Do Bond Rating Changes Affect Information Risk of Stock Trading?

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Do Bond Rating Changes Affect Information Risk of Stock Trading?

Yan Hea , Junbo Wangb, K.C. John Weic

aSchool of Business, Indiana University Southeast, New Albany, Indiana, U.S.A bDepartment of Economics and Finance, City University of Hong Kong, Hong Kong cDepartment of Finance, Hong Kong University of Science and Technology, Hong Kong

This version: January 18, 2007

Abstract

Using a sample of 279 upgrades and 310 downgrades from 1996 to 2004, we find that bond rating changes affect asymmetric information of stock trading and other measures of information risk. More specifically, when a firm’s bond rating is upgraded (downgraded), its stock information asymmetry and its analysts’ earnings forecast dispersion are significantly reduced (increased), while the institutional equity holdings of its shares are significantly increased (reduced). In addition, the degree of the change in stock asymmetric information is positively associated with the magnitude of the bond rating changes. Our evidence supports the hypothesis that a firm’s bond rating change influences investors’ perceptions of the firm’s disclosure level, which, in turn, affects the information asymmetry of its stock trading and other measures of information risk.

JEL Classification: G10

Key Words: Bond rating changes; Information asymmetry; Information risk

* Earlier versions of this work have benefited from conversations with seminar participants at the City University of Hong Kong and the Hong Kong University of Science and Technology. The authors wish to thank Dr. Virginia Unkefer for editorial assistance. Junbo Wang acknowledges financial support from the RGC Research Grant of the Hong Kong Special Administration Region, China (CityU1423/06H). John Wei acknowledges financial support from the RGC Research Infrastructure Grant of the Hong Kong Special Administration Region, China (RI93/94.BM02).

Corresponding author: K.C. John Wei, Tel: +852-2358-7676; fax: +852-2358-1749. E-mail address: johnwei@ust.hk (K.C. John Wei).

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Do Bond Rating Changes Affect Information Risk of Stock Trading? 1. Introduction

Information risk reflects uncertainty about a firm’s value and it should be priced into stock and bond prices, which, in turn, should affect the cost of capital. The measures of information risk usually include asymmetric information of stock trading, the quality of accounting disclosure, the dispersion of analysts’ earnings forecasts, and institutional equity holdings, among others. Easley and O’Hara (2004) develop an asymmetric information asset-pricing model that provides a theoretical argument that the cost of equity capital is positively related to stock information asymmetry. Using the probability of information-based trades developed by Easley, Kiefer, and O’Hara (1997a; 1997b) as a proxy for asymmetric information of stock trading, Easley, Hvidkjaer, and O’Hara (2002) provide empirical evidence that supports the prediction of the Easley and O’Hara’s (2004) asymmetric information asset-pricing model.

Several studies find that other measures of information risk also affect the cost of capital or credit spreads. Botosan (1997) and Botosan and Plumlee (2002) find that greater accounting disclosure lowers the cost of equity capital. Other studies show that greater accounting disclosure (Sengupta (1998)) or transparency (Yu (2005)) lowers the cost of debt capital. Mansi, Maxwell, and Miller (2005) report a positive relation between the dispersion of analysts’ earnings forecasts and credit spreads. Bhojraj and Sengupta (2003) and Wang and Zhang (2006) document that increases in institutional equity holdings lead to decreases in credit spreads. Odders-White and Ready (2006) find that a higher level of stock information asymmetry is associated with a poorer credit rating.1 Economic theory suggests that disclosure is related to the cost of capital either

through the disclosure effect on the reduction of information asymmetry or estimation risk or

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both. Investors are compensated for the non-diversifiable risk that includes the estimation of an asset’s unknown risk and of its return distribution parameters (Coles et al., 1995).

Since both information asymmetry of stock trading and disclosure affect the cost of equity capital, one may argue that disclosure affects information asymmetry. In fact, Brown et al. (2004) document that firms that conduct conference calls enhance their disclosure and reduce their information asymmetry of stock trading. In addition, Chen, Hong, and Stein (2001) and others argue that managers of firms, especially those that are less monitored, have some degree of discretion over the disclosure of information and that they prefer to announce good news immediately but allow bad news to dribble out slowly.2 This managerial behavior is called the discretionary disclosure hypothesis (Bae, Lim, and Wei, 2006). Since firms with lower credit ratings in general have more bad news and less good news and are less likely to be monitored by market participants than are firms with higher credit ratings, the hypothesis suggests that high credit rating firms should have better disclosure quality and lower asymmetric information than low credit rating firms.

Credit rating agencies evaluate publicly traded companies and communicate their findings and opinions to investors. Credit rating agencies claim to receive inside information unavailable even to stock analysts such as the minutes of board meetings, profit breakdowns by products, and new product plans (Ederington and Yawita, 1987). Therefore, bond rating changes should convey information that uninformed investors do not possess and may influence investors’ perceptions of a firm’s financial condition and disclosure behavior. Therefore, we hypothesize that bond rating upgrades (downgrades) should strengthen (weaken) investors’

2 The implication of this managerial behavior is that it will impart a degree of positive skewness in stock returns. Furthermore, this managerial discretion tends to be more pronounced in small-capitalization firms or in firms followed by fewer analysts, since managers of these firms have a wider scope for hiding bad news from the market.

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perceptions about firms’ disclosure quality, which, in turn, reduces (increases) the information risk of their stock trading.

The main objective of this study is to investigate whether or not an upgrade or a downgrade of a firm’s bond rating affects the information asymmetry of its stock trading and other measures of information risk. We measure stock information asymmetry by using the probability of information-based trades (PIN). PIN is a firm-specific estimate of the probability that a particular trade order originates from a privately informed investor. It directly captures the extent of information asymmetry among investors in the secondary market. In addition to PIN, we also employ the dispersion of analysts’ earnings forecasts and institutional equity holdings to measure information risk. Since greater dispersion of analysts’ earnings forecasts conveys a higher degree of uncertainty about future cash flows and profitability, it represents higher information risk (Wang and Zhang, 2006). Firms with higher institutional equity holdings tend to have better corporate governance, which can mitigate the agency cost and therefore reduce information risk (see, for example, Bhojraj and Sengupta (2003)).

Using a sample of 279 upgrades and 310 downgrades from the period 1996 to 2004, we document that bond rating changes affect the information asymmetry of stock trading. When a firm’s bond rating is upgraded (downgraded), the information asymmetry of its stock trading is significantly reduced (increased). Furthermore, the magnitude of the change in asymmetric information is positively associated with the magnitude of the bond rating changes. The evidence indicates that disclosure manifested by bond rating changes does have a significant effect on stock information asymmetry. In addition, bond rating changes also affect other measures of information risk, such as the dispersion of analysts’ earnings forecasts and institutional equity holdings. When a firm’s bond rating is upgraded (downgraded), the dispersion of analysts’

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earnings forecasts tends to decrease (increase), while institutional equity holdings tend to increase (decrease), implying lower (higher) information risk. In sum, a firm’s bond rating change does affect its information risk, which is consistent with our hypothesis.

The reminder of this paper is organized as follows. Section 2 discusses the hypothesis development. In particular, we develop three hypotheses regarding the relation between bond rating changes and changes in information asymmetry. Section 3 describes the PIN estimation procedure based on a market microstructure model proposed by Easley et al. (1997a; 1997b). Section 4 discusses the sample selection. Section 5 reports empirical results on the relation between bond rating changes and changes in information asymmetry. Section 6 reports the results from robustness checks. Finally, Section 7 concludes the paper.

