2008. Seasonality in the cross-section of stock returns. J. Financ. Econ. 87, 418–445.Hong, H., Lim, T., Stein, J., 2000. Bad new travels slowly: size, analyst coverge, and the profitability of momentum strategies. J. Finance

55, 265–295.

Hong, H., Stein, J., 1999. A unified theory of underreaction, momentum trading, and overreaction in asset markets. J. Finance 54,2143–2184.

Jegadeesh, N., Titman, S., 1993. Returns to buying winners and selling losers: implications for stock market efficiency. J. Finance 48,65–91.

Johnson, T., 2002. Rational momentum effects. J. Finance 57, 585–608.Moskowitz, T., Grinblatt, M., 1999. Do industries explain momentum? J. Finance 54, 1249–1290.

Moskowitz, T., Ooi, Y., Pedersen, L., 2012. Time series momentum. J. Financ. Econ. 104, 228–250.

Novy-Marx, R., 2012. Is momentum really momentum? J. Financ. Econ. 103, 429–453.Parnes, D., 2007. Time series patterns in credit ratings. Finance Res. Lett. 4, 217–226.

Rouwenhorst, G., 1998. International momentum strategies. J. Finance 53, 267–284.Sagi, J., Seasholes, M., 2007. Firm-specific attributes and the cross-section of momentum. J. Financ. Econ. 84, 389–434.

Yao, Y., 2012. Momentum, contrarian, and the january seasonality. J. Banking Finance 36, 2757–2769.Zhang, F., 2006. Information uncertainty and stock returns. J. Finance 61, 105–137.

Empir EconDOI 10.1007/s00181-015-0952-9

The market price of credit risk and economic states

Klaus Grobys1 · Jesper Haga2

Received: 7 April 2014 / Accepted: 2 March 2015© Springer-Verlag Berlin Heidelberg 2015

Abstract This paper proposes a market-wide credit risk factor for the US stock marketand investigates its properties that are dependent on economic conditions. The marketprice of credit risk is found to be statistically significantly negative, supporting earlierstudies. However, a sample-split analysis reveals that this negative payoff is nonexistentin a later subsample, indicating that the credit risk puzzle is based on temporarymispricing related to the earlier subsample. Further investigation shows that mispricingin the earlier period was mainly driven by positive payoffs of low credit risk firms, whilehigh credit risk firms did not generate significant returns in any of the sub-periods.

Keywords Asset pricing · Credit rating · Credit risk · Economic states · Businesscycle · Market price of credit risk

JEL Classification G12 · G14

1 Introduction

Fundamental finance theory suggests that assets bearing higher risk should generatehigher returns. However, several studies have asserted that firms exhibiting high credit

B Klaus Grobysklaus.grobys@uwasa.fi; grobys.finance@gmail.com

Jesper Hagajesper.haga@hanken.fi

1 Department of Accounting and Finance, University of Vaasa, Wolffintie 34,65200 Vaasa, Finland

2 Department of Statistics and Finance, Hanken School of Economics,Handelsesplanaden 2, 65100 Vaasa, Finland

123

K. Grobys, J. Haga

risk generate statistically lower returns compared to firms with low credit risk. Thiscross-sectional effect is often referred to as the credit risk anomaly or the credit riskpuzzle. Avramov et al. (2007) established a link between the momentum anomaly andcredit ratings and asserted that momentum profitability is large and significant amonglow-grade firms but is nonexistent among high-grade firms. Moreover, Avramov et al.(2013) investigated commonalities across asset pricing anomalies and found thatdistressed stocks subsequently generate noticeably low returns, thus producing anom-alous profits from the short side of the trading strategy. They argued that financialdistress provides a link between the anomalies’ conditioning variables and subsequentprofitability of an anomaly-based trading strategy.

On the one hand, Denis and Denis (1995) and Vassalou and Xing (2004) both arguedthat default risk is systematic because the number of defaults varies with the businesscycle. On the other hand, Avramov et al. (2009) supported the opposite view, namelythat credit risk is an idiosyncratic risk. In contrast to Avramov et al. (2007, 2009,2013) and Campbell et al. (2008), who explored credit risk as an equity characteristic,Avramov et al. (2012) were the first to propose a systematic credit risk factor basedon S&P issuer credit ratings. The proposed world credit risk factor is related to thepricing of a cross section of global equity markets. No study has yet been undertakenthat investigates the asset pricing implications of a market-wide credit risk factorassociated with a domestic stock market.

The purpose of this paper is to formulate a market-wide credit risk factor for theUS stock market and investigate its properties that may be dependent on economicconditions. First, we created 15 zero-cost portfolios long in high credit risk stocks andshort in low credit risk stocks. Then, we controlled for previously documented associ-ations between both credit risk and size and credit risk and momentum. In doing so, wemade use of triple sorts by momentum, size, and credit rating. Our sample comprisesa total of 4,473 stocks and runs from March 1987 to December 2011. We employedboth Carhart’s (1997) four-factor model, and Novy-Marx’s (2013) recently proposedfour-factor model to price the 15 zero-cost portfolios. Furthermore, we made use ofthe Gibbons et al. (1989) test to investigate whether these zero-cost strategies couldbe explained by common risk factors. Second, we constructed a market-wide creditrisk factor based on S&P issuer credit ratings. We used different asset pricing modelspecifications and explored the properties of the market price of credit risk duringeconomic expansionary periods and recessions. Moreover, we investigated whetherthe loadings against common risk factors changed when the state of the economyalters. Third, we split the sample into two subsamples of equal length and investigatedwhether the properties of the market price of credit risk have changed over time.

The study contributes to the existing literature in various ways. First, we proposea systematic credit risk factor based upon S&P issuer credit ratings related to theUS stock market. In contrast to Avramov et al. (2012), who proposed a systematicworld credit risk factor based on a country’s long-term credit rating (issued by S&P asmeasure of credit risk), we construct a market-wide credit risk factor for the US stockmarket based on S&P long-term firm-specific credit ratings, which are also employedas measure of credit risk in studies by Avramov et al. (2007, 2009, 2013). Doing someant we were able to explore whether the market price of credit risk depends on theeconomic state.

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The market price of credit risk and economic states

Second, we investigate whether the market price of credit risk has changed over time.Apparently anomalous patterns in a cross section of stock returns may be subject tosample bias, and hence, the question arises as to whether this abnormal pattern relatedto credit risk persists. Schwert (2003) examined the profitability of the size, book-to-market, and momentum anomalies and Jegadeesh and Titman (2001) investigatedthe persistence of the momentum anomaly. This paper extends the studies of Schwert(2003) and Jegadeesh and Titman (2001) by exploring whether the credit risk anomalypersists. Additionally, we make use of Fama and French’s (1993, 2008) portfolioapproach, as expounded in Schwert (2003) and Jegadeesh and Titman (2001). Byexpanding the sample used in Avramov et al. (2007), we can investigate whetherthe payoffs associated with high credit risk stocks continue to be statistically lowercompared to low credit risk stocks, as argued in Avramov et al. (2013).

Third, we extend Novy-Marx’s (2013) study by employing his four-factor modelto price zero-cost portfolios that are long in high credit risk stocks and short in lowcredit risk stocks. Employing the financial distress measures proposed in Campbellet al. (2008) and Ohlson (1980), Novy-Marx (2013) asserted that his four-factor modelperforms well in pricing these two credit-risk-based strategies. When lower returnsof financially distressed firms are driven by positive loadings against the profitabilityfactor, as argued by Novy-Marx (2013), we would expect the model to also correctlyprice test assets sorted by alternative measures of credit risk, such as those proposedin Avramov et al. (2007, 2009, 2013).

Our finding that higher credit risk firms appear to generate lower expected returnsis in line with the majority of prior literature (Dichev 1998; Griffin and Lemmon 2002;Campbell et al. 2008; Garlappi et al. 2008; Avramov et al. 2009).1 Neither Carhart’s(1997) traditional asset pricing model nor Novy-Marx’s (2013) four-factor model isable to capture this negative risk premium. Moreover, the conducted market-wide creditrisk factor appears to be significantly negatively priced as well. However, a sample-split analysis reveals this negative relationship to be driven only by the first subsample,and hence, it is rather temporary in nature. The spread between high and low creditrisk portfolios is not statistically different from zero in the second subsample. We findalso that the negative risk–return relationship in the first subsample is driven solely bythe short position, supporting Avramov et al. (2013).

This remainder of the paper is organized as follows: In the next section, we presentthe data used for our study. In Sect. 3, the empirical framework is presented. The lastsection provides conclusions.

2 Data

We extracted monthly returns on all NYSE, AMEX, and NASDAQ stocks listed in theCRSP database. First, stocks were required to have at least 12 consecutive monthlyreturn observations. From the universe of stocks, we choose those rated by S&P,

1 Negative distress premiums are determined with different distress measures: Dichev (1998) with Altman’sZ-score and Ohlson’s O-score, Griffin and Lemmon (2002) with an O-score measure, Garlappi et al. (2008)with default risk measures from Moody’s KMV, and Campbell et al. (2008) and Avramov et al. (2009) withS&P credit ratings.

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K. Grobys, J. Haga

leaving us with 4,473 rated stocks in the period from March 1987 to December 2011.S&P credit ratings, which reflect the current S&P opinion on the borrower’s capacity tomeet its financial liabilities, were collected from the COMPUSTAT database. Avramovet al. (2007) highlighted that S&P assigns these ratings to a firm, not an individualbond, and detailed the issuer’s rating process. S&P credit ratings prior to 1998 werebased only on a firms’ most senior public outstanding debt, while after 1998, creditratings were based on all outstanding debt, both public and private. We employed thesame transformation as Avramov et al. (2007) and converted the S&P ratings intoconventional numerical scores, where a score of 1 represents an AAA rating and ascore of 22 reflects a D rating.2 In other words, a higher numerical score correspondsto a lower credit rating and consequently to higher credit risk. In our sample, the ratioof rated to unrated firms was 1:2.6 as of December 2011. Since we extended the sampleused in Avramov et al. (2007), we assume our sample is representative for the samereasons their study was (Avramov et al. 2007, p. 2506).

3 Empirical framework

3.1 Portfolio sorts and controlling for momentum and size

We investigated the asset pricing implications of a market-wide credit risk factor. Wefollowed Avramov et al. (2007, 2009) and employed S&P issuer ratings as a credit riskmeasure. We employed a portfolio-based approach in line with Fama and French (1996,2008), which is common practice in empirical asset pricing research. Previous studieshave found a significant correlation between credit risk and other firm characteristics.Vassalou and Xing (2004) argued for a strong correlation between firm size and creditrisk and a weaker correlation between credit risk and book-to-market value. Moreover,Fama and French (1996) mentioned that the correlation between financial distress andfirm size is negative. They argued further that their size factor may be a proxy forfinancial distress. This strong relationship between size and credit risk is of interestin our study. Vassalou and Xing (2004) controlled for size and book-to-market valuebased on the correlation structure between default risk, size, and book-to-market value.Garlappi et al. (2008) derived a theoretical link between credit risk and momentum,which was empirically confirmed by Avramov et al. (2007). In the spirit of Avramovet al. (2007) and Vassalou and Xing (2004), we controlled for both momentum andsize and, as a result, employed a three-way sort on momentum, size, and credit rating.

First, we controlled for momentum and divided the overall sample into five groups.The first group contained the 20 % of companies that exhibited the highest cumulativereturns over the previous 12 months (“winner portfolio”), whereas the last groupcontained the 20 % of companies that exhibited the lowest cumulative returns overthe previous 12 months (“loser portfolio”). The momentum portfolios were estimatedusing a rolling time window for each month. After controlling for momentum, we

2 The entire set of transformations is as follows: AAA = 1, AA+ = 2, AA = 3, AA− = 4, A+ = 5, A = 6,A− = 7, BBB+ = 8, BBB = 9, BBB− = 10, BB+ = 11, BB = 12, BB− = 13, B+ = 14, B = 15, B− = 16,CCC+ = 17, CCC = 18, CCC− = 19, CC = 20, C = 21, and D = 22.

123

The market price of credit risk and economic states

sorted each group by size. This approach resulted in three different sized groups,with the first group comprising the largest companies and accounting for 30 % ofthe total of companies; the second group comprised medium-sized companies andaccounted for 40 % of the total; and the third group comprised the smallest companiesand accounting for 30 % of the total. Market capitalizations were measured at thebeginning of each month. This sorting methodology produced 15 subgroups. Aftercontrolling for momentum and size, we sorted each of these subgroups by their creditrankings into three further subgroups. The first group contained the 30 % of companieswith the highest credit ranking; the second group contained the 40 % of companies witha medium credit ranking; and the third group contained the 30 % of companies withthe lowest credit ranking. The portfolio allocation procedure was re-estimated everymonth. Thus, the sorting of the momentum portfolios was based on their cumulativereturns over the 12-month period preceding the assembly of the portfolio. The three-way sorts produced a total of 45 portfolios. Then, for each momentum and size group,we constructed long–short portfolios by buying the group of stocks exhibiting thehighest credit risk and selling the group of stocks with the lowest credit risk, resultingin 15 equally weighted zero-cost strategies.

Four different measures of credit risk dominate the literature on credit risk: defaultspreads, structural models, credit default swaps (CDS), and credit ratings. However,our use of S&P credit ratings has several advantages over these measures. First, defaultspreads that have been used to measure credit risk default are often defined as thereturn spread between long-term BAA and long-term AAA corporate bonds. Whileempirical evidence has shown that a large part of the default spread is unrelated to creditrisk, according to Elton et al. (2001) and Huang and Huang (2012), it instead arisesfrom liquidity effects and risk premiums for bearing systematic risk. Second, differentstructural models, often derived from Merton (1974), as, for example, in Longstaff andSchwartz (1995) and Vassalou and Xing (2004), have been used to measure credit risk.Friewald et al. (2014) criticized structural models for not being sufficiently informativeregarding expected stock returns. It is evident that the findings relating to the distresspuzzle supplied by studies applying structural models have varied considerably. Third,CDS can be most easily interpreted as an insurance contract in the event that a specificfirm defaults, and while CDS can be used to measure credit risk, doing so presents twomain problems. First, CDS prices can contain an illiquidity premium unrelated to thecredit risk of the firm. Moreover, and most importantly, only a limited number of thelisted stocks have CDS contracts. The majority of firms with tradable CDSs are largefirms, and hence, the use of CDS prices as a measure of credit risk would probably havebiased our study. Therefore, we consider the use of S&P credit ratings as a measure ofcredit risk available for a wide range of stocks to offer clear advantages. Credit ratingsare non-tradable and are thus a more suitable measure for the purpose of our study.

