Institutional Investors and Loan Market

Information Spillover

Victoria Ivashina *

Harvard Business School

Zheng Sun **

Stern School of Business New York University

First draft: February, 2007

PLEASE DO NOT QUOTE WITHOUT PERMISSION

Over the past decade, the most important development in the corporate loan market has been an explosive participation of the institutional investors in the lending syndicates. As lenders, institutional investors routinely receive private information about the borrowers. However, most of these investors also trade in public securities. This leads to a controversial question: do institutional investors use information received in the loan market to trade in other securities? This paper looks at performance and trading practices of institutional investors that hold both stocks and loans. We document that institutional investors who buy commercial loans outperform their peers in the stock markets. Furthermore, we examine amendments to credit agreements and find a causal relationship between release of private information in loan renegotiations and loan investors’ trading in related stocks. Finally, we provide evidence supporting private information spill-over from loan to stock prices of the same company. To the best of our knowledge, this is the first paper that directly looks at the investments across different asset classes and explains the channel through which the information is disseminated.

Key words: Institutional investors, Banks, Syndicated loans, Private information

JEL classification: G11, G14, G21, G22, G23

* Harvard Business School, Baker Library 233, Boston, MA 02163. E-mail: vivashina@hbs.edu. ** Email: zsun@stern.nyu.edu. This paper greatly benefited from Andre Perold and Guhan Subramanian insightful comments.

Over the past two decades, commercial loan market was transformed by loan syndication

and multiple financial innovations that accompanied it. Nowadays, most of the corporate

loans are funded by a group, or a syndicate of lenders. With due diligence and loan

administrative duties typically delegate to the leading bank, loan syndication created an

opportunity for passive lenders. Participants in the loan syndicates did not need to have

banks’ “special” skills or expertise. Thus, loans became just an alternative investment,

triggering a massive entry of the institutional investors into the syndicated loan market.

A growing supply of institutional money allowed banks to reduce their risk

through diversification, noticeably improving loan market liquidity and ultimately

benefiting borrowers through easier and cheaper access to credit.1 However, it also

created concern about separation between public and private information. We typically

think of the bank debt as an informed debt. Indeed, most of the loans are unmistakably

private and flexible agreements with regular material information flow between borrower

and lenders.2 In addition to the primary market, where loans are syndicated, institutional

investors can enter a lending syndicate by purchasing a fraction of the loan in the

secondary loan market. Consequently, it is not unusual that lending syndicates can reach

over thirty or forty members. All lenders in the lending syndicate are governed by the

same credit agreement, they all hold a direct and interdependent claim against the

borrower, and they all need to agree if there are amendments or waivers to the credit

agreement. Thus, loan renegotiations narrow to a private conference call that includes all 1 Mutual funds have a restriction on the fraction of the portfolio invested in illiquid securities. However, according to an opinion of one of the most prominent mutual funds, these days, most of the loans would classify as liquid securities. We were not pointed to any other regulatory restriction that could affect loan investments. 2 “Beyond the credit agreement, there is a raft of ongoing correspondence between issuers and lenders that is made under confidentiality agreements, including quarterly or monthly financial disclosures, covenants compliance information, amendment and waiver requests, and financial projections, as well as plans for acquisitions or dispositions.” Standard and Poor’s (2006).

the lenders, where the borrower explains its financial conditions and reasons for

renegotiation.3

There is no doubt that institutional investors have access to fundamental private

information in the loan market. The doubt is if these large investors are effective in

protecting this private information from their investments in public securities. Formally,

public securities can not be traded on basis of material non-public information, i.e.,

private information that is likely to substantially affect the stock price. In practice, large

institutional investors create “ethical walls” by adopting preemptive paper procedures

meant to preclude trading on private information received from the loan markets. These

“ethical walls” are a softer version of what is came to be known as “Chinese walls”

between investment and commercial banking which are typically associated with physical

and functional separation within a bank, as well as formal wall-crossing procedures.

In this paper, we look at the institutional managers that invest in the syndicated

loan market and stock market. We first document that stock portfolio of the institutional

managers that invest in both markets outperform that of the investors that specialize in

stock market. This is consistent with the fixed setup costs of participating in a given

market. As Brennan (1975) points out, this can be viewed as the cost of obtaining and

using information. Therefore, we provide direct and aggregate evidence that this superior

performance is at least partially attributed to the use of private information received in the

loan market for trading in the stock market. To establish a causal relationship between

private information release and trading, we collect data on loan renegotiations that can be

unambiguously associated with private negative news about the borrower. We use

difference-in-differences approach and find that, following private negative information 3 Anecdotal evidence suggests that most of the passive lenders remain silent in these negotiations.

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releases, institutional investors holding loans in their portfolio sell stocks of the related

companies. In other words, institutional investors sell stocks of the companies that have

high earning or stocks returns correlation with the borrower. We also find out that

negative private information leads to selling stock of the companies that are in the same

industry as the borrower, and when the industry is concentrated. These same factors

appear to influence the choice of loan investments and stock portfolio performance.

We want to emphasize that our central test does not focus on the insider trading in

its traditional definition: using private information about company A to trade public

securities of company A. Indeed, in our current sample, circumstances where institutional

managers hold equity and loans of the same company are very rare. Our central

hypothesis is that private information about company A is used to infer information and

trade in stock of company B. Can private information about Coca-Cola be used to trade in

stock of Pepsi? Put in these words, the answer may seem very clear; however, this is

actually a very complex and important legal question.4 In what follows, we will focus on

the economic evidence for transmission of public information from loan to stock market.

We believe that regulatory implications of such activities can not be drawn without

understanding the full scope of impact of institutional investment in the loan market. For

instance, restricting institutional investors’ access to private information would severally

aggravate the agency within the lending syndicate and increase the cost of corporate

lending.5

This is the first paper that directly looks at the institutional managers investment

in the loan market and, more broadly, across different security classes. However, there is

4 See Allen, Kraakman and Subramanian (2007). 5 Ivashina (2006) finds that information asymmetries within a lending syndicate lead to large economic costs for the borrower.

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an important and growing literature that investigates cross-market information flow.

Thus, our findings contribute to those of Hotchkiss and Ronen (2002), Longstaff, Mithal,

and Neis (2003), Blanco, Brennan and Marsh (2005), Altman, Saunders and Gande

(2005) and Acharya and Johnson (2005). This paper also helps understand the exact

channel of information transmission between the loan and equity market and, in that

sense, adds to the findings by Massa and Rehman (2005).

