THE INFORMATIONAL CONTENT OF THE LOAN MARKET: AN … · 2017-02-21 · also examine the factors...

1

THE INFORMATIONAL CONTENT OF THE LOAN MARKET: AN EQUITY

PORTFOLIO-BASED APPROACH

by

Claudia Champagne*, Stéphane Chrétien** and Frank Coggins***

Current Version: October 2016

* Department of Finance, Sherbrooke University, 2500 Blvd. de l’Université, Sherbrooke, P.Q.,

Canada, J1K 2R1. Telephone 819-821-8000, ext. 62976. E-mail:

[email protected].

** Finance, Insurance and Real Estate Department, Laval University, 2325 rue de la Terrasse,

Québec, P.Q, Canada, G1V 0A6. Telephone 418-656-2131, ext. 3380. E-mail:

[email protected] .

*** Department of Finance, Sherbrooke University, 2500 Blvd. de l’Université, Sherbrooke,

P.Q., Canada, J1K 2R1. Telephone 819-821-8000, ext.65156. E-mail:

[email protected]

The authors would like to thank Mario Lavallée, Mohamed Al Guindy and seminar participants at the

Mathematical Finance Days 2012, the Northern Finance Association Conference 2013 and Sherbrooke

University for their comments, David Lamoureux for his outstanding research assistance, as well as

Alexandre Deschamps and Eric Sylvestre for their help. Financial support from the Desjardins Sustainable

Development Management Chair, the Institut de Finance Mathématique de Montréal (IFM2) and the

Investors Group Chair in Financial Planning is gratefully acknowledged. The authors are research affiliates

at CIRPÉE (Chrétien and Coggins), GReFA and LABIFUL (Chrétien).

mailto:[email protected]



2

THE INFORMATIONAL CONTENT OF THE LOAN MARKET: AN EQUITY

PORTFOLIO-BASED APPROACH

ABSTRACT

This paper examines the informational content of the loan market by testing whether loan

market activities and loan terms provide valuable information regarding the equity

performance and risk of borrowers. Using a portfolio approach with a conditional

performance evaluation model that controls for the economic conditions, we document

evidence that the loan market is informative. Signals from primary loan announcements,

first secondary market loan sales and secondary market price variations generate significant

performance for portfolios invested in borrower stocks. Loan terms also provide valuable

conditioning information about the systematic and specific equity risks of borrowers.

Keywords: syndicated loans; secondary market; informational content; capital markets;

portfolio strategy; conditional performance model;

JEL Classifications: G14, C58, G12, G21

3

1. Introduction

Capital market information is crucial to investors and portfolio managers looking to

understand investment opportunities, corporate and treasury officers interested in financing

opportunities, and market regulators and economists worried about efficiency. This market

information can come from varied sources, including another asset class or another market

altogether. For instance, adding the term structure premium or the credit spread in an equity

model is intuitive and commonly done since Fama and French (1989) documented that

these variables are predictors of the equity premium. Ferson and Schadt (1996) and

Jagannathan and Wang (1996) are prominent examples of using such bond market

information into the conditional CAPM to evaluate equity portfolios.

Although largely overlooked, the syndicated loan market, which represents one third

of all international corporate financing, including stocks, bonds and commercial papers, is

another potential candidate from which to gather valuable information.1 The presence of

an increasingly liquid and voluminous secondary loan market makes it even more

informative and useful. Because the loan market is primarily driven by financial

institutions, retail investors cannot directly use loan securities to diversify their portfolio.2

Nevertheless, the information available on the loan market regarding borrowers can be

insightful, especially considering that an institutional market is less noisy, thus increasing

the quality and value of the information.

1 Dennis and Mullineaux (2000) provide a very good description of the syndicated loan market. The authors

also examine the factors that influence the decision to syndicate a loan as well as agency problems that may

arise in a syndicate. 2 Trading in the syndicated loan market is limited to qualified institutional investors as a result of the

designation of syndicated loans as Rule 144a securities.

4

The general objective of this paper is to study the informational content of the

syndicated loan market. We analyse two sources of information: direct information via loan

market activities (primary market loan announcements, first secondary market loan sales

and secondary market loan price variations) and additional indirect information captured

by loan terms (spread, amount and maturity). In order to determine if the two sources

provide valuable information regarding the borrowers’ performance and risk, we first

construct equity portfolios based on trading signals from loan market activities. We then

evaluate these portfolios in a conditional performance framework that can also include

controls for the loan terms. Our comprehensive loan market information comes from more

than 9,000 loans issued to American borrowers from 1998 to 2009 and over 436,000

secondary market transactions during the same period.

The contributions of the research are twofold. The first innovation of the paper lies in

our characterization of the informational content of the syndicated loan market. It is well

known that financial intermediaries, through quality monitoring and surveillance, have

superior knowledge regarding the quality and risk of a borrower than arms’ length

investors. We argue that if these financial institutions issue loans and trade on the loan

market based (even partly) on this private information, primary market issues and

secondary loan market trading can become informative to investors. Moreover, because

lenders go through extensive evaluations of the borrower’s performance and risk, loan

terms can also reveal valuable information because they reflect the lenders’ assessment of

the borrower. Results show that this is indeed the case. The specialness of financial

institutions or the value of bank loans has been demonstrated in the literature, but has

mostly been restricted to certain types of information such as loan announcements. Existing

5

research is also generally based on traditional event studies that do not control for the

financial or economic context surrounding the event. The next section of the paper presents

a review of the relevant literature that further highlights this first innovation.

The second innovation concerns our methodology, a portfolio-based approach with

conditional performance measurement. This approach is not only relevant in a portfolio

management context, but it also provides a complement to event studies to test the value

of bank loans and the informational content of the loan market. Specifically, in order to

obtain the most accurate measures of performance and risk, we apply a methodology that

combines two econometric methods commonly used in the empirical finance literature:

conditional measures based on predetermined information variables and GARCH models.

This methodology allows us to i) evaluate the impact of loan market information on equity

performance and risk (specific and systematic), ii) control for the financial and economic

context during which the event takes place, and iii) test the impact of static loan information

(such as loan terms) in a dynamic investment framework. To our knowledge, the impact of

loan market information has not been tested on risk, either specific or systematic. Also, we

are the first to look at whether loan terms can offer additional insights regarding the

informational content of loan market signals.

Our empirical results provide evidence that the loan market is informative regarding

the equity performance and risk of borrowers and that this information can be extracted

either from loan market activities such as loan announcements, first loan sales and loan

price movements, or from loan terms that provide complementary information. We find

that signals based on loan announcements on the primary market provide reliably positive

portfolio performance, consistent with the literature on the positive impact of bank loan

6

announcements on stock prices. First loan sales and large loan price variations (positive or

negative) on the secondary market are also associated with statistically significant

performance and provide valuable positive signals to equity investors. These findings

support the argument that secondary loan market trading is typically seen as favorable by

equity investors, potentially because of benefits for the borrowers such as lower borrowing

costs, a better access to financing and a certification role by the loan-buying institution.

Both primary and secondary market signals are associated with significant risk changes.

All results are robust to controls for the financial and economic context.

Further, our analysis suggests that the financial gains that can be achieved by using

the information from the loan market are economically important. For all signals

considered, we find that the annualized average abnormal returns vary generally between

6% and 12%, depending on the holding periods and performance models. The gains are the

largest when the stocks of a borrower are held for five days after its loan announcement on

the primary market, ten days after its first secondary market loan sale, one day after a large

increase in its loan price or five days after a large decrease in its loan price. These findings

suggest that the speed of information transmission between the loan market and the equity

market differs by loan market activities.

Finally, our results show that while loan terms are not important determinants of the

performance of equity portfolios constructed with signals from loan market activities, they

are informative with respect to the risk of these portfolios, both systematic and specific.

Specifically, loan terms are especially informative regarding the systematic risk of

portfolios based on signals from primary loan announcements. Likewise, they are

especially informative regarding the specific risk of portfolios based on signals from the

7

secondary loan market. Overall, our results suggest that equity investors and analysts can

gain an informational advantage by following the syndicated loan market.

The rest of the paper is divided as follows. The second section reviews the literature

on the loan market with respect to its informational content. The third section defines the

research hypotheses on whether the information from the syndicated loan market is

relevant for the equity market. The fourth section details the sample and describes the

methodology for portfolio construction. The fifth section presents the portfolio evaluation

models and the empirical results on our hypotheses. The last section concludes the paper.

2. Literature

2.1 Information from the primary loan market

Bank loans are generally considered to be “special” when compared to other types of

financing. Specifically, because of the better monitoring and restructuring offered by bank

loans, they are often seen as generating positive benefits to the borrower. Consistent with

this view, researchers generally find a robust and positive abnormal stock return following

bank loan announcements (e.g., James, 1987; Billett et al., 1995) or loan restructurations

(e.g., Lummer and McConnell, 1989; Best and Zhang, 1993). Preece and Mullineaux

(1996) focus on syndicated loans and also find a positive market response to primary loan

announcements. This positive impact appears to remain even when the loan is subsequently

resold on the secondary market.