In Table 1, we report the average returns and corresponding t-values for each ofthe 15 long–short strategies. We also report the number of stocks included in eachgroup, the average credit rating, and the average market capitalization. We estimatedthe mean and the corresponding t-statistic of the 15 zero-cost credit risk portfoliosusing the seemingly unrelated regression (SUR) estimation technique to account forcross-sectional correlation. The results reported in Table 1 indicate that seven of the15 estimated sample means for the equally weighted zero-cost portfolios were statis-

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K. Grobys, J. Haga

Table 1 Descriptive statistics of the zero-cost portfolios sorted by momentum, size, and credit risk

First sort Secondsort

Averagereturn

Average numberof stocks

Averagecredit rating

Average marketcapitalization

Momentum 1 (“winner”) Large 0.07 70.45 7.93 16,241,000.00

(0.30)

Momentum 1 (“winner”) Medium 0.60* 94.22 10.79 1,785,400.00

(1.73)

Momentum 1 (“winner”) Small 1.15** 69.53 12.85 330,520.00

(2.22)

Momentum 2 Large −0.05 70.29 6.30 20,613,000.00

(−0.69)

Momentum 2 Medium 0.12 94.14 8.65 2,400,100.00

(0.23)

Momentum 2 Small 0.33 69.35 10.77 461,840.00

(0.85)

Momentum 3 Large −0.42*** 70.31 6.04 20,374,000.00

(−2.65)

Momentum 3 Medium −0.10 94.10 8.33 2,279,600.00

(−0.46)

Momentum 3 Small −0.40 69.39 10.41 459,090.00

(−1.16)

Momentum 4 Large −0.46** 70.29 6.20 18,911,000.00

(−1.98)

Momentum 4 Medium −0.65** 94.14 8.43 2,005,400.00

(−2.50)

Momentum 4 Small −0.21 69.35 10.82 385,310.00

(−0.50)

Momentum 5 (“loser”) Large −0.80** 68.89 7.72 12,311,000.00

(−2.20)

Momentum 5 (“loser”) Medium −1.74*** 93.54 10.88 943,610.00

(−3.67)

Momentum 5 (“loser”) Small −1.94** 68.96 13.53 140,200.00

(−2.19)

For each month t , all stocks rated by S&P with available return data for months t − 12 through t − 2were first sorted according to their performance in quintiles from the 20 % best performing companies tothe 20 % worst performing companies. These five portfolios were sorted in the second step according totheir size (smallest 30 %, medium 40 % and largest 30 %), resulting in 15 different portfolios. These groupswere sorted in the third step with respect to their credit rating and divided into three subgroups (best 30 %,medium 40 % and worst 30 %). Then, for each credit risk group a zero-cost portfolio was created by sellingthe portfolio that contained 30 % of the companies that exhibited the worst credit rating and buying theportfolio that contained the 30 % of the companies with the best credit rating at time t −1. The strategy wasupdated at the beginning of each month. The sample period runs from March 31, 1987, until December 30,2011. Robust t-statistics are given in parentheses* Statistically significant at a 10 % significance level** Statistically significant at a 5 % significance level*** Statistically significant at a 1 % significance level

123

The market price of credit risk and economic states

tically significantly different from zero (see column “average return” in Table 1) on acommon 5 % level. Without risk adjusting these portfolios, it would be difficult to drawunambiguous conclusions since two estimated sample mean parameters (i.e., 0.60 and1.15 % per month) were significantly positive at the 10 and 5 % significance levels.The other six estimated sample mean parameters, which are statistically significantlydifferent from zero, were estimated to be negative. The largest negative sample meanparameter in absolute terms (i.e., −1.94 % per month) was estimated for the zero-costportfolio for small firms in the loser portfolio with a corresponding t-statistic of −2.19(see last entry in the average return column in Table 1). We also observed a spread inthe average credit rating as we moved from small to large stocks. Smaller stocks have,on average, a higher credit risk compared to larger stocks. As a result of our dependenttriple sorts, the average number of stocks is relatively constant in each portfolio.

Table 1 shows that there is a link between momentum and credit rating, confirmingthe finding of Avramov et al. (2007). The credit risk spread was not significantlynegative in the momentum groups consisting of the best performing stocks. Past winnerstocks with a low credit rating did not generate lower returns than past winner stockswith a high credit rating. On the other hand, and contradicting a finding of Avramovet al. (2007), the negative payoff pattern for momentum groups 3–5 does not seem tobe related only to small stocks or loser stocks. Table 1 illustrates the finding that largestocks with average performance (e.g., momentum group 3) do indeed show the samenegative patterns as small stocks in the loser momentum quintile (e.g., momentumgroup 5).

3.2 Testing the zero-cost portfolios

We estimated multivariate asset pricing models for the 15 zero-cost portfolios. Wefirst employed the traditional four-factor model as proposed by Carhart (1997) (1),and then, we used the four-factor model specification proposed by Novy-Marx (2013)(2).

WMBi,t = αi + βi1MRFt + βi2SMBt + βi3HMLt + βi4MOMt + εi,t , (1)

WMBi,t = γi + δi1MRFt + δi2HML∗t + δi3UMD∗

t + δ j4PMU∗t + εi,t , (2)

and i = 1, . . . , 15. As defined by Fama and French (1993) and Carhart (1997), MRFt

denotes the market factor, SMBt denotes the size factor, HMLt denotes the risk fac-tor related to the book-to-market ratio, and MOMt denotes the momentum factor.Moreover, as defined by Novy-Marx (2013), HML∗

t denotes the industry-adjustedHML factor, UMD∗

t denotes the industry-adjusted momentum factor, and PMU∗t

denotes the industry-adjusted profitability factor. Furthermore, WMBi,t (“worst-minus-best”) denotes the 15 zero-cost portfolios sorted by momentum, size, andcredit rating, and εi,t is assumed to be a white noise process. Since Novy-Marx(2013) suggested that lower returns of financially distressed firms are driven by pos-itive loadings against the profitability factor, we would expect that his four-factormodel would also correctly price test assets sorted by the S&P issuer’s credit risk

123

K. Grobys, J. Haga

Table 2 Pricing errors of 15 test assets sorted by momentum, size, and credit rating

Winner 2 3 4 Loser

Panel A: Pricing errors employing Carhart’s (1997) model

Large −0.02 −0.07 −0.46*** −0.56** −1.00***

(−0.09) (−0.92) (−2.81) (−2.33) (−2.66)

Medium 0.37 −0.02 −0.27 −0.81*** −2.15***

(1.04) (−0.08) (−1.23) (−3.04) (−4.31)

Small 0.84 0.10 −0.61* −0.36 −2.39***

(1.58) (0.25) (−1.71) (−0.83) (−2.64)

Panel B: Pricing errors employing Novy-Marx’s (2013) model

Large 0.10 0.01 −0.27 −0.50* −0.96**

(0.37) (0.15) (−1.51) (−1.87) (−2.31)

Medium 0.52 0.07 −0.29 −0.80*** −2.27***

(1.34) (0.26) (−1.18) (−2.72) (−4.10)

Small 0.89 −0.49 −0.91** −0.74 −2.94***

(1.52) (−1.15) (−2.31) (−1.57) (−2.94)

We used 15 zero-cost portfolios sorted by momentum, size, and credit rating as test assets and ran multivariatetime-series regressions using Carthart’s (1997) four-factor model and alternatively Novy-Marx’s (2013)four-factor model. The corresponding pricing errors are reported in Panel A and Panel B, respectively. TheMRF factor included in these asset pricing models is the excess return of the CRSP index while the SMB,HML, and MOM factors are risk factors based on Size, Book-to-Market value, and Momentum as proposedby Fama and French (1996) and Carhart (1997), respectively. The data for the MRF, SMB, HML, and MOMfactors were downloaded from Kenneth French’s web site. The data for Novy-Marx’s (2013) risk factor weredownloaded from Novy-Marx’s web site. The sample period runs from March 31, 1987, until December30, 2011. Robust t-statistics are given in parentheses* Statistically significant at a 10 % significance level** Statistically significant at a 5 % significance level*** Statistically significant at a 1 % significance level

measure. In Table 2, we report the estimated pricing errors of the 15 zero-cost strate-gies.

Next, we used the Gibbons et al. (1989) test (GRS test) to investigate whether theestimated intercepts in this multivariate regression model were jointly equal to zero.We tested

H0: α1= α2 = . . .= α15 =0 versus H1: αi �= 0 for at least one i ={1, . . . , 15} , and

H0: γ1= γ2 = . . .= γ15 =0 versus H1: γi �= 0 for at least one i ={1, . . . , 15} .

If the null hypothesis is rejected, the corresponding asset pricing model is not capableof explaining the spreads of the zero-cost strategies. The results reported in Table 3indicate that the estimated pricing errors increased even after risk adjustment. Carhart’s(1997) four-factor model produced statistically significant pricing errors ranging from−0.46 to −2.39 % per month with t-statistics between −2.33 and −4.31. However,Novy-Marx’s (2013) four-factor model did not produce better results. This model gen-erated statistically significant pricing errors varying between −0.80 and −2.94 % per

123

The market price of credit risk and economic states

month with corresponding t-statistics between −2.31 and −4.10. Unreported resultsalso show that only one zero-cost portfolio exhibited a statistically significantly nega-tive loading against the profitability factor. Moreover, the asymptotic GRS test statisticsof 62.48 (p value 0.0000) and 60.55 (p value 0.0000) for testing the pricing errors ofCarhart’s (1997) and Novy-Marx’s (2013) models indicate that neither appear to becapable of correctly pricing these test assets.

3.3 The market-wide credit risk factor and economic states

Constructing the market-wide credit risk factor appears to be ultimately an empiricalquestion. The results reported in Tables 1 and 2 provide strong empirical evidencethat credit risk is related only to momentum groups 3 through 5. As a result, wegenerated the market-wide credit risk factor as the arithmetic mean of nine zero-cost strategies. That is, we employed the zero-cost strategies for all size groups withinmomentum groups 3–5. This portfolio approach is similar to Fama and French (1996),who constructed their size factor and value factor in a similar manner. To investigatewhether our market-wide credit risk factor can be explained by traditional asset pricingfactors, we regressed the spread on both Carhart’s (1997) four-factor model and Novy-Marx’s (2013) four-factor model. Novy-Marx (2013, p. 14) used the same approachto investigate the extent to which anomalies can be explained by these models. SinceNovy-Marx’s (2013) results provided evidence that his asset pricing model was capableof explaining credit risk spreads based on financial distress measures, as proposed inCampbell et al. (2008) and Ohlson (1980), we expect that this model would alsocorrectly price test assets sorted by alternative measures of credit risk, as proposed inAvramov et al. (2007, 2009, 2013). Since Vassalou and Xing (2004) and Denis andDenis (1995) suggested that credit risk is a systematic risk because it can vary with thebusiness cycle, we also included a recession dummy variable in our analysis. We usedthe recession dates obtained by the National Bureau of Economic Research (NBER)web site and coded the dummy variable as equal to one if the corresponding monthwas in a recession period, as reported by the NBER, and zero if it was not. The resultsare reported in Table 3.

As discussed in Sect. 3.1, only the zero-cost strategies in momentum group 3 through5 were affected by our credit risk factor. Hence, we estimated the market price of creditrisk as the arithmetic mean of the zero-cost strategies associated with these groups.Table 3 shows that the market price of credit risk is statistically significantly negativein the overall sample period. This supports the findings of Avramov et al. (2007, 2009,2013) and Campbell et al. (2008). Furthermore, the recession dummy variable did notaffect the intercept of the spread directly but did affect the loadings against size, value,and momentum factors. Interestingly, only in recessionary periods did the market priceof risk appear to be driven by these factors. This state-dependent correlation betweencredit risk and size, value, and momentum factors supports Vassalou and Xing (2004)in the sense that credit risk varies with the business cycle. The positive loadings againstthese factors during recessions suggest that high credit risk firms tend to be small firms,value firms, and past winners.

Next, to evaluate the efficiency of the market-wide credit risk factor, we estimatedfour different asset pricing model specifications for single companies:

123

K. Grobys, J. Haga

Tabl

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the

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MR

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BH

ML

MO

MM

RF

×d

SMB

×d

HM

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−0.7

6***

(−2.

79)

−0.7

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0.06

(−2.

94)

(0.8

1)

−0.7

9***

0.07

−0.0

60.

04

(−2.

97)

(1.0

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*

(−3.

42)

(1.7

0)(−

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)(1

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(2.6

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−1.2

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(−2.

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(−1.

29)

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−1.0

10.

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(−2.

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(−1.

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(0.6

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(−2.

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(−0.

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(2.3

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123

The market price of credit risk and economic states

Rexcessj t = φ j0 + φ j1MRFt + φ j2SMBt + φ j3HMLt + u jt , (3)

Rexcessj t = κ j0 + κ j1MRFt + κ j2SMBt + κ j3HMLt

+ κ j4MOMt + κ j5WMB∗t + u jt (4)

Rexcessj t = λ j0 + λ j1MRFt + λ j2HMLt + λ j3UMD∗

t + λ j4PMU∗t + u jt , (5)

Rexcessj t = θ j0 + θ j1MRFt + θ j2HMLt + θ j3UMD∗

t

+ θ j4PMU∗t + θ j5WMB∗

t + u jt , (6)

where j = {1, . . . , 26}. In Eqs. (3)–(6), Rexcessj t denotes the excess returns of single firm

j , WMB∗t (“worst-minus-best”) denotes our market-wide credit risk factor, described

in the previous section, and u jt denotes a white noise process. All other factors are as inEqs. (1) and (2). The model M1 in Eq. (3) corresponds to Carhart’s (1997) four-factormodel in Eq. (1), whereas model M3 in Eq. (5) corresponds to Novy-Marx’s (2013)model in Eq. (2). In models M2 and M4, we added the market-wide credit risk factorWMB*. Again, we estimated all parameters simultaneously in a multivariate time-series model, as in the previous section. As test assets, we used all blue-chip stockslisted on the Dow Jones 30 index apart from Cisco Systems, Goldman Sachs Group,United Health Group Inc., and Visa Inc. because no historical data were publiclyavailable until March 1987 for those stocks. Hence, we compounded the monthlyexcess returns by subtracting the risk-free rate for a total of 26 stock companies.The sample period runs from March 1987 to December 2011. In Table 4, we reportthe pricing errors associated with each firm. The results indicate that Carhart’s (1997)model (e.g., M1) produced seven pricing errors that are statistically different from zeroon a common level. The largest pricing error was produced for Microsoft Corporation,which had an economic magnitude of 1.70 % per month with a t-statistic of 3.61. Onthe other hand, Novy-Marx’s (2013) model (e.g., M3) produced only three pricingerrors that were statistically significantly different from zero on a common level.

Next, we included the market-wide credit risk factor in both model specificationsand estimated the models again. It is evident that accounting for the credit risk factorgenerally decreases both the economic magnitude of the pricing errors and the statis-tical significance (see Table 4). To investigate the overall efficiency of the proposedcredit risk factor, we next examined whether its inclusion results in lower GRS teststatistics. We again ran the GRS test for model specifications M1–M4. The resultsindicate that when moving from M1 to M2 and from M3 to M4, the GRS test statisticsdecrease from 51.67 to 46.91 and 25.20 to 22.49, respectively. Therefore, the market-wide credit risk factor appears to be efficient in this time-series framework. Moreover,we explored whether the test assets’ sensitivities against the credit risk factor in modelsM2 and M4 are statistically significant. To do so, we tested

H0: κ1,5 = κ2,5 = . . . = κ26,5 = 0 versus H1: κ j,5 �= 0 for atleast one j = {1, . . . , 26} and

H0: θ1,5 = θ2,5 = . . . = θ26,5 = 0 versus H1: θ j,5 �= 0 for atleast one j = {1, . . . , 26} .

If the null hypotheses are not rejected, movements in the credit risk factor can becaptured by other risk factors. The Wald test statistic is under the null hypothesesasymptotically Chi-square distributed with 26 degrees of freedom. The test statisticsare estimated at 41.93 for M2 and 39.58 for M3. As the corresponding critical valueof the underlying Chi-square distribution at a common 5 % significance level was

123

K. Grobys, J. Haga

Table 4 Composition of the Dow Jones 30 and pricing errors

Symbol Name Pricing errors

M1 M2 M3 M4

AXP American ExpressCompany

0.14 0.38 0.23 0.49

(0.34) (0.94) (0.51) (1.08)

BA The Boeing Company 0.23 0.28 0.04 0.07

(0.40) (0.67) (0.10) (0.16)

CAT Caterpillar Inc. 0.34 0.48 −0.04 0.11

(0.58) (1.09) (−0.09) (0.23)

CSCO Cisco Systemsa N.A. N.A. N.A. N.A.

CVX Chevron Corporation 0.31 0.41 0.05 0.18

(1.07) (1.37) (0.14) (0.52)

DD E. I. du Pont de Nemoursand Company

−0.06 0.00 −0.27 −0.21

(−0.18) (0.01) (−0.77) (−0.59)

DIS The Walt Disney Company 0.13 0.23 0.42 0.56

(0.37) (0.67) (1.11) (1.43)

GE General Electric Company 0.05 0.12 −0.01 0.07

(0.16) (0.41) (−0.04) (0.20)

GS The Goldman Sachs Groupa N.A. N.A. N.A. N.A.