2. Hypothesis Development

Information asymmetry exists when investors are differentially informed about a firm’s value and when it allows investors with superior information to trade profitably at the expense of other investors. To compensate for the risk of these expected losses, uninformed investors demand a return premium that increases with the risk of trading against privately informed investors. Such risk is not diversifiable since uninformed investors are always in a disadvantageous position relative to informed investors. Easley and O’Hara (2004) formally develop the model and Easley et al. (2002) provide empirical evidence that the level of information asymmetry is positively associated with a firm’s cost of equity capital.3

The discretionary disclosure hypothesis proposed by Bae et al. (2006) and Chen et al. (2001) suggests that managers of firms, particularly those that are less monitored, have some degree of discretionary power over the disclosure of information and that they prefer to announce

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good news immediately but allow bad news to dribble out slowly. In addition, firms with better credit ratings have more good news and less bad news and are less monitored than are firms with lower credit ratings. All these arguments suggest that firms with higher credit ratings should be more transparent in terms of information disclosure than are firms with lower credit ratings. Botosan (1997), Botosan and Plumlee (2002) and Sengupta (1998) document that greater disclosure reduces the cost of capital. Finally, Brown at el. (2004) show that greater disclosure (conference calls) reduces the extent to which information asymmetry can be developed. Taking all these studies together, one would expect that a firm’s bond rating is associated with the information asymmetry of its stock trading. The above discussion leads to the following hypothesis:

H1: Firms with higher bond ratings have lower asymmetric information of their stock trading than do firms with lower bond ratings.

An upgrade of a bond rating raises the transparency of a firm, which reduces information asymmetry by reducing the relative amount of informed trading in the equity market. The models proposed by Merton (1987) and Fishman and Hagerty (1989) imply that more informative disclosure reduces the costs associated with processing and assimilating public information. As a result, greater disclosure induces more trading by uninformed liquidity traders. Diamond and Verrecchia (1991) show that under certain conditions, the amount of uninformed trading by large investors increases as the firm discloses more information. These models suggest that uninformed investors are more likely to trade the stocks of firms with greater disclosure levels. We, therefore, expect that when a firm’s bond rating is upgraded, there will be more uninformed

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trading. Ceteris paribus, more uninformed trading reduces the risk of trading against privately informed investors.

However, this ceteris paribus condition is unlikely to hold, since previous research indicates that increases in uninformed trading are also associated with more informed trading. Kyle (1985) demonstrates that if investors are risk neutral and are not capital constrained, then the amount of informed trading varies proportionately with the expected amount of uninformed trading. Thus, the relative proportion of informed trading remains unchanged when the expected amount of uninformed trading changes. In practice, however, informed traders are likely to be risk averse and/or capital constrained, which results in changes in the intensity of informed trading that are less than fully proportional to the changes in uninformed trading. Consequently, the net effect of an upgrade is relatively smaller for informed trading than for uninformed trading. As for a downgrade, one would expect an opposite effect, i.e., the proportion of informed trading is increased.

Based on the above discussions, we expect that, by upgrading a bond rating, the rating agencies reduce the information search incentives by privately informed traders and affect the trading behavior of both uninformed and informed investors. That is, the change in the incentives of investors’ information search leads to a long lasting impact on the level of information asymmetry. The above discussion leads to the following hypothesis:

H2: When a firm’s bond rating is upgraded (downgraded), the information asymmetry of its stock trading is reduced (increased).

The above discussions suggest that a firm’s bond rating change affects the level of information asymmetry in its stock trading. Furthermore, we expect that a different magnitude of

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a bond rating change may give rise to a different degree of the change in disclosure and thus may cause a different magnitude of the change in asymmetric information. We therefore would also like to test the following hypothesis:

H3: The magnitude of a firm’s bond rating change is positively related to the magnitude of the change in its stock information asymmetry.

3. The Measure of Information Asymmetry: PIN

The traditional asset-pricing models with symmetric information assume that prices are always fully revealing. In contrast, the microstructure models explicitly account for the process of price discovery. That is, the microstructure models study how private information is incorporated into prices through trading. Information asymmetry manifests itself when investors trade on the basis of their private information. While it is not possible to identify which trades are based on private information, the presence of privately informed traders in the market can be inferred from large imbalances between the number of buy orders and the number of sell orders. This operability provides the intuition behind the microstructure model developed by Easley et al. (1997a; 1997b), called the EKOH model of information asymmetry. The EKOH model is a learning model in which the market maker draws inferences about the presence and the type of private information based on the observed order flow. Over a trading day, prices converge to their full information levels as private information is fully revealed through the trading activities of informed investors. Thus, one can estimate the probability of information-based trades (PIN) for a given stock over a particular period based on the daily order flow during the period.

We employ the EKOH model to estimate the probability of information-based trades. This parsimonious structural model has been shown to be very effective in capturing the process

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of a market maker’s learning about private information from trades. The model has been successfully implemented in previous studies to address many important microstructure issues.4 As demonstrated by these studies, the great advantage of this model is that its parameters can be easily estimated from the trade data by a maximum likelihood method. We briefly describe the structure of the EKOH model below.

The model assumes that, at the beginning of each day, there is a probability, α, for the arrival of new information. This new information is a signal about the value of the underlying asset that is traded. Good news means that the value of the asset is high (Vi), while bad news means that the value of the asset is low (Vi ). Good news and bad news occur with the probabilities of 1−δ and δ, respectively. On each trading day, traders arrive according to independent Poisson processes throughout the day. The market maker sets prices as traders arrive, conditional on the information at the time of trade. Orders from risk neutral and competitive informed traders arrive randomly at the daily arrival rate of μ only on good- and bad-news event days. Orders from uninformed buyers and sellers arrive randomly at the daily rates of εb and εs, respectively, on every trading day. Informed traders buy if they have learned good news and sell if they have learned bad news. All of the arrival processes are assumed to be independent and their parameters are common knowledge across all traders and the market maker. The market maker sets prices to buy or sell shares at each point in time based on his/her current information set and executes orders as they randomly arrive.

The trading process described above leads to one of the three general patterns (which are similar to branches in a decision tree) of trade orders. On a no-news day, the model predicts a

4 For example, the

PIN methodology has been employed to investigate how informed trading varies across different

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roughly equal number of buyer- and seller-initiated trade orders. On a good news day, there will be a large imbalance in the order flow with buyer-initiated trades predominating. On a bad news day, there will be more sell-initiated trades. Although unaware of the branch chosen by nature on any given day, the market maker knows the probability of each branch and the expected order process associated with each branch. He uses the observed numbers of buys and sells to update his/her beliefs throughout the trading day via the Bayes rule.

The structural parameters of the EKOH model can be easily estimated using trade data. EKOH show that the likelihood function of the model for a single trading day can be written as:

(

) (

)

(

)

( )

(

)

( )

(

)

, ! ! ! ! 1 ! ! 1 , | S e B e S e B e S e B e S B L S s B b S s B b S s B b s b s b S b ε μ ε αδ ε ε μ δ α ε ε α θ ε μ ε ε ε μ ε ε + + + − + − = + − − − + − − − (1)

where B and S are the total numbers of buy and sell orders for that day, respectively.