The remainder of the paper is structured into four sections. In the first section, we

provide more details on institutional background and discuss our sample. The second

section analyzes we analyze performance of stock portfolio for institutional investors

who participate in both stock and loan markets. In the third section, we test causality of

information flow between loan and stock markets. Finally, in the section four, we

summarize our conclusions.

I. Institutional Background and Sample Construction

A. Institutional environment

The banking industry considers syndicated lending to be the largest and most profitable

corporate financing business.6 New corporate issuance of syndicated loans is estimated to

be at least twice as large as total bond issuance and over five times larger than equity

issuance. The essence of syndicated lending is that instead of one lender there is a group

of lenders, or lending syndicate that funds loan under the same loan agreement.7 An

increasing trend in this major financial market is that most of the participants in the

6 PaineWebber Equity Research, “The Biggest Secret of Wall Street” May 14, 1999. 7 For more information on syndicated loans please see Standard & Poor’s (2006).

4

lending syndicates are not banks but institutional investors, including insurance

companies, mutual funds, pension funds and hedge funds. 8

[FIGURES 1 & 2]

As Figures 1 and 2 illustrate, over the past decade institutional investors’ participation in

the loan market was steadily growing in number and volume. Institutional managers, as a

percent of the total number of participants in the syndicated loan market, steadily

increased from approximately 25% in the mid 90s to nearly 70% by the end of 2005. For

the same period, the total volume of new loans where institutional lenders were part of

the original syndicate increased from approximately $30 to $130 billion. These numbers

correspond to the primary market. In addition, loan participations are traded in the

secondary loan market. As a result, institutional investor can, and do, enter lending

syndicates by purchasing a fraction of loans in the secondary market.9

Syndicated loans are typically senior secured debt and that is the central feature

that attracts institutional investors into loan markets. Starting in late 1995, syndicated

loan market underwent several important changes including the creation of a trading

association, adoption of several contractual and settlement conventions, and the

introduction of loan ratings. The standardization process dramatically improved loan

liquidity and, as a consequence, triggered the explosive trends in institutional entry into

the loan market.

Notwithstanding financial innovations, loan market remains largely private.

Indeed, on October 16, 2006, the Loan Syndications and Trading Association drafted a

8 Pilgrim Prime Rate Trust created in 1988 is typically credited with being the first institutional investor in the loan market. 9 Formally speaking, when part of the loan is sold as a claim against the borrower it is called an “assignment.” “Participation” is a name reserved for the secondary market sales where the claim is against the seller (e.g. bank).

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set of principles designed to help loan market participants handle confidential

information. Coincidently, it was the same day that suspicions of hedge funds trading on

private information received in the loan market made it to the cover of The New York

Times.10 This news was related to a specific incident and, in what follows here, we

analyze market-wide patterns that could be attributed to spill-over of private information

between the loan and stock markets.

B. Sample description

Our main sample links three different data sources: Reuters LPC DealScan loan database,

CDA/Spectrum 13f and S12 institutional stock holdings database, and CRSP stock

returns database. In DealScan we observe names of the borrower and members of the

lending syndicate at the loan issuance. In the U.S., approximately 17% of the syndicate

corporate loans have institutional investors in the lending syndicate and over 200

different institutional investors participate in the primary loan market. Using Spectrum

data, we were able to identify stock holdings for 75 institutional investors. To assure that

an investor’s portfolio composition is not driven by regulatory aspects, throughout the

paper, we restrict our sample to the manager that invests in stock and loan markets.

DealScan provides only loan information; therefore, we also match borrowers to the

CRSP database.

[TABLE I]

As can be observed from Table I, in 2004 our sample includes 26 different institutional

investors that participated in 87 loans. There are several loans with multiple institutional

investors; consequently, there are 112 unique manager-loan observations with 44% 10 The New York Times cover story, “As Lenders, Hedge Funds Draw Insider Scrutiny” by Jenny Anderson, October 16, 2006.

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corresponding to publicly traded companies. In approximately 6% of the cases,

institutional investors hold stocks and loans of the same company.

Table I shows that institutional presence in the primary loan market goes back at

least to the early 90s. However, it is likely that the dynamic of the market was

significantly affected by the standardization process and improvements in liquidity that

took place in the mid-90s. To account for this structural change, we restrict our sample to

1995-2004.

II. Performance Differences

We calculate stock performance of institutional investor’s using the Grinblatt and Titman

(1993) approximation (GT). The measure calculates the sum of time series co-variance

between portfolio weight change and returns of each asset included in the evaluating

portfolio. In particular, the GT measure uses a four-quarter change in portfolio weights

and multiplies it by the future return. For example, the performance from quarter t to t+m

is calculated as:

, , , , 41 1

( )m N

t t m j t j t j t ii j

w w Rα + −= =

= −∑∑ , +

where wj,t is the weight of stock j in a institutional investors portfolio at quarter t, and

Rj,t+i is stock i’s return from (t+i-1) to (t+i).

As pointed out by Roll (1978), traditional performance measures, that use a

benchmark portfolio to adjust for portfolio risk, are often times sensitive to the choice of

the benchmark portfolio. In contrast, the GT measure does not require any benchmark

portfolio, yet it has been shown to effectively adjust for the risk of the underlying

portfolio. Another advantage of the measure is that it allows us to compute performance

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for various types of institutional investors besides mutual funds. Unlike mutual funds,

there is generally little information on performance of other type of investors.

[TABLE II]

We want to see if there are differences in equity investment performance for

institutional managers that invest in the loan market as compared to similar investors that

do not invest in the loan market. Therefore, except in mutual funds, each investor in our

sample is matched to two different groups of controls. The first control group of

comparable investors is selected using managers in the same size quintile among all types

of institutions. The second control group is constructed using portfolio size quintile, as

well as the type of the institutions.11 For mutual funds, we used CRSP mutual fund data

to identify comparable funds. For size-matched control group we consider all funds in the

same assets under management quintile. For size and style-matched control sample, we

use investment objective, as well as, assets under management quintile. Differences in

performance are presented in Table II. On the aggregate level, institutional investors that

participate in the loan market outperform their peers in the stock market. In particular,

stock portfolios of mutual funds with loan investments outperform similar mutual funds

without loan investments by over 2%.12

[TABLE III]

11 The type of the institutional investor is provided by Spectrum 13f. There is some possible inaccuracy on this type variable for year 1999 and beyond. As a consequence, a lot more institutions are misclassified into type 5 (“other institutional type”). To correct this problem, for managers classified as type 5 in year 1999 and afterwards, we replace the value with the one reported by the end of 1998, if such information is available. The problem seems to be alleviated to some degree, but we still find a sharp increase in the number of type 5 managers around that period. It is unlikely, however, that this limitation explains our results. 12 The economic magnitude of this result is comparable to the effect found by Massa and Rehman (2005) for mutual funds that form part of bank hold companies.