HD The Home Depot 1.07*** 0.97** 0.83* 0.72

(2.59) (2.30) (1.83) (1.55)

IBM International BusinessMachines Corporation

0.86** 0.69* 1.06** 0.87*

(2.22) (1.76) (2.46) (1.97)

INTC Intel Corporation 1.35** 1.32** 1.22** 1.20**

(2.52) (2.41) (2.10) (2.01)

JNJ Johnson & Johnson 0.68** 0.61** −0.01 −0.07

(2.35) (2.07) (−0.02) (−0.21)

JPM JP Morgan Chase & Co. 0.13 0.20 0.74 0.81

(0.29) (0.44) (1.47) (1.57)

KO The Coca-Cola Company 0.58* 0.52 0.00 −0.06

(1.79) (1.55) (0.00) (−0.16)

MCD McDonald’s Corp. 0.60* 0.67** −0.05 0.03

(1.89) (2.06) (−0.14) (0.09)

MMM 3M Company 0.19 0.10 −0.10 −0.19

(0.63) (0.34) (−0.30) (−0.56)

MRK Merck & Co. Inc. 0.72* 0.81 0.02 0.12

(1.92) (2.11) (0.05) (0.27)

MSFT Microsoft Corporation 1.70*** 1.51*** 1.40** 1.16**

(3.61) (3.14) (2.65) (2.16)

NKE Nike 1.35** 1.32** 0.69 0.67

(2.56) (2.44) (1.21) (1.16)

123

The market price of credit risk and economic states

Table 4 continued

Symbol Name Pricing errors

M1 M2 M3 M4

PFE Pfizer Inc. 0.56 0.51 −0.11 −0.17

(1.57) (1.38) (−0.27) (−0.41)

PG The Procter & Gamble Company 0.57* 0.53 0.13 0.10

(1.73) (1.58) (0.37) (0.27)

T AT&T 0.26 0.29 −0.12 −0.04

(0.76) (0.81) (−0.30) (−0.10)

TRV The Travelers Companies 0.29 0.19 −0.24 −0.32

(0.78) (0.49) (−0.57) (−0.73)

UNH United Health Group Incorporateda N.A N.A. N.A. N.A.

UTX United Technologies Corp. 0.39 0.41 −0.22 −0.22

(1.28) (1.30) (−0.68) (−0.66)

V Visa Inc.a N.A. N.A. N.A. N.A.

VZ Verizon Communications Inc. 0.36 0.36 0.02 0.90

(1.04) (1.04) (0.06) (0.23)

WMT Wal-Mart Stores Inc. 0.77** 0.66* −0.03 −0.15

(2.22) (1.84) (−0.08) (-0.40)

XOM Exxon Mobil Corporation 0.40* 0.46* 0.17 0.26

(1.67) (1.87) (0.62) (0.92)

We used a sample of single firms listed on the Dow Jones 30 index and compounded the excess returns. Weused these firms as test assets and ran multivariate regressions employing four different regressor sets. Themodel M1 contains the risk factors for Carhart’s (1997) four-factor model, whereas in model M2, the WMBfactor is added. Model M3 contains the risk factors for Novy-Marx’s (2013) four-factor model, whereas inmodel M4, the WMB factor is added. The data for the MRF, SMB HML, and MOM factors and the risk-free rate were downloaded from Kenneth French’s web site. The data for HML*, UMD* and PMU* wereobtained from Novy-Marx’s web site. Table 4 reports the pricing errors estimated via multivariate regressionmodels. The sample period runs from March 31, 1987, until December 30, 2011. Robust t-statistics aregiven in parentheses* Statistically significant at a 10 % significance level** Statistically significant at a 5 % significance level*** Statistically significant at a 1 % significance levela These stocks were not included in the sample because for these firms, there were no historical data publiclyavailable until March 1987

38.89, the null hypotheses can be rejected in both cases. This result indicates that thecorrelation of the test assets (single firms) with the credit risk factor does indeed matterin this time-series framework.

Finally, we investigated the marginal ability of the credit risk factor to predictreturns. We followed Fama and French (2008) and used the cross-sectional regressionapproach of Fama and MacBeth (1973) to answer this question. As test assets, we usedthe excess returns of the 45 portfolios sorted by momentum, size, and credit rating,as described in the previous section. In Table 5, results are reported for different asset

123

K. Grobys, J. Haga

Tabl

e5

Fam

a–M

acB

eth

cros

s-se

ctio

nalr

egre

ssio

ns

cM

RF

SMB

HM

LM

OM

HM

L*

UM

D*

PMU

*W

MB

R-s

quar

edW

ald

−0.3

0***

−2.2

0−0

.28

0.04

1.89

*0.

5392

.45

(−21

.78)

(−1.

42)

(−0.

32)

(0.0

5)(1

.85)

−0.2

2***

−3.0

3**

0.18

−0.1

72.

32**

1.42

0.56

91.9

2

(−22

.51)

(−1.

97)

(0.1

9)(−

0.21

)(2

.20)

(1.5

8)

−0.3

0***

−2.8

7**

0.18

1.30

0.64

*0.

5092

.57

(−23

.57)

(−1.

98)

(0.5

8)(1

.48)

(1.9

3)

−0.3

0***

−3.1

8**

0.18

1.46

**0.

71**

*0.

420.

5191

.70

(−18

.99)

(−2.

09)

(0.5

8)(2

.01)

(2.6

5)(0

.39)

We

used

the

exce

ssre

turn

sof

the

45po

rtfo

lios

sort

edby

mom

entu

m,s

ize,

and

cred

itra

ting

aste

stas

sets

inth

ecr

oss-

sect

iona

lreg

ress

ion

anal

ysis

.We

also

acco

unte

dfo

rth

em

arke

t-w

ide

cred

itri

skfa

ctor

deno

ted

asW

MB

.We

mad

eus

eof

the

cros

s-se

ctio

nala

ppro

ach

asde

taile

din

Fam

aan

dM

acB

eth

(197

3).T

heda

tafo

rth

eM

RF

,SM

B,H

ML

,an

dM

OM

fact

ors

wer

edo

wnl

oade

dfr

omK

enne

thFr

ench

’sw

ebsi

te.T

heda

tafo

rthe

HM

L*,

UM

D*,

and

PM

U*

wer

eob

tain

edfr

omN

ovy-

Mar

xw

ebsi

te.T

hesa

mpl

epe

riod

runs

from

Mar

ch31

,198

7,un

tilD

ecem

ber

30,2

011.

Cro

ss-s

ectio

nalt

-sta

tistic

sar

egi

ven

inpa

rent

hese

s*

Stat

istic

ally

sign

ifica

ntat

a10

%si

gnifi

canc

ele

vel

**St

atis

tical

lysi

gnifi

cant

ata

5%

sign

ifica

nce

leve

l**

*St

atis

tical

lysi

gnifi

cant

ata

1%

sign

ifica

nce

leve

l

123

The market price of credit risk and economic states

pricing model specifications with and without the market-wide credit risk factor.3

Surprisingly, the credit risk factor does not appear to be priced because the risk pre-miums associated with the market-wide credit risk factor is statistically not differentfrom zero in this cross-sectional framework. However, Fama and French (2008, p.1666) pointed out that FM regressions face potential problems as they

impose a functional form on the relation between anomaly variables and returns.This structure is what gives regressions the power to disentangle the return effectsof multiple anomalies. The functional form may, however, be incorrect.

Considering the models in Table 5, it is evident that both the economic magnitude andthe statistical significance of both the market and momentum factors (MRF, MOM,and UMD*) notably increase as the credit risk factor is accounted for. An explanationfor this result may be that the marginal ability of the credit risk factor to predict futurereturns is absorbed by the stronger correlations between the test assets and the marketand momentum factors. However, the disagreement between the cross-sectional andtime-series evidence appears to be a puzzle and is left for future research.

3.4 Sample-split analysis

To check the robustness of our results, we performed a sample-split analysis. Wedivided the overall sample into two subsamples of equal length. The first subsampleran from March 1987 until June 1999, and the second subsample ran from July 1999until December 2011. We used the same model specifications as in the previous sectionand accounted for a recession dummy variable as well. The results are reported inTables 6 and 7. The time-series regression analysis of the first subsample revealed thatthe intercept is statistically significant for all regression specifications. This suggeststhat the credit risk factor had a negative risk premium during this period. Further, theinformation collated in Table 6 provides evidence of a strong relationship between themarket price of credit risk and the interaction factors. Results recorded in the last rowof Table 6 indicate that the interactions between the recession dummy variable and themarket, size, and value factors are statistically significant at a 1 % level. Interestingly,we observed a striking change in the sensitivity of the market price of credit risk againstthe value factor in bear markets between the entire sample and the first subsample.In the entire sample, the loading is 0.86 and statistically significant but in the firstsubsample, the same coefficient is statistically significantly negative with an economicmagnitude of −1.56.

3 We implemented the cross-sectional regressions as follows: In the first stage, we estimated the fac-tor loadings by running time-series regressions in line with Eqs. (3)–(6). For instance, we employedthe estimated factor loadings from Eq. (3) (i.e., φ̂ j1, φ̂ j2 and φ̂ j3), and stacked them into a matrix

φ̂ =(

1, φ̂ j1, φ̂ j2, φ̂ j3

), where 1 denotes a N × 1 vector of ones, and φ̂ is a N × 4 matrix. In the

second stage, we then estimated the corresponding risk premiums at time t by λ̂t =(φ̂

Tφ̂)−1

φ̂T

R̄ext ,

where the vector R̄ext = (

1, R̄ex1t , . . . , R̄ex

Nt

)T contains the excess returns of the test assets at time t . In

Table 5, the average risk premiums ¯̂λ are reported.

123

K. Grobys, J. Haga

Tabl

e6

Tim

e-se

ries

regr

essi

ons

from

the

mar

ket-

wid

ecr

edit

risk

fact

oron

asse

tpri

cing

mod

els

for

the

first

subs

ampl

e

Con

stan

td

MR

FSM

BH

ML

MO

MM

RF

×d

SMB

×d

HM

L×

dM

OM

×d

−0.8

1**

(−2.

42)

−0.7

7**

−0.0

5

(−2.

30)

(−0.

96)

−0.8

4***

0.00

−0.0

10.

20

(−2.

78)

(0.0

6)(−

0.10

)(1

.58)

−1.0

1***

0.00

0.04

0.29

***

0.21

**

(−3.

10)

(0.0

7)(0

.34)

(2.6

8)(2

.60)

−0.8

0**

−0.4

3

(−2.

32)

(−0.

43)

−0.7

5**

−0.4

6−0

.06

−0.0

0

(−2.

19)

(−0.

44)

(−0.

86)

(−0.

04)

−1.0

4***

−0.5

5−0

.01

−0.0

30.

30**

*0.

26**

*−0

.86*

**1.

71**

*−1

.56*

**0.

25

(−3.

05)

(−1.

51)

(−0.

12)

(−0.

24)

(2.8

1)(2

.88)

(−7.

73)

(8.9

9)(−

8.11

)(1

.33)

We

ran

regr

essi

ons

from

this

cons

truc

ted

mar

ket-

wid

ecr

edit

risk

fact

or(W

MB

)on

stan

dard

asse

tpr

icin

gm

odel

san

don

the

sam

em

odel

spec

ifica

tions

cond

ition

edon

adu

mm

yva

riab

le(e

.g.,

d)

indi

catin

gre

cess

ions

.The

data

for

the

MR

F,S

MB

,HM

L,a

ndM

OM

fact

ors

wer

edo

wnl

oade

dfr

omK

enne

thFr

ench

’sw

ebsi

te.T

heda

tafo

rth

ere

cess

ions

wer

edo

wnl

oade

dfr

omth

eN

BE

Rw

ebsi

te.T

hesa

mpl

epe

riod

runs

from

Mar

ch31

,198

7,un

tilJu

ne30

,199

9.N

ewey

and

Wes

t(1

987)

t-st

atis

tics

are

give

nin

pare

nthe

ses

*St

atis

tical

lysi

gnifi

cant

ona

10%

sign

ifica

nce

leve

l**

Stat

istic

ally

sign

ifica

nton

a5

%si

gnifi

canc

ele

vel

***

Stat

istic

ally

sign

ifica

nton

a1

%si

gnifi

canc

ele

vel

123

The market price of credit risk and economic states

Tabl

e7

Tim

e-se

ries

regr

essi

ons

from

the

mar

ket-

wid

ecr

edit

risk

fact

oron

asse

tpri

cing

mod

els

for

the

seco

ndsu

bsam

ple

Con

stan

td

MR

FSM

BH

ML

MO

MM

RF

×d

SMB

×d

HM

L×

dM

OM

×d

−0.6

9

(−1.

63)

−0.7

1*0.

15

(−1.

83)

(1.3

5)

−0.6

3*0.

18−0

.13

−0.0

4

(−1.

69)

(1.5

6)(−

0.99

)(−

0.35

)

−0.7

8**

0.24

**−0

.11

0.16

0.21

*

(−2.

01)

(2.3

0)(−

0.78

)(0

.89)

(2.6

0)

−0.4

1−1

.51

(−0.

82)

(−1.

11)

−0.4

8−1

.25

0.14

*−0

.02

(−1.

02)

(−1.

14)

(1.9

4)(−

0.08

)

−0.3

6−1

.06

0.17

*−0

.19

−0.1

9−0

.01

0.17

0.55

*1.

17**

*0.

77**

*

(−0.

81)

(−0.

70)

(1.7

0)(−

0.93

)(1

.27)

(−0.

09)

(0.8

0)(1

.90)

(2.7

7)(3

.27)

We

ran

regr

essi

ons

from

this

cons

truc

ted

mar

ket-

wid

ecr

edit

risk

fact

or(W

MB

)on

stan

dard

asse

tpr

icin

gm

odel

san

don

the

sam

em

odel

spec

ifica

tions

cond

ition

edon

adu

mm

yva

riab

le(e

.g.,

d)

indi

catin

gre

cess

ions

.The

data

for

the

MR

F,S

MB

,HM

L,a

ndM

OM

fact

ors

wer

edo

wnl

oade

dfr

omK

enne

thFr

ench

’sw

ebsi

te.T

heda

tafo

rth

ere

cess

ions

wer

edo

wnl

oade

dfr

omth

eN

BE

Rw

ebsi

te.T

hesa

mpl

epe

riod

runs

from

July

31,1

999,

until

Dec

embe

r30

,201

1.N

ewey

and

Wes

t(19

87)

t-st

atis

tics

are

give

nin

pare

nthe

ses

*St

atis

tical

lysi

gnifi

cant

ona

10%

sign

ifica

nce

leve

l**

Stat

istic

ally

sign

ifica

nton

a5

%si

gnifi

canc

ele

vel

***

Stat

istic

ally

sign

ifica

nton

a1

%si

gnifi

canc

ele

vel

123

K. Grobys, J. Haga

Table 7 presents the regression results corresponding to the later sub-period. Wedetected a notable difference in the market price of credit risk between the earlierand later periods. The information recorded in the last row of Table 7 shows that theintercept of the market price of credit risk is statistically not different from zero inthe later subsample. Additionally, the loading against the value factor in recessionschanges from significantly negative (see the last row of Table 6) to significantly positive(see the last row of Table 7).