(

α μ ε ε δ

)

θ = , , b, s, is the vector of model parameters described above. The above likelihood function has a very intuitive interpretation. On a no-news day, which happens with the probability of 1−α , buy and sell orders are purely from uninformed traders who arrive with the intensities of εb for buyers and of εs for sellers. On a good-news day, which happens with the probability of α

(

1−δ

)

, informed traders who arrive with the intensity of μ will purchase the asset. Therefore, the buy and sell orders will arrive with the intensities of μ+εb (uninformed plus informed buyers) and εs (uninformed sellers), respectively. On a bad-news day, which happens with the probability of αδ , informed traders who arrive with the intensity of μ will sell the asset. Thus, the buy and sell orders will arrive with the intensities ofεb (uninformed buyers) and μ +εs (informed plus uninformed sellers), respectively.

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By imposing an independent structure across trading days, we have the following likelihood function for observations over I days:

(

)

(

)

= = = I i i i S B L M L V 1 , , | | θ θ (2)

where

(

Bi,Si

)

is the trade data for day i = 1,…, I. As can be seen from above two equations, the daily numbers of buy and sell orders are sufficient statistics for the data. As one observes the order flow over an increasing number of days, one can estimate the parameter vector of

(

α μ ε ε δ

)

θ = , , b, s, with an increasing precision, assuming that the parameter vector of

(

α μ ε ε δ

)

θ = , , b, s, is stationary. Essentially, the model uses the normal levels of buy orders and sell orders to identifyεb andεs . An abnormal buy or sell order volume is interpreted as information-based and it identifies μ. The number of days on which there is an abnormal buy or sell volume identifies α andδ . The maximum likelihood that estimates for each firm’s parameter vector of θ =

(

α,μ,εbs

)

over a particular period allows us to calculate firm- and period-specific PINs. From the estimated model parameters, we can infer the unobservable information events through observed trade data. One variable of particular interest is the probability of informed trades (PIN), which represents a measure of information risk. The PIN is defined as , b s PIN ε ε αμ αμ + + = (3)

where αμ+εsb is the arrival rate of all trades and αμ is the arrival rate of information-based trades.

Our use of a direct measure for information asymmetry contrasts the indirect bid-and-ask spread-based proxies for asymmetric information used by prior studies, such as Welker (1995)

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and Leuz and Verrecchia (2000). A direct measure avoids the numerous econometric problems and interpretation difficulties that occur when spread-based proxies are used (Callahan et al., 1997; O’Hara, 1995). An additional advantage of the PIN methodology is that it enables us to analyze the sources of the underlying relation between the changes in bond ratings and the changes in stock information asymmetry.

4. Data Description

Our data sample consists of bond rating changes for companies listed on the NYSE, AMEX, and Nasdaq stock exchanges. Our sample period spans the period from January 1996 to April 2004. The sample of bond rating changes is retrieved from FISD and Bloomberg; the accounting information is retrieved from Compustat; and the intraday transaction information is retrieved from TAQ.

To begin, we collect the full sample of all bond rating changes from January 1996 through April 2004. To create our final sample, we use several criteria to select bond rating changes from the large original sample. First, we eliminate observations from the rating changes for all preferred stocks, stock rights and warrants, stock funds, and American deposit receipts (ADRs). This is to remove any unique security whose buy and sell information might reflect idiosyncratic factors instead of the volume-related properties that we seek to investigate. Second, we require that the firms must be present in the Compustat tapes for the fiscal year of the bond rating changes. This is to make sure that we can perform robustness checks by matching the sample firms with firms that do not experience bond rating changes. Third, the firms must be on the TAQ tapes for at least three months before and three months after the rating change date. Finally, following Healy and Palepu (1990), we exclude bond rating changes for the same firm

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during the three months before or after a rating change. We exclude these changes in order to reduce dependence in the statistical tests. Thus, when a firm has two or more bond rating changes within a six-month period, the bond rating changes after the first one are not included in our final sample.

Easley et al. (1997b) point out that a sixty-day trading window is sufficient to allow reasonably precise estimation of the parameters specified in equations (1) and (2). It is also short enough so that the stationarity built into the trade model is not too unreasonable. To compute the likelihood function given in the EKOH model, we need to estimate the number of buys and sells on each day for each of our sample stocks. We can determine all these numbers of buys and sells from the TAQ data.

First, we know that large trades sometimes have multiple participants on one side of the trades. Reporting conventions may treat such a transaction as multiple trades. To mitigate this problem, all trades occurring within five seconds of each other at the same price and with no intervening quote revisions are collapsed into one trade. Second, trades are classified into buys and sells using the technique developed by Lee and Ready (1991). Trades at prices above the midpoint of the bid and ask are called buys; those below the midpoint are called sells. The rationale for this classification is that trades originating from buyers are most likely to be executed at or near the ask price, while sell orders trade at or near the bid price. This scheme classifies all trades except those that occur at the midpoint of the bid and ask prices. These trades are classified using the “tick test.” Trades executed at a price higher than the previous trade are called buys, and those executed at a lower price are called sells. If the trade occurs at the midpoint and is at the same price as the last trade, its price is compared to the next most recent trade. This is continued until the trade is classified. This procedure undoubtedly misclassifies

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some trades, but it is standard and it has been proved to work reasonably well (Lee and Ready, 1991).

To examine if bond rating changes affect information asymmetry of stock trading, we first estimate the parameters of the asymmetric information structural model by using three months of trading transaction information both before and after a rating change. After obtaining the estimates of the PIN parameter vector, we eliminate observations with extreme parameter estimates using the following filters: (1) 50µ>ε or 50ε>µ, where ε=εb+εs; (2) α<0.02 or α>0.98;

(3) δ<0.02 or δ>0.98; and (4) Min (µ,ε)<1 (Brown et al. (2004)).

After we employ all of above filters, our final bond rating change sample includes 279 upgrade and 310 downgrade events. To check the robustness, we replicate all our tests using all usable bond rating changes without applying the above four filters for the estimates of the PIN parameters. It turns out that the results remain virtually the same for any of the analyses. More specifically, all of the coefficients on the bond rating changes retain the same signs and remain significant at the 1% level.

We obtain the analysts’ earnings forecast data from the Institutional Broker Estimation System (I/B/E/S). The analysts’ earnings forecast dispersion (DISP) is the standard deviation of forecasts across analysts on earnings per share (EPS) divided by the absolute value of forecasted EPS 90 days before the bond rating changes or 90 days after the bond rating changes.5 Five different dispersion measures (Q1, Q2, Q3, Q4, and Y1) are used to represent the dispersion of earnings forecasts for the next one to four quarters and for the next fiscal year.

Institutional equity holding data are collected from the Thomson Financial Ownership database. Institutions are categorized into three groups: the transient group (TRA), the

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indexing group (QIX), and the dedicated group (DED). For each firm, we follow Bushee (1998, 2001) to calculate the percentage of outstanding shares held by all types of institutional investors (Total%), the percentage of outstanding shares held by transient institutional investors (TRA%), the percentage of outstanding shares held by quasi-indexing institutional investors (QIX%), and the percentage of outstanding shares held by dedicated institutional investors (DED%).