2.2 Information from the secondary loan market

Once the initial contract is signed, in which loan terms are determined and the

syndicate is formed, syndicate members can sell their loan shares on the secondary market.3

3 The exponential growth of the syndicated loan market is partly attributable to the emergence of this

secondary market in the 90s, when new regulations allowed non-bank institutions to trade loans. The creation

8

While lenders can sell their loan shares if they receive private negative information about

the borrower, this behavior would not be sustainable, as it would lead to a lemons problem

(Akerlof, 1970). Further, lenders can sell their loan shares for a variety of other reasons

including diversification or regulation compliance. Although the literature on the

secondary loan market is still at an early stage, there is some evidence that additional

information can be gathered from this market. Regarding the effects of loan sales on the

borrower, they appear to be both positive and negative.

Berndt and Gupta (2009) argue that loan sales on the secondary market can lead to a

lower cost of capital, better access to loan financing and informational benefits. On the

other hand, these loan sales can weaken banking relationships, induce insufficient

monitoring which may lead to inefficient executive decisions, higher collateral

requirements, and difficulties to renegotiate. Consistent with a negative impact, Berndt and

Gupta (2009) observe that loans on the secondary market are associated with a 9%

underperformance for borrower stocks on the equity market. Dahiya et al. (2003) also

observe negative effects for borrowers when loans are traded on the secondary market,

such as a decline in bank monitoring incentives and a negative impact on bank-borrower

relationships. Using a basic event study methodology, the authors find negative

announcement returns for borrowers following a loan sale, especially for subpar loans.

They conclude that the transaction yields valuable information for investors regarding the

weakness of these borrowers. Drucker and Puri (2009) find that borrowers whose loans are

sold have more restrictive covenants.

of the Loan Syndication and Trading Association (LSTA) in 1995 and the introduction of loan credit ratings

by major rating agencies brought structure, information and liquidity to the market.

9

However, consistent with a positive impact, Gupta et al. (2008) find that borrowers

whose loans are likely to be traded in the secondary market pay lower interest rates in the

primary market. Further, Santos and Nigro (2009) find that borrowers whose loans enter

the secondary market and become liquid benefit from an interest rate discount on their

subsequent loans. This reduced borrowing cost is also observed by Kamstra et al. (2015).

Gande and Saunders (2012) use an event-study methodology to examine the stock returns

of borrowers around the first trading day of their previously non-traded loans and find

significantly positive announcement effects. They attribute their findings to the fact that

borrowers are able to obtain easier and greater financing with the existence of the secondary

market than they would otherwise, which may ease their financial constraints.

2.3 Integration of the loan and other capital markets

Although the size of the syndicated loan market puts it at least on the same level as

other extensively researched markets, theoretical and empirical research on its

informational efficiency is much less developed.

Using unconditional asset pricing models, Altman et al. (2010) provide evidence of

the informational efficiency of the loan market relative to the bond market. Among other

results, they observe that the price reactions of loans are less adverse than that of bonds

around default dates and that recovery rates (proxied by the price at default) are higher for

loans. They attribute this stronger efficiency of the loan market to banks’ superior

monitoring ability. Allen and Gottesman (2006) study the informational efficiency of the

equity market as compared to the syndicated loan market. According to their results, the

two markets are integrated and information flows freely across markets. However, their

conclusion is based on the aggregate, or average, information reflected in the loan market

10

returns and, therefore, does not rule out that some information can be profitable. Further,

they use an unconditional seemingly unrelated regression approach which does not control

for the financial and economic context.

Allen et al. (2008) observe that secondary loan prices react to earnings news one month

prior to their announcements, which the authors attribute to the loan contract clause

regarding information divulgation 30 days prior to public announcements.4 On a similar

topic, Park and Wu (2009) compare the impact of financial restatements on the loans and

stocks of firms and conclude that the loan market negatively reacts to financial statement

revisions and partially anticipates the changes in the 30 days prior to the announcements.

They also show that the loan market appears to use superior information than the stock

market and that information flows from one market to the other.

Ivashina and Sun (2011) analyze if institutional investors use private information

received in the loan market (as lenders) to trade in public securities. They identify

institutional managers that had access to private information disclosed by borrowers in the

process of loan renegotiation and find that such managers tend to trade in the stocks of

these borrowing companies and outperform other managers. Exploring a similar theme

with a different methodology, Bushman et al. (2010) come to the same conclusion.

Massoud et al. (2011) find evidence consistent with the short-selling of the equity of hedge

fund borrowers prior to public announcements of both loan originations and loan

amendments. Finally, Allen et al. (2012) find that the presence of “super-informed” dual

market makers (i.e., financial institutions that are active in both the loan market and the

equity market) has an impact on market liquidity and the price discovery process.

4 The SEC’s “Regulation Fair Disclosure” rule issued in 2000, which requires public information to be

disseminated equally to all market participants, does not apply to syndicated loan market participants.

11

Overall, the literature reviewed in this section gives clear indications that some

valuable information can be gathered from the loan market. However, the channels and

types of information that are transmitted are still unclear. Further, it’s not yet established

whether or not this information is linked to the borrowers’ performance and/or risk.

3. Research hypotheses

As reported in the literature review, the loan market has an informational advantage,

notably relative to the stock market (e.g., Allen et al., 2008; Park and Wu, 2009; Bushman

et al., 2010; Ivashina and Sun, 2011). In addition, there seems to be information diffusion

between the two markets, as observed by Allen and Gottesman (2006), Bushman et al.

(2010) or Ivashina and Sun (2011). However, the literature is not conclusive in terms of

the speed of diffusion or transmission channels.

We argue that the informational content of the loan market comes from two distinct

sources which are related to the borrowers’ performance and risk: i) specific market

activities by lenders (e.g., loan announcements, secondary market sales, etc.) and ii)

indirect information captured by loan terms. Regarding the first source of information,

following the literature discussed in section 2, we argue that if “insiders” (i.e., lenders)

issue or trade loans based on private information, then loan announcements, sales and

prices provide useful public information about the borrowers.5 This is highlighted in the

first research hypothesis:

5 In contrast to Allen and Gottesman (2006), we argue that while aggregate secondary loan market returns

can convey some information (as reflected in the significance of lagged loan market returns to explain equity

returns), they don’t necessarily convey the best-quality information. Instead, we rely on borrower-specific

loan market activities and prices that are based on actions by financial institutions, who are theoretically more

informed. In other words, instead of looking at aggregate information that is reflected in loan market returns

(as in Allen and Gottesman, 2006), we study borrower-specific signals generated by actions from loan

syndicate members.

12

1

0H Loan market activities provide valuable information and signals regarding the

performance of borrowers

From the literature and our knowledge of the syndicated loan market, we define three

types of market activities that can capture the lenders’ superior information, one on the

primary market and two from the secondary market:

(a) Loan announcements on the primary market

(b) First loan sales (for a specific loan) on the secondary market

(c) Significant loan price variations on the secondary market

The first type of loan market activity is motivated by the informational advantage of

the syndicated loan market and the positive value of loans described in the literature review,

as well as the results of studies on bank loan announcements. The remaining two types of

activities are motivated by Dahiya et al. (2003) and Berndt and Gupta (2009), who show

examples of information that can be used from secondary loan market transactions and

included in security prices.

As a complement to market activities, the informational content of the loan market can

also come from indirect information reflected in loan terms. Loan terms, such as spread,

maturity or amount, are negotiated between the borrower and the lead arranger. They are

generally based on the performance and risk of the borrower, as evaluated by the lenders,

as well as agency problems between the borrower and the lenders, which are typically

related to risk. Loan terms are also related to the lender syndicate structure which is

indirectly linked to borrower risk. Specifically, the structure of the syndicate is typically

determined to reduce moral hazard and adverse selection problems among lenders which

are related, among other things, to borrower risk (see, for e.g., Sufi, 2007; Ivashina, 2009;

13

Do and Vu, 2010; Panyagometh and Roberts, 2010; Champagne and Coggins, 2012;

Chaudhry and Kleimeier, 2015). If observed loan terms effectively reflect the performance

and risk of borrowers, they can provide useful indirect information to investors.6 This

discussion justifies the second research hypothesis:

2

0H Loan market information captured by loan terms adds valuable information

regarding the performance and risk of borrowers.

4. Sample description and portfolio construction

4.1 Sample description

Primary loan market data are available from Dealscan by Reuters’ Loan Pricing

Corporation (LPC). The database contains loan characteristics on loan issues since 1982.

To ensure sufficient portfolio transactions and minimize reporting biases, we focus solely

on loans to American borrowers from 1998 to 2009. Overall, more than 9,000 loan issues

on the primary market meet our criteria and have all the associated variables and returns

required for the analysis. Information regarding the secondary loan market is obtained

directly from the Loan Syndications and Trading Association (LSTA). Almost 700 distinct

loans sold on the secondary market are included in our analysis. These loans generate

436,367 secondary market transactions recorded between 1998 and 2009.

Macroeconomic data and individual stock returns are obtained from Bloomberg, while

the returns on the CRSP value-weighted index required for portfolio construction are

obtained from the Center for Research in Security Prices (CRSP) database. Finally, the

6 Loan terms do not include borrower-related corporate information reported in financial statements, such as

leverage or profitability, which should already be integrated into stock prices.

14

Fama-French factors included in the performance evaluation models come from the

Kenneth R. French’s website. The data frequency for these series is daily.

4.2. Strategic portfolio construction

To test our hypotheses, we use a methodology based on the performance evaluation of

constructed portfolios. Specifically, we construct strategic portfolios with positions in

borrower stocks that depend on loan market signals regarding their loans. The trading

signals come from the three types of loan market activities exposed in the preceding

section. We then use unconditional and conditional models to evaluate the performance

and risk of the portfolios. If the constructed portfolios generate abnormal returns based on

loan market signals, it implies that valuable information can be gathered from these signals.