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In the Table III we reexamine differences in performance in a multivariate

framework. The dependent variable is an average difference in performance for a given

manager as compared to its control group. In addition to institutional type, we now

control for year fixed effects. Private fundamental information about a given company

may be relevant to investor from a portfolio prospective. For instance, if a company A is

considering an acquisition, there could be fundamental implication for other companies

directly or indirectly related to company A. We create two different variables to capture

potential scope of information, let’s call it informativeness: absolute value of stock

returns and earnings correlations between manager’s value-weighted stock portfolio and

the borrower. We find that, for a given institutional type, investors that hold loans of the

companies that are more informative about their stock portfolio perform better in the

equities market. Therefore, if a mutual fund holds one loan of a company that is perfectly

correlated with its equity portfolio, it would outperform comparable mutual funds by

1.89% on the risk-adjusted basis.

[TABLE IV]

If private information about company A can be used to make inferences about

company B, institutional investors could have an incentive to participate in loans that are

more related to their existing stock portfolios. In Table IV, we study managers’ loan

selection behavior at the time of loan syndication. The evidence is consistent but weak.

Although not statistically significant, we find that a fund is more likely to participate in

loans if the stock returns or earnings correlation between the borrowing company and the

fund’s existing equity portfolio is higher. It is important to notice that we control for the

previous relationship between the same arranging bank and institutional manager. As

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expected, institutional managers are likely to participate in the deals syndicated by the

same banks. However, this effect is not statistically significant. Overall, it doesn't seem

that clientele effect with banks explains choice of loan investments.

Of course, the private information effect may not be the only possible explanation

for the outperformance. Another potential explanation is portfolio manager’s skill. For

example, institutional managers who invest in related companies could have better skill

in understanding a single risk factor, therefore, their performance is generally better.

Although, we control for the skill effect using sample matched by size and style, these

factors may still be too rough to account for all the skill variation between institutional

investors. Unfortunately, skill of the manager is unobservable and the adequacy of skill

proxies can always be questioned. In what follows, we provide direct evidence that

institutional investors use private information gathered in the loan market to trade in

stock.

III. Information flow from loan to equity market

A. Stock portfolio rebalancing following loan renegotiations

To understand causality of the information flow between the loan and equity markets, we

look at the stock portfolio rebalancing following loan renegotiations. Loan renegotiations

are very frequent and can be triggered by covenants violation, changes in market

conditions or company’s financing needs.13 Changes in the credit agreement concerning

repayment schedule, maturity, loan amount or interest rate require consent of all lenders.

In practical terms, this means that when a borrower needs to renegotiate terms of the loan

it will do it by holding a private conference call for all of its lenders. Such loan

13 Dichev and Skinner (2002) document that covenants violations occur in approximately 30% of loans.

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renegotiations, therefore, represent exogenous events when institutional managers receive

private information in the loan market.

Amendments to the credit agreements are typically disclosed by the borrower as

part of its 8-K, 10-Q and 10-K SEC filings, wherefrom DealScan collects the date of the

renegotiation and changes in the contract. However, it is not enough to know the dates of

the private information release; we need to understand if private information disclosed in

the process of the renegotiation was fundamentally good or bad. This is a very difficult

task, as most often loan amendments represent simultaneous changes in multiple financial

covenants, repayment terms and pricing schedules tying borrower’s performance to the

interest rate. To get around this problem, we focus on the loan amendments that result in

interest rate increases without change or with decrease in loan amount or maturity.14

These events credibly identify bad news about the borrower. Accordingly, for our sample

we find 86 loan renegotiations (84 different borrowers) that were associated with the

release of negative private information about the borrower.15

To verify the accuracy of the data and to assure that loan renegotiations do not

become public until several days later, we randomly selected and reviewed the original

SEC filings for 20 loan amendments in our sample. The shortest period, that we found,

between a loan amendment taking place and it being disclosed by the borrower to the

public was 3 days, however, the mean time lag in disclosure was 32 days.16 Loan

14 Performance pricing is a common feature of a loan contract and it implies automatic adjustment of the interest rate paid by the borrower as a step function of its financial ratios. Basically, performance pricing allows state pricing and it is believed to be a positive contractual feature (See Tchistyi (2006)). In our sample, we observe some cases where performance pricing is replaced by a flat interest rate, typically the highest under a performance pricing schedule. If that is the case, we also categorize it as negative news about the borrower. 15 Additionally, we assure that we pick up only the first price increase for a given credit agreement. 16 Disclosure of information to the public is governed by the SEC Regulation Fair Disclosure. While loan offerings are not regulated by the Securities Act, they are not exempt of Regulation FD. However, it only

11

amendments language tends to be very technical, and we rarely find companies

explaining in their reports to shareholders what triggered credit amendments. Here is an

example of Integrated Health Services mentioning its loan renegotiation that took place

on March 25, 1999 in the Quarterly Report filed on May 17, 1999:17

“In March 1999, the Company amended its Credit Facility, which amendments

loosened the financial covenants, increased interest rates and accelerated the

reduction in the availability under the Credit Facility.”

Overall, this seems consistent with our intuition. In addition, loan amendments have to be

signed by all lending syndicate members at the day of the amendment’s ratification.

Therefore, by looking at the actual filings, we were able to confirm that the institutional

investors that were part of the original loan syndicate did not sell their position on the

secondary loan market and had access to private information about the borrower.

[TABLE V]

In Table V, we test whether institutional investors trade their stocks in their

portfolio following the loan amendments. Institutional holdings data is quarterly, so we

look at the changes in stock positions in the quarter of the loan amendment. Accordingly,

for a given amendment we would follow movements in the entire stock portfolio of

institutional investors that received private information in the process of loan

renegotiation. Our hypothesis is that institutional investors use this private news to

infer information and trade in related stocks. We conjecture that information about

company A is more relevant for company B if: (i) absolute earnings correlation is higher;

requires public disclosure of information “under circumstances in which it is reasonably foreseeable” that the lenders will trade on the basis of the information in the public markets. In addition, a typical loan would include a confidentiality agreement between lenders and borrowers. 17 Integrated Health Services registered losses at the end of 1998 and during 1999 undertook several attempts to reorganize, ultimately filing for bankruptcy under Chapter 11 on February 2, 2000.