5. Empirical Results

5.1 Summary statistics for the final sample

Table 1 presents the number of bond rating changes for the sample over the period from January 1996 to December 2004. Panel A of Table 1 shows that the number of bond rating changes is evenly distributed throughout the sample years for both the upgraded and downgraded groups. Panel B of Table 1 summarizes the number of bond rating changes classified by the previous bond rating category. The previous bond rating category (AA and above, A, BBB, BB, B, and CCC and below) is the rating category before the change and it is based on the Fitch, Moody’s, or Standard & Poor’s ratings. We notice that the number of rating changes distributes evenly for the upgraded group. For the downgraded group, firms with a rating category of BBB are more likely to be downgraded and firms with a rating category of CCC and below are less likely to be downgraded than firms in other categories. Panel C provides the number of rating changes by the number of jumps. The number of jumps is the number of changes in bond rating grades. We convert the alphabetical bond rating grades into 22 numerical integers. More specifically, we assign a rating grade of AAA to 1, AA+ to 2, AA to 3,…, CCC- to 19, CC to 20, C to 21, and D to 22. For example, a rating change from AA to AAA is equal to two jumps for the upgraded group, and a rating change from AAA to AA is also equal to two jumps for the

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downgraded group. It is observed that one jump accounts for the majority of the bond rating changes.

[Insert Table 1 Here]

5.2 Summary statistics for the estimates of model parameters and PIN

Table 2 presents the mean and median estimates of the model parameters and PIN for firms whose bond ratings have been upgraded or downgraded during our sample period. Panel A reports the results for the upgraded group. The difference in means is equal to the mean after the bond rating changes minus the mean before the bond rating changes. We observe that when a firm’s bond rating is upgraded, the probability of an information event (α) reduces significantly, the arrival rate of uninformed trades (εsand εb) increases significantly, and the arrival rate of informed trades (μ) does not change at all. In addition, we also check the percentage of the arrival rate for informed (uninformed) trades among all the trades and notice that the percentage of the arrival rate of informed trades (μ(%)) reduces significantly, while the percentages of the arrival rates of uninformed sell orders ( (%)εs ) and uninformed buy orders (εb(%)) increase significantly.6 All the changes in the model parameters lead to a lower probability of informed trades (PIN). Thus, the improvement in a firm’s bond rating not only sends a positive signal to the stock market but also reduces the asymmetric information between the informed and uninformed investors on the firm’s stock.

6 The percentage of the arrival rate of informed trades is equal to the arrival rate of informed trades divided by the sum of μ,εs, and εb, i.e., μ/(μ+εsb). We also compute the percentages of the arrival rates of uninformed buys

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Panel B reports the estimation results for the downgraded group. As expected, the probability of an information event (α) increases significantly, the arrival rate of uninformed trades (εsand εb) decreases significantly, and the arrival rate of informed trades (μ) does not change at all. Based on the percentages of the arrival rates of informed and uninformed trades, we obtain exactly the opposite results compared with those upgraded bonds (both are unreported). As the results show, when a firm’s bond rating is downgraded, the probability of information-based trades (PIN) increases significantly on its stock trading. Thus, a downgrade in a firm’s bond rating not only sends a warning signal to the stock market but also increases the asymmetric information between the informed and uninformed investors on the firm’s stock.

In summary, the results from Table 2 support our hypothesis 2. That is, when a firm’s bond rating is upgraded, the information asymmetry on its stock trading is reduced and it is the opposite for a downgrade.

[Insert Table 2 Here]

Table 3 reports the mean estimates of the model parameters and PIN by the level of the previous bond rating category. Panel A provides the estimation results for the upgraded group. First, the results show that when a firm’s bond rating is upgraded from a previous rating category of BBB, BB, B, or CCC and below, the arrival rates of uninformed buy and sell trades increase significantly, whereas the arrival rate of informed trades does not experience any considerable change.7 In addition, the probability of information-based trades (PIN) reduces significantly. However, the probability of information-based trades (PIN) does not change much in firms with a previous rating category of AA or A. These results indicate that the effect of a rating upgrade on the reduction of information asymmetry is significant only in firms with a previous rating of

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BBB or below. Second, by comparing the PIN estimates across different categories of bond ratings before rating changes, we note that the higher (lower) the previous bond rating, the lower (higher) the PIN. For example, the PIN is 0.11 for the rating category of AA and above and 0.30 for the rating category of CCC and below. This indicates that a firm’s bond rating conveys information about its stock trading. Firms with a higher (lower) credit rating tend to have lower (higher) asymmetric information on their stock trading.

[Insert Table 3 Here]

Panel B reports the estimation results by the previous bond rating category for the downgraded group. First, we observe that when a firm’s bond rating is downgraded from a previous rating category of AA, A, BBB, BB, or B, the probability of information-based trade (PIN) on its stocks increases significantly. Second, we find that the relationship between the PIN estimates and the bond rating categories before the rating changes is the same as that in Panel A of Table 3. Specifically, the higher (lower) the bond rating, the lower (higher) the PIN estimate. For example, the PIN is 0.10 for the rating category of AA and above and 0.31 for the rating category of CCC and below. The results in Table 3 not only support our hypothesis 2 but also our hypothesis 1. That is, firms with higher (lower) bond ratings tend to have lower (higher) asymmetric information about their stock trading.

Table 4 presents the mean estimates of the model parameters and PIN by the number of rating jumps. The number of jumps ranges from one to three for the upgraded group and one to four for the downgraded group. Panel A reports the estimation results for the upgraded group. We note that when a firm’s bond rating jumps two or three grades in the AAA direction, the probability of an information event (α) reduces significantly, the arrival rate of informed trades drops significantly, the arrival rates of uninformed buy and sell trades increase significantly, and

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indicate that the greater the number of jumps in the AAA direction, the larger the reduction in PIN. For example, the PIN is reduced by only 0.002 for one jump, 0.052 for two jumps, and 0.140 for three jumps. Therefore, firms with their bond ratings more dramatically upgraded tend to have more dramatic reduction in asymmetric information about their stock trading.

[Insert Table 4 Here]

Panel B provides the estimation results for the downgraded group. The results indicate that when a firm’s bond rating jumps 1, 2, 3, or 4 grades toward the CCC direction, the probability of an information event (α) increases significantly, the arrival rate of informed trades increases significantly (for 3 or 4 jumps), the arrival rates of uninformed buy and sell trades decrease significantly (for 2 or 3 jumps), and the probability of informed trades (PIN) of its stock increases significantly. In addition, the greater the number of jumps in the CCC direction, the greater the increase in PIN. For example, the PIN increases 0.020 for one jump, 0.107 for two jumps, 0.150 for three jumps, and 0.176 for four jumps. Hence, firms with their bond ratings more dramatically downgraded tend to have more dramatic increases in the asymmetric information of their stock trading. In summary, Table 4 not only confirms our hypothesis 2 but also verifies our hypothesis 3. More specifically, the magnitude of a firm’s bond rating change is positively related to the magnitude of the change in its stock information asymmetry.

5.3 Regression results

To strengthen our analysis, the change in PIN is regressed on whether the bond rating is upgraded or downgraded, the previous bond rating category, and the number of jumps as specified below: Jump D Jump Rating D Rating D PIN = + + + × + + × Δ α0 α1 β0 β1 γ0 γ1 , (3)

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where ΔPIN is the difference in the PIN estimates between the after and before the bond rating

changes. D is a dummy variable, which is equal to one for an upgrade and is zero for a downgrade. “Rating” is the before-change rating category and is classified into six categories with AA and above = 1, A = 2, BBB = 3, BB = 4, B = 5, and CCC and below = 6. “Jump” denotes the number of jumps in the absolute value. As discussed earlier, when we compute the number of jumps, we classify the bond ratings into 22 grades, each with a corresponding integer.