Further, it implies that the information has not been (fully) integrated into the stock

market.7 The portfolio construction procedure is defined below. The performance

evaluation models are introduced in the next section.

To isolate the effects of the three loan market signals, three classes of actively managed

portfolios are constructed. Each class concentrates on the signals from a different type of

loan market activities. Within each class, the portfolios differ by their holding period of

borrower stocks. The first class of portfolios, Portfolios A, concentrates on loan market

activity (a), i.e., loan announcements on the primary loan market. As mentioned in section

2, loan announcements are usually considered to be positive news regarding the borrowing

firms, signalling that reputable and credible lenders assessed the financial situation of the

firms and approved the loan. In Portfolios A, the loan announcement date is therefore

7 Our methodology tests the transfer of information from the loan market to the stock market and not the

reverse. Results that show a lag in the integration of information from the loan to the stock market therefore

do not imply that the stock market is less efficient than the loan market, as they don’t preclude a lag in the

other way around (i.e., from the stock to the loan market).

15

considered to be a signal to buy the borrower stocks. The stocks of borrowers are therefore

added to the portfolios when activity/signal (a) occurs for them and are removed from the

portfolios after predetermined holding periods.

Loan market activity (b), i.e., first loan sales on the secondary market, does not provide

a clear buy or sell signal because of mixed evidence, as reviewed in section 2.2.

Specifically, the literature is ambiguous on whether the impact of a secondary loan sale

should be positive or negative for the borrower’s stock. We arbitrarily chose to interpret

the first transaction on the secondary market as a negative signal, and thus sell borrower

stocks.8 The stocks of borrowers are therefore sold on their first trading day on the

secondary loan market. The portfolios created from these signals form the second class of

portfolios, Portfolios B.

Finally, the third class of portfolios, Portfolios C, focus on loan price movements on

the secondary market. Price increases (decreases) are considered positive (negative)

information because they imply an improvement (deterioration) in the credit quality of the

borrower.9 Price increases are therefore interpreted as signals to buy while price decreases

are signals to sell. To limit the number of transactions and focus only on valuable

information-related transactions, we use a threshold of one standard deviation for price

increases and decreases to trigger a transaction.

All the portfolios are held from January 1998 to September 2009. Different holding

periods for the stocks are tested, from 1 to 20 days. The portfolios are built with an

8 Even if our choice is wrong, the informativeness of the signals can still be inferred. Specifically, if the first

loan sales on the secondary market are positive for the borrowers, the performance of portfolios constructed

by (erroneously) selling their stocks will still be significant but negative. In other words, we will nonetheless

be able interpret the information coming from the loan market. 9 Syndicated loans are typically floating-rate loans in which borrowers are charged a specific credit spread

above a reference rate (often the LIBOR). Price movements can therefore be attributed to changes in credit

quality as opposed to other market factors.

16

investment in the market portfolio that is held permanently while the remaining

investments vary according to the strategies described above. When a positive signal

occurs, the portfolio is composed equally of the equity market index and the portfolios

based on loan market signals. When a negative signal occurs, we short sell the borrower

stocks for 50% of the portfolios and use the proceedings to invest in the market index. In

all cases, when no signal occurs for a given day and holding periods for past transactions

are over, the portfolio is composed entirely of the market index. In other words, we follow

the market index when there are no signals and deviate from it when trading signals occur.

Only the latter case has the potential to yield abnormal returns.10 This methodology avoids

an empty portfolio problem that may arise if active strategies have ended and no new signal

occurs during one or more days. The CRSP value-weighted index return is used as a proxy

for market return. Appendix A provides an example of the portfolio construction procedure

and calculation of returns for the first class of portfolios, based on loan market activity (a),

and assuming a holding period of 5 days for each borrower stock added to the portfolio.

5. Evaluation models and empirical results

In the preceding section, we created strategic portfolios based on loan market signals.

We now use these portfolios in a performance evaluation setting to test the two research

hypotheses.

5.1 Information from loan market activities

10 Untabulated statistics show that our signal strategies lead to active portfolio management (i.e. portfolio

holdings differ from those of the market portfolio) for more than 50% of the days in our sample for all but

one of the strategic portfolios. For example, the secondary loan market sale signals (signals b) for one-day

holding periods lead an active management portfolio for 10% of the observations in the sample. However,

when these signals are applied to 20-day holding periods, active management increases to 70% of the sample.

Specifically, for signal strategies (a), (b) and (c), active portfolio management varies, respectively, between

74% (one-day holding period) and 97% (20-day holding period), between 10% and 70%, and between 53%

and 88% of the days in the sample.

17

5.1.1 Unconditional model

We start with a common unconditional performance model to evaluate the strategic

portfolios constructed in the preceding section:

𝑟𝑝,𝑡 = 𝛼𝑝 + 𝛽𝑝𝑚 ∗ 𝑟𝑚,𝑡 + 𝛽𝑝𝑚− × 𝑟𝑚,𝑡−1 + 𝛽𝐻𝑀𝐿 ∗ 𝐻𝑀𝐿𝑡 + 𝛽𝑆𝑀𝐵 × 𝑆𝑀𝐵𝑡 +

𝛽𝑈𝑀𝐷𝑈𝑀𝐷𝑡 + 𝛽𝐽𝑎𝑛𝐽𝐴𝑁𝑡 + 𝛽𝑊𝐸𝑊𝐸𝑡 + 𝑢𝑝,𝑡, (1)

where portfolio p return, ,p tr , and market return, tmr , , are measured in excess of the one

day risk free rate at time t. 𝛼𝑝 is the performance or average abnormal return measure, also

known as the portfolio’s alpha. The term 𝛽𝑝𝑚− × 𝑟𝑚,𝑡−1 is added to avoid an econometric

bias that may arise when using daily data because of the non synchronous trading issue

(Scholes and Williams, 1977). In our case, the consistent market beta is 𝛽𝑝𝑚 + 𝛽𝑝𝑚− . Other

standard risk premium variables are added: 𝐻𝑀𝐿𝑡and 𝑆𝑀𝐵𝑡 are Fama and French (1993)

high-minus-low and small-minus-big factors to control for the value and size effects, and

𝑈𝑀𝐷𝑡 is the up-minus-down factor that controls for the momentum effect. Finally, 𝐽𝐴𝑁𝑡

and 𝑊𝐸𝑡 control for the January and weekend effects, respectively. Coefficients 𝛽𝑗 are

related to these variables, for j =HML, SMB, UMD, JAN and WE. ,p tu is the error term.

Results from the estimation of model (1) for the three classes of strategic portfolios

are available in table 1.11 Panel A shows that signals from primary market loan

announcements yield positive and significant alphas for holding periods greater than 1 day.

These results generally confirm the findings from the literature on the positive impact of

bank loan announcements on stock prices (e.g., James, 1987; Billett et al., 1995; Preece

11 In this table as well as the others, the statistical significance of the parameter estimates is established by

using OLS standard errors. Additional robustness results adjusted for conditional heteroskedasticity and

serial correlation with the method of Newey and West (1987) are available upon request.

18

and Mullineaux, 1996), but demonstrate that announcement-day effects (1-day) are, on

average, not significant after controlling for standard stock market effects, such as size,

value, momentum, January and weekend effects. The performance is positive for all

portfolios, suggesting that economically valuable signals can be extracted from loan

announcements on the primary market. For example, for the 5-day holding period, the

portfolio earns approximately an annualized average abnormal return of 12.725% (0.0509

× 250).

[Please insert table 1 here.]

Results in panel B provide evidence that first loan trade signals are informative for

holding periods greater than 1 day. However, the negative alphas imply that investors

should buy the borrower stocks (instead of selling them) to yield a superior performance.

These results support the notions that the trading of a loan is seen by investors as positive

for borrowers because of expected benefits such as lower borrowing costs and better access

to financing.

Portfolios based on secondary market price movements also generate significant

abnormal returns in seven of the eight cases, as displayed in panel C. The buying of

borrower stocks following loan price increases provide positive performance as expected.

However, the selling of borrower stocks following loan price decreases generates an

unexpected negative performance. Large loan price decreases thus also provide positive

information regarding the future returns of borrowers. This result is surprising at first

glance, but it is consistent with at least two explanations.

First, there are potential differences in the way that debtholders and equity holders

respond to the same information. For example, if information reflects an increase in the

19

volatility of asset values, then loan prices will decline whereas equity prices will increase.

This stems from the short put option nature of debt versus the call option qualities of equity.

Also, from a lender’s point of view, negative information regarding a borrower is more

important than positive information because lenders are exposed to downside losses but do

not share in upside gains (Smith, 1979).

Although the literature is very scarce on the subject of secondary loan market prices,

Allen et al. (2008) find evidence of an asymmetric loan return reaction to earnings data and

that loan returns are much less sensitive to news of increasing earnings than are equity

returns. Consistent with the above arguments, our results provide evidence of a negative

correlation between loan and equity prices following loan-related bad news. The larger

effects obtained from negative price movements (vs positive price movements) are also

consistent with the asymmetrical reactions observed by Allen et al. (2008). Specifically,

for a given price change (in absolute value), equity investors give more weight to the

information coming from a price decrease because they know that lenders react more

strongly to negative news.