Finally, we turned our attention to the long and short positions of the market-widecredit risk factor and repeated the sample-split analysis separately for the high creditrisk (long position) and low credit risk (short position) portfolios. The results arereported in Tables 8 and 9. Table 8 presents the result for the first subsample. Webegan by investigating the long position of the strategy, which is reported in PanelA of Table 8. We observed that the intercept is statistically not different from zero,which means that high credit risk stocks did not generate any significant returns inthe earlier sample, supporting Avramov et al. (2013). However, during recessions, thelong portfolio exhibits a positive and significant correlation with the size factor anda negative correlation with the value factor. In Panel B of Table 8, the correspondingregression results for the short position are reported. We observed that the interceptsfor all different model specifications are statistically significant. This implies that thenegative payoff of the market price of credit risk in the first subsample was generatedbecause low credit risk stocks generated positive returns in the earlier sample. Inaddition, we observed that during recessions the market price of credit risk has asignificantly positive correlation with the value and size factors, which again supportsthe argument of Vassalou and Xing (2004).

Finally, in Table 9, the regression results for the later subsample are reported. Weinvestigated the behavior of the long and short positions for the period from July 1999to December 2011. Panel A of Table 9 presents the results for the high credit riskportfolio (long position) and shows an insignificant positive intercept. This result isconsistent with the finding relating to the earlier sample that high credit risk stocksdid not generate any statistically significant returns. In contrast to the earlier sample,however, high credit risk stocks in the later subsample appear to be correlated only withthe momentum factor. This correlation is particularly driven during recession periods.In Panel B of Table 9, the corresponding results for the short position (low credit riskstocks) are presented. We found that the intercepts are insignificant for all models untilwe included the recession dummy variable. This variable and its interactions changethe intercept to be significantly positive, although none of the independent variablesare significant apart from the loading against the interaction variable of the marketfactor and the recession dummy (i.e., MRF × d), as shown in the sixth row of PanelB in Table 9.

From these results, we determined that the anomalous negative market price ofcredit risk found for the entire sample is driven by the earlier part of the sample.Moreover, we documented that the exposure of the market price of credit risk to thevalue factor during recessions differs among the subsamples. In fact, it changes froma significantly negative correlation in the earlier subsample to a significantly positiverelationship in the later subsample. Moreover, we found that there is an economicallysignificant difference in the market price of credit risk. Even though the last three rows

123

The market price of credit risk and economic states

Tabl

e8

Tim

e-se

ries

regr

essi

ons

from

the

mar

ket-

wid

ecr

edit

risk

fact

or’s

long

posi

tion

and

shor

tpos

ition

onas

setp

rici

ngm

odel

sfo

rth

efir

stsu

bsam

ple

Con

stan

td

MR

FSM

BH

ML

MO

MM

RF

×d

SMB

×d

HM

L×

dM

OM

×d

Pane

lA:

Tim

e-se

ries

regr

essi

ons

from

the

long

posi

tion

onas

setp

rici

ngm

odel

s

0.47

(0.7

5)

0.57

−0.1

1

(0.9

3)(−

1.24

)

0.61

−0.1

10.

270.

13

(1.1

3)(−

0.99

)(1

.07)

(0.5

5)

0.26

−0.1

10.

390.

34*

0.45

**

(0.4

5)(−

1.01

)(1

.42)

(1.7

6)(2

.13)

0.57

−1.5

8

(1.0

1)(−

0.37

)

0.69

−1.7

4−0

.13

0.24

(1.2

7)(−

0.43

)(−

1.45

)(0

.65)

0.28

0.91

−0.1

60.

180.

29*

0.53

**−0

.82*

4.12

***

−1.5

0**

0.62

(0.5

2)(0

.90)

(−1.

53)

(0.7

6)(1

.73)

(2.3

3)(−

1.77

)(1

3.73

)(2

.40)

(1.4

7)

123

K. Grobys, J. Haga

Tabl

e8

cont

inue

d

Con

stan

td

MR

FSM

BH

ML

MO

MM

RF

×d

SMB

×d

HM

L×

dM

OM

×d

Pane

lB:

Tim

e-se

ries

regr

essi

ons

from

the

shor

tpos

itio

non

asse

tpri

cing

mod

els

1.30

***

(3.4

3)

1.34

***

−0.0

5

(3.7

4)(−

0.77

)

1.45

***

−0.1

10.

29*

−0.0

6

(4.3

2)(−

1.27

)(1

.76)

(−0.

36)

1.27

***

−0.1

10.

35*

0.05

0.23

(3.4

7)(−

1.22

)(1

.87)

(0.3

1)(1

.36)

1.37

***

−1.1

4

(4.3

3)(−

0.35

)

1.44

***

−1.2

7−0

.08

0.24

(4.8

7)(−

0.42

)(−

1.21

)(0

.82)

1.32

***

1.47

−0.1

5*0.

21−0

.09

0.27

0.04

2.41

***

3.06

**0.

38

(4.2

2)(1

.32)

(−1.

83)

(1.3

4)(−

0.07

)(1

.53)

(0.0

6)(9

.97)

(2.5

3)(0

.85)

Pane

lAW

era

nre

gres

sion

sfr

omth

isco

nstr

ucte

dm

arke

t-w

ide

cred

itri

skfa

ctor

’slo

ngpo

sitio

n(e

.g.,

high

cred

itri

sk)o

nst

anda

rdas

setp

rici

ngm

odel

san

don

the

sam

em

odel

spec

ifica

tions

cond

ition

edon

adu

mm

yva

riab

le(e

.g.,

d)i

ndic

atin

gre

cess

ions

.The

data

fort

heM

RF

,SM

B,H

ML

,and

MO

Mfa

ctor

sw

ere

dow

nloa

ded

from

Ken

neth

Fren

ch’s

web

site

.The

data

for

the

rece

ssio

nsw

ere

dow

nloa

ded

from

the

NB

ER

web

site

.The

sam

ple

peri

odru

nsfr

omM

arch

31,1

987,

until

June

30,1

999.

New

eyan

dW

est(

1987

)t-

stat

istic

sar

egi

ven

inpa

rent

hese

sPa

nelB

We

ran

regr

essi

ons

from

this

cons

truc

ted

mar

ket-

wid

ecr

edit

risk

fact

or’s

shor

tpos

ition

(e.g

.,lo

wcr

edit

risk

)on

stan

dard

asse

tpri

cing

mod

els

and

onth

esa

me

mod

elsp

ecifi

catio

nsco

nditi

oned

ona

dum

my

vari

able

(e.g

.,d

)ind

icat

ing

rece

ssio

ns.T

heda

tafo

rthe

MR

F,S

MB

,HM

L,a

ndM

OM

fact

ors

wer

edo

wnl

oade

dfr

omK

enne

thFr

ench

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ebsi

te.T

heda

tafo

rth

ere

cess

ions

wer

edo

wnl

oade

dfr

omth

eN

BE

Rw

ebsi

te.T

hesa

mpl

epe

riod

runs

from

Mar

ch31

,198

7,un

tilJu

ne30

,199

9.N

ewey

and

Wes

t(19

87)

t-st

atis

tics

are

give

nin

pare

nthe

ses

*St

atis

tical

lysi

gnifi

cant

ona

10%

sign

ifica

nce

leve

l**

Stat

istic

ally

sign

ifica

nton

a5

%si

gnifi

canc

ele

vel

***

Stat

istic

ally

sign

ifica

nton

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123

K. Grobys, J. Haga

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The market price of credit risk and economic states

in Panel B of Table 9 indicate a significant positive payoff of low credit risk stocksin the later sample, a comparison of the last three rows in Panel A of Table 9 withthe corresponding intercept in Panel A of Table 9, in association with the results inTable 7, reveals that the spread is statistically not different from zero. In other words,the credit risk puzzle does not exist in the later sample.

4 Conclusions

This paper investigated the market price of credit risk and its properties in differentstates of the economy and demonstrated that the market price of credit risk has changedconsiderably over time. We began by constructing the market price of credit risk basedon S&P credit ratings. Then, we employed three-way sorts on momentum, size, andcredit rating to control for previously documented associations between both creditrisk and size and credit risk and momentum. We showed that this market premiumfor credit risk cannot be explained by Carhart’s (1997) four-factor model or Novy-Marx’s (2013) four-factor model. We made use of a sample-split test to check therobustness of the market price of credit risk. The regression analysis revealed that inthe earlier subsample, the market price of credit risk was negative, which supports thefindings of Avramov et al. (2007, 2009, 2013) and Campbell et al. (2008). However,we found that this negative relationship between the high credit risk and low credit riskportfolios is nonexistent in the second subsample. This empirical evidence suggeststhat the credit risk puzzle is based on temporary mispricing during the first subsample,which disappeared along with the dotcom boom.

Acknowledgments We received valuable comments from Peter Nyberg at the 2013 summer workshoporganized by the Graduate School of Finance. An earlier version of this paper entitled Asset Pricing andCredit Risk was presented at the Midwest Finance Conference 2014 in Orlando, where we received additionalhelpful comments. We also want to thank the participants in the Accounting & Finance Seminar at theUniversity of Vaasa. In particular, we are grateful to Sami Vähämaa, Seppo Pynnönen, and Bernd Papefor their useful advice. Finally, we are grateful to an anonymous reviewer for helpful comments. We areresponsible for all errors and omissions.

References

Avramov D, Chordia T, Jostova G, Philipov A (2007) Momentum and credit rating. J Financ 62:2503–2520Avramov D, Chordia T, Jostova G, Philipov A (2009) Credit ratings and the cross-section of stock returns.

J Financ Mark 12:469–499Avramov D, Chordia T, Jostova G, Philipov A (2012) The world price of credit risk. Rev Asset Pricing Stud

2:112–152Avramov D, Chordia T, Jostova G, Philipov A (2013) Anomalies and financial distress. J Financ Econ

108:139–159Campbell JY, Hilscher J, Szilagyi J (2008) In search of distress risk. J Financ 63:2899–2939Carhart M (1997) On persistence in mutual fund performance. J Financ 52:57–82Denis DJ, Denis DK (1995) Causes of financial distress following leveraged recapitalizations. J Financ

Econ 37:128–157Dichev I (1998) Is the risk of bankruptcy a systematic risk? J Financ 53:1141–1148Elton EJ, Gruber MJ, Agrawal D, Mann C (2001) Explaining the rate spread on corporate bonds. J Financ

56:247–278Fama E, MacBeth J (1973) Return, risk and equilibrium: Empirical tests. J Polit Econ 81:607–636

123

K. Grobys, J. Haga

Fama EF, French KF (1993) Common risk factors in the returns on stocks and bonds. J Financ Econ 33:3–56Fama EF, French KF (1996) Multifactor explanations of asset pricing anomalies. J Financ 51:55–84Fama EF, French KF (2008) Dissecting anomalies. J Financ 63:1653–1678Friewald N, Wagner C, Zechner J (2014) The cross-section of credit risk premia and equity returns. J Financ

69:2419–2469Garlappi L, Shu T, Yan H (2008) Default risk, shareholder advantage, and stock returns. Rev Financ Stud

21:2743–2778Gibbons M, Ross S, Shanken J (1989) A test of the efficiency of a given portfolio. Econometrica 57:

1121–1152Griffin J, Lemmon M (2002) Book-to-market equity, distress risk, and stock returns. J Financ 57:2317–2336Huang J-Z, Huang M (2012) How much of the corporate-treasury yield spread is due to credit risk? Rev

Asset Pricing Stud 2:153–202Jegadeesh N, Titman S (2001) Profitability of momentum strategies: an evaluation of alternative explana-

tions. J Financ 56:699–720Longstaff FA, Schwartz E (1995) A simple approach to valuing risky fixed floating rate debt. J Financ

50:789–821Merton R (1974) On the pricing of corporate debt: the risk structure of interest rates. J Financ 29:449–470Novy-Marx R (2013) The other side of value: the gross profitability premium. J Financ Econ 108:1–28Newey WK, West KD (1987) A simple, positive semi-definite, heteroskedasticity and autocorrelation con-

sistent covariance matrix. Econometrica 55:703–708Ohlson JA (1980) Financial ratios and the probabilistic prediction of bankruptcy. J Account Res 18:109–131Schwert GW (2003) Anomalies and market efficiency. In: Constantinides George M, Harris Milton, Stulz

Rene M (eds) Handbook of the economics of finance. North Holland, AmsterdamVassalou M, Xing Y (2004) Default risk in equity returns. J Financ 59:831–868

123

1

Essay 3

Individual stock volatility and reporting

frequency

Jesper Haga † Hanken School of Economics, Biblioteksgatan 16, 65101 Vaasa, Finland

Abstract

This paper examines the impact of reporting frequency on stock return volatility. The paper uses data from 14 member states of the European Union (EU). The impact of reporting frequency on volatility is examined by comparing means, difference-in-difference regression, and Fama-MacBeth regression. Further, three measures of risk are examined: total volatility, idiosyncratic systematic volatility, and idiosyncratic volatility. The results indicate that higher reporting frequency can decrease volatility during large market declines. In addition, higher reporting frequency slightly increases the average firm specific systematic volatility on the market.

: G12, G14 Keywords: Capital market regulation, disclosure, European Union, volatility

1 Introduction

A firm’s financial reports are given a high degree of attention by the firm’s stakeholders, since stakeholders need to analyze the firm’s financial reports to be able to assess the riskiness and magnitude of the firm’s future cash flows. Even though this is well known, relatively little academic attention has been given to the impact of

Corresponding author. E-mail: jesper.haga@hanken.fi. Telephone: +358443797918

† I would like to thank Nader Virk for all the help with collecting the data. The paper was written during my stay as Research Aff .

2

reporting frequency on the stock price volatility of individual firms. The aim of this study is to address this important issue.

In finance literature, the volatility of an asset is often considered to be the risk of the asset. Moreover, one of the key concepts in asset pricing is that investors should demand higher returns from riskier assets. In addition, a highly volatile stock market is usually seen in a negative light by commenters and criticized for being the result of short-termism among investors. However, it is important to remember that there exists both good and bad volatility. Interestingly, a country’s characteristics and legislation can impact the amount of good and bad volatility. For example, Obstfeld (1994) shows that countries where there are more incentives for company investments usually have more volatile companies on their stock markets. This is an example of good volatility, since investments are associated with both risk and future economic growth. In contrast, an increase in volatility that is harmful can decrease economic growth and destabilize the economy. Bad volatility can be increased, for example, by an increase in political risk or legislation that gives short-term incentives to managers or investors.

This paper examines whether the reporting frequency affects the volatility of stock prices. This question is easy to relate to the finance literature, since a higher reporting frequency leads to more information disclosure. There are several studies that examine the relation between information disclosure and stock price volatility. However, there are disagreements about the impacts from more information disclosure on stock price volatility. To start, LeRoy and Porter (1981) show that in a framework where the market is efficient and discount rates are constant, a higher level of information disclosure decreases the volatility on the market. In line with this, Teoh, Yang and Zhang (2009) suggest that less information disclosure gives investors more unknown information to disagree about. Because of this the stock price volatility should increase with less information disclosure. In contrast to these studies, Li and Myers (2006) suggest that in a framework where insiders are concerned about their private benefits, more information disclosure leads to more volatility. As shown, the theoretical models present different predictions on how the levels of information disclosure impact the volatility. Furthermore, price volatility can be divided into systematic and idiosyncratic volatility. More information disclosure also impacts on the idiosyncratic volatility. Morck, Yeung and Yu (2000) suggest that more disclosure increases the firm-specific information incorporated in the stock price, since stock returns are more affected by idiosyncratic shocks. With this logic, Brockman and Yan (2009) use idiosyncratic volatility to predict informativeness in stock prices. According to them, higher idiosyncratic volatility leads to more informative stock prices. Further, supporting evidence is shown by Durnev, Morck, Yeung (2003). They suggest that firms' prices that reflect more information have a higher level of idiosyncratic volatility. On the other hand, Krishnaswami and Subramaniam (1999) suggest that higher idiosyncratic volatility is a sign of less informative stock prices. In a recent paper, Lee and Liu (2011) present a theoretical model in which there exists either a U-shaped or negative relationship between price informativeness and idiosyncratic volatility; the relationship being dependent on the calibration of the model. In their model, both noise and information have an impact on idiosyncratic volatility. Moreover, the noise component constantly decreases when information disclosure increases. The information component of idiosyncratic volatility first decreases when information

3

disclosure increases. However, at some level of information disclosure, the information component of idiosyncratic volatility starts to increase when information disclosure increases. These two effects create the U-shaped or negative relationship between price informativeness and idiosyncratic volatility. Overall, more empirical evidence could provide further knowledge about this dilemma. At present, the literature does not discuss the possible impact of more information disclosure on systematic risk. However, in a recent paper, Savor and Wilson (2014) suggest a link between earnings announcements and systematic risk. This link arises because investors use earnings announcements to update their expectations about both the announcing firm and non-announcing firms. As an effect, the covariance between firm-specific and market news peaks around the firm’s announcement. Further, according to Savor and Wilson (2014), this increase in systematic risk is compensated with higher expected returns surrounding the earnings announcement. Patton and Verardo (2012) also show that investors use the information from the announcing firm to update expectation about the aggregated economy. Firm betas are significantly higher on the announcement day. In line with this, the systematic risk is expected to increase with a higher reporting frequency. The discussion above shows that the relationship between volatility and reporting frequency is of interest to both academic literature and policy makers.