The first regression reported in Table 5 shows that when the bond ratings are upgraded, the changes in PIN are significantly negative (0.035-0.065=-0.030).8 When the bond ratings are downgraded, the changes in PIN are significantly positive (0.035).9 The results validate that the asymmetric information on a firm’s stock trading reduces (increases) significantly, when its bond rating is upgraded (downgraded). The second regression indicates that the before-change bond rating category has a significantly negative effect on the change in PIN when a bond rating is downgraded. However, this negative effect is reduced when a bond rating is upgraded. That is, when a bond rating is changed, the better the before-change bond rating category, the greater the decrease in stock asymmetric information, especially for a downgrade. More specifically, one rating-category better is accompanied by a decrease in PIN by 0.023 (i.e., the coefficient on Rating) for a downgrade, while it is only decreased in PIN by 0.009 (=-0.023+0.014) for an upgrade. The third regression suggests that the number of jumps is positively and significantly linked to the change in PIN. In other words, when a bond is upgraded, the greater the number of the rating grades it jumps, the greater the decrease in stock asymmetric information. When a bond is downgraded, the greater the number of the rating grades it jumps, the greater the increase in the asymmetric information on the stock. More specifically, one rating upgrade is

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accompanied by a decrease in PIN by 0.064 (=0.058+0.122), while one rating downgrade is associated with an increase in PIN by 0.058. The regression results are all consistent with those from Tables 2-4 and support our three hypotheses.

[Insert Table 5 Here]

6. Robustness Checks

6.1 Matched firms without bond rating changes

In order to test whether the change in PIN is actually caused by the bond rating changes, we conduct a robustness test by matching the sample firms with firms whose bond ratings do not experience any changes during the same test period, based on trading volume, size, industry, and/or the book-to-market ratio.10 Table 6 shows the mean and median estimates of model parameters and PIN for stocks whose firms’ bond ratings do not change during the sample period. These stocks are matched with the stocks whose firms’ bond ratings have been upgraded or downgraded during the sample period, based on both the before-change bond ratings (the same ratings) and the closest stock trading volume. The results from Panels A and B of Table 6 suggest that firms without bond rating changes do not show any significant changes in the model parameters (α, μ, εb, εs, and δ) as well as in the probability of informed trades (PIN). These contrast those results reported in Table 2 and strengthen our argument that the decrease (increase) in a firm’s asymmetric information on its stock is attributed to its bond rating upgrade (downgrade).

[Insert Table 6 Here]

10 Easley et al. (1996b) show that the trading frequency affects the level of asymmetric information. Since the results for matched firms with different firm characteristics are very similar, to save space, we only report the result for the

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In the following, we examine if our results are robust to other information risk measures, including traditional measures of asymmetric information (such as buy/sell numbers, size, and transaction costs), analysts’ earnings forecast dispersion, and institutional equity holdings. We provide summary statistics on the above measures for firms whose bonds are upgraded or downgraded.11

6.2 Trading activities and bond rating changes

To investigate trading activities before and after bond rating changes, we focus on the traditional measures of asymmetric information. The first traditional measure of asymmetric information is Buy-Num (Sell-Num), which is defined as the number of stock transactions initiated by bid orders (ask orders). This measure reflects the change in the whole market environment. We use the following procedures to estimate this measure. First, we obtain the daily Buy-Num (Sell-Num) with each bond rating change. We then compute the average of the daily Buy-Num (Sell-Num) with each bond rating change in the before or after rating change periods (3 months in each period). Finally, we compute the mean (median) for all stocks in the upgraded (downgraded) group in both the before-change and after-change periods. The second measure is Buy-Size (Sell-Size), which is the sum of the trading volume for bid-initiated (ask-initiated) stock transactions. We follow the same above procedures to obtain the mean (median) for all stocks in the upgraded (downgraded) group in both the before-change and after-change periods.

The last four measures (E-spread, E-spread_bp, Q-spread, and Q-spread_bp) represent the stock transaction costs for investors. spread is the effective bid-ask spread in dollars and

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spread_bp is the effective bid-ask spread in basis points. Q-spread is the quoted bid-ask spread in dollars and Q-spread_bp is the quoted bid-ask spread in basis points. E-spread is calculated as two times the difference between the transaction price and the midquote. Q-spread is calculated as the difference between ask and bid prices. E-spread_bp is calculated as the effective spread (E-spread) divided by the transaction price and then multiplied by 100. Q-spread_bp is calculated as the quoted bid-ask spread (Q-spread) divided by the transaction price and then multiplied by 100.

The results from Panel A1 of Table 7 suggest that a firm’s stock trading characteristics are affected in several ways when its bond rating is upgraded. The numbers of buy orders (Buy-Num) and sell orders (Sell-(Buy-Num) increase significantly; the size of buy transactions (Buy-Size) and sell transactions (Sell-Size) also increases significantly after the bond rating is upgraded. The bid-ask spreads (including the effective bid-ask spreads in dollars or in basis points and the quoted bid-ask spreads in basis points) decrease significantly. These findings indicate that an upgrade in a firm’s bond rating increases the liquidity and reduces the transaction costs of its stock trading.

[Insert Table 7 Here]

Panel A2 of Table 7 reports the results on trading characteristics for the downgraded group. We observe that when a firm’s bond rating is downgraded, the numbers of buy and sell orders decrease significantly, the size of sell transactions also decreases significantly, and the bid-ask spreads (including the effective and quoted bid-ask spreads in dollars and in basis points) increase significantly. These results suggest that a downgrade in a firm’s bond rating reduces the liquidity and increases the transaction costs of its stock trading.

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6.3 Stock market reactions to the announcements of bond rating changes

The examination of stock performance surrounding the announcements of bond rating changes is a good way to check if the bond rating changes bring new information to the stock market. Excess stock returns are defined as the stock return prediction errors calculated from a market model. Since previous studies have shown that a rating downgrade (an upgrade) is preceded by negative (positive) average excess returns, we use a post-rating change period (day +60 through +315) to estimate a market model for each firm. The market model is estimated using an equally weighted market index, which is the CRSP equally weighted New York and American Stock Exchange Index. Abnormal stock returns are defined in the usual manner by subtracting the expected returns implied by the estimated market model from the daily returns for each firm. We also report the proportion of positive abnormal stock returns. The t-test on the mean excess stock return is based on the cross-sectional standard deviations.

Panel B of Table 7 reports the abnormal stock returns surrounding the announcements of bond rating changes. Consistent with virtually all earlier studies such as Holthausen and Leftwich (1986), Hand et al. (1992), and Ederington and Goh (1998), we observe a significant negative stock market reaction to downgrades. The average abnormal return on the announcement day for downgrades is -0.28% with a t-statistic of -2.22. Of the abnormal returns on the announcement day, 54.19% are negative, which is significantly different from 50% at the 0.01 level. The market reaction to upgrades is positive and the average abnormal return on the announcement day is 0.54% with a t-statistic of 2.48. Of the abnormal returns on the announcement day, 53.41% are positive, which is significantly different from 50% at the 0.01

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level. The findings from the event study indicate that bond rating downgrades and upgrades do introduce some new information to the stock market.12

6.4 Analysts’ earnings forecast dispersion and bond rating changes

Table 8 presents the means and medians of analysts’ earnings forecast dispersion for firms whose bonds have been upgraded or downgraded. Panel A of Table 8 shows that the dispersions of analysts’ forecasts on earnings for all five forecasted periods reduce significantly after the bond ratings are upgraded, implying that information risk is reduced significantly. Panel B of Table 8 shows that the dispersion of analysts’ forecasts on earnings for the next one and four quarters and for the next fiscal year increase significantly after the bond ratings are downgraded, implying that information risk increases significantly. Overall, the results from analysts’ earnings forecast dispersion are consistent with but not as strong as the results from PIN.