Second, the presence of a buying institution following a large loan price decrease may

represent a vote of confidence toward the borrower, which is favorable news for the equity

market. This is consistent with the certification value of bank loans which is well

documented in the literature (see, for e.g., James, 1987; Lummer and McConnell, 1989 or

Preece and Mullineaux, 1994, Cook et al., 2003). Borrowers derive value from the loan,

beyond the funding, because the lender creates the presumption that the borrower has the

capacity to honor the loan. Interested parties, such as stockholder, assume that the

institution screens the borrower’s private information prior to buying the loan (Allen,

20

1990).Overall, given our choices of types of loan market activities and portfolio

construction strategies, our results show that, after controlling for typical equity market

effects, information from the secondary loan market (both in terms of first trade and large

price movements) provides economically valuable signals to investors. These signals tend

to be positive for the borrower stocks and are associated with significant performance.

Finally, results from the three panels also show that primary and secondary market

signals are associated with positive and significant average betas. More specifically, all

strategic portfolios show average market betas that are close to one, especially once

accounting for the lagged beta term. Given that our portfolios hold the market index when

no signal occurs, these results imply that the borrower stock components of the portfolios

also have market betas close to one.12

5.1.2 Conditional model

To better account for the financial and economic context from 1998 to 2009, we test

the performance of the three classes of strategic portfolios with conditional models. This

framework allows us to study the performance and risk of the portfolios based on signals

from the loan market activities while controlling for macroeconomic variables. Led by

Gibbons and Ferson (1985) followed by Jagannathan and Wang (1996), many authors have

studied conditional versions of the CAPM by defining the alpha and market beta of

portfolio p as a function of public information Zt−1. For instance, Ferson and Schadt (1996),

Christopherson et al. (1998), Ferson and Qian (2004) and Coggins et al. (2009) propose

conditional performance measures. Their results show that the conditional approach not

12 For brevity, we do not report the estimates of the other coefficient of the performance evaluation model.

We generally find that size and value betas are highly significantly positive for the buying signal portfolios

and significantly negative for the selling signal portfolios. The other control variables are generally weakly

significant (momentum and January betas) or not significant (weekend betas).

21

only gives potentially better performance and risk estimations than the unconditional

parameterization, but also controls for some portfolio management effects.

Based on these contributions, we assume that the performance evaluation model is as

follows:

𝑟𝑝,𝑡 = 𝛼𝑝(𝑍𝑡−1) + 𝛽𝑝𝑚(𝑍𝑡−1)𝑟𝑚,𝑡 + 𝛽𝑝𝑚− 𝑟𝑚,𝑡−1 + 𝛽𝐻𝑀𝐿𝐻𝑀𝐿𝑡 + 𝛽𝑆𝑀𝐵𝑆𝑀𝐵𝑡 +

𝛽𝑈𝑀𝐷𝑈𝑀𝐷𝑡 + 𝛽𝐽𝑎𝑛𝐽𝐴𝑁𝑡 + 𝛽𝑊𝐸𝑊𝐸𝑡 + 𝑢𝑝,𝑡. (2)

As Christopherson et al. (1998), we suppose that the conditional alpha, 1( )p tZ , and the

conditional market beta, 𝛽𝑝𝑚(𝑍𝑡−1), are linear functions of the information included in a

vector of demeaned variables known at t − 1, 𝑧𝑡−1 = 𝑍𝑡−1 − 𝐸(𝑍):

𝛼𝑝 (𝑍𝑡−1) = 𝑎0𝑝 + A𝑝′ 𝑧𝑡−1, (3)

𝛽𝑝𝑚(𝑍𝑡−1) = 𝑏0𝑝𝑚 + B𝑝𝑚′ 𝑧𝑡−1. (4)

Since 𝑧𝑡−1 is defined with mean zero, the coefficients 𝑎0𝑝 and 𝑏0𝑝𝑚 represent the average

conditional alpha and beta, respectively. The coefficients in vectors A𝑝′ and B𝑝𝑚

′ measure

the sensitivity of the conditional alpha and beta, respectively, to macroeconomic variables

𝑧𝑡−1. To determine the macroeconomic variables to be included in vector 𝑍𝑡−1, we test the

predictive power of different variables for the market premium and include variables that

have a significant forecasting ability in our sample: the S&P500 dividend yield, the

corporate bond spread, the term structure premium and the lagged market return squared.13

Results from the estimation of conditional model (2) are available in table 2. Panel A

gives the results for the first type of signals, loan announcements on the primary market,

and shows that average conditional performance is significant and positive for holding

13 Test results are not tabulated to save space but are available upon request.

22

periods greater than 1 day, confirming the unconditional results that valuable positive

signals can be inferred from loan announcements. A closer examination of the impact of

the macroeconomic variables on alpha reveals that conditional alphas are weakly sensitive

to the financial and economic context. In particular, the performance is generally lower

(higher) when the dividend yield (market premium volatility) increases. These results

reinforce those from the literature by confirming that the positive value of bank loan

announcements remains even after controlling for the financial and economic context

during which these loans occur.


Panel B shows results for the second class of portfolios. We see that first loan sales

on the secondary market are associated with negative performance for holding periods

greater than 1 day. The negative sign implies that purchasing the borrower stocks (instead

of selling them) following the signals would yield a positive average abnormal return.

Further, a weak positive relationship exists between alphas and the term premium for two

of the four portfolios. Overall, these results show that the first sale of a loan is associated

with positive information regarding the short-term returns of the borrower.

Panel C gives the results for the third class of portfolios, constructed from signals

associated with loan price movements in the secondary market. For loan price increases,

average conditional performance is significantly positive for all holding periods. Further,

performance is enhanced when the bond market default spread increases, indicating that an

increase in loan prices during toughening credit conditions provides even more valuable

information about the borrowers. For loan price decreases, performance results are

significantly negative for holding periods greater than 1 day, which imply that buying

23

(instead of selling) the borrower stocks following a negative loan price movement would

lead to a positive performance. Overall, these results provide evidence that secondary loan

market price variations are informative for equity investors and that large variations in loan

price of any sign (positive or negative) provide positive information regarding the

borrowers, even after controlling for the financial and economic context.

Results about the conditional market beta show that both strategies based on buying

borrower stocks (i.e., primary market loan announcements and positive secondary market

movements) obtain significantly higher (lower) portfolio betas during good (bad) economic

conditions and during periods of low (high) market volatility. Specifically, in panel A and

in the top of panel C, betas are higher when the dividend yield is high and the default

spread, term structure slope and market volatility are low. Results in panel A suggest that

financial institutions have an increased willingness to provide loans to high (low)

systematic risk firms when economic conditions are good (bad) and when markets are less

volatile. Results in panel C reveal a similar pattern in which higher-risk firms are more

likely to be associated with increases in their loan prices in good economic conditions.

This, in turn, leads to buy signals for these firms, which explain the higher (lower) betas in

good (bad) economic conditions. Results for the strategies based on selling borrower stocks

reveal the opposite conditional beta pattern. Specifically, the bottom of panel C shows that

negative price variations are more likely for higher-risk firms during bad economic

conditions, which lead to higher (lower) portfolio betas during bad (good) economic

conditions.

Overall, the macroeconomic variables added in the conditional model are helpful to

understand the investments strategies based on loan market signals. Their significance also

24

indicates that they are useful controls for the financial and economic context in our

performance evaluation model.

5.2 Information from loan terms

5.2.1 Specification and definition of loan term variables

To test the second research hypothesis, we include a conditional residual volatility

specification and add loan terms as conditioning variables in the performance and risk

evaluation model. In other words, we test whether loan terms are helpful in the estimation

of the conditional alpha, conditional beta and conditional residual volatility of the

portfolios constructed based on the loan market signals. To do so, we use an expanded

conditional framework that jointly considers the vector of macroeconomic variables 𝑍𝑡−1

and a vector of syndicated loan information variables, 𝐼𝑝,𝑡−1, reflecting the average loan

terms of the borrowers whose stocks are included in portfolio p at time t. Because each

portfolio is rebalanced regularly to account for loan market activity signals, vector 𝐼𝑝,𝑡−1 is

time-varying. Similar to the previously defined vector 𝑍𝑡−1, vector 𝐼𝑝,𝑡−1 represents

information that is known at time t, with 𝑖𝑝,𝑡−1 being its standardized version. The

performance evaluation model is then defined as follows:

𝑟𝑝,𝑡 = 𝛼𝑝(𝑍𝑡−1, 𝐼𝑝,𝑡−1) + 𝛽𝑝𝑚(𝑍𝑡−1, 𝐼𝑝,𝑡−1)𝑟𝑚,𝑡 + 𝛽𝑝𝑚− 𝑟𝑚,𝑡−1 + 𝛽𝐻𝑀𝐿𝐻𝑀𝐿𝑡 +

𝛽𝑆𝑀𝐵𝑆𝑀𝐵𝑡 + 𝛽𝑈𝑀𝐷𝑈𝑀𝐷𝑡 + 𝛽𝐽𝑎𝑛𝐽𝐴𝑁𝑡 + 𝛽𝑊𝐸𝑊𝐸𝑡 + 𝑢𝑝,𝑡, (5)

where the control variables are as described above.