For this study, the EU capital market has several desirable features. First, the variation in domestic legislation regarding quarterly reporting presents a good research setup. In addition, these legislations have changed for some countries, but at different points of time. Together, these details present a better research setup than if all the legislation changes had occurred in one single event at one point of time. Second, financial and disclosure regulation is very similar across EU countries with one exception, which is the reporting frequency. This is also one argument for the inclusion of only 14 countries out of the 27 member countries. Since all these 14 countries have been members of the EU from 1995, they have had several years to harmonize their disclosure regulation.

In this paper, we use three methodologies to examine whether reporting frequency has an impact on stock price volatility. This study also investigates three volatility measures: total risk, systematic risk and idiosyncratic risk. With a systematic risk model, the total risk is divided into systematic and idiosyncratic risk. The systematic risk model used includes a world market factor, a domestic market factor, a regional size factor and a regional value factor. According to the asset pricing models, only systematic risk is important. However, idiosyncratic risk still has an important role in many areas, even though it can be removed using diversification. Importantly, idiosyncratic risk can be related to stock price informativeness. In addition, there is evidence in asset pricing literature that firms with a higher idiosyncratic risk have lower expected returns (Ang, Hodrick, Xing and Zhang, 2006; Ang, Hodrick, Xing and Zhang, 2009). In corporate finance literature, insiders that co-invest heavily with outsiders are not able to diversify away the idiosyncratic risk from that investment. Finally, the microstructure literature has shown that idiosyncratic risk and illiquidity are related issues, since market makers take smaller positions in firms with higher idiosyncratic risk to decrease their exposure to risk. In line with this discussion, it is important to study all three risk measures.

4

First, a comparison is made between the mean and median risk levels for quarterly reporting firms with matched non-quarterly reporting firms. A propensity score matching procedure is used to match the quarterly reporting firms with similar non-quarterly reporting firms. The results from the comparison between means and medians provides mixed evidence on the relationship between reporting frequency and total risk. From 2003 to 2009, a higher reporting frequency had a negative impact on total risk. However, from 2010 to 2013 there was a positive impact on total risk. Interestingly, the negative relationship between total risk and reporting frequency from 2003 to 2009 is driven by idiosyncratic risk and the positive relationship from 2010 to 2013 is driven by systematic risk. Second, we use difference-in-difference regressions to examine if a higher reporting frequency has an impact on the risk measures. For the difference-in-difference procedure, a so-called treatment needs to be specified. Here, firms are considered treated if they have changed from non-quarterly reporting to quarterly reporting. These difference-in-difference regressions are estimated for every two years, for example 2000-2001 and 2001-2002. In addition, the results from the difference-in-difference regressions suggest that a higher reporting frequency has a stabilizing impact during recessions. In contrast, there is no evidence to suggest that a higher reporting frequency impacts the risk measures on average. Third, Fama-MacBeth regressions are used to investigate whether there is a relationship between a higher reporting frequency and the risk measures. In the Fama-MacBeth regressions, the independent variables are a dummy for the quarterly reporting and control variables. On average, although firms with a higher reporting frequency have a lower risk level, when all control variables are included in the regression, this relationship disappears. On the other hand, with all control variables included, the Fama-MacBeth regressions’ results suggest that a higher reporting frequency increases the firms’ systematic risk. Furthermore, it is found that both systematic risk and idiosyncratic risk are driven by the same firm and country characteristics. One of those characteristics is age. Our Fama-MacBeth regressions show that young firms have a higher systematic as well as idiosyncratic risk. According to Bartram, Brown and Stulz (2012), young firms are often more innovative. We also find that firms with more R&D expense in relation to total assets have higher volatility, both systematic and idiosyncratic. Moreover, the level of plant, property and equipment to total assets has a negative relationship with all risk measures. A similar relationship is found between debt maturity and risk measures.

To further examine the impact from a higher reporting frequency on our risk measures during recession, we run a panel regression in which we include a recession dummy. The results again show that a higher reporting frequency decreases the level of risk during recessions.

Overall, my study contains two highlights: First, a higher reporting frequency leads to lower stock price volatility during times of recession. Possibly, the impact from a higher reporting frequency is larger in times of recession, because of more unknown information. Second, an increase in systematic risk occurs for firms with a higher reporting frequency. This supports the model by Savor and Wilson (2014). According to them, investors use earnings announcements to update beliefs for the whole market, which explains why systematic risk increases with a higher reporting frequency.

5

The rest of my paper is organized as follows: Section 2 of this paper describes the reporting frequency regulation in the EU countries. Section 3 presents the dataset. In Section 4 the results are presented. Finally, Section 5 concludes the paper.

2 Regulation Background

To investigate how higher reporting frequency has an impact on stock-market risk, quarterly and non-quarterly reporting firms in the EU are compared. The EU regulated market presents a perfect setup to study the impacts from reporting frequencies, since the market has very harmonized financial disclosure and reporting regulation, although the market still has variation in reporting frequency. This variation gives a possibility to analyze the impact from reporting frequency on the volatility.

In 1999, the EU established the Financial Services Action Plan (FSAP), for the purpose of improving and harmonizing the European Union’s capital markets. As a first step toward harmonizing the EU's capital market, the European Commission proposed changes in legislation for the capital markets. One such proposed change was to introduce mandatory quarterly reporting for all firms listed on the EU regulated market. According to the European Commission, this would improve the transparency of the firms from an investor’s and analyst’s point of view. As earlier mentioned, the accounting and finance literature present both empirical and theoretical evidence suggesting that higher reporting frequency increases the transparency and decreases the asymmetric information on the capital markets. Even though there are clear benefits with a higher reporting frequency, some countries (e.g. the UK, the Netherlands, Denmark, and Austria) strongly opposed the proposal by the European Commission. This opposition led to the dismissal of the proposal. One of the main arguments mentioned by opposing countries was that a higher frequency of earnings reports would increase short-termism among investors and managements.

Even though the European Commission failed to make quarterly earnings reports mandatory, in 2004 the EU approved the Transparency Directive (TD). Further, although the TD had to be included into the countries national law by January 2007, only two countries (the United Kingdom and Germany) had included the TD in their legislation by that time. Moreover, the last country in this study to implement the TD was Italy in August 2009 (Christensen, Hail and Leuz, 2015). The TD regulates the minimum disclosure required from a firm listed on the EU regulated market. There are three main parts of the TD: minimum information in the financial reports, notification and reporting of major holdings, and obligations on the timing and storage of this information (European Commission, 2004). In addition, all firms listed on the EU regulated market have to follow the directives in the TD. For this study, the most important aspect of the TD is that it requires companies to provide an interim management statement (IMS) 1 at least quarterly. In practice, this means that all companies that only report semi-annually have to provide an IMS report for the first and third quarter. Moreover, an IMS only has to contain a general description of the financial position and performance of the company and an explanation of events and

1 In addition to the IMS requirement, the TD also requires all EU countries to have an Officially Appointed Mechanism (OAM). This OAM should be used as an information storage from which investors can access up-to-date information free of charge.

6

transactions that have taken place during the relevant period (European Commission, 2004).

Already from the start, the Transparency Directive was criticized in many EU countries, especially in the UK. Due to criticism of the TD, the EU started an independent investigation of the directive (Mazars, 2009). The main findings of this investigation were that the minimum requirements in the IMS are not specific enough and that the costs of disclosing the IMS are high for the small and medium-sized firms (SME). After this investigation, the EU Commission started reviewing the TD and in May 2011 they published a new report on the subject. This updated version of the TD was presented in October 2011. In this not yet approved version of the TD, the EU Commission has changed tack and is now planning to abolish mandatory quarterly financial reports for companies listed on EU regulated markets. Furthermore, the new TD in its current form would restrict the ability of EU member nations to implement stricter rules than in the TD (European Commission, 2004).

3 Data

Price data are downloaded from Datastream and accounting data from the Worldscope database from 1995 through 2013. For each country, local currency at the end of the time period is used as the currency for the data. Several variables are collected: total assets, shares outstanding, book value of total debt, and book-to-market. Those firm observations that have missing values for these variables are dropped. In addition, all secondary listings are excluded from the sample. Other firms that are removed are real estate firms, investment trusts, and companies with suspicious phrases in their name.2 For firms with multiple stock series only the most liquid stock series are used, the rest of the series are removed from the sample. Especially in Scandinavia, there are companies with multiple stock series, usually the difference between the series are the voting rights per share. This cleaning procedure generates a final sample that contains 76,220 firm-year observations.3 Furthermore, the United Kingdom has the most firm-year observations with 26,310 and Ireland has the least with 684.

The focus of the study is on risk and reporting frequency. In this study three risk measures are used, and each risk measure is calculated using local currency, meaning that the euro is used for all countries except Sweden (Swedish krona), Denmark (Danish krone) and the United Kingdom (British pound). In addition, this study measures firm risk as total risk, systematic risk, and idiosyncratic risk. The first risk measure, total risk, is the annualized standard deviation of monthly stock returns. The systematic risk and idiosyncratic risk are breakdowns of the total risk. A systematic risk model has to be used in order to make this breakdown possible. As a systematic risk model, a model with both a local and a global market factor is used. The CAPM with a

2 These suspicious phrases are collected from previous studies (Ince and Porter, 2006; Griffin, Kelly and Nardari, 2010; Watanabe, Xu, Yao and Yu, 2013), and from the homepages of stock exchanges. In practice, when a company’s name contains one of the pre-specified phrases, that company is removed from the dataset. 3 According to Ince and Porter (2006), return data from Datastream can contain large reversals due to incorrect price data. This issue is corrected, as Ince and Porter (2006) suggested, by setting and to missing when absolute of is less than 200 % or is less than 200 % and (1 + ) (1 + ) 1 <50%.

7

local market premium is preferred in a segmented market, while a global market premium is preferred in a fully integrated market. To avoid this issue, this study uses both a local and a global market factor. In addition to market factors, the model also accounts for regional size (SMB) and value factors (HML). As in Bekaert, Hodrick and Zhang (2012), regional SMB and HML factors are used. However, although local Fama-French factors are commonly utilized as determinants of cross-sectional expected returns, in this sample there are too few firms in the majority of countries to calculate stabile country SMB and HML factors. , = + + + + + , (1) where , is the company i's return in local currency month t, is the return in excess of the risk-free rate in local currency for the local market, is the return in excess of the risk-free rate for the global market index, is the return for the European SMB factor, is the return for the European HML factor, and , is the error term for company i at time t.4 Further, idiosyncratic risk for each company is an annualized standard deviation of , . The systematic risk for each company is calculated as the square root of the subtraction between the company return variance and variance of error term.

As earlier mentioned, the accounting data for this test is collected from the Worldscope database. In total 11 variables are collected, and these variables are used to calculate ratios comparable between companies. A detailed description of the ratios calculated is presented in Appendix 1. Furthermore, both accounting and return data are winsorized at the top and bottom 0.5 %. The winsorizing procedure is done to handle outliers in the data. Further, a threshold level at 0.5 % is modest and should not bias the result. In addition to accounting data, following Lesmond (2005) the number of non-trading days is used as a proxy for liquidity. The main variable for this study is FREQ, which is an indicator as to whether the company reported quarterly or not. FREQ is a dummy variable that has the value of one if the company reports quarterly and the value of zero in all other situations.5

[Insert Table 1 near here]

Table 1 reports a summary of individual firm statistics for each country. The median total risk varies between countries. The highest median total risk is 0.478 (Greece) and the lowest is 0.245 (Belgium). In addition to the total risk, Table 1 reports the percentage of quarterly reporting companies. In this sample, Sweden and Finland have the highest percentage of quarterly reporting companies. These two countries have had a strict regulation enforcing quarterly reporting since the beginning of 2000 (see Appendix 2 for more information regarding each country’s regulations). In contrast, the UK (2.6 %) and France (3.6 %) have the lowest percentage of quarterly reporting companies. Notably, France has a regulation that demands listed companies to report at least their sales each quarter. Table 1 also reports the median value of total assets. The median values of total assets between the countries vary heavily. In Sweden the median firm has a total asset value of 63.2 million euros and in Italy the median firm 4 The factor data is downloaded from Kenneth French’s homepage. 5 Worldscope reports four different reporting frequency values: quarterly reporting, semi-annually, annually and undefined.

8

has a total asset value of 503.7 million euros. The size of the firm is expected to have a negative impact on our risk measures. Another variable that is expected to have a negative impact on the risk measures is the age of the firm. The median firm age for each country is presented in column eight. Here, Denmark has the highest median firm age and Sweden has the lowest, and the difference is five years between the medians. Overall, Table 1 does not show a clear relationship between any of the risk measures and the percentage of firms that report quarterly. On the other hand, these results do not adjust for possible differences in firms and industries between countries.

In addition to the firm-specific variables, we also collect variables for country characteristics. To proxy shareholder protection and corporate governance, we collect the anti-directors rights index from Djankov, Roll, de Silanes and Shleifer (2008). A higher value for anti-directors rights index implies that the country has better shareholder protection and governance. The risk measures are expected to be greater for countries with higher values of the anti-directors rights index. Further, an index of creditor rights is collected from Djankov, McLeish and Shleifer (2007). Here, a higher value for the creditor rights indicates better creditor rights. In addition, we use two proxies for equity market development; these are the ratio of stock market capitalization to GDP and stock market turnover. Stock market turnover is calculated as the total value of shares traded during the year divided by the average total market capitalization for the market that year. The data is collected from the World Bank database.

[Insert Table 2 near here]

Table 2 provides a summary of the additional variables for each country. In this table, column four shows the median capital expenditures (CAPEX) proportion to total assets for all countries. Moreover, Finland is the country with the highest median proportion of CAPEX (0.043), and Italy has the lowest proportion (0.020). Firms with a higher value of CAPEX/total assets are expected to have higher values for the risk measures. Furthermore, this is an example of a good volatility increase, because more CAPEX spending can lead to greater economic growth. For each country, column five and six report the anti-director rights and creditor rights, respectively. Column seven of Table 2 shows that Spain has the fastest and Belgium has the slowest stock-market turnover. In addition, column ten of Table 2 shows the reporting situation before the TD and column eleven shows the reporting situation after the TD. Italy and Spain changed their regulation regarding quarterly reporting when the TD was introduced. These countries changed from mandatory to voluntary quarterly reporting. Further, Finland, Portugal and Sweden still have mandatory quarterly reporting. There are also segments on the German and Austrian stock market where quarterly reporting is mandatory. Table 1 and 2 show that there are differences in the risk measures between the countries, but that there are as well differences in firm-specific and country-specific characteristics.