[Insert Table 8 Here]

6.5 Institutional equity holdings and bond rating changes

Institutional holdings can be used as a proxy for corporate governance and therefore information risk. As discussed earlier, institutions can be categorized into three groups. The transient group (TRA) has high turnover and holds highly diversified positions. The quasi-indexing group (QIX) has low turnover and holds highly diversified portfolios. The dedicated group (DED) has low turnover and has high concentrations in their investments. We expect that the TRA group is the most sensitive to changes in information risk, the DED group is the least

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since it is more likely to possess superior information about their investment (Bushee, 1998, 2001; Wang and Zhang, 2006), and the QIX group lies between. Table 9 provides the means and medians of institutional equity ownership for firms whose bond ratings have been upgraded or downgraded. Panel A shows that when bond ratings are upgraded, the percentages of outstanding shares held by all types (Total%) of institutional investors and transient institutional investors (TRA%) increase significantly, indicating that information risk is reduced. In Panel B, when bond ratings are downgraded, the percentages of outstanding shares held by all types of institutional investors (Total%) and transient institutional investors (TRA%) decrease significantly, indicating that information risk increases. In summary, the results based on institutional equity holdings are in line with those based on PIN.13 That is, both show that bond rating changes negatively affect the information asymmetry on stocks measured by either PIN or institutional equity holdings.

[Insert Table 9 Here]

7. Conclusions

Previous studies on bond rating changes focus on the announcement effects on bond prices or stock prices. For example, Weinstein (1977) finds that there is no bond price reaction at the time of bond rating changes. However, Katz (1974), Grier and Katz (1976), and Ingram, Brooks, and Copeland (1983) document a significant bond price reaction upon bond rating changes. Ederington and Goh (1998) show that bond rating downgrades result in negative equity returns and that equity analysts tend to revise earnings forecasts sharply downward following

13 The holdings of quasi-indexing and dedicated institutional investors are much smaller than those for transient institutional investors, and particularly those for quasi-indexing institutional investors. In addition, the effect of

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bond rating downgrades. Other studies also confirm that the stock market reacts negatively to the announcements of bond rating downgrades.14

In contrast, in this paper, we examine whether bond rating changes affect the information risk of stock trading. We argue that bond rating changes should be negatively associated with stock information asymmetry based on the linkage among the following studies. First, recent studies show that both asymmetric information (Easley and O’Hara, 2004; Easley et al., 2002) and disclosure (Botosan, 1997; Botosan and Plumlee, 2002; Sengupta, 1998) affect the cost of capital. Second, the models proposed by Merton (1987), Fishman and Hagerty (1989), and Diamond and Verrecchia (1991) suggest that uninformed investors are more likely to trade the stocks of firms with higher disclosure levels. By considering these studies together, one can argue that disclosure should reduce the stock information asymmetry. In fact, Brown et al. (2004) document that conference calls (i.e., disclosure) can reduce stock information asymmetry. In addition, the discretionary disclosure hypothesis suggested by Chen et al. (2001) and Bae et al. (2006) suggests that firms with better bond ratings should have greater disclosure than firms with lower bond ratings have. Linking all these studies together, one can conclude that bond ratings should be negatively associated with stock information asymmetry.

Using data from bond rating changes during the period 1996 to 2004, we find that a firm’s bond upgrade (downgrade) causes a significant decrease (increase) in the private information on its stock trades. Other measures of information risk, including analysts’ earnings forecast dispersion and institutional equity holdings, are also affected by bond rating changes in a similar manner. In addition, the magnitude of bond rating changes is positively related to the magnitude of changes in the probability of informed trades. All these results appear to support

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our hypothesis that bond rating changes have a significant effect on the information asymmetry of stock trading.

Two important implications can be derived from our study. First, we verify the signaling theory. The corporate bond market and the equity market are interrelated. They both reveal information about a firm’s expected future performance and uncertainty. An update in a firm’s credit quality sends out a signal regarding a change in the firm’s financial condition. Stock investors catch the upgrade or downgrade signal, interpret the signal as good or bad news, and revise their expectations about the firm’s future performance and uncertainty. As a result, the distribution of informed and uninformed stock traders is affected accordingly, which, in turn, affects the information risk of its stock trading.

Second, we validate the effect of disclosure on the measures of information risk, including asymmetric information, transaction costs, analysts’ earnings forecast dispersion, and institutional equity holdings. Bond rating upgrades reveal good news about a firm’s financial condition, give rise to more disclosure transparency, reduce the probability of informed trades, transaction costs, and analysts’ earnings forecast dispersion, and increase institutional equity holdings. The reverse is true for bond rating downgrades. Previous studies find that changes in a firm’s bond rating have short-term effects on stock prices. Our study contributes to the literature by documenting that changes in a firm’s bond rating also have long lasting effects on the information risk of the firm’s stock trading.

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References

Bae, Kee-Hong, Chanwoo Lim, and K.C. John Wei, 2006, Corporate governance and conditional skewness in the world’s stock markets, Journal of Business 79, 2999-3028.

Bhojraj, S. and P. Sengupta, 2003, Effect of corporate governance on bond ratings and yields: The role of institutional investors and outside directors, Journal of Business 76, 455-75. Botosan, C.A., 1997, Disclosure level and the cost of equity capital, The Accounting Review 72,

323-349.

Botosan, C.A., and M.A. Plumlee, 2002, A re-examination of disclosure level and the expected cost of equity capital, Journal of Accounting Research 40, 21-40.

Brown S., S. Hillegeists, and K. Lo, 2004, Conference calls and information asymmetry, Journal of Accounting and Economics 37, 343-366.

Bushee, B., 1998, The influence of institutional investors on myopic R&D investment behavior, The Accounting Review 73: 305-333.

Bushee, B., 2001, Do institutional investors prefer near-term earnings over the long-run value? Contemporary Accounting Research 18, 207-246.

Callahan, C.M., C.M.C. Lee, and T.L. Yohn, 1997, Accounting information and bid-ask spreads, Accounting Horizons 11, 50-60.

Chen, Joseph, Harrison Hong, and Jeremy Stein, 2001, Forecasting crashes: Trading volume, past returns, and conditional skewness in stock prices, Journal of Financial Economics 61, 345-381.

Coles, J.L., U. Loewenstein, and J. Suay, 1995, On equilibrium pricing under parameter uncertainty, Journal of Financial and Quantitative Analysis 30, 347-364.

Cornell, B., W. Landsman, and A. Shapiro, 1989, Cross-sectional regularities in the response of stock prices to bond rating changes, Journal of Accounting, Auditing, and Finance 4, 460-479.

Diamond, D.W. and R.E. Verrecchia, 1991, Disclosure, liquidity, and the cost of capital, Journal of Finance 46, 1325-1360.