The conditional alpha and beta are then linear functions of the macroeconomic and

syndicated loan term variables:

𝛼𝑝 (𝑍𝑡−1, 𝐼𝑝,𝑡−1) = 𝑎0𝑝 + A𝑝,𝑧′ 𝑧𝑡−1 + A𝑝,𝑖

′ 𝑖𝑝,𝑡−1, (6)

𝛽𝑝𝑚(𝑍𝑡−1, 𝐼𝑝,𝑡−1) = 𝑏0𝑝𝑚 + B𝑝𝑚,𝑧′ 𝑧𝑡−1 + B𝑝𝑚,𝑖

′ 𝑖𝑝,𝑡−1. (7)

25

In this context, A𝑝,𝑖′ and B𝑝𝑚,𝑖

′ measure the sensitivity of the conditional alpha and beta,

respectively, to the loan terms associated with the portfolio.

The choice of variables that compose vector 𝐼𝑝,𝑡−1 is based on the literature. To capture

the information in loan terms, we include the loan spread, defined as the all-in loan spread

above LIBOR, the loan amount and the loan maturity. The loan terms for a portfolio then

correspond to the equally-weighted values of the loan terms for each borrower security in

the portfolio. Each element of vector 𝐼𝑝,𝑡−1 =

(𝐼𝑆𝑃𝑅𝐸𝐴𝐷,𝑝,𝑡−1, 𝐼𝐴𝑀𝑂𝑈𝑁𝑇,𝑝,𝑡−1, 𝐼𝑀𝐴𝑇𝑈𝑅𝐼𝑇𝑌,𝑝,𝑡−1) is therefore obtained as follows:

𝐼𝑐,𝑝,𝑡−1 =1

𝑁∑ 𝐼𝑗,𝑐,𝑝,𝑡−1

𝑁𝑗=1 , for c = SPREAD, AMOUNT and MATURITY, (8)

where 𝐼𝑐,𝑝,𝑡−1 is the average value of loan term c for portfolio p at time t − 1 and 𝐼𝑗,𝑐,𝑝,𝑡−1

represents the specific value of loan term c for security j, one of N borrower securities

included in portfolio p at time t − 1. Because we work on a portfolio level, each element

of vector 𝐼𝑝,𝑡−1 becomes time-varying even if loan terms for individual firms are static and

defined at loan origination.

Results on testing the second research hypothesis are obtained by considering the

impact of the loan characteristics on the conditional alpha, conditional beta (or systematic

risk) and conditional residual volatility (or specific risk) by looking at the significance of

the coefficients in vectors A𝑝,𝑖′ , B𝑝𝑚,𝑖

′ and 𝛵′.

5.2.2 Impact of loan terms on performance

Table 3 examines the impact of loan terms on performance. The table shows that

conditioning on loan terms does not provide additional information on conditional

performance. Primary and secondary markets signals are still associated with significant

performance for three of the four portfolios (panels A and B) and secondary market

26

movements yield significant performance for seven of the eight portfolios (panel C). The

conditional alphas are still weakly sensitive to the financial and economic context reflected

in the macroeconomic variables.


5.2.3 Impact of loan terms on systematic risk

As discussed in sections 2 and 3, because loan market activity and information should

be closely related to risk and agency problems, it is intuitive that they provide valuable

information regarding the risk of borrowers. However, to our knowledge, the impact of

loan market information on the risk of portfolios composed of borrower stocks has not yet

been researched. To study the implications for risk of the second research hypothesis, we

analyze the effect of loan terms on the systematic portfolio risk defined in equation (7).

Results from table 4 reveal that loan terms can provide valuable information to equity

investors regarding the systemic risk of their portfolios of borrowers. More specifically,

panel A indicates that loans with high credit spread significantly increase the market beta

of portfolios built on signals from primary market loan announcements for holding periods

greater than 1 day.14 In other words, loan spreads, by providing information about the risk

of borrowers, significantly explain the risk of the strategic portfolios. Results in panel B

also suggest that loan spread has a positive impact on portfolio beta for the second class of

portfolios, based on signals following first loan sales on the secondary market. Overall, in

14 The average conditional betas are lower than 1, but the values would be close to 1 if accounting for the

unreported lagged beta term.

27

partial support of the second hypothesis, our results indicate that information regarding

equity systematic risk is available from loan terms, especially loan spreads.15


5.2.4 Impact of loan terms on specific risk

To test the impact of loan terms the specific risk of borrowers, we modify model (5)

to include a GARCH-type volatility for the error term. The conditional residual (or

specific) risk of portfolio p is thus estimated as follows and is a function of the vector 𝑖𝑝,𝑡−1

of the demeaned loan term variables:

ℎ𝑝,𝑡 = 𝜛 + 𝛼 𝑢𝑝,𝑡−12 + 𝛽ℎ𝑝,𝑡−1 + 𝛾𝑆𝑡−1

− 𝑢𝑝,𝑡−12 + 𝛵′ 𝑖𝑝,𝑡−1, (9)

where ℎ𝑝,𝑡 is a conditional GJR-GARCH variance (e.g., Engle, 1982; Bollerslev, 1986;

Glosten et al., 1993). Bollerslev et al. (1992) show the efficiency of GARCH models in a

wide range of financial applications. 𝑆𝑡− = 1 if up,t < 0 and 0 if up,t ≥ 0. The coefficient 𝜛

is a constant. The coefficients 𝛼, 𝛽 and 𝛾 are associated with the ARCH, GARCH and

asymmetric GJR effects, respectively. The coefficient vector 𝛵′ measures the marginal

impact of syndicated loan terms on the conditional variance of portfolio p.

Results for the analysis of specific risk through the GJR-GARCH model described in

equation (9) are available in table 5. The parameters related to the ARCH and GARCH

effects are almost always significant, while the GJR-GARCH parameters related to the

asymmetric effect are not different from zero. In terms of the effect of loan terms on

specific risk, our results provide strong evidence that loan spread, amount and maturity

have an impact on the estimation of conditional residual risk, especially when considering

15 The fact that the portfolio beta is related to loan terms suggests that aggregate information variables from

the loan market could be used for the conditional estimation of any beta, in addition to common

macroeconomic variables. We leave this for future research.

28

portfolios built on signals from the secondary loan market (panels B and C). In particular,

in panel B, loan spread, amount and maturity are all positively and significantly related to

portfolio residual risk. This indicates that portfolios with large investments in the stocks of

borrowers whose loans traded for the first time in the secondary market have more residual

risk when the loans were riskier at initiation, i.e., have higher spread (typically related to

higher credit risk), larger amount (implying a larger commitment) and longer maturity. In

panel C, all three loan terms are negatively (positively) related to specific risk when the

portfolios are composed of borrower stocks with large loan price increases (decreases).

This implies valuable information that the residual equity risk of borrowers whose loans

just had a large price increase or decrease is impacted by the initial riskiness of the loans.


Overall, in partial support of the second research hypothesis, we observe that loan

terms are informative in terms of specific risk, especially for portfolios based on signals

from the secondary loan market.

5.3 Speed of information transmission from the loan market to the stock market

Our empirical results thus far find significant performance that documents that loan

market activities provide valuable information regarding borrower stocks. Controls for

loan terms and the economic and financial context do not affect the average performance

results, although we obtain some evidence that they add valuable information regarding the

risk of borrower stocks. This section focuses on the implication from our results on the

speed of information transmission from the loan market to the stock market by looking

more closely at the impact of the holding periods on the performance results.

29

Specifically, figure 1 illustrates our average performance results from table 1 (lines

with circle symbols), table 2 (lines with square symbols) and table 3 (lines with triangle

symbols) for different holding periods. For ease of comparison, we present the annualized

average alpha in absolute value for all portfolio investigated, in effect assuming that all

loan market activities considered provide buy signals for the borrower stocks. From figure

1, it is easy to see that the performance results are economically important, with annual

abnormal returns varying generally between 5% and 15%, and are robust to the

performance models used.

[Please insert figure 1 here.]

Figure 1 is particularly well suited to illustrate that the speed of information

transmission from the loan market to the equity market differs by loan market activities.

The average abnormal returns from the strategic portfolios are the highest when the

borrower stocks are held for five days following signals from primary market loan

announcements (panel A), ten days following signals from first loan sale on the secondary

market (panel B), one day following signals from secondary market loan price increases

(panel C) and five days following signals from secondary market loan price decreases

(panel D). The stock market thus appears the most efficient in processing the informational

content of loan price increases and the least efficient in processing the informational

content of first loan sales. While further investigations will be needed to understand these

differences, it takes at least a week for the borrower stock prices to adjust to news

associated with three out of the four types of loan market signals investigated. Our findings

thus suggest that the stock market is relatively inefficient in incorporating the valuable

informational content in loan market activities.

30

6. Conclusion

In this study, we test the hypothesis that the loan market contains useful information

regarding the equity of borrowers. Results confirm this hypothesis and show that direct

information from loan market activities as well as indirect information from loan terms are

informative about the equity performance and risk of borrowers.

In terms of performance, we find that three types of loan market activities provide

valuable information regarding borrowers, even after controlling for the financial and

economic context. Loan announcements on the primary market, first secondary market loan

sales and large secondary market loan price movements (increases or decreases) provide

positive signals about the future equity performance of borrowers. While the performance

is robust to controls for the economic and financial context, there is only weak evidence

that loan terms at loan initiation add to the informativeness of the signals.

Further, our results show that loan terms are important determinants of the conditional

equity risk of borrowers, both systematic and specific. Specifically, loan spread is

especially informative regarding the systematic risk of portfolios based on signals from

primary loan announcements and, to a lesser extent, first secondary market loan sales.