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(ii) absolute stock returns correlation is higher; and (iii) companies A and B are in the

same industry and the industry is highly concentrated. These hypotheses correspond to

the three models presented in Table V.

Naturally, there is a concern that stock trading could be attributed to release of

other public information within the same quarter. To assure that rebalancing in a stock

portfolio is caused by private information received by the institutional investors during

loan renegotiation, for each manager we constructed a control sample where we follow

trading in the same stock, in the same quarter, by comparable managers who do not have

access to the information from the loan market.18 This empirical approach is the so called

difference-in-differences. Access to private information is the treatment in our model,

and we are interested in its effect on the trade. Given trade is a flow variable, formally,

we are dealing with triple differences: before vs. after loan renegotiation, high (H) vs.

low (L) informativeness, and institutional investors that have private information (P) vs.

investors that don’t (NP). Our central hypothesis is about effect of private information

for related stocks, and it is equal to:

(Trade PH – Trade PL) - (Trade NPH – Trade NP

L)

In the regressions, this effect is reflected in the coefficient on the interaction term

between dummy identifying institutions with private information and dummy identifying

stocks with high correlation. In addition, we control for past trade direction and change in

portfolio weight, stock and market return over a quarter and changes in price-to-earnings

ratio.

18 As earlier, comparable managers are matched by size quintile and institutional type. Mutual funds are matched by size quintile and investment objective.

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The dependent variable in Table V is trade direction. We find that when there is

negative private information, institutions sell shares of the companies that have high

earning and stock returns correlation with the distressed borrower. This result is

statistically and largely economically significant. Similarly, when there is negative

private information, institutions sell shares of the companies in the same industry, if this

industry is concentrated. This result, however, is not statistically significant.

B. Relationship between stock and loan prices for the same company

Thus far, we found that, following loan amendments, institutional investors rebalance

their portfolio in related stock. Preliminary evidence suggests that there are very few

cases where institutional investors hold stock and loan of the same company. However it

could be limitation of our current sample. As we mentioned earlier, there is small number

of cases but a big concern surrounding possibility of institutional investors using private

information from the loan market to trade stock of the same company. To address this

point, we look at two different aspects of aggregate relationship between stock returns

and secondary market loan returns: lead lad analysis of the returns and event study

surrounding loan amendments.

Lead-lag relationship between loan and equity markets is conducted using daily

returns. Loan returns are calculated using quotes from the LSTA/LPC secondary market

price database. We estimate pooled regression for each equation the following system:19

5 5 5 5

, , , ,1 0 0 0

L S B Mi t j i t j j i t j j i t j j i t j i t

j j j jRL RL RS RB RM , ,α β β β β− − − −

= = = =

= + + + + +∑ ∑ ∑ ∑ ε

(1)

19 Robust standard errors are computed assuming contemporaneous correlations across error terms but no time series autocorrelations. We also estimate the equations jointly using seemingly unrelated regression (SUR). Both estimation techniques produce the same point estimators. The statistical significances are qualitatively similar, except that the standard errors obtained by SUR are generally slightly lower.

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5 5 5 5

, , , ,1 0 0 0

S L B Mi t j i t j j i t j j i t j j i t j i t

j j j jRS RS RL RB RM , ,α β β β β− − − −

= = = =

= + + + + +∑ ∑ ∑ ∑ ε

(2)

RLi,t stands for returns on syndicated loan i at time t, and RSi,t represents the stock

return of the loan issuing company for loan i at time t. We also control for market level

return for fixed income market and equity market. RB and RM are daily returns of

Lehman Brothers Aggregate Bond Index, and daily returns of S&P Index.20

[TABLE VI]

Table VI reports the results of the return generating process (1) and (2). The

results on equation (1) indicate that equity returns lead subsequent loan returns at all lags.

This may be due to the fact that equity market is in general more liquid than secondary

loan market. Therefore, public information on the same company may be incorporated

into the equity prices more quickly than loan prices. Even if there is private information

generated in the loan market, high transaction cost and low liquidity may significantly

reduce the incentive of informed trading in loan market. This could explain a significant

delay in information incorporation into loan prices. Evidence of different speed of price

adjustment between equity and loan markets are also reflected in the autocorrelation

within loan and stock returns themselves. For loan market, current loan return can

significantly predict future returns into the next five days. In contrast, current equity

returns do not predict future equity returns after 2 days.

[TABLE VII]

The results on equation (2) suggest that loan returns also affect current and future

equity returns to some degree. Some information is incorporated into loan market more

20 Allen and Gottesman (2005) conduct a similar analysis. Using weekly data, they find both the equity and syndicated loan markets are highly integrated. In contrast, we focus on daily returns in our analysis since a lot of news may be incorporated into prices within several days and aggregate daily data into weekly may lose some information of the timing on the price adjustment.

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quickly than equity market. Given the lending syndicate participants have access to

private information about the borrowing firms, this information could be first

incorporated into loan prices and, only later, when the information becomes public, it

becomes reflected in equity prices. The results, therefore, suggest that time difference in

information release between two markets can provides an opportunity for the institutional

investors to exploit information advantage through its equity investments. In 1999 the

syndicated market introduced mark-to-market loan pricing facilitating exposure of private

information conducive of loan trading. Indeed Table VII indicates that time delay of

information becomes shorter as the loan becomes more liquid.

[TABLE VIII & FIGURE 3]

To better understand the nature of information released in the market we look at

the returns behavior surrounding loan renegotiations. We only focus on loan amendments

associated with borrower’s distress. For that reason, if there is a spillover of the

information to the stock market, we expect to find negative stock reaction surrounding

loan amendment. Table VIII shows a large and significant negative abnormal return in

the stock market in the day of the loan renegotiation and for the two days window

following loan renegotiation. By looking at the Figure 3 we can conclude that negative

returns in the stock market are accompanied by positive abnormal turnover in the two

days following the loan amendment. It is interesting to observe that the equity prices start

to drop even before the official amendment date, specifically, one day before the loan

amendment, suggesting that there might be information leak into the equity market even

preceding formal amendment of the loan.