Easley, David, Nicholas M. Kiefer, and Maureen O’Hara, 1996a, Cream-skimming or profit-sharing? The curious role of purchased order flow, Journal of Finance 51, 811-833.

Easley, David, Nicholas M. Kiefer, Maureen O’Hara, and Joseph Paperman, 1996b, Liquidity, information, and less-frequently traded stocks, Journal of Finance 51, 1405-1436.

Easley, David, Nicholas M. Kiefer, and Maureen O’Hara, 1997a, The information content of the trading process, Journal of Empirical Finance 4, 159-186.

Easley, David, Nicholas M. Kiefer, and Maureen O’Hara, 1997b, One day in the life of a very common stock, Review of Financial Studies 10, 805-835.

(30)

Easley, David, Maureen O’Hara, and Joseph Paperman, 1998a, Financial analysts and information-based trade, Journal of Financial Markets 1, 175-201.

Easley, David, Maureen O’Hara, and P. S. Srinivas, 1998b, Option volume and stock prices: Evidence on where informed traders trade, Journal of Finance 53, 431-465.

Easley, David, Maureen O’Hara, and Gideon Saar, 2001, How stock splits affect trading: A microstructure approach, Journal of Financial and Quantitative Analysis 36, 25-51.

Easley, David, Soeren Hvidkjaer, and Maureen O’Hara, 2002. Is information risk a determinant of asset returns? Journal of Finance 57, 2185-2221.

Ederington, L. and J. Goh, 1998, Bond rating agencies and stock analysts: who knows what and when? Journal of Financial and Quantitative Analysis 33, 569-585.

Ederington, L., and J. Yawitz, 1987, The bond rating process, Handbook of financial Markets and Institutions, 6th ed., E. Altman, ed. New York, NY: John Wiley and Sons.

Fishman, M.J. and K.M. Hagerty, 1989, Disclosure decisions by firms and the competition for price efficiency, Journal of Finance 44, 633-646.

Grier, P. and S. Katz, 1976, The differential effects of bond rating changes on industrial and public utility bonds by maturity, Journal of Business 49, 226-239.

Hand, J., R. Holthausen, and R. Leftwich, 1992, The effect of bond rating agency announcements on bond and stock prices, Journal of Finance 47, 733-752.

Harvey, Campbell R., and Akhtar Siddique, 2000, Conditional skewness in asset pricing tests, Journal of Finance 55, 1263-1295.

Healy, P. and K.G. Palepu, 1990, Earnings and risk changes surrounding primary stock offers, Journal of Accounting Research 28, 25-48.

Holthausen, R. and R. Leftwich, 1986, The effect of bond rating changes on common stock prices, Journal of Financial Economics 17, 57-89.

Ingram, R., L. Brooks, and R. Copeland, 1983, The information content of municipal bond rating changes: A note, Journal of Finance 38, 997-1003.

Katz, S., 1974, The price adjustment process of bonds to rating reclassifications: A test of bond market efficiency, Journal of Finance 29, 551-559.

Kyle, A., 1985, Continuous auctions and insider trading, Econometrica 53, 1315-1335.

Lee, C.M.C. and Ready, M.J., 1991, Inferring trade direction from intraday data, Journal of Finance 46, 733-747.

Leuz, C. and R. Verrecchia, 2000, The economic consequences of increased disclosure, Journal of Accounting Research 38, 91-124.

Mansi, S., W. Maxwell, and D. Miller, 2005, Information risk and the cost of debt capital, Working paper, University of Arizona.

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Merton, R.C., 1987, A simple model of capital market equilibrium with incomplete information, Journal of Finance 42, 483-510.

Odders-White, E. and M. Ready, 2006, Credit rating and stock liquidity, Review of Financial Studies 19, 119-157.

O’Hara, M., 1995, Market Microstructure Theory, Blackwell, Malden, MA.

O’Hara, M., 2003, Presidential address: Liquidity and price discovery, Journal of Finance 58, 1335-1364.

Sengupta, P., 1998, Corporate disclosure quality and the cost of debt, The Accounting Review 73, 459-474.

Wang, A. and G. Zhang, 2006, Institutional equity investment, asymmetric information and credit spreads, Working paper.

Weinstein, M., 1977, The effect of a rating change announcement on bond price, Journal of Financial Economics 5, 329-350.

Welker, M., 1995, Disclosure policy, information asymmetry, and liquidity in equity markets, Contemporary Accounting Research 11, 801-827.

Yu, F., 2005, Accounting transparency and the term structure of credit spreads, Journal of Financial Economics 73, 53-84.

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Table 1

Summary of the number of bond rating changes

This table presents the number of bond rating changes by year, by the previous rating category, and by the number of jumps. The previous rating category (AA & above, A, BBB, BB, B, and CCC & below) is the bond rating category before changes and it is based on the Fitch, Moody’s, or S&P’s ratings. The number of jumps is the changes in bond rating categories. We convert the bond rating categories to 22 numerical integers (i.e., AAA = 1, AA+ = 2, …, CCC = 19, CC = 20, C = 21, and D = 22).

Panel A: Number of bond rating changes by year

Year Upgrades Downgrades

1996 8 2 1997 29 14 1998 46 29 1999 38 49 2000 37 51 2001 38 52 2002 36 52 2003 34 53 2004 13 8 Total 279 310

Panel B: Number of bond rating changes by the previous rating category

Rating category Upgrades Downgrades

AA & above 21 21 A 36 54 BBB 56 104 BB 73 67 B 54 58 CCC & below 39 6 Total 279 310

Panel C: Number of bond rating changes by the number of jumps

Jumps Upgrades Downgrades

1 180 264

2 66 30

3 33 12

4 0 4

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Table 2

Summary statistics of estimates of parameters and PIN for firms with bond rating changes

This table presents the mean and median estimates of parameters and PIN for firms whose bond ratings have been upgraded or downgraded during the sample period. αis the probability of an information event, μis the arrival rate of informed trades, εb is the arrival rate of uninformed buy orders, εs is the arrival rate of uninformed sell orders, and δ is the probability of a low signal (i.e., bad news). PIN is the probability of informed trades and is defined as PIN = αμ/(αμ +εb+εs). Diff is the difference in the respective estimate between the after and before the bond rating changes. The t-statistic is used to test the null hypothesis that Diff = 0. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Before (1) After (2) Diff = (2) – (1)

Mean Median

Median

std error Mean Median

Median

std error Mean t-stat Panel A: Upgrades α 0.6459 0.6272 0.0662 0.6025 0.5278 0.0617 -0.0434 -3.05*** μ 56.2087 32.8724 4.9320 56.7112 34.7687 5.0248 0.5025 0.23 b ε 63.3812 26.6489 2.2820 67.7088 30.2988 2.4485 4.3276 1.98** s ε 58.5676 25.4067 2.6652 65.9952 30.1143 2.8036 7.4296 2.86*** δ 0.7038 0.7946 0.0808 0.6846 0.7233 0.1003 -0.0192 -1.01 PIN 0.2408 0.2515 0.2110 0.2124 -0.0298 -6.89*** Panel B: Downgrades α 0.5639 0.4740 0.0654 0.6556 0.6313 0.06905 0.0917 6.26*** μ 54.6879 39.2549 5.5444 54.0850 34.6827 4.4808 -0.6028 -0.28 b ε 65.7084 40.2420 2.8774 64.1318 34.6405 2.5464 -1.5766 -1.69* s ε 64.1328 41.2620 3.0966 60.1074 34.4605 2.7828 -4.0254 -1.97** δ 0.6370 0.6785 0.1062 0.6694 0.7455 0.0851 0.0324 1.69* PIN 0.2015 0.1986 0.2369 0.2521 0.0354 7.77***