Likewise, all three loan terms (loan spread, amount and maturity) are especially

informative regarding the specific risk of portfolios based on signals from first trades on

the secondary market as well as secondary market price variations, both positive and

negative.

Overall, our results provide evidence that the loan market is informative regarding the

equity performance and risk of borrowers and that this information can be extracted either

from loan market activities such as loan announcements, loan sales and loan price

31

variations, or from loan terms that provide complimentary information. Equity investors

can thus gain an informational advantage by following the syndicated loan market.

7. Acknowledgements

The authors would like to thank Mario Lavallée, Mohamed Al Guindy and seminar

participants at the Mathematical Finance Days 2012, the Northern Finance Association

Conference 2013 and Sherbrooke University for their comments, David Lamoureux for his

outstanding research assistance, as well as Alexandre Deschamps and Eric Sylvestre for

their help. Financial support from the Desjardins Sustainable Development Management

Chair, the Institut de Finance Mathématique de Montréal (IFM2) and the Investors Group

Chair in Financial Planning is gratefully acknowledged. The authors are research affiliates

at CIRPÉE (Chrétien and Coggins), GReFA and LABIFUL (Chrétien).

32

References

Akerlof, G. A., 1970, The Market for “Lemons”: Quality Uncertainty and the Market

Mechanism, Quarterly Journal of Economics 84 (3), 488-500.

Allen, F., 1990, The Market for Information and the Origin of Financial Intermediation,

Journal of Financial Intermediation 1, 3-30.

Allen, L., Gottesman, A., and Peng, L., 2012, The Impact of Joint Participation on

Liquidity in Equity and Syndicated Bank Loan Markets, Journal of Financial

Intermediation 21 (1), 50-78.

Allen, L., Guo, H., and Weintrop, J., 2008, The Information Content of Quarterly Earnings

in Syndicated Bank Loan Prices, Asia-Pacific Journal of Accounting & Economics 15 (2),

91-122.

Allen, L., and Gottesman, A., 2006, The Informational Efficiency of the Equity Market as

Compared to the Syndicated Bank Loan Market, Journal of Financial Services Research

30 (1), 5-42.

Altman, E., Gande, A. and Saunders, A., 2010, Informational Efficiency of Loans versus

Bonds: Evidence from Secondary Market Prices, Journal of Money, Credit and Banking

42 (4), 755-767.

Aragon, G. O., and Ferson, W. E., 2006, Portfolio Performance Evaluation, Foundations

and Trends in Finance 2 (2), 83-190.

Berndt, A., and Gupta, A., 2009, Moral Hazard and Adverse Selection in the Originate-to-

distribute Model of Bank Credit, Journal of Monetary Economics 56 (5), 725-743.

Best, R., and Zhang, H., 1993, Alternative Information Sources and the Information

Content of Bank Loans, Journal of Finance 48 (4), 1507-1522.

Billett, M. T., Flannery, M. J., and Garfinkel, J. A., 1995, The Effect of Lender Identity on

a Borrowing Firm's Equity Return, Journal of Finance 50 (2), 699-718.

Bollen, N. P., and Busse, J. A., 2004, Short-Term Persistence in Mutual Fund Performance,

Review of Financial Studies 18 (2), 569-597.

Bollerslev, T., 1986, Generalized Autoregressive Conditional Heteroskedasticity, Journal

of Econometrics 31 (3), 307-327.

Bollerslev, T., R. Y. Chou, and Kroner, K. F., 1992, ARCH Modeling in Finance: A

Selective Review of the Theory and Empirical Evidence, Journal of Econometrics 52 (1-

2), 5-59.

33

Bushman, R., Smith, A., and Wittenberg-Moerman, R., 2010, Price Discovery and

Dissemination of Private Information by Loan Syndicate Participants, Journal of

Accounting Research 48 (5), 921-972.

Champagne, C., and Coggins, F., 2012, Common Information Asymmetry Factors in

Syndicated Loan Structures, Journal of Banking and Finance 36 (5), 1437-1451.

Chaudhry, S.M., and Kleimeier, S., 2015, Lead Arranger Reputation and the Structure of

Loan Syndicates, Journal of International Financial Markets, Institutions and Money 38,

116-126.

Christopherson, J. A., Ferson, W. E., and Glassman, D. A., 1998, Conditioning Manager

Alphas on Economic Information: Another Look at the Persistence of Performance, Review

of Financial Studies 11 (1), 111-142.

Coggins, F., Beaulieu, M.-C., and Gendron, M., 2009, Mutual Fund Daily Conditional

Performance, Journal of Financial Research 32 (2), 95-122.

Cook, D.O., Schellhorn, C.D., and Spellman, L.J., 2003, Lender Certification Premiums,

Journal of Banking and Finance 27 (8), 1561-1579.

Dahiya, S., Puri, M., and Saunders, A., 2003, Bank Borrowers and Loan Sales: New

Evidence of the Uniqueness of Bank Loans, Journal of Business 76 (4), 563-581.

Dennis, S.A., and Mullineaux, D.J., 2000, Syndicated Loans, Journal of Financial

Intermediation 9, 404-426.

Do, V., and Vy, T., 2010, The Effects of Reputation and Relationships on Lead Banks’

Certification Roles, Journal of International Financial Markets, Institutions and Money 20

(5), 475-489.

Drucker, S., and Puri, M., 2009, On Loan Sales, Loan Contracting, and Lending

Relationships, Review of Financial Studies 22 (7), 2835-2872.

Engle, R. F., 1982, Autoregressive Conditional Heterosckedasticity with Estimates of the

Variance of U.K. Inflation, Econometrica 50 (4), 987-1008.

Fama, E. F., and French, K. R., 1989, Business Conditions and the Expected Returns on

Stocks and Bonds, Journal of Financial Economics 25 (1), 23-49.

Fama, E. F., and French, K. R., 1993, Common Risk Factors in the Returns on Stocks and

Bonds, Journal of Financial Economics 33 (1), 3-56.

Ferson, W. E., and Qian, M., 2004, Conditional Performance Evaluation, Revisited,

Research Foundation of the Association for Investment Management and Research

(AIMR).

34

Ferson, W. E., and Schadt, R. W., 1996, Measuring Fund Strategy and Performance in

Changing Economic Conditions, Journal of Finance 51 (2), 425-461.

Gande, A., and Saunders, A., 2012, Are Banks still Special when there is a Secondary

Market for Loans? Journal of Finance 67 (5), 1649-1684.

Gibbons, M. R., and Ferson W. E., 1985, Testing Asset Pricing Models with Changing

Expectations and an Unobservable Market Portfolio, Journal of Financial Economics 14

(2), 217-236.

Glosten, L., Jagannathan, R., and Runkle, D., 1993, On the Relation between the Expected

Value and the Volatility of the Nominal Excess Return on Stocks, Journal of Finance 48,

1779-1801.

Ivashina, V., 2009, Asymmetric Information Effects on Loan Spreads, Journal of Financial

Economics 92 (2), 300-319.

Ivashina, V., and Sun, Z., 2011, Institutional Stock Trading on Loan Market Information,

Journal of Financial Economics 100 (2), 284-303.

Jagannathan, R., and Wang, Z., 1996, The Conditional CAPM and the Cross-Section of

Expected Returns, Journal of Finance 51 (1), 3-53.

James, C., 1987, Some Evidence on the Uniqueness of Bank Loans, Journal of Financial

Economics 19 (2), 217-235.

Kamstra, M.J., Roberts, G.S. and Shao, P., 2014, Does the Secondary Loan Market Reduce

Borrowing Costs? Review of Finance 18 (3), 1139-1181.

Lummer, S. L., and McConnell, J. J., 1989, Further Evidence on the Bank Lending Process

and the Capital-Market Response to Bank Loan Agreements, Journal of Financial

Economics 25 (1), 99-122.

Massoud, N., Nandy, D., Saunders, A., and Song, K., 2011, Do Hedge Funds Trade on

Private Information? Evidence from Syndicated Lending and Short-Selling, Journal of

Financial Economics 99 (3), 477-499.

Newey, W. K., and West, K. D., 1987, A Simple, Positive Semi-Definite,

Heteroskedasticity and Autocorrelation Consistent Covariance Matrix, Econometrica 55

(3), 703-708.

Panyagometh, K., and Roberts, G.S., 2010, Do Lead Banks Exploit Syndicate Participants?

Evidence from Ex Post Risk, Financial Management 39 (1), 273-299.

Park, J. C., and Qiang Wu, 2009, Financial Restatement, Cost of Debt and Information

Spillover, Journal of Business Finance and Accounting 36 (9-10), 1117-1147.

35

Preece, D., and Mullineaux, D., 1994, Monitoring by Financial Intermediaries: Banks vs.

Nonbanks, Journal of Financial Services Research 8, 193-202.

Preece, D., and Mullineaux, D., 1996, Monitoring, Loan Renegotiability, and Firm Value:

The Role of Lending Syndicates, Journal of Banking and Finance 20 (3), 577-593.

Scholes, M., and Williams, J., 1977, Estimating Betas From Nonsynchronous Data,

Journal of Financial Economics 5 (3), 309-327.

Smith, C., 1979, Applications of Options Pricing Analysis. In: Bicksler, J.L. (Ed.),

Handbook of Financial Economics. North-Holland, Amsterdam, 79-121.

Sufi, A., 2007, Information Asymmetry and Financing Arrangements: Evidence from

Syndicated Loans, Journal of Finance 62 (2), 629-668.