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On the other hand, we find a significant increase in the loan price on the day when

a loan renegotiation reaches to its final stage. This may look surprising at the first glance

since the loan holders should have learned the information about the borrower through

the renegotiation process and there should be no more price adjustment on the last day of

the process. However, until a renegotiation reaches to its final stage and become

successful, there is always some concern that a new agreement can not be reached

between the borrower and the lenders, resulting in borrower’s default. If renegotiation is

successful, it is reflect in the increase in the loan price on the date of loan amendment.

Since our sample only considers cases that ended in amendments, we only observe

positive price adjustments.

IV. Conclusions

Institutional investment in syndicated loans is the biggest, yet the least understood

development in the corporate loan market. In this paper, we provide first insight into why

institutional investors choose to participate in the loan market, and how the news received

in the loan market propagate through institutional managers’ investments and into the

equity market.

We look at the institutional investors that invest in equity and loan markets. We

find that stock investments of the managers that hold loans generally outperform stock

investments of comparable mangers that do not hold loans. To verify causal relationship

between superior information and outperformance, we introduce a direct and novel test

that looks at the institutional investors’ trading behavior following loan renegotiations.

We carefully select loan renegotiations to credibly identify time and nature of private

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information released in the loan market. Thus, we focus only on the loan amendment

events that are unambiguously triggered by bad news about the borrower. Our results

suggest that when a loan contract is subject to such an amendment, institutions that hold

the loan tend to sell shares of the companies that have high earning and stock returns

correlation with the distressed borrower, while institutional investors who do not hold the

loan have significantly less selling behavior.

The economics benefit of institutional entry into the loan market should not be

understated. Thus, because of the controversial nature of the issue raised in this paper,

more thorough analysis is required before final conclusions can be reached. To continue

with this effort, we are currently working on expanding our sample to 2006 (two

additional years). Given the exponential trend in the institutional investors’ participation

in the loan market over the past few years, we expect that it will enlarge our sample in an

important way. In addition, using SEC filings, we are collecting lender’s identities at the

moment of the loan amendments. Our initial results indicate that there is a significant

group of investors that enter loans through the secondary loan market, as opposed to

acquiring part of the loan at the loan syndication. Moreover, to directly link the out-

performance to the private information gained through loan amendment event, we will

also look at the performance of the stocks purchased and sold by the institutional

investors during and immediately following the loan amendment event, the private

information should allow these institutional investors to better tell future winners from

losers. Finally, we are gathering bond data for the companies that issue loans. We believe

that it will be an important factor in understanding investment choices of the institutional

managers.

18

References Acharya, Viral V., and Tomothy C. Johnson, 2005, Insider Trading in Credit Derivatives, Working paper. Allen, Linda and Aron A. Gottesman, 2005, The Informational Efficiency of the Equity Market As Compared to the Syndicated Bank Loan Market, Working paper. Allen, T. William, Reinier Kraakman and Guham Subramanian, 2007. “Trading in the corporation’s securities” in Commentaries and cases on the law of business organization (Aspen Publishers, New York). Altman, Edward I., Amar Gande, and Anthony Saunders, 2005, Informational Efficiency of Loans versus Bonds: Evidence from Secondary Market Prices, Working paper. Blanco, Robert, Simon Brennan and Ian W. Marsh, 2004, An empirical analysis of the dynamic relationship between investment grade bonds and credit default swaps, Journal of Finance 60, 2255-2281. Brennan, Michael, 1975, The Optimal Number of Securities in a Risky Asset Portfolio When There are Fixed Costs of Transacting: Theory and Some Empirical Results, Journal of Financial and Quantitative Analysis 10, 483-496. Dichev, Ilia D. and Douglas J. Skinner, 2002, Large sample evidence on the debt covenants hypothesis, Journal of Accounting Research 40, 1091-1123. Grinblatt, Mark, and SheridanTitman, 1993, Performance Measurement without Benchmark: An Examination of Mutual Fund Returns, Journal of Business 66, 47-68. Hotchkiss, Edith S., and Tavy Ronen, 2002, The informational efficiency of the corporate bond market: An intraday analysis, Review of Financial Studies 15, 1325-1354. Ivashina, Victoria, 2006, Agency Effects on Syndicated Loan Rates, HBS Working Paper. Longstaff, Francis A., Sanjay Mithal and Eric Neis, 2005, Corporate yield spreads: Default risk or liquidity? New evidence from the credit-default swap market, Journal of Finance 60, 2213-2253. Massa, Massimo, and Zahid Rehman, 2005, Information flows within financial conglomerates: evidence from the banks-mutual fund relationship, Working Paper. Roll, Richard, 1978, Ambiguity when information is measured by the securities market lne. Journal of Finance 33, 1051-1069. Standard & Poor’s, 2006, A Guide to the Loan Market (Standard & Poor’s, New York).

19

Tchistyi, Alexei, 2006, Security Design with Correlated Hidden Cash Flows: The Optimality of Performance Pricing, NYU Working Paper.

20

FIGURE 1 COMPOSITION OF LOAN INVESTORS

0%

20%

40%

60%

80%

100%

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Institutional Investors

Includes investments in the secondary loan market Source: Standard and Poor’s

FIGURE 2 CORPORATE LOAN ISSUANCE WITH INSTITUTIOANAL INVESTORS

$0

$20

$40

$60

$80

$100

$120

$140

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

billi

on

Including all loans with institutional investors in the primary loan market Source: Reuters LPC/DealScan

21

FIGURE 3 ABNORMAL STOCK TURNOVER AROUND LOAN AMENDMENTS

-0.002

0

0.002

0.004

0.006

0.008

0.01

-5 -4 -3 -2 -1 0 1 2 3 4 5

Mean Median

Abnormal turnover is defined as deviation from the average historical turnover. Source: CRSP

22

APPENDIX

VARIABLE DESCRIPTION

Variable Definition Source

Log (Assets)

Logarithm of total value of equity portfolio.

CRSP & Spectrum 13f and S12

Portfolio earnings correlation Specific to a given institutional manager in a given quarter, measures absolute value of earnings correlation between manager’s value-weighted stock portfolio and the borrower. Correlation is calculated using quarterly earnings over the 5-year window previous to a given quarter. We take the maximum if an investor holds more than one loan. Loans are considered to be outstanding until its maturity.