9

4 Analysis

This section presents the results from the comparison of risk measures between quarterly and non-quarterly reporting firms. Three methodologies are used to investigate the impact from a higher frequency reporting on the risk measures. First, we examine the difference in risk level between quarterly reporting and matched non-quarterly reporting firms. Second, difference-in-difference (DID) regressions are used to examine whether there is a change in the risk measures when a firm changes to quarterly reporting. Third, Fama-MacBeth regressions are used to provide evidence on how the risk measures are affected from a higher reporting frequency.

The mean and median of the risk measures are compared between quarterly reporting firms and matched non-quarterly reporting firms. This matching procedure enables an examination between comparable firms. There are different methods to match firms, for example firms can be matched according to a single dimension, such as size or book-to-market. In this study, propensity score matching is used to find matching firms. This econometric model is preferable because it enables researchers to match objects along multiple dimensions. Each year, quarterly reporting firms are matched with similar non-quarterly reporting firms. Specifically, we use propensity score matching to match each quarterly reporting firm with a similar non-quarterly reporting firm. The propensity score matching procedure consists of two steps. The first step is a logit regression. In this regression, the dependent variable is a dummy variable with a value equal to one for quarterly reporting firms and to zero otherwise. The independent variables in this regression consist of firm-specific characteristics. These characteristics are so-called matching characteristics. In the second step, the coefficients from the logit regression are used to calculate predicted values. These predicted values are used to match each quarterly reporting firm with the chosen characteristics most similar to each non-quarterly reporting firm. The matching procedure is done each year and the matching variables are size, age and R&D/Total assets. To avoid that the matching variables and the risk measures are determined at the same point in time, the matching variables are used with a one year lag. Further, we have followed Bartman et al. (2012) in choosing matching variables. They also used size and firm age as matching variables. Furthermore, they also used a market-to-book ratio, although here we instead use R&D/Total assets. This is because we do not find any clear evidence in the Fama-MacBeth regressions, suggesting that there exists a relationship between market-to-book and total risk and systematic risk.

[Insert Table 3 near here]

For each year, Table 3 shows the number of quarterly reporting firms, the percentage of quarterly reporting firms, and the median for matching characteristics. Columns two and three of Table 3 show that the proportion of quarterly reporting firms has been increasing during the sample. The largest increase occurred between 2000 and 2002. In 2000, my dataset contained 366 quarterly reporting firms (9.8 % of the total amount of firms) and in 2002 the dataset contained 1,212 quarterly reporting firms (30.1 % of the total amount of firms). Moreover, Table 3 reports for both the quarterly reporting and the matched firms the median value of total assets, age and R&D/Total assets. Overall, Table 3 suggests that the matching process has succeeded, because the

10

descriptive values shown are similar firms in both groups of firms. In addition, one trend is that the median value of total assets is decreasing over time. There is a drop in the median age of the quarterly reporting firms from 2001 to 2002. This drop is driven by the increase in quarterly reporting firms.

[Insert Table 4 near here]

For each year, Table 4 reports the difference in means for all risk measures between quarterly reporting firms and matched non-quarterly reporting firms. In addition to the difference in means, Table 4 also reports the p-values for the difference from both the two-sample t-test and Wilcoxon rank sum test. Examining differences in total risk, we find that there are both significant positive and negative differences. From 2003-2009, all differences in total risk are negative and in five out of seven years this difference is significant. On the other hand, from 2010-2013 all differences are positive and significant. A possible explanation for the change in the pattern is that from 2010 all firms on the EU regulated market that did not report quarterly had to disclose an IMS. Further, the higher total risk for quarterly reporting from 2010-2013 originates mostly from an increase in systematic risk. Moreover, the year with the largest negative difference is 2009 and the year with the largest positive difference is 2012, with differences of -0.057 and 0.058 respectively. The difference in systematic risk between quarterly and non-quarterly reporting firms is significantly positive during six years and significantly negative in three years. In contrast, the difference in idiosyncratic risk is significantly negative in six years and significantly positive in three years. This evidence suggests that a higher reporting frequency can have different effects on systematic and idiosyncratic risk.

The second approach we use to examine whether the reporting frequency affects the risk measures is a difference-in-difference (DID) regression. DID regressions are able to eliminate bias in estimating a single difference as long as the underlying trends are linear and parallel. In the DID regression we compare the risk measures for the treatment group before and after the treatment. The treatment in this case is a change to quarterly reporting. We also use a control group of firms that have not changed to quarterly reporting. In the DID regressions, the risk is measured for the year before the change and compared to the risk for the year after the change. Because of this, all DID regressions are done separately for every two-year period. Furthermore, by including control variables my coefficients will only pick up the impact from the change in reporting frequency.

I estimate the following DID regression: , = + + + ( ) , + , + , (1) where , is the risk measure for firm i in time period t, is a dummy variable which has the value one for firms that have changed to quarterly reporting and zero otherwise. Further, is a dummy variable with the value one if the observation is from the second time period and zero otherwise. Finally, , is a matrix that contains control variables.6 Table 5 presents the coefficient of interest from the DID

6 These control variables are: PPE/total assets, profitability, R&D/total assets, percentage of zero returns, log of age, log of total assets, book-to-market, leverage, debt maturity, creditor rights, anti-director rights,

11

regressions and corresponding t-statistics. For the majority of the DID regressions, the coefficient of interest is insignificant, with three exceptions 2001-2002, 2007-2008, and 2009-2010. Interestingly, two of these exceptions (2001-2002 and 2007-2008) occur in times of market distress. The Dot-com bubble collapsed in 2000 and 2001, but the stock market continued to decline in 2002. Moreover, stock markets in Europe experienced huge market declines in 2008 in the wake of the housing bubble crash in the United States. In both cases, DID regressions find a significant negative impact from a higher reporting frequency on the risk measures. Moreover, the impact is both statistically and economically significant, the coefficient for total risk is -0.079 for 2001-2002 and -0.089 for 2007-2008. The value of the coefficient can be compared with the median value of total risk which is 0.341. This result suggests that a higher reporting frequency can decrease the stock price volatility for a firm during times of market distress. Furthermore, the absence of significant coefficients for the majority of the years suggests that on average the risk measures are not affected by a higher reporting frequency.

[Insert Table 5 near here]

Finally, Fama-MacBeth regressions are used to examine whether the reporting frequency affects the risk measures. These regressions have the risk measures as the dependent variable and the FREQ dummy as the independent variable. In addition, the Fama-MacBeth regressions include country and firm characteristics as control variables. Table 6 reports the Fama-MacBeth coefficients and corresponding t-statistics. In the regressions when only FREQ is used as an independent variable (not tabulated), firms that report quarterly have a significantly lower total risk and idiosyncratic risk. The coefficient for FREQ without control variables for total and idiosyncratic risk is -0.033 and -0.009, respectively. In the Fama-MacBeth regressions, where all control variables are used, the results do not show the same relationship between a higher reporting frequency and risk. Instead, the result shows a significantly higher systematic risk. In contrast, neither total risk nor idiosyncratic risk is significantly affected by the reporting frequency. In the Fama-MacBeth regression for systematic risk, the FREQ coefficient when all control variables are used is 0.003. This evidence suggests that a higher reporting frequency increases the systematic risk of firms.

[Insert Table 6 near here]

Overall, the Fama-MacBeth regression coefficients are in line with expectations. Table 6 shows significant negative coefficients for PPE/total assets, profitability, percent zero returns, age, total assets, debt maturity, stock market cap, and GDP per capita. These variables have a positive impact on the risk measures: R&D/total assets, book-to-market, leverage, and anti-director rights. Previous literature, such as Pastor and Veronesi (2003) and Bartam et al. (2012), has also shown a negative relation between volatility and total assets, percent zero returns, and age. According to Bartam et al. (2012), younger firms are more innovative and more innovative firms are also riskier. stock market turnover, stock market to GDP, and GDP per capita. In addition, all these control variables are measured at the end of the first year.

12

Similar argumentation can be done for why firms with higher R&D/Total assets contain more risk. By investing more, firms increase their riskiness, since there is uncertainty regarding how the new investments will pay off. The percentage of zero returns has a negative relationship with the risk measures. This suggests that more illiquid stocks have less risk measured with volatility. Stock market turnover and GDP per capita both have a negative impact on all three risk measures.

In summary, the results from these Fama-MacBeth regressions suggest that a higher reporting frequency, on average, does not affect the total risk and idiosyncratic risk. On the other hand, we find that a higher reporting frequency significantly increases the firm specific systematic risk.

4.1 Robustness To further support the results, we run a panel regression to investigate the effects from recessions. In the panel regression we include a recession dummy. This dummy variable has the value one if more than one quarter of the year is classified as a recession by NBER.7 To explore the effect on our risk measures from a higher reporting frequency during recession, we, in the panel regression, include FREQ, the recession dummy and an interaction between FREQ and the recession dummy. Table 7 reports the coefficients from the panel regression. The key result is that the interaction term is negative and significant for total risk and idiosyncratic risk. Furthermore, the interaction coefficient is negative, albeit insignificant for the systematic risk. This implies that a higher reporting frequency reduces the risk measured as volatility during recessions. Table 7 also shows, as expected, a significant positive coefficient for the recession dummy in all three regressions. This suggests that the stock market is more volatile in times of recession.

[Insert Table 7 near here]

To ensure the robustness of our results we run the Fama-MacBeth regressions with risk measures estimated with weekly returns instead of monthly. The local and the global market were used to estimate the risk measures. Table 8 presents the coefficients from the Fama-MacBeth regressions. Taken together, the results confirm the previous conclusion. Firms with a higher reporting frequency bear more systematic risk. The FREQ coefficient for systematic risk is 0.003 with a t-statistic of 7.89. Furthermore, the FREQ coefficient is positive also for the total risk and idiosyncratic risk regressions, albeit the coefficients are insignificant in both cases.

[Insert Table 8 near here]

7 The years classified as recession years are 2001, 2008 and 2009. The recession data was downloaded from: http://www.nber.org/cycles.html.

13

5 Conclusion

This paper investigates the impact from higher reporting frequency on total risk, systematic risk, and idiosyncratic risk. Moreover, this paper relies on a data set containing firms from 14 EU countries during the time period January 1995 to September 2013. This data set has several desirable features as variations in reporting frequency between firms and changes in reporting frequency regulations. The main finding in this paper is that a higher reporting frequency can decrease the stock price volatility of firms during recessions. With the exception of recessions, however, it was not found that a higher reporting frequency significantly affects total stock price volatility. On the other hand, an increase in systematic risk for firms with a higher reporting frequency is found.

The first result partly supports the suggestions made by Teoh et al. (2009). They suggested that more information disclosure leads to lower volatility, because then investors have less unknown information to disagree about. Moreover, since the amount of unknown information is large during large recessions, the effects from higher reporting frequency is increased during those time periods. This is the reason why firms with a higher reporting frequency have significantly lower stock price volatility during recessions. However, on average there is no clear impact from higher reporting frequency on total and idiosyncratic risk.

The second result shows that firms with a higher reporting frequency are exposed to more systematic risk. This increase in systematic risk for quarterly reporting firms is in line with the model by Savor and Wilson (2014). In their framework, the co-movement of firms increases during announcement days; because the co-movement increases, the systematic risk also increases.

The discussion about reporting frequency regulations has drawn considerable attention and passionate arguments in the EU. One of the arguments against a mandatory quarterly reporting has been that a higher reporting frequency increases the short-terministic behavior among investors and managers. This study shows that if such behavior exists it does not significantly impact the risk measures. We further show evidence suggesting that during times of market declines, a higher reporting frequency can help to stabilize the equity market.

14

REFERENCES

Ang, A., Hodrick, R., Xing, Y., Zhang, X., 2006. The cross-section of volatility and expected returns, Journal of Finance, 61, 259–299.

Ang, A., Hodrick, R., Xing, Y., Zhang, X., 2009. High idiosyncratic volatility and low returns: International and further U.S. evidence, Journal of Financial Economics, 91, 1–23.

Bartram, S., Brown, G., Stulz, R., 2012. Why are U.S. stocks more volatile? Journal of Finance 67, 1329–1370.

Bekaert, G., Hodrick, R., Zhang, X., 2012. Aggregate idiosyncratic volatility. Journal of Financial and Quantitative Analysis 47, 1155–1185.

Brockman, P., Schutte, M., Yu, W., 2009. Is idiosyncratic risk priced? The international evidence. Working paper. Available at SSRN: http://papers.ssrn.com/ abstract =1364530.

Christensen, H., Hail, L., Leuz, C., 2015. Capital-market effects of securities regulation: prior conditions, implementation and enforcement. Working paper. Available at SSRN: http://papers.ssrn.com/abstract=1745105.

Djankov, S., McLiesh, C., Shleifer, A., 2007. Private credit in 120 countries. Journal of Financial Economics 84, 299–329.

Djankov, S., Roll, R., de Silanes, F. L., Shleifer, A., 2008. The law and economics of self dealing. Journal of Financial Economics 88, 430–465.

Doidge, C., Karolyi, A., Stulz, R., 2007. Why do countries matter so much for corporate governance? Journal of Financial Economics 86, 1–39.

Durnev, A., Morck, R., Yeung, B., 2003. Value-enhancing capital budgeting and firm-specific stock returns variation, Journal of Finance, 25, 65–105.

European Commission, 2004. Directive 2004/109/EC of the European parliament and of the council. Official Journal of European Union, 390, 38–57.

European Commission, 2011. Proposal for a directive of the European parliament and the European council amending directive 2004/109/E.

15

Griffin, J., Kelly, P., Nardari, F., 2010, Do market efficiency measures yield correct? A comparison of developed and emerging markets, Review of Financial Studies, 20, 905–951.

Ince, O., Porter B., 2006. Individual equity return data from Thomson Datastream: Handle with care! Journal of Financial Research, 29, 463–479.

Krishnaswami, S., Subramaniam, V., 1999. Information asymmetry, valuation, and the corporate spin-off decision. Journal of Financial Economics 53, 73–112.

Lee, D., Liu, M., 2011. Does more information in stock price lead to greater or smaller idiosyncratic return volatility? Journal of Banking and Finance 35, 1563–1580.

LeRoy, S., Porter, R., 1981. The present-value relation: Tests based on implied variance bounds. Econometrica 49, 555–574.

Lesmond, D., 2005. Liquidity of emerging markets. Journal of Financial Economics 77, 411–452.15

Li, J., Myers, S., 2006. R-squared around the world: New theory and new tests. Journal of Financial Economics 79, 257–292.

Mazars, 2009. Transparency directive assessment report, prepared for the European Commission Internal Market and Services DG, Mazars. Available at: http://ec.europa.eu/internal_market/securities/docs/transparency/report-application_summary_en.pdf

Morck, R., Yeung, B., Yu, W., 2000. The information content of stock markets: why do emerging markets have synchronous stock price movements? Journal of Financial Economics 58, 216–260.

Newey, W., West, K., 1987. A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix, Econometrica, 55, 703–708.

Obstfeld, M., 1994. Risk- American Economic Review 84, 1310–1329.

Pastor, L., Veronesi, P., 2003. Stock valuation and learning abou Journal of Finance 58, 1749–1790.

Patton, A., Verardo, M., 2012. Does beta move with news? Firm-specific information flows and learning about profitability, Review of Financial Studies, 25, 2789–2839.

16

Savor, P., and M. Wilson 2014, Earnings announcements and systematic risk, Journal of Finance, Forthcoming.

Teoh, S., Y. Yang and Y. Zhang 2009, R-square and market efficiency, Working paper. Available at SSRN: http://papers.ssrn.com/abstract=926948.

Watanabe, A., Xu, Y., Yao, T., Yu, T., 2013. The asset growth effect: Insights from international equity markets, Journal of Financial Economics, 108, 529–563.

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Appendix A: Variable Description

Variable Definition

Firm characteristics

FREQ A dummy variable that has the value of one if the firm reports quarterly and zero otherwise. Source: Datastream.

Total assets Total Assets. Source: Datastream

Age Difference between month of observation and the first observed price for the stock +1. Source: Datastream.