36

Appendix A – An example of portfolio construction and return calculation for a

strategy based on signals from primary market loan announcements and a 5-day

holding period

In this example, we suppose that the first signal to buy (following a loan announcement) occurs on day 51

and the second signal (for stock 2) occurs on day 54. The portfolio would therefore be composed entirely of

the market index from day 1 to day 50. Stock 1 is added to the portfolio on day 51 and removed on day 56.

Stock 2 is added to the portfolio on day 54 and removed on day 59. And so on with the third, fourth, …, nth

signal, until the end of the evaluation period.

Borrower stock returns are calculated as follows:

𝑅𝑖,𝑡 =P𝑖,𝑡+𝐷𝑖,𝑡−𝑃𝑖,𝑡−1

𝑃𝑖,𝑡−1

where 𝑃𝑖,𝑡 is the price of security i at time t and D is the dividend. In the days when individual stocks are held

following buy signals, the portfolio return is composed of 50% in the market return and 50% in the return of

an equally-weighted portfolio of all borrower stocks held.

Day

... 50 51 52 53 54 55 56 57 58 59 60

Holding period stock 1

Holding period stock 2

Purchase Purchase

Day

Borrowers'

stocks included

in PF Portfolio return

50 no stock 100% x R m

51 stock 1 50% x R 1 + 50% x R m

52 stock 1 50% x R 1 + 50% x R m

53 stock 1 50% x R 1 + 50% x R m

54 stock 1, stock 2 25%(R 1 + R 2 ) + 50% x R m

55 stock 1, stock 2 25%(R 1 + R 2 ) + 50% x R m

56 stock 2 50% x R 2 + 50% x R m

57 stock 2 50% x R 2 + 50% x R m

58 stock 2 50% x R 2 + 50% x R m



37

Table 1 – Impact of loan market activity signals on the unconditional performance of

equity portfolios

This table presents results for the estimation of model (1). For each portfolio, returns are computed over

2,928 trading days between January 1998 and September 2009. Portfolios are composed of borrower stocks

and the market index as described in section 4.2. Market index returns are based on the CRSP value-weighted

index. Each panel presents unconditional alpha and market beta coefficients as well as lagged market beta

coefficients with their t-statistics. The panels show results for portfolios based on signals from primary

market loan announcements (panel A), from first loan sale on the secondary market (panel B), and from

secondary market loan price movements (panel C). Number of days represent the holding period for each

borrower stock bought or sold in the portfolios. *, ** and *** indicate significance at the 10%, 5% and 1%

levels, respectively.

Value (x 100) t-stat Value t-stat Value t-stat

Panel A - Primary market signals

1 day 0.0276 1.2082 1.0053 62.5102 *** 0.0283 1.9800 **

5 days 0.0509 3.2529 *** 0.9798 89.0126 *** 0.0043 0.4406

10 days 0.0355 3.6479 *** 0.9859 143.8065 *** 0.0166 2.7349 ***

20 days 0.0262 3.8622 *** 0.9707 203.1294 *** 0.0093 2.1850 **

Panel B - Secondary market sale signals

1 day -0.0062 -0.6381 0.9996 145.5242 *** 0.0015 0.2442

5 days -0.0343 -1.8686 * 1.0123 78.3471 *** -0.0010 -0.0843

10 days -0.0500 -2.4767 ** 1.0303 72.4268 *** -0.0098 -0.7740

20 days -0.0379 -2.0193 ** 1.0229 77.4629 *** -0.0184 -1.5740

Panel C - Secondary market movement signals

1 day (+ 1 std.dev.) 0.0553 2.0397 ** 0.9900 51.8250 *** 0.0366 2.1599 **

5 days (+ 1 std.dev.) 0.0418 2.3841 ** 0.9753 79.0782 *** 0.0286 2.6096 ***

10 days (+ 1 std.dev.) 0.0255 1.7938 * 0.9655 96.3813 *** 0.0246 2.7664 ***

20 days (+ 1 std.dev.) 0.0305 2.5618 ** 0.9609 114.5354 *** 0.0259 3.4776 ***

1 day (- 1 std.dev.) -0.0209 -0.6266 0.9262 39.3815 *** -0.0407 -1.9506 *

5 days (- 1 std.dev.) -0.0859 -2.5832 *** 0.9859 42.1275 *** -0.0194 -0.9343

10 days (- 1 std.dev.) -0.0477 -2.4094 ** 1.0157 72.8954 *** -0.0223 -1.8018 *

20 days (- 1 std.dev.) -0.0394 -2.6176 *** 1.0214 96.2602 *** -0.0275 -2.9164 ***

Portfolios (holding

periods)

Unconditional market betaUnconditional alpha Lagged beta term

38

Table 2 – Impact of loan market activity signals on the conditional performance of equity portfolios

This table presents results for the estimation of model (2). For each portfolio, returns are computed over 2,928 trading days between January 1998 and September

2009. Portfolios are composed of borrower stocks and the market index as described in section 4.2. Market index returns are based on the CRSP value-weighted

index. Each panel presents average conditional performance and conditional alpha and market beta coefficients related to the economic variables. The panels show

results for portfolios based on signals from primary market loan announcements (panel A), from first loan sale on the secondary market (panel B), and from

secondary market loan price movements (panel C). Number of days represent the holding period for each borrower stock bought or sold in the portfolios. *, ** and

*** indicate significance at the 10%, 5% and 1% levels, respectively.

Dividend

yield

Default

spread

Term

structure

Market

premium

squared

Dividend

yield

Default

spread

Term

structure

Market

premium

squared


1 day 0.0275 -0.0006 -0.0002 -0.0002 1.0086 *** 0.1697 *** -0.0759 * 0.03977 -33.8193 **

5 days 0.0488 *** -0.0008 * 0.0003 -0.0002 0.5182 ** 0.1106 *** -0.0240 -0.05441 *** 11.7934

10 days 0.0354 *** -0.0005 * 0.0004 -0.0002 -0.1365 0.1137 *** -0.0313 * -0.02037 * -9.1831

20 days 0.0259 *** -0.0003 0.0001 -0.0001 -0.0638 0.1029 *** -0.0499 *** -0.01373 -15.6367 ***


1 day -0.0062 0.0000 0.0001 -0.0001 -0.0296 0.0227 -0.0172 0.00388 0.2171

5 days -0.0354 * -0.0004 0.0004 0.0005 * -0.0433 0.0588 -0.0410 -0.01724 2.5518

10 days -0.0521 *** -0.0004 0.0005 0.0008 ** 0.0787 0.0278 -0.0254 -0.02850 23.6393 *

20 days -0.0411 ** 0.0006 -0.0008 0.0004 0.9732 *** -0.0368 0.0392 -0.03268 25.5292 **


1 day (+ 1 std.dev.) 0.0562 ** -0.0013 * 0.0020 *** -0.0004 -0.1606 0.1112 * -0.0453 -0.03715 -16.4439

5 days (+ 1 std.dev.) 0.0413 ** -0.0006 0.0013 ** -0.0001 -0.7126 ** 0.1435 *** -0.0876 ** -0.05359 ** -10.2436

10 days (+ 1 std.dev.) 0.0240 * -0.0004 0.0008 ** 0.0000 -0.1772 0.1424 *** -0.0963 *** -0.04698 *** -7.6377

20 days (+ 1 std.dev.) 0.0293 ** -0.0007 ** 0.0007 * 0.0000 -0.1804 0.1006 *** -0.0822 *** -0.03591 ** 0.3724

1 day (- 1 std.dev.) -0.0185 -0.0017 * 0.0001 -0.0010 * 0.1136 -0.1872 ** 0.0062 0.08747 ** 60.0367 ***

5 days (- 1 std.dev.) -0.0859 *** 0.0006 -0.0012 -0.0006 0.6591 -0.1178 0.0147 0.05738 27.4205

10 days (- 1 std.dev.) -0.0468 ** 0.0007 -0.0006 -0.0002 0.3218 -0.0971 ** 0.0398 0.05034 ** 7.5071

20 days (- 1 std.dev.) -0.0385 ** 0.0004 -0.0005 -0.0001 0.1264 -0.1143 *** 0.0656 ** 0.03601 * 12.1403

Sensitivity of conditional alpha to economic variables Sensitivity of conditional market beta to economic variables

Average

conditional

performance

Value (x 100)

39

Table 3 – Impact of loan market activities and loan terms on the conditional performance of equity portfolios



index. Each panel presents average conditional performance and conditional alpha coefficients related to loan terms and economic variables. The panels show

results for portfolios based on signals from primary market loan announcements (panel A), from first loan sale on the secondary market (panel B), and from

secondary market loan price movements (panel C). Number of days represent the holding period for each borrower stock bought or sold in the portfolios. *, ** and