Compustat & Spectrum 13f and S12

Portfolio returns correlation Specific to a given institutional manager in a given quarter, measures absolute value of correlation between return on the manager’s value-weighted equity portfolio and return on the borrower’s stock. Correlation is calculated using monthly returns over the 3-year window previous to a given quarter. We take the maximum if an investor holds more than one loan. Loans are considered to be outstanding until its maturity.

CRSP & Spectrum 13f and S12

Previous relationship with the bank Equal 1 if institutional investor participated in other loans syndicated by the same lead bank and 0 otherwise.

DealScan

Trade direction Equal -1 if there was a decrease, 0 if there was no change and 1if there was an increase in number of shares held at the end of the quarter.

Spectrum 13f and S12

Portfolio weight change Value based change in the fraction of the portfolio allocated to a given stock.

Spectrum 13f and S12

Private information Investor specific variable defined around loan renegotiation; it is equal 1 if the investor holds a renegotiated loan and 0 otherwise; 1 indicates access to private information about the company that renegotiated a loan.

DealScan & Spectrum 13f and S12

Stock return over quarter Stock return over the previous quarter. CRSP Market return over quarter Value weighted market return over the previous quarter. CRSP ∆ P/E ratio Quarterly change in price-to-earnings ratio: DATA14 /

DATA27. Compustat

Earnings correlation Specific to loan renegotiation, measures absolute value of earnings correlation between a given stock in the investor’s portfolio and stock of the company that renegotiated its loan. Correlation is computed using quarterly earnings over the 5-year window previous to the loan amendment

Compustat

Returns correlation Specific to loan renegotiation, measures absolute value of stock returns correlation between a given stock in the investor’s portfolio and stock of the company that amended its loan; correlation is calculated using monthly returns over the 5-year window previous to the loan renegotiation.

CRSP

Same industry Dummy variable specific to a loan renegotiation, equals 1 if a given stock in the investor’s portfolio is in the same 2-digit SIC industry as the company that renegotiated its loan.

Compustat

Concentrated industry Equal one if Herfindahl industry concentration index is above the median and 0 otherwise; Herfindahl index is computed at the 2-digit SIC level using annual assets data previous to the year of the loan renegotiation.

Compustat

23

TABLE I SAMPLE DESCRIPTION

This table presents results of matching loan level data from Reuters LPC/DealScan with institutional investors’ data from CDA/Spectrum 13f and S12 and CRSP. The sample is restricted to institutional investors that invest in stock and loan market.

All borrowers Publicly traded borrowers

Year Loans Firms Inst. Inv. Inst. Inv. x Loan

Inst. Inv. x Equity

Inst. Inv. x Loan

Inst. Inv. x (Equity & Loan)

1990 104 98 21 158 1,109 106 16 1991 120 109 24 158 1,232 91 16 1992 117 107 26 155 1,979 73 1 1993 129 122 27 184 3,081 125 19 1994 128 122 23 175 4,343 86 11 1995 99 94 25 154 6,587 83 8 1996 133 131 30 208 8,159 138 17 1997 102 95 27 158 10,747 102 5 1998 95 92 26 135 7,796 96 4 1999 104 99 27 201 7,942 133 13 2000 68 67 24 115 8,339 76 8 2001 47 46 26 65 9,692 35 4 2002 61 59 30 88 7,666 54 4 2003 71 67 29 112 7,851 69 9 2004 87 81 26 112 5,844 49 7

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TABLE II DIFFERENCE IN STOCK PORTFOLIO PERFORMANCE

This table presents differences in quarterly returns on stock portfolios between institutional managers that invest in loans and institutional managers that do not invest in loans. Numbers correspond to averages between 1995 and 2004. First column presents average returns for the managers that invest in loans, subsequent columns present differences in performance for comparable managers. The first group of controls is matched by the size quintile. The second group of controls is matched by size and institutional type. For mutual funds we use investment objective as style.

Differences in performance

Matched by Size Matched by Size & Style

Institutional Type Mean (%) StdErr Controls Mean (%) StdErr Controls Mean (%) StdErr Panel A: 6 month horizon

Bank Trust 2.54 1.27 14,846 0.91 0.07 1,422 2.48 0.17 Insurance Company 1.65 0.32 118,063 -0.25 0.02 4,916 0.19 0.10 Mutual Fund 5.83 1.80 12,028 1.03 0.12 507 0.79 0.25 Independent Advisor 0.19 0.63 21,531 -1.15 0.05 13,611 -1.56 0.07 Other 0.99 0.53 15,545 0.02 0.06 4,860 -0.18 0.13 Panel B: 1 year horizon Bank Trust 3.51 1.57 14,846 1.34 0.09 1,422 3.77 0.22 Insurance Company 1.52 0.47 118,063 -0.88 0.04 4,916 -0.18 0.15 Mutual Fund 11.07 2.56 12,028 2.84 0.16 507 2.21 0.38 Independent Advisor 0.60 0.78 20,305 -1.26 0.06 13,125 -1.70 0.09 Other 1.50 0.77 15,545 -0.63 0.08 4,860 -1.44 0.18

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TABLE III DIFFERENCE IN STOCK PORTFOLIO PERFORMANCE: MULTIVARIATE ANALYSIS

This table presents multivariate analysis of the differences in quarterly returns on stock portfolios between institutional managers that invest in loans and institutional managers that do not invest in loans. The sample is the same as in Table II. The dependent variable is a quarterly average difference in performance for a given manager investing in loans, as compared to its control sample. Each regression includes fixed effects for the year interacted with an institutional type. General econometric specification of the models presented in this table is:

Portfolio Return i,t = β0* Dummy Type, Year + β1*Log(Assets i,t-1)+ β2*Portfolio correlation i,t-1 +ε i,t

where i stands for the investor, and t stand for the quarter. We postulate that receiving information on a given company would be more relevant for the investor’s total portfolio if this company’s earnings and/or stock returns are highly correlated with earnings and/or stock returns of the portfolio. Therefore, of particular interest are coefficients on Returns correlation and Earnings correlation, β2. These are correlations between loan portfolio and stock portfolio outstanding in a given quarter t, for a given investor i. For definitions of other explanatory variables, please see the appendix. ***, **, and * indicate p values of 1%, 5%, and 10%, respectively.