R&D/Total assets R&D divided by total assets. The value of zero when R&D is missing. Source: Datastream.

PPE/Total assets Total PPE divided with total assets. Source: Datastream.

Cash/(Total assets-Cash) Cash and short-term investments divided by total assets minus cash and short-term investments. Source: Datasteam

Debt maturity Total long-term debt divided by total debt. Source: Datastream.

Percent of zero returns Percentage of a firm’s daily returns in local currency that equals zero. Source: Datastream.

Total debt Total debt. Source: Datastream.

Leverage (Total debt+preferred shares) divided by total assets. Source: Datastream.

Country characteristics

GDP per capita GDP per capita in USD. Source: World Bank.

Market Cap/GDP End of year market capitalization divided by nominal GDP. Source: World Bank.

Stock market turnover Traded shares during a year divided by outstanding shares. Source: World Bank.

Anti-director rights Source: Djankov et al. (2007)

Creditor rights Source: Djankov et al. (2008)

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Appendix B : Overview of interim disclosure

This Appendix presents the situation in the countries in this study. The majority of the countries consists of a major EU regulated market and a smaller regulated unofficial market.

Austria: The main stock market is the Vienna Stock Exchange. The Vienna Stock Exchange can be divided into three segments, of which one requires quarterly earnings reports. Those three segments are: Prime Market, Mid Market, and Standard Market. Of these three segments, only companies listed on the Prime Market have to report their earnings quarterly. In addition, to be included in the main stock market index, ATX (Austrian Traded Index, which contains the 20 largest publicly traded firms in Austria), the companies have to be listed on the Prime Market. Austria’s market for small to mid-sized companies is the Mid Market. Companies listed on the Mid Market only have to report their earnings semi-annually. Both the Prime Market and Mid Market are EU regulated markets, while the Standard Market is a regulated unofficial market. Moreover, companies listed on the Standard Market are usually micro companies with low liquidity.

Belgium: In Belgium, the main stock market operator is NYSE Euronext Brussels. Furthermore, NYSE Euronext Brussels can be divided into two segments: the Euronext and Alternext. Neither of these segments requires companies to issue quarterly earnings reports. The Euronext is the main segment and is an EU regulated market. The Alternext is a regulated unofficial market.

Denmark: The main stock market in Denmark is NASDAQ OMX Copenhagen. Again, this market can be divided into two segments, the main market, which is an EU regulated market, and the First North, which is a regulated unofficial market. Listed companies do not have to disclose their earnings reports quarterly in either of these markets.

Finland: All public companies in Finland are required to report their earnings quarterly. This has been the situation since January 2000 (Finanssivalvonta, 2011). The main stock market operator in Finland is NASDAQ OMX Helsinki, the stock market has two segments: the main market and First North.

France: The stock market in France, as in Belgium, is operated by NYSE Euronext. In France, the main operator is NYSE Euronext Paris. As in Belgium, France follows the same structure with the Euronext and Alternext segments. Listed companies in France are not obligated to report quarterly, but all listed companies are obligated to report the sales after each quarter.

Germany: In similarity with Belgium, Germany has one market segment that requires quarterly earnings reports. This segment is the Prime Standard, but there are also three other market segments: First Quotation Market, Entry Standard, and General Standard. Two of these, the First Quotation Market and Entry Standard are so-called regulated unofficial markets. On the other hand, General Standard and Prime Standard are EU regulated markets. Furthermore, the main market operator in Germany is Deutsche Borse.

Greece: In Greece all public companies are required by regulation to publish quarterly reporting. The regulation authority in Greece is the Hellenic Capital Market

19

Commission (HCMC). The main stock market operator in Greece is the Athens Exchange Securities Market.

Italy: Italy is one of two countries which, after the adoption of TD, only require listed companies to issue the IMS statement. The main stock market operator in Italy is Borsa Italiana. The Italian stock market can be divided into three segments: the MTA, the STAR, and the AIM. Both the MTA and the STAR are EU regulated markets, while the AIM, like the LSE (LSE also owns Borsa Italiana since 2007), is a regulated unofficial market suited for small and growing companies. The Italian market regulator is the Commissione Nazionale per le Societa elaBorsa (CONSOB). Even though quarterly reporting is voluntary, the CONSOB encourages companies to report quarterly to increase comparability and transparency.

Portugal: In Portugal, the Comissao do Mercado de Valores Mobiliarios requires listed companies to report their earnings quarterly. Not all companies have to report quarterly, only companies which meet two of these three criteria: total book value over 100 million Euro, total sales over 150 million Euro, or over 150 employees in 2 consecutive years. Further, the main stock market operator is NYSE Euronext Lisbon.

Sweden: In Sweden, the main stock market operator is NASDAQ OMX Stockholm. The stock market has two segments: Main Segment (EU regulated market) and First North (regulated unofficial market). Companies listed on the Swedish stock exchange are required by the financial supervisory authority (Finansinspektionen) to report companies’ earnings quarterly. Finansinspektionen does not specify the information that has to be issued each quarter. Due to this, NASDAQ OMX Stockholm requires companies to report information according to IAS 34.

Spain: Like Italy, Spain has downgraded their requirements from mandatory quarterly reporting to only IMS after the adoption of the TD. The main stock market operator in Spain is Bolsas y Mercados Espanoles (BME). However, the regulator Comision Nacional del Mercado de Valores (CNMV) recommends companies to issue quarterly reports instead of only IMS for better comparability and transparency.

The Netherlands: The main market operator in the Netherlands is NYSE Euronext Amsterdam. Neither the Dutch stock market regulator nor NYSE Euronext Amsterdam requires listed companies to report quarterly. The NYSE Euronext Amsterdam has two segments: Euronext and Alternext.

The United Kingdom: The London Stock Exchange (LSE) is the main market operator in the United Kingdom. The LSE have one EU regulated market, which is the main market, and one regulated official market, which is the Alternative Investment Market (AIM). The AIM suits young, fast growing, and small companies. Neither British regulations nor the LSE requires listed companies to report their earnings quarterly.

20

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1 0.

174

24.6

%

356.

3 11

0.

562

0.00

4 0.

219

0.21

9 0.

054

Port

ugal

87

9 0.

284

0.05

9 0.

168

47.0

%

304.

2 10

0.

769

0.07

5 0.

337

0.06

9 0.

034

Spai

n2,

351

0.27

6 0.

061

0.16

0 63

.8%

72

1.8

10

0.59

5 0.

034

0.27

4 0.

237

0.06

1Sw

eden

5,

725

0.37

3 0.

079

0.23

1 76

.0%

63

.3

7 0.

526

0.03

5 0.

145

0.14

2 0.

101

UK

26,3

1 0.

372

0.07

5 0.

241

2.6%

66

.2

8 0.

535

0.05

3 0.

140

0.30

1 0.

098

Ove

rall

76,2

20

0.34

1 0.

071

0.21

2 23

.5%

12

1.7

9 0.

602

0.03

80.

184

0.24

9 0.

098

21

Tab

le 2

S

um

mar

y st

atis

tic

B

This

tab

le r

epor

ts c

ount

ry c

hara

cter

isti

cs a

nd c

ount

ry m

edia

n an

d m

ean

valu

es f

or t

he f

irm

-yea

r ch

arac

teri

stic

s. A

ppen

dix

A c

onta

ins

a de

taile

d de

scri

ptio

n of

the

cha

ract

eris

tics

. The

tab

le p

rese

nts

med

ian

valu

es f

or a

ll ch

arac

teri

stic

s ex

cept

CA

PEX

/Tot

al a

sset

s. T

he t

ime

peri

od c

over

s Ja

nuar

y 19

95 to

Sep

tem

ber

2013

.

Cou

ntry

D

ebt

mat

urit

y PP

E/T

A

CA

PEX

/TA

A

nti-

Dir

ecto

r R

ight

s

Cre

dito

r R

ight

s St

ock

mar

ket

turn

over

M

arke

t C

ap/G

DP

GD

P/C

apit

a M

anda

tory

QR

be

fore

TD

M

anda

tory

QR

be

fore

TD

Aus

tria

0.

603

0.28

6 0.

028

2.5

3 50

.1

0.16

8 31

,075

N

o N

o B

elgi

um

0.60

1 0.

189

0.03

0 3

2 25

.9

0.55

6 30

,065

N

o N

o D

enm

ark

0.56

7 0.

200

0.02

9 4

3 71

.4

0.55

9 38

,115

N

o N

o Fi

nlan

d 0.

700

0.24

2 0.

043

3.5

1 83

.5

0.93

4 30

,810

Ye

s Ye

s Fr

ance

0.59

9 0.

125

0.02

43.

50

78.8

0.

728

28,4

15N

oN

oG

erm

any

0.58

6 0.

151

0.04

1 3.

5 3

121.

2 0.

421

30,3

30

No

No

Gre

ece

0.31

4 0.

294

0.03

72

146

.5

0.45

0 16

,635

Yes

Yes

Irel

and

0.76

4 0.

205

0.02

9 5

1 40

.5

0.52

8 28

,680

N

o N

o It

aly

0.50

8 0.

155

0.02

02

210

7.1

0.37

9 24

,815

Yes

No

Net

herl

ands

0.

651

0.22

6 0.

031

2.5

3 10

1.4

0.85

6 32

,115

N

o N

o Po

rtug

al

0.58

4 0.

330

0.02

1 2.

5 1

54.7

0.

344

14,4

85

Yes

Yes

Spai

n0.

567

0.28

0 0.

026

52

163.

9 0.

734

19,4

80Ye

sN

oSw

eden

0.

752

0.09

8 0.

022

3.5

1 10

5.1

1.00

6 33

,935

Ye

s Ye

s U

K0.

640

0.17

8 0.

024

54

94.8

1.

268

32,2

45N

oN

oO

vera

ll 0.

603

0.17

3 0.

027

3.5

3 86

.8

0.85

0 29

,500

22

Tab

le 3

D

escr

ipti

ve s

tati

stic

s fo

r m

atch

ed s

amp

les

For

each

yea

r, t

his

tabl

e re

port

s nu

mbe

r of

qua

rter

ly r

epor

ting

firm

s in

the

sam

ple,

per

cent

age

of q

uart

erly

rep

orti

ng fi

rms,

med

ian

size

, m

edia

n ag

e an

d m

ean

R&

D/T

otal

ass

ets

for

quar

terl

y re

port

ing

firm

s an

d m

atch

ed n

on-q

uart

erly

rep

orti

ng fi

rms.

The

tim

e pe

riod

cov

ers

Janu

ary

1995

to S

epte

mbe

r 20

13.

QR

firm

s M

atch

ed fi

rms

Year

N

umbe

r of

QR

fir

ms

Perc

enta

ge o

f QR

fir

ms

Size

A

ge

R&

D/T

A

Size

A

ge

R&

D/T

A19

9595

3.3%

2828

.5

7.3

0.01

2 26

33.7

7.

3 0.

010

1996

102

3.2%

3202

.2

8.3

0.01

1 34

50.6

8.

3 0.

009

1997

109

3.2%

3960

.4

9.3

0.02

3 43

78.6

9.

3 0.

018

1998

228

6.5%

833.

6 9.

0 0.

016

851.

6 10

.3

0.01

119

9930

88.

7%77

9.2

9.6

0.01

7 89

8.6

10.3

0.

016

2000

366

9.8%

731.

5 9.

4 0.

015

748.

6 10

.3

0.02

020

0172

117

.6%

568.

9 8

0.01

7 70

8.9

7.4

0.01

520

021,

212

30.1

%30

7.9

5.4

0.01

8 30

1.9

5.5

0.01

920

031,

231

32.0

%33

1.8

6.4

0.02

5 28

6.2

6.5

0.02

220

041,

261

33.0

%31

0.0

7.4

0.02

4 29

2.2

8.1

0.02

520

051,

225

31.2

%33

6.1

8.4

0.02

2 29

7.7

9.5

0.02

220

061,

282

31.2

%35

.3

9.2

0.01

9 32

5.5

11.6

0.

020

2007

1,32

830

.5%

374.

1 9.

6 0.

019

325.

9 12

.7

0.02

120

081,

470

34.4

%32

5.8

9.6

0.01

9 25

3.7

11.9

0.

021

2009

1,51

337

.5%

322.

4 10

.4

0.02

0 23

0.3

11.5

0.

026

2010

1,50

938

.9%

301.

9 11

.4

0.01

922

6.3

12.5

0.

022

2011

1,41

738

.5%

334.

1 12

.4

0.01

9 25

7.9

13.2

0.

020

2012

1,35

939

.0%

390.

9 13

.3

0.01

9 29

5.1

14.6

0.

019

2013

1,19

936

.0%

437.

6 14

.3

0.02

4 42

6.8

15.4

0.

023

23

Tab

le 4

M

atch

sam

ple

tes

ts o

f ris

k m

easu

res

For

each

ris

k m

easu

re t

his

tabl

e re

port

s di

ffer

ence

in

mea

ns b

etw

een

quar

terl

y re

port

ing

firm

s an

d m

atch

ed n

on-q

uart

erly

rep

orti

ng

firm

s. T

his

tabl

e al

so r

epor

ts p

-val

ues

from

two-

sam

ple

t-te

st a

nd W

ilcox

on s

igne

d-ra

nk te

st. B

old

indi

cate

s si

gnifi

cant

var

iabl

es. T

he ti

me

peri

od c

over

s Ja

nuar

y 19

95 to

Sep

tem

ber

2013

.

Tota

l ris

k Sy

stem

atic

ris

k Id

iosy

ncra

tic

risk

Year

D

iffer

ence

in

mea

ns

p-va

lue

Wilc

oxon

p-

valu

e D

iffer

ence

in

mea

ns

p-va

lue

Wilc

oxon

p-

valu

e D

iffer

ence

in

mea

ns

p-va

lue

Wilc

oxon

p-

valu

e 19

950.

028

(0.0

76)

(0.3

84)

0.00

7 (0

.055

) (0

.060

)0.

018

(0.1

47)

(0.6

58)

1996

-0.0

08

(0.6

59)

(0.4

95)

0.00

2 (0

.606

) (0

.702

)-0

.019

(0

.169

) (0

.182

)19

97-0

.007

(0

.604

) (0

.635

)0.

001

(0.7

93)

(0.7

43)

-0.0

16

(0.1

60)

(0.0

52)

1998

0

.040

(0

.038

) (0

.019

) 0

.00

9 (0

.04

2)

(0.0

17)

0.0

26(0

.057

) (0

.027

) 19

99

-0.0

27(0

.137

) (0

.018

)-0

.00

6 (0

.147

) (0

.04

5)-0

.015

(0

.202

) (0

.022

) 20

000.

001

(0.9

54)

(0.5

96)

0.00

0 (0

.955

) (0

.526

)0.

000

(0.9

53)

(0.7

40)

2001

0.02

7 (0

.087

) (0

.383

)0

.00

8

(0.0

24)

(0.0

77)

0.00

3 (0

.791

) (0

.351

)20

020.

003

(0.7

77)

(0.5

48)

0.00

3 (0

.288

) (0

.072

)-0

.009

(0

.247

) (0

.107

)20

03

-0.0

22

(0.0

46)

(0

.128

)-0

.002

(0

.377

) (0

.481

)-0

.024

(0

.00

1)

(0.0

09

) 20

04

-0.0

16

(0.0

44

) (0

.04

3)

-0.0

00

(0.9

24)

(0.7

57)

-0.0

22

(0.0

00

) (0

.00

0)

2005

-0.0

04

(0.6

05)

(0.9

15)

-0.0

00

(0.9

58)

(0.4

66)

-0.0

05

(0.3

69)

(0.0

77)

2006

-0

.015

(0

.031

) (0

.722

)0.