*** indicate significance at the 10%, 5% and 1% levels, respectively.

Dividend

yield

Default

spread

Term

structure

Market

premium

squared


1 day 0.0274 0.0001 -0.0004 * 0.0001 -0.0006 -0.0003 -0.0001 1.0151 ***

5 days 0.0470 *** 0.0000 -0.0003 ** 0.0000 -0.0008 * 0.0003 -0.0002 0.4750 *

10 days 0.0350 *** 0.0000 0.0000 0.0000 -0.0005 * 0.0004 -0.0002 -0.1354

20 days 0.0248 *** -0.0001 0.0000 0.0000 -0.0003 0.0001 -0.0001 -0.0779


1 day -0.0057 0.0001 -0.0002 ** -0.0003 0.0000 0.0001 -0.0002 -0.0383

5 days -0.0336 * 0.0008 *** -0.0001 -0.0011 *** -0.0007 0.0005 0.0003 0.0836

10 days -0.0487 ** -0.0005 -0.0001 0.0003 -0.0003 0.0004 0.0008 ** 0.1327

20 days -0.0374 ** -0.0001 -0.0001 -0.0002 0.0005 -0.0007 0.0003 1.0254 ***


1 day (+ 1 std.dev.) 0.0544 ** 0.0002 0.0001 -0.0002 -0.0014 * 0.0018 ** -0.0005 -0.1664

5 days (+ 1 std.dev.) 0.0403 ** 0.0002 0.0001 -0.0002 -0.0006 0.0012 ** 0.0000 -0.7269 **

10 days (+ 1 std.dev.) 0.0217 0.0002 0.0001 -0.0003 -0.0003 0.0008 * 0.0001 -0.1728

20 days (+ 1 std.dev.) 0.0279 ** -0.0002 0.0003 *** -0.0001 -0.0007 * 0.0004 0.0000 -0.1983

1 day (- 1 std.dev.) -0.0164 0.0003 0.0000 -0.0005 -0.0017 * 0.0001 -0.0011 * 0.1614

5 days (- 1 std.dev.) -0.0829 ** 0.0010 ** -0.0002 -0.0011 ** 0.0007 -0.0015 -0.0009 0.6480

10 days (- 1 std.dev.) -0.0439 ** 0.0004 0.0000 -0.0006 * 0.0007 -0.0007 -0.0004 0.3012

20 days (- 1 std.dev.) -0.0371 ** 0.0000 0.0000 -0.0001 0.0004 -0.0005 -0.0001 0.1235

Portfolios (holding

periods)

Sensitivity of conditional alpha to economic variables

Spread Amount Maturity

Sensitivity of conditional alpha to loan termsAverage

conditional

performance

Value (x 100)

40

Table 4 – Impact of loan terms on the systematic risk of equity portfolios



index. Each panel presents average conditional market beta (systematic risk) and conditional market beta coefficients related to loan terms and economic variables.

The panels show results for portfolios based on signals from primary market loan announcements (panel A), from first loan sale on the secondary market (panel

B), and from secondary market loan price movements (panel C). Number of days represent the holding period for each borrower stock bought or sold in the

portfolios. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.

Portfolios (holding

periods)Dividend

yield

Default

spread

Term

structure

Market

premium

squared


1 day 0.9704 *** -0.0097 0.0276 ** 0.0727 0.1581 *** -0.0678 0.0365 -32.0219 **

5 days 0.9407 *** 0.0261 ** 0.0102 0.0228 0.0894 ** -0.0223 -0.0459 ** 15.2474

10 days 0.9540 *** 0.0144 ** 0.0017 0.0034 0.1050 *** -0.0349 * -0.0145 -8.4561

20 days 0.9549 *** 0.0176 *** 0.0039 0.0022 0.0945 *** -0.0578 *** -0.0072 -12.3632 ***


1 day 0.9985 *** 0.0154 0.0197 * -0.0134 0.0220 -0.0161 0.0034 0.5831

5 days 1.0128 *** 0.0706 *** 0.0286 -0.0384 0.0590 -0.0359 -0.0084 -1.4992

10 days 1.0320 *** 0.0427 0.0071 -0.0051 0.0249 -0.0214 -0.0107 15.8737

20 days 1.0181 *** 0.0637 *** -0.0019 -0.0279 -0.0128 0.0162 -0.0080 17.7994


1 day (+ 1 std.dev.) 0.9745 *** 0.0669 0.0003 -0.0236 0.1127 * -0.0233 -0.0385 -16.0352

5 days (+ 1 std.dev.) 0.9628 *** -0.0075 0.0042 0.0438 0.1344 *** -0.0750 ** -0.0620 *** -9.8086

10 days (+ 1 std.dev.) 0.9574 *** 0.0185 0.0010 0.0155 0.1330 *** -0.0777 *** -0.0470 ** -7.4119

20 days (+ 1 std.dev.) 0.9570 *** -0.0093 0.0117 * 0.0429 * 0.0907 *** -0.0735 *** -0.0372 ** -0.8469

1 day (- 1 std.dev.) 0.9857 *** -0.1172 ** -0.0177 0.1244 ** -0.1786 ** 0.0093 0.1086 ** 60.9842 ***

5 days (- 1 std.dev.) 1.0233 *** -0.0689 -0.0056 0.0885 -0.1088 0.0137 0.0764 * 27.8192

10 days (- 1 std.dev.) 1.0290 *** 0.0643 * 0.0157 -0.0364 -0.0943 ** 0.0203 0.0336 7.4448

20 days (- 1 std.dev.) 1.0306 *** 0.0312 0.0201 -0.0068 -0.1102 *** 0.0544 * 0.0294 12.1552

Average

conditional market

beta

Sensitivity of conditional beta to economic variables


Sensitivity of conditional beta to loan terms

41

Table 5 – Impact of loan terms on the specific risk of equity portfolios This table presents results for the estimation of model (5). For each portfolio, returns are computed over 2,928 trading days between January 1998 and September


index. Each panel presents all coefficients in the conditional variance (specific risk) model, such as the constant, ARCH, GARCH and asymmetric GJR coefficients

and the coefficients related to loan terms. The panels show results for portfolios based on signals from primary market loan announcements (panel A), from first

loan sale on the secondary market (panel B), and from secondary market loan price movements (panel C). Number of days represent the holding period for each

borrower stock bought or sold in the portfolios. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.


1 day 0.0090 *** 0.1442 *** 0.0476 0.5880 *** 0.0064 -0.0088 *** -0.0052 **

5 days 0.0045 *** 0.1500 0.0500 0.5972 *** 0.0076 *** 0.0002 -0.0023 ***

10 days 0.0000 0.0479 ** 0.0466 0.9286 *** -0.0001 -0.0001 * 0.0000

20 days 0.0000 0.0357 *** 0.0221 0.9519 *** 0.0000 0.0000 0.0000


1 day 0.0005 *** 0.1496 *** 0.0498 ** 0.5982 *** 0.0100 *** 0.0022 *** 0.0057 ***

5 days 0.0044 *** 0.1468 ** 0.0482 0.5841 *** 0.0244 *** 0.0080 *** 0.0342 ***

10 days 0.0066 *** 0.1441 *** 0.0466 0.5725 *** 0.0317 *** 0.0094 *** 0.0318 ***

20 days 0.0047 *** 0.1467 *** 0.0479 0.5803 *** 0.0159 *** 0.0060 *** 0.0173 ***


1 day (+ 1 std.dev.) 0.0158 *** 0.1447 0.0454 0.6051 *** -0.1034 *** -0.0317 *** -0.0434 ***

5 days (+ 1 std.dev.) 0.0037 *** 0.1468 *** 0.0485 0.5841 *** -0.0101 *** -0.0033 *** -0.0078 ***

10 days (+ 1 std.dev.) 0.0009 *** 0.1517 *** 0.0511 0.5953 *** -0.0015 *** 0.0001 *** -0.0023 ***

20 days (+ 1 std.dev.) 0.0008 *** 0.1518 *** 0.0516 0.5975 *** -0.0024 *** -0.0010 *** -0.0003 ***

1 day (- 1 std.dev.) 0.0056 *** 0.1442 * 0.0722 0.5397 *** 0.0464 *** 0.0118 0.0053

5 days (- 1 std.dev.) 0.0206 *** 0.1797 0.0852 0.5352 *** 0.0219 *** 0.0255 *** 0.0324 ***

10 days (- 1 std.dev.) 0.0047 *** 0.1602 0.0592 0.5955 *** 0.0165 *** 0.0069 *** 0.0023 ***

20 days (- 1 std.dev.) 0.0034 *** 0.1506 * 0.0507 0.5940 *** 0.0173 *** 0.0053 *** -0.0054 ***

Portfolios (holding

periods)GARCH termARCH term

Variance conditional on loan characteristics

Value (x 1000)


Constant term

(x 100)

Asymmetric

GJR term

42

Figure 1 – Impact of loan market activity signals on the performance of equity

portfolios

This figure illustrates the performance results for the estimation of model (1) (lines with circle symbols),

model (2) (lines with square symbols) and model (3) (lines with triangle symbols) for different holding

periods. For each portfolio, returns are computed over 2,928 trading days between January 1998 and

September 2009. Portfolios are composed of borrower stocks and the market index as described in section

4.2. Market index returns are based on the CRSP value-weighted index. Annualized average performance

(%) is the absolute value of the annualized average alpha in percentage, which is computed as the daily alpha

estimated from the models multiplied by 25,000. Holding period (Days) is the holding period in days for each

borrower stock bought or sold in the portfolios. The panels show results for portfolios based on signals from

primary market loan announcements (panel A), from first loan sale on the secondary market (panel B), and

from secondary market loan price increases (panel C) and decreases (panel D).



Panel C - Secondary market price increase signals

Panel D - Secondary market price decrease signals

THE INFORMATIONAL CONTENT OF THE LOAN MARKET: AN … · 2017-02-21 · also examine the factors...

Documents

Transcript of THE INFORMATIONAL CONTENT OF THE LOAN MARKET: AN … · 2017-02-21 · also examine the factors...