Matched by Size Matched by Size & Style

Coeff t-value Coeff t-value Coeff t-value Coeff t-value Panel A: 6 month horizon Log (Assets) -0.18 -1.7 * -0.17 -1.59 -0.16 -1.42 -0.16 -1.42 Portfolio returns correlation 2.64 3.16 *** 1.89 2.15 ** Portfolio earnings correlation 2.37 2.44 ** 0.42 0.41 Fixed effects: Institutional type x Year Yes Yes Yes Yes Adjusted R2 0.11 0.10 0.12 0.11 Observations 595 595 595 595 Panel B: 1 year horizon Log (Assets) 0.03 0.16 0.04 0.24 0.08 0.47 0.09 0.5 Portfolio returns correlation 5.30 4.26 *** 3.81 2.84 *** Portfolio earnings correlation 3.70 2.54 ** 1.98 1.27 Fixed effects: Institutional type x Year Yes Yes Yes Yes Adjusted R2 0.12 0.10 0.12 0.11 Observations 595 595 595 595

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TABLE IV DETERMINANTS OF LOAN SELECTION

This table presents results of a logit analysis that examines how institutional investors select the loans. In the sample, all companies issue stocks and loans and all managers invest in stocks and loans. The dependent variable equals 1 if an institutional manager invests in a given loan, and 0 otherwise. Lagged stock portfolio performance is measured over the 6 months previous to the loan active date. Econometric specification of the model is:

Pr(Invest in loan i,l,t) = β0* Dummy Type, Year + β1*Log(Assets i,t-1)+ β2*Portfolio return i,t- 1+ β3*Previous relationshipi,l,t-1 + β4*Portfolio correlation i,l,t-1 +ε i,l,t

where i stands for the investor, l stands for the loan, and t stand for the end of the quarter in which loan was syndicated. As in Table III, the focus is on the coefficient on the informativeness measure, β4. For definitions of other explanatory variables, please see the appendix. ***, **, and * indicate p values of 1%, 5%, and 10%, respectively.

Coeff Wald Coeff Wald

Log (Assets) 0.11 11.7 *** 0.11 11.1 ***

Lag Stock portfolio performance -2.18 3.4 * -2.31 3.8 **

Previous relationship with the bank 21.21 0.0 21.22 0.0 Portfolio returns correlation 0.54 1.3 Portfolio earnings correlation 0.20 0.6 Fixed effects: Institutional type x Year Yes Yes Pseudo R2 0.69 0.69 Max-rescaled R2 0.92 0.92 Observations 12,761 12,967

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TABLE V STOCK PORTFOLIO REBALANCING AROUND LOAN RENEGOTIATIONS

This table presents results an ordinal logit for stock trades following loan renegotiations; specifically, we focus on the loan renegotiations associated with negative news about the borrower. The dependent variable is Trade direction equal to -1, 0 or 1 if over the past quarter the investor reduced, did not change or increased his position in a given stock. Private information is a dummy variable, equal 1 if the investor holds a renegotiated loan, and 0 otherwise. Earnings correlation and Returns correlation are specific to the renegotiation, and measure absolute value of earnings and returns correlation between a given stock in the investor’s portfolio and the stock of the company that renegotiated its loan. Model 1 corresponds to the following specification:

Prob(Trade direction i,s,t) = β0* Dummyi + β1*Portfolio weight change i,s,t-1+ β2* Trade direction i,s,t-1 + β3*Returns,t +β4*Market returnt +β5*∆P/Es,t-1 +

β6*Private information i,t-1 +β7*Earnings correlation i,s,t-1 + β8*Private information i,t-1*Earnings correlation i,s,t-1+ε i,s,t

where i stands for the investor, s stands for the stock, and t stand for the quarter end after the loan amendment. The focus of the Model 1 is on β8; it measures difference-in-differences trading effects of private information for related stocks. Dummyi is equal to 1 for a given case (or treatment) and control group. Econometric specification is similar for Models 2 and 3. For definitions of the explanatory variables, please see the appendix. ***, **, and * indicate p values of 1%, 5%, and 10%, respectively.

Model 1 2 3 Variable Coeff Wald Coeff Wald Coeff Wald

Lag Portfolio weight change -0.0382 9.8 *** -0.0241 4.2 ** -0.0008 34.3 ***

Lag Trade direction 0.2003 2560.4 *** 0.2034 2433.6 *** 0.2051 2982.2 ***

Stock return over quarter 0.2441 282.2 *** 0.2532 275.2 *** 0.2287 273.4 ***

Market return over quarter -0.0503 1.3 -0.0466 1.1 0.0157 0.2 ∆ P/E ratio -0.0001 76.0 *** -0.0001 69.0 *** -0.0001 67.5 ***

Private information -0.0335 1.5 -0.0230 0.8 -0.1793 121.0 ***

Earnings correlation -0.0228 2.3 Private info.* Earnings correlation -0.3102 37.8 *** Returns correlation -0.0501 4.0 ** Private info.* Returns correlation -0.5018 27.0 *** Same industry 0.0084 0.1 Private info.* Same industry 0.0780 1.0 Concentrated industry -0.0310 13.4 ***

Private info.* Concentrated industry -0.0385 1.6 Concentrated industry*Same industry 0.2628 22.4 ***

Private info.* Same & Concentrated ind. -0.3391 2.5 Manager fixed effects Yes Yes Yes Pseudo R2 0.019 0.020 0.019 Max-rescaled R2 0.023 0.024 0.023 Observations 273,481 251,481 306,210

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TABLE VI LEAD-LAG RELATIONSHIP BETWEEN LOAN AND EQUITY MAREKTS

This table presents the results on the study of the lead lag relationship between daily returns of stocks and syndicated loans issued by the same companies. Model specification is shown by Equation (1) and Equation (2) in the main text. The left panel records results for equation (1) studying how past and contemporaneous stock returns affect current loan returns. The right panel records the corresponding results for Equation (2) – past loan returns’ effect on current stock returns. The models are estimated in a pooled regression. Robust standard errors are used to adjust for contemporaneous correlation of error terms across different companies. RL, RS, RB and RM stand for daily returns for individual loans in the secondary market, daily equity returns, daily returns of Lehman Brothers Aggregate Bond Index, and daily returns of S&P Index, respectively. X(t) is the observation at time t for variable X. ***, **, and * indicate p values of 1%, 5%, and 10%, respectively.

