Stock Return Analysis Literature Review

Yang Xu9/18/2019

Fundamental theories – cross-sectional

• Markowitz (1952), Merton (1972): optimal portfolio choice – efficient frontier – determined by returns and covariance matrix.

• Ross (1976), Chen, Roll and Ross (1986): APT – premia associated with economic factors.

• Connor and Korajczyk (1988): identify market factor that drives most premium – by PCA.

• Fama and French (1993): characteristic sorting procedure identifies three more factors.

Further factors – firm characteristics• RMW and CMA (Fama and French, 2015)

• Profitability and investment.• PMU (Novy-Marx, 2013)

• Profitability, measured by gross profits-to-assets.• ISU (Daniel and Titman, 2006)

• Book-to-market ratio forecasts returns because it is a good proxy for the intangible return.

• QMJ (Asness, Frazzini, and Pedersen, 2018)• long high-quality stocks (profitable, growth, safety, payout) and shorts low-quality.

• BAB (Frazzini and Pedersen, 2014)• Long leveraged low-beta assets and short high-beta assets, produces significant

positive risk-adjusted returns.

RMW and CMA (Fama and French, 2015)

Y: earningsdB: investment

For portfolios formed in June of year t, profitability (measured with accounting data for the fiscal year ending in t1) is operating profitability minus interest expense, scaled by book equity at year t1.Inv is the growth of total assets for the fiscal year ending in t1 divided by total assets at the end of t2.

There are patterns in average returns related to Size, B/M, profitability, and investment. The GRS test easily rejects a five-factor model directed at capturing these patterns, but we estimate that the model explains between 71% and 94% of the cross-section variance of expected returns for the Size, B/M, OP, and Inv portfolios we examine.

ISU (Daniel and Titman, 2006)

• Distress risk / overreact to the information contained in accounting growth rates vs. intangible return.

BAB (Frazzini and Pedersen, 2014)

• Why high-risk assets often deliver lower expected returns than low-risk assets?

• High-beta – sensitive to aggregate disagreement – overpriced due to short-sales constraints.

MF – aCannot short

E(z) = lambda1/2

E(z) = -lambda1/2

HF – (1-a)Can short

Homogenous

Anomaly zoo – economic states

• RX and HML-FX (Lustig, Roussanov, and Verdelhan, 2011)• By investing in high interest rate currencies and borrowing in low interest rate

currencies, U.S. investors load up on global risk.

• LIQ (Pastor and Stambaugh, 2003)• Order flow induces greater return reversals when liquidity is low.

Anomaly zoo – behavior

• MOM (Titman, 1993, 1999, 2011)• Empirically observed tendency for rising asset prices to rise further and falling prices

to keep falling.• Apply to stock performance and earnings.

• Attention (Tan, and Tas, 2018)• Long position in high attention stocks and short position in low attention stocks.

• Sentiment (Stambaugh, Yu and Yuan, 2012)• Higher long-short anomaly profits following high sentiment.

• Sticky expectations and profitability (Bouchaud, Kruger, Landier, and Thesmar, 2019)

• Investors forecast future return using a signal (past profit) and sticky belief dynamics.• The profitability anomaly is stronger for stocks with more persistent profits.

Sentiment (Stambaugh, Yu and Yuan, 2012)

• Whether sentiment affects the profitability of momentum strategies?• Hypothesize: news that contradicts investors’ sentiment causes

cognitive dissonance. Thus, losers (winner) become underpriced under optimism (pessimism).

• Empirically: • Momentum profits arise only under optimism.• Momentum-based hedge portfolios formed during optimistic periods

experience long-run reversals.

• Consumer Confidence Index_ published by the Conference Board (CB) (orthogonalized with respect to a set of macroeconomic variables).

• Table 2 presents the results for strategies that are based on a 6-month ranking period (J) and holding periods (K) of 3, 6, and 12 months sorted by investor sentiment.

• 5 years after portfolio formation

Sticky expectations and profitability (Bouchaud, Kruger, Landier, and Thesmar, 2019)• Abnormal return

• Risk premium• Behavior bias + limits to arbitrage – non-Bayesian expectation.

Note that, taken together, assumption (4), assumption (5), and normality require that profits follow an ARMA(1,1) process.

• c = 0. • λ = b/(1 + b).

Reconciling factors

• Idiosyncratic cash flows (Babenko, Boguth, and Tserlukevich, 2016)• A positive idiosyncratic shock decreases the sensitivity of firm value to priced

risk factors and simultaneously increases firm size.• Explain book-to-market and size anomalies.

• Cash flow and discount rate news (Lochstoer, and Tetlock, 2018)• Decompose the returns of five well-known anomalies into cash flow

(measured by dividend growth) and discount rate news.• Main source of anomaly return variation is news about cash flows.

Idiosyncratic cash flows (Babenko, Boguth, and Tserlukevich, 2016)• Goal: Reject that standard asset pricing assumptions, only systematic risk is

priced. This paper argues that unpriced idiosyncratic cash flow shocks can also be important for asset prices as they contain valuable conditioning information in a dynamic asset pricing framework.

• Assumption: firm value is additive in two types of shocks and only systematic risk is priced.

• Larger idiosyncratic cash flows – larger market capitalization, lower book-to-market, lower equity betas – large firms and growth firms have low expected returns.

• idiosyncratic cash flow risk is the variance of the regression residuals, var(εi,τ )

Back to the beginning

• Andrei (2019)• Anomalies are not the reason to reject CAPM.

• Daniel, Mota, Rottke, and Santos (2018)• Factor-portfolios are likely to capture not only the priced risk associated with

the characteristic, but also unpriced risk. • Unpriced risk can be hedged out, leading to a higher SR of the optimal

combination.

Andrei (2019)

Potential directions

• Horizon/frequency• Becher and Giglio (2016) decompose fluctuations at different frequency and

show that long-run risk is significantly priced.• Chinco and Ye (2016) decompose trading volume variance at different

frequency and show that stocks dominated by short-run fluctuations in trading volume have abnormal returns that are 1% per month higher than otherwise similar stocks where short-run fluctuations in volume are less important — i.e., stocks with less of a short-run tilt.

• Covariance matrix estimation• Johannes, Korteweg, and Polson (2014) also incorporate time-varying

volatility based on sequential process of investors learning.

Chinco and Ye (2016)

Johannes, Korteweg, and Polson (2014)