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Table 3

Mean estimates of parameters and PIN by the previous bond rating category

This table presents the mean estimates of parameters and PIN by the previous bond rating category for firms whose bonds have been upgraded or downgraded during the sample period. The previous rating category (AA & above, A, BBB, BB, B, and CCC & Below) is the bond rating category before the rating changes and it is based on the Fitch, Moody’s, or S&P’s ratings. αis the probability of an information event, μis the arrival rate of informed trades, εb is the arrival rate

of uninformed buy orders, εs is the arrival rate of uninformed sell orders, and δ is the probability of a low signal (i.e., bad news). PIN is the probability of

informed trades and is defined as PIN = αμ / (αμ +εb+εs). Diff is the difference in the respective estimate between the after and before the bond rating

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

AA A BBB BB B CCC & below

Before After Diff Before After Diff Before After Diff Before After Diff. Before After Diff Before After Diff

Panel A: Upgrades α 0.36 0.47 0.11* 0.50 0.50 0.00 0.53 0.46 -0.07 0.74 0.66 -0.09*** 0.82 0.76 -0.06** 0.68 0.65 -0.03 μ 37.31 44.77 7.47 53.06 45.83 -7.23 37.10 39.77 2.67 56.24 59.57 3.33 71.78 74.08 2.33 75.12 68.12 -7.00** b ε 52.16 70.24 18.18** 58.48 55.22 -3.26 39.47 44.52 5.05* 67.26 70.85 3.36* 84.85 89.80 4.45* 71.30 75.39 4.08* s ε 59.53 75.66 16.13** 58.81 51.81 -7.00 37.54 46.88 9.35** 59.72 69.73 10.01** 72.05 83.77 11.72*** 67.20 69.73 2.53 δ 0.61 0.66 0.05 0.65 0.71 0.06 0.67 0.67 -0.00 0.72 0.67 -0.05 0.79 0.71 -0.08** 0.70 0.68 -0.02 PIN 0.11 0.13 0.02 0.19 0.18 -0.01 0.22 0.18 -0.04*** 0.27 0.23 -0.04*** 0.277 0.25 -0.03*** 0.30 0.25 -0.05*** Panel B: Downgrades α 0.31 0.59 0.28*** 0.46 0.66 0.19*** 0.49 0.60 0.12** 0.66 0.67 0.01 0.77 0.79 0.01 0.63 0.36 -0.27*** μ 49.10 59.87 10.77 51.97 51.81 -0.16 43.29 37.81 -5.48 53.06 55.28 2.22 81.60 83.31 1.71 54.33 40.61 -13.72* b ε 61.69 73.79 12.11 67.62 66.33 -1.29* 48.08 42.24 -5.84 65.52 63.59 -1.94 99.67 100.98 1.30 41.90 39.93 -1.98 s ε 74.59 83.16 6.61 74.66 62.54 -12.12*** 46.96 42.11 -4.85 59.38 58.19 -1.19 89.77 86.006 -3.78* 28.92 40.76 11.84* δ 0.57 0.66 0.09 0.58 0.74 0.16*** 0.63 0.62 -0.01 0.66 0.64 -0.02 0.69 0.74 0.05 0.75 0.50 -0.25 PIN 0.10 0.18 0.08*** 0.14 0.22 0.07*** 0.19 0.22 0.04*** 0.24 0.26 0.02*** 0.26 0.28 0.01** 0.31 0.20 -0.12

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Table 4

Mean estimates of parameters and PIN by the number of jumps

This table reports the mean estimates of parameters and PIN by the number of jumps for firms whose bond ratings have been upgraded or downgraded during the sample period. α is the probability of an information event, μis the arrival rate of informed trades, εb is the arrival rate of uninformed buy orders, εs is the

arrival rate of uninformed sell orders, and δ is the probability of a low signal (i.e., bad news). PIN is the probability of informed trades and is defined as PIN =

αμ/(αμ +εb+εs). Diff is the difference in the respective estimate between the after and before the bond rating changes.

*, **, and *** indicate significance at the

10%, 5%, and 1% levels, respectively. Panel A: Upgrades

Number of jumps

1 2 3

Before After Diff Before After Diff Before After Diff

α 0.674 0.702 0.027* 0.556 0.428 -0.128*** 0.672 0.410 -0.261*** μ 67.394 70.926 3.532* 31.807 30.672 -1.135 43.999 31.251 -12.748** b ε 78.833 85.960 7.128** 34.329 33.761 -0.568 37.205 36.051 -1.155 s ε 71.905 78.866 6.961*** 35.543 40.102 4.559** 31.868 47.580 15.712*** δ 0.734 0.732 -0.002 0.630 0.595 -0.035 0.686 0.606 -0.079 PIN 0.238 0.236 -0.002 0.221 0.169 -0.052*** 0.295 0.155 -0.140*** Panel B: Downgrades Number of jumps 1 2 3 4 Before After Diff Before After Diff Before After Diff Before After Diff

α 0.583 0.626 0.044*** 0.461 0.786 0.325*** 0.455 0.887 0.432*** 0.428 0.920 0.492*** μ 55.627 54.613 -1.014 49.173 43.615 -5.559 44.601 52.840 8.239** 64.340 101.491 37.151* b ε 66.131 64.532 -1.599 61.039 52.711 -8.328* 64.710 66.052 1.342 75.833 117.623 41.786 s ε 63.426 60.573 -2.853 66.285 51.524 -14.760* 71.828 63.107 -8.722** 71.553 84.753 13.200 δ 0.640 0.663 0.023 0.649 0.745 0.095 0.480 0.5807 0.099 0.789 0.781 -0.008 PIN 0.210 0.230 0.020*** 0.156 0.263 0.107*** 0.140 0.290 0.150*** 0.154 0.330 0.176*

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Table 5

Cross-sectional regressions of changes in PIN on bond rating changes This table presents the estimates for the following regression:

Jump D Jump Rating D Rating D PIN= + + + × + + × Δ α0 α1 β0 β1 γ0 γ1 ,

where ΔPIN is the change in the PIN estimate between the before and after the bond rating changes. D is a dummy variable that has a value of 1 for an upgrade

and is 0 for a downgrade. Rating is the bond rating category before the rating change and is classified into six categories with AA & above = 1, A = 2, BBB = 3,

BB = 4, B = 5, and CCC & Below = 6. Jump is the absolute number of jumps and is the number of rating grades that a bond rating has changed. We classify

bond ratings into 22 grades each with a corresponding integer. More specifically, AAA = 1, AA+ = 2, AA = 3, …, CCC- = 19, CC = 20, C = 21, and D = 22. The t-statistics are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Model Intercept D Rating D×Rating Jump D×Jump Adj. R2

0.035*** -0.065*** 1 (8.15) (-10.33) 0.15 0.113*** -0.106*** -0.023*** 0.014*** 2 (9.25) (-6.13) (-6.76) (2.96) 0.23 0.024* 0.068*** -0.018*** 0.010*** 0.058*** -0.122*** 3 (1.68) (3.57) (-5.91) (2.54) (9.10) (-14.53) 0.44

Figure

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References

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