Equation (1): Dependent Variable - RL(t) Equation (2): Dependent Variable - RS(t) Variable Coeff t-value Variable Coef t-value

Intercept 2.64E-05 6.58 *** Intercept 0.0003 1.44 RL(t-1) 0.0116 5.83 *** RS(t-1) -0.0121 -2.53 **

RL(t-2) 0.0089 4.8 *** RS(t-2) -0.0124 -3.01 ***

RL(t-3) 0.0080 4.96 *** RS(t-3) -0.0016 -0.36

RL(t-4) 0.0060 5.91 *** RS(t-4) -0.0033 -0.79

RL(t-5) 0.0020 2.41 ** RS(t-5) 0.0066 1.57

RS(t) 0.0004 3.58 *** RL(t) 0.4038 3.58 ***

RS(t-1) 0.0008 7.01 *** RL(t-1) 0.0674 2.18 **

RS(t-2) 0.0006 5.02 *** RL(t-2) 0.0348 1.48

RS(t-3) 0.0006 5 *** RL(t-3) 0.0235 0.96

RS(t-4) 0.0006 5.03 *** RL(t-4) 0.0882 3.54 ***

RS(t-5) 0.0004 3.4 *** RL(t-5) 0.0148 0.69

RB(t) -7.50E-06 -0.69 RM(t) 0.8743 45.51 ***

RB(t-1) -1.40E-06 -0.1 RM(t-1) 0.0913 4.72 ***

RB(t-2) 6.60E-06 0.49 RM(t-2) 0.0515 2.92 ***

RB(t-3) -2.40E-05 -1.73 * RM(t-3) 0.0327 1.74 *

RB(t-4) 6.30E-06 0.41 RM(t-4) 0.0391 2.23 **

RB(t-5) -1.50E-06 -0.12 RM(t-5) 0.0059 0.33

RM(t) -0.0003 -0.6 RB(t) -0.0002 -0.39

RM(t-1) -0.0001 -0.27 RB(t-1) 0.0007 0.99

RM(t-2) 0.0001 0.25 RB(t-2) -0.0010 -1.54

RM(t-3) 0.0004 1.01 RB(t-3) 4.87E-05 0.08

RM(t-4) -2.00E-05 -0.05 RB(t-4) 0.0015 2.27 **

RM(t-5) 0.0003 0.7 RB(t-5) -0.0009 -1.62

R2 0.01 R2 0.11

Observations 283,518 observations 283,518

29

TABLE VII IMPACT OF LOAN LIQUIDITY ON THE LEAD LAG RELATIONSHIP

BETWEEN LOAN AND EQUITY MAREKTS This table summarizes the results of lead lag analysis for the stock market returns (Equation (2) sorted by loan liquidity. For each month, loans are sorted into “High Liquidity”, “Median Liquidity” and “Low Liquidity” portfolios based on their average number of quotes for each day. The definitions are as in table VI. Separate analyses are done for separate liquidity portfolios. ***, **, and * indicate p values of 1%, 5%, and 10%, respectively.

Low Liquidity Median Liquidity High Liquity Variable Coef t-value Coef t-value Coef t-value

RS(t-1) -0.0140 -2.47 ** -0.0231 -3.3 *** -0.0006 -0.09 RS(t-2) -0.0132 -2.51 ** -0.0090 -1.53 -0.0147 -2.53 **

RS(t-3) -0.0028 -0.54 -0.0005 -0.09 -0.0012 -0.21

RS(t-4) -0.0016 -0.32 -0.0045 -0.76 -0.0036 -0.57

RS(t-5) 0.0056 1.09 0.0041 0.7 0.0093 1.51

RL(t) 0.0777 0.38 0.4066 2.56 ** 0.5170 2.94 ***

RL(t-1) 0.0069 0.16 0.1054 2.72 *** 0.0526 1.02

RL(t-2) 0.0896 2.6 *** 0.0310 0.93 -0.0014 -0.04

RL(t-3) 0.0414 1.06 0.0171 0.54 0.0244 0.63

RL(t-4) 0.0437 1.12 0.0805 2.51 ** 0.1326 4.12 ***

RL(t-5) -0.0017 -0.05 0.0093 0.3 0.0377 1.35

R2 0.09 0.09 0.12

Observations 98,414 76,032 109,072

30

TABLE VIII LOAN AND STOCK RETURNS AROUND LOAN AMENDMENTS

This table presents loan and equity returns surrounding loan amendments. RL stands for returns in the secondary loan market and RS standards for returns in stock market. Assuming loan amendment event date is t, RL(t-j) and R(t+j) represents average loan returns on the jth day before and after the event date, respectively. Abnormal return for each company is calculated as raw return minus average return during the 25th to 5th day before the loan amendment dates. ***, **, and * indicate p values of 1%, 5%, and 10%, respectively.

Return Abnormal Return Variable N Mean t-stat Mean t-value

Panel A: Loan Returns RL(t-5) 53 -0.0013 -1.44 -0.0013 -1.48

RL(t-4) 52 -0.0001 -1.90 * -0.0001 -1.18

RL(t-3) 52 0.0004 0.72 0.0003 0.63

RL(t-2) 54 -0.0007 -1.34 -0.0007 -1.40

RL(t-1) 50 0.0008 1.78 0.0008 2.20 **

RL(t) 55 0.0005 2.23 ** 0.0006 2.30 **

RL(t+1) 55 -0.0001 -0.58 0.0000 0.27

RL(t+2) 55 -0.0001 -0.31 0.0000 -0.09 RL(t+3) 55 0.0006 1.86 * 0.0007 2.30 **

RL(t+4) 55 -0.0003 -0.95 -0.0002 -0.61

RL(t+5) 55 0.0004 1.53 0.0005 2.28 **

Panel B: Equity Returns RS(t-5) 55 0.0148 2.91 *** 0.0097 1.86

RS(t-4) 55 0.0104 1.67 0.0053 0.72

RS(t-3) 55 0.0042 0.65 -0.0009 -0.17

RS(t-2) 55 -0.0012 -0.20 -0.0063 -1.12

RS(t-1) 55 -0.0032 -1.09 -0.0083 -2.28 **

RS(t) 55 -0.0146 -3.63 *** -0.0196 -5.36 ***

RS(t+1) 55 -0.0011 -0.14 -0.0062 -0.73

RS(t+2) 55 -0.0159 -3.24 *** -0.0209 -3.91 ***

RS(t+3) 55 0.0024 0.36 -0.0026 -0.37

RS(t+4) 55 0.0110 1.69 * 0.0059 0.89

RS(t+5) 55 0.0012 0.26 -0.0039 -0.83

31