000

(0.8

77)

(0.0

70)

-0.0

23

(0.0

00

) (0

.00

3)

2007

-0.0

00

(0.9

58)

(0.9

19)

0.00

1 (0

.446

) (0

.183

)-0

.005

(0

.339

) (0

.166

)20

08

-0.0

33

(0.0

00

) (0

.013

) -0

.00

5 (0

.00

5)

(0.1

91)

-0.0

28

(0.0

00

) (0

.00

3)

2009

-0

.057

(0

.00

0)

(0.1

37)

-0.0

10

(0.0

00

) (0

.866

) -0

.04

7 (0

.00

0)

(0.0

00

) 20

10

0.0

01

(0.9

05)

(0.0

36)

0.0

03

(0.0

69)

(0.0

00

) -0

.012

(0

.057

) (0

.108

) 20

11

0.0

16

(0.0

28)

(0.0

06

) 0

.00

6 (0

.00

0)

(0.0

00

) -0

.001

(0

.820

) (0

.786

) 20

12

0.0

58

(0.0

00

) (0

.00

0)

0.0

16

(0.0

00

) (0

.00

0)

0.0

24

(0.0

00

) (0

.012

) 20

13

0.0

41

(0.0

00

) (0

.00

0)

0.0

10

(0.0

00

) (0

.00

0)

0.0

20

(0.0

03)

(0

.061

)

24

Tab

le 5

D

iffe

ren

ce-i

n-d

iffe

ren

ce r

egre

ssio

ns

This

tab

le r

epor

ts t

he c

oeff

icie

nts

for

the

inte

ract

ion

term

fro

m t

he d

iffer

ence

-in-

diff

eren

ce r

egre

ssio

n. T

he d

iffer

ence

-in-

diff

eren

ce

regr

essi

ons

are

done

for

pai

rs o

f ye

ars.

The

fir

st y

ear

is t

he p

re-p

erio

d an

d th

e se

cond

yea

r th

e po

st-p

erio

d. T

he t

reat

men

t in

the

di

ffer

ence

-in-

diff

eren

ce r

egre

ssio

n is

the

cha

nge

to q

uart

erly

rep

orti

ng.

Thes

e re

gres

sion

s al

so i

nclu

de u

n-ta

bula

ted

cont

rol

vari

able

s.

Thes

e co

ntro

l var

iabl

es a

re: P

PE/T

A, p

rofit

abili

ty, R

&D

/TA

, per

cent

age

of z

ero

retu

rns,

log

of a

ge, l

og o

f TA

, boo

k-to

-mar

ket,

leve

rage

and

de

bt m

atur

ity,

sto

ck m

arke

t tur

nove

r, s

tock

mar

ket t

o G

DP

and

GD

P pe

r ca

pita

. In

this

tabl

e al

l sta

ndar

d er

rors

are

cor

rect

ed w

ith

New

ey

and

Wes

t (19

87) p

roce

dure

. Bol

d in

dica

tes

sign

ifica

nt c

oeff

icie

nts.

The

tim

e pe

riod

cov

ers

Janu

ary

1995

to S

epte

mbe

r 20

13.

Year

s To

tal r

isk

Syst

emat

ic r

isk

Idio

sync

arti

c ri

sk

1995

-199

6 0.

038

(0.9

4)

0.01

4 (1

.24)

-0

.001

(-

0.05

) 19

96-1

997

-0.0

07

(-0.

24)

0.00

7 (0

.88)

-0

.036

(-

1.75

) 19

97-1

998

-0.0

09

(-0.

25)

0.00

1 (0

.13)

-0

.014

(-

0.55

) 19

98-1

999

-0.0

70

(-1.

19)

-0.0

25

(-1.

79)

-0.0

07

(-0.

17)

1999

-200

0 -0

.017

(-

0.56

) -0

.005

(-

0.66

) -0

.008

(-

0.38

) 20

00-2

001

-0.0

37

(-1.

09)

-0.0

11

(-1.

24)

-0.0

12

(-0.

58)

2001

-200

2 -0

.079

(-

3.23

) -0

.018

(-

3.12

) -0

.044

(-

2.57

)20

02-2

003

-0.0

22

(-0.

46)

-0.0

04

(-0.

35)

-0.0

12

(-0.

47)

2003

-200

4 0.

016

(0.3

0)

0.00

7 (0

.54)

0.

003

(0.0

8)

2004

-200

5 0.

008

(0.2

3)

-0.0

06

(-0.

71)

0.03

0 (1

.41)

20

05-2

006

0.00

5 (0

.14)

0.

008

(1.0

2)

-0.0

25

(-0.

94)

2006

-200

7 0.

017

(0.5

1)

0.00

6 (0

.90)

0.

005

(0.1

6)

2007

-200

8 -0

.08

9 (-

3.39

) -0

.011

(-

1.90

) -0

.08

8

(-4

.14

)20

08-2

009

-0.0

06

(-0.

12)

0.00

7 (0

.52)

-0

.027

(-

0.73

) 20

09-2

010

0.1

53

(3.1

1)

0.0

32

(2.8

2)

0.1

04

(3.0

2)

2010

-201

1 -0

.054

(-

0.80

) -0

.008

(-

0.47

) -0

.042

(-

0.89

) 20

11-2

012

0.00

7 (0

.10)

0.

002

(0.1

3)

0.00

4 (0

.08)

20

12-2

013

0.06

4 (1

.73)

0

.020

(2

.94

) 0.

000

(0.0

1)

Ove

rall

-0.0

21

(-1.

57)

-0.0

03

(-1.

04)

-0.0

15

(-1.

84)

25

Tab

le 6

F

ama-

Mac

Bet

h r

egre

ssio

ns

This

tab

le s

how

s co

effic

ient

s fr

om f

irm

-lev

el F

ama-

Mac

Bet

h re

gres

sion

s. B

oth

firm

-spe

cific

and

cou

ntry

-spe

cific

var

iabl

es a

re u

sed.

All

inde

pend

ent v

aria

bles

are

list

ed in

the

first

col

umn.

The

def

init

ion

of th

e in

depe

nden

t var

iabl

es c

an b

e fo

und

in A

ppen

dix

A. I

n th

is ta

ble

all

stan

dard

err

ors

are

corr

ecte

d w

ith

the

New

ey a

nd W

est

(198

7) p

roce

dure

. T-

stat

isti

cs a

re p

rese

nted

in

the

pare

nthe

sis

and

bold

in

dica

tes

sign

ifica

nt c

oeff

icie

nts.

The

tim

e pe

riod

cov

ers

Janu

ary

1995

to S

epte

mbe

r 20

13.

Inde

pend

ent v

aria

bles

To

tal r

isk

Syst

emat

ic r

isk

Idio

sync

rati

c ri

sk

FRE

Q-0

.001

(-

0.22

) 0.

009

(1.3

3)

0.00

2 (1

.07)

0

.00

3 (2

.12)

-0

.00

9

(-2.

36)

0.00

1 (0

.17)

PP

E/T

otal

ass

ets

-0.0

73(-

4.3

3)

-0.0

83

(-4

.61)

-0

.017

(-

4.1

1)

-0.0

19

(-4

.28

) -0

.037

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4.6

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45

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Prof

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(-0.

38)

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7 (0

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-0.0

11

(-1.

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(-3.

56)

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ets

0.3

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(-6

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3.53

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og)

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31

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(-7.

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6.4

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tal a

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t mat

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16

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DP

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capi

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00

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k m

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0

(-2.

29)

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00

(-

2.9

0)

Inte

rcep

t 0

.828

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5.20

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.96

4

(19

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0

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9.2

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94

(1

6.2

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89

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0.6

63

(21.

00

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26

Tab

le 7

P

anel

reg

ress

ion

wit

h a

rec

essi

on d

um

my

This

tabl

e sh

ows

coef

ficie

nts

from

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-lev

el p

anel

reg

ress

ions

. Bot

h fir

m-s

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aria

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d. A

ll in

depe

nden

t va

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d in

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firs

t co

lum

n. T

he d

efin

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inde

pend

ent v

aria

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be

foun

d in

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endi

x A

. In

this

tab

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rd

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wit

h W

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-sta

tist

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are

pres

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d in

the

par

enth

esis

and

bol

d in

dica

tes

sign

ifica

nt c

oeff

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The

ti

me

peri

od c

over

s Ja

nuar

y 19

95 to

Sep

tem

ber

2013

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Inde

pend

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) PP

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81

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11)

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20

(-3.

11)

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40

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07)

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ty

-0.0

49

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85)

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8

(-2.

85)

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(-4

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R

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82

(-4

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6.4

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32

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81)

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35

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8

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9)

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25

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Tab

le 8

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Bet

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a

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how

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a-M

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st c

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n. T

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n of

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nden

t va

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les

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thi

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ble

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est

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tist

ics

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pare

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07)

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0

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)

EKONOMI OCH SAMHÄLLE Skrifter utgivna vid Svenska handelshögskolan

ECONOMICS AND SOCIETY Publications of the Hanken School of Economics

260. FRANS SAXÉN: Essays on the Economics of Retailing: Payments, Finance andVertical Restraints. Helsinki 2013.

261. HILAL ANWAR BUTT: Asset Pricing in Small Sized Developed Markets. Helsinki2013.

262. PAUL CATANI: Misspecification and Bootstrap Tests in Multivariate Time SeriesModels with Conditional Heteroskedasticity. Helsinki 2013.

263. HELI HOLTTINEN: Cultural Ideals, Practices and Value Propositions in ConsumerEveryday Value Creation. Helsinki 2013.

264. MIKKO VESA: There be Dragons! An Ethnographic Inquiry into the StrategicPractices and Process of World of Warcraft Gaming Groups. Helsinki 2013.

265. HENRICH NYMAN: Service Profitability: An Augmented Customer Lifetime ValueApproach. Helsinki 2013.

266. HANNA-RIITTA HARILAINEN: Managing Supplier Sustainability Risk. Helsinki2014.

267. JACOB MICKELSSON: Customer Activity: A Perspective on Service Use. Helsinki2014.

268. MIKAEL LAAKSO: Measuring Open Access: Studies of Web-enabled Innovation inScientific Journal Publishing. Helsinki 2014.

269. HANNA KIEHELÄ: Dimensionality of the Consumer Perceived Value of ProductColour. Helsinki 2014.

270. LINDA TALLBERG: Processing Puppies: An Animal Shelter Ethnography. Helsinki2014.

271. PIA HELLMAN: The Effect of Communicating E-service Benefits on Consumer E-service Adoption. Helsinki 2014.

272. PENG WANG: Four Essays on Corporate Finance of Chinese Listed Firms. Helsinki2014.

273. DHANAY MARÍA CADILLO CHANDLER: The Role of Patents in Latin AmericanDevelopment: 'models of protection' of pharmaceutical patents and access tomedicines in Brazil, Chile and Venezuela. Helsinki 2014.

274. CARLOS A. DIAZ RUIZ: Market Representations in Action: Foundations for thePerformativity of Representations in Marketing. Helsinki 2014.

275. IRA HAAVISTO: Performance in Humanitarian Supply Chains. Helsinki 2014.

276. SALLA PÖYRY: Essays on Financial Market Frictions and Imperfections. Helsinki2014.

277. HELENA LIEWENDAHL: What Motivates Employees to Live up to Value Prom-ises: An Employee Discourse. Helsinki 2014.

278. ALEXEI KOVESHNIKOV: Micro-Political Perspectives on MultinationalCorporations: Legitimation, Stereotyping and Recontextualization. Helsinki 2014.

279. FRÉDÉRIC DÉLÈZE: Essays in Quantitative Analysis of the Effects of MarketImperfections on Asset Returns. Helsinki 2014.

280. KAI HUOTARI: Experientializing – How C2C Communication Becomes Part of theService Experience. The Case of Live-tweeting and TV-viewing. Helsinki 2014.

281. RICHARD KEDZIOR: How Digital Worlds Become Material: An Ethnographic andNetnographic Investigation in Second Life. Helsinki 2014.

282. MARIA FORSS: Fortbildning är mer ”fort” än ”bildning”. En kritisk granskning avfortbildning för sjukskötaren. Helsingfors 2014.

283. MAGNUS BLOMKVIST: Essays on Market States, Firm Growth and CorporateFinance Decisions. Helsinki 2014.

284. ANNAMARI TUORI: Doing Intersectional Identity Work: Social Categories,Inequalities, and Silences. Helsinki 2014.

285. SANNE BOR: A Theory of Meta-Organisation: An Analysis of Steering Processes inEuropean Commission-Funded R&D ‘Network of Excellence’ Consortia. Helsinki2014.

286. RINAT MUKMINOV: Deposit Markets, Lending Markets and Bank ScreeningIncentives. Helsinki 2015.

287. LINUS NYMAN: Understanding Code Forking in Open Source Software. AnExamination of Code Forking, its Effect on Open Source Software, and How it isViewed and Practiced by Developers. Helsinki 2015.

288. HENRIK VIRTANEN: Integrerat och sekventiellt samarbete mellan konkurrenter:En studie av små och medelstora företag i en internationell kontext. Integrated andSequential Cooperation between Competitors: A Study of Small and Medium-sizedEnterprises in an International Context. With an English Summary. Helsinki 2015.

289. JOHN PETTERSSON: Essays on Momentum and Risk. Helsinki 2015.

290. LING ELEANOR ZHANG: On Becoming Bicultural: Language Competence,Acculturation and Cross-cultural Adjustment of Expatriates in China. Helsinki2015.

291. ALAIN VAILLANCOURT: Consolidation in Humanitarian Logistics. Helsinki 2015.

292. HARRY SALONAHO: Förändringsbehov i Finlands arbetslagstiftning och arbets-marknadsmekanismer. Helsingfors 2015.

293. OLUGBENGA OLUFEAGBA: Essays on the Currency Effect on Stock MarketRelationships and Stock Return Forecast. Helsinki 2016.

Jesper Ha

ga

– essays on

asset pric

ing

an

om

alies, in

form

atio

n flo

w a

nd

risk

essays on asset pricing anomalies, information flow and riskJesper Haga

ekonomi ocH samHälle economics and society

294

Jesper Haga

essays on asset pricing anomalies, information flow and risk

Asset pricing models provide investors with a rela-tion between risk and expected returns. Higher risk levels should be linked to higher expected returns. In addition, trading strategies that earn risk adjusted abnormally high or low returns are referred to as asset pricing anomalies. These asset pricing anomalies present an important chal-lenge for us researchers. Either our asset pricing models are incorrect or there exist frictions in the capital markets allowing such anomalies to persist. A better understan-ding of these anomalies can help in the development of asset pricing models. Knowledge about these anomalies is of course gained by studying them, which is where my thesis comes in.

This dissertation investigates three different topics in asset pricing literature. The first two papers study anoma-lies. In the first essay the momentum anomaly is investi-gated. In this respect, the momentum strategy consists of buying previous outperformers and selling previous underperformers. Moreover, this strategy generates ab-normal returns. More specifically, the first essay studies the robustness of intermediate-term momentum. The result suggests that the difference found between short-term and intermediate-term momentum is mainly driven by low cre-

dit risk firms and that the optimal momentum strategy can be dependent on firm characteristics.

In the second essay we investigate the credit risk puzzle. Previous studies have shown that firms with a high credit risk exhibit lower excepted returns than firms with a low credit risk. This phenomenon is referred to as the cre-dit risk puzzle. Contrary to previous findings, we suggest that the credit risk puzzle is only a temporary occurrence. Furthermore, the reason for this temporary mispricing of high credit risk firms could be the result of stronger limits to arbitrage during the subsample or possibly due to a sud-den increased power to the debtholders during the early subsample.

The third essay shows that a higher reporting frequen-cy can act as a stabilizing factor in times of market dist-ress. Firms that report quarterly instead of semi-annually experience lower stock price volatility during times of mar-ket distress. However, the important systematic volatility is higher for stock prices of firms that report quarterly. Ultimately, there exists a trade-off between higher firm specific systematic volatility on average and lower total volatility in times of market distress.

HANKENSCHOOL OF ECONOMICS

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