Insider Trading in Credit Derivatives
Viral V. Acharya and Timothy C. Johnson1
DRAFT
May, 2005
authors are at London Business School, Regent’s Park, London - NW1 4SA, United Kingdom. Tel: +44 (0)20 7262 5050, Fax: +44 (0)20 7724 3317, e-mails: vacharya@london.edu, tjohnson@london.edu. Viral Acharya is also a Research Affiliate of the Centre for Economic Policy Research (CEPR). A part of this work was undertaken while Timothy Johnson was visiting MIT. Pedro Saffi and Jason Sturgess provided research assistance. We are indebted to Sreedhar Bharath, Anand Srinivasan, Amir Sufi, and Ilya Strebulaev for sharing data with us, and to the Grant Approval Committee at Fondation Banque de France, seminar participants at Goldman Sachs Asset Management (GSAM), David Goldreich, Jose Liberti, and Lucie Tepla for useful inputs. We acknowledge financial support in the form of a grant for this project from Fondation Banque de France. This is a preliminary draft. Comments are welcome.
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Abstract Insider trading in the credit derivatives market has become a significant concern for regulators and participants. This paper attempts to quantify the problem. Using news reflected in the stock market as a benchmark for public information, we report evidence of significant incremental information revelation in the CDS market, consistent with the occurrence of insider trading. We show that the degree of this activity increases with the number of banks having lending/monitoring relations with a given firm, and is robust to controls for non-informational trade. Furthermore, information revelation in the CDS market is asymmetric, consisting exclusively of bad news, consistent with hedging activity by banks with loan exposure and private information. We find no evidence, however, that the degree of insider activity adversely affects prices or liquidity in either the equity or credit markets. If anything, the reverse appears to be true.
Keywords: adverse selection, default, bank relationship, credit default swaps, asset pricing. JEL CLASSIFICATIONS: G12, G13, G14, G20, D8
‘ ‘[B]anks must not use private knowledge about corporate clients to trade instruments such as credit default swaps (CDS), says a report drawn up by five bodies including the International Swaps and Derivatives Association and the Loan Market Association... The warning highlights the challenges credit derivatives pose to banks and regulators trying to build a functioning market infrastructure... [M]any banks and institutions are trading CDS instruments in the same companies they finance - sometimes because they want to reduce the risks to their own balance sheets.” (Financial Times, April 25, 2005 - ‘Banks warned on insider trading threat posed by market for credit derivatives’)
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Introduction
Credit derivatives have been perhaps the most important and successful financial innovation of the last decade. The use of credit derivatives has been suggested as an important reason for the observed robustness of banks and financial institutions to the historically high levels of corporate defaults all over the globe during the period 2000 to 2004.1 In addition, markets for credit derivatives have helped banks create synthetic liquidity in their otherwise illiquid loan portfolios.2 Not surprisingly, the growth in the size of this market remains unabated as products are expanding to emerging markets, and indices such as iBoxx and iTraxx are becoming industry benchmarks for credit conditions. If credit derivatives are to provide seamlessly the insurance and liquidity-creation roles for banks, then the liquidity of credit derivatives itself becomes a desirable feature of marFor instance, Alan Greenspan, Chairman of the Federal Reserve of the United States, in his speech “Economic Flexibility” before Her Majesty (HM)’s Treasury Enterprise Conference (London, 26 January 2004), mentions with reference to credit derivatives: “The new instruments of risk dispersion have enabled the largest and most sophisticated banks in their credit-granting role to divest themselves of much credit risk by passing it to institutions with far less leverage. These increasingly complex financial instruments have contributed, especially over the recent stressful period, to the development of a far more flexible, efficient, and hence resilient financial system than existed just a quarter-century ago. One prominent example was the response of financial markets to a burgeoning and then deflating telecommunications sector.” 2 In an important recent example, Citigroup had distributed a large portion of its exposure to Enron through issuance of credit-linked notes at regular intervals (in the two-year period preceding default of Enron). The final effect of Enron’s collapse on the balance sheet of Citigroup was as a result small compared to the size of its loan exposures to Enron.
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kets. Credit derivatives however, like all forms of insurance, are subject to moral hazard (see, Duffee and Zhou, 2001) and asymmetric information risks. In this paper, we are concerned with the latter of these risks. Specifically, if some creditor of Company X has private information about the likelihood of default, or can itself influence default, then this creditor may try to exploit its privileged information by buying credit insurance from a less well-informed counterparty. Or if loan officers, who deal directly with a bank’s borrowers, pass on inside information to the traders buying credit derivatives, the institution on the other side of the trade may get a rotten deal. Indeed, some recent episodes in the credit derivatives markets reveal that this issue may be real with potentially important implications for the efficiency of credit-risk transfer across institutions. In striking recent episodes, managers of Pacific Investment Management Co. (PIMCO), the largest bond investor in the U.S., have cited several cases of insider trading in credits such as Household International Inc., AT&T Wireless, and Sprint Corp.: “Credit default markets are a mechanism with which friendly commercial bankers ... can profit by betraying and destroying their clients through the use of inside information,” and “... firms with large lending departments would always come in and buy protection at exactly the right moment.”3 The underlying hypothesis behind these complaints is that first, the price at which default protection is bought conveys potentially valuable inside information about likelihood of default, and second, these prices may get affected also by temporary buying pressures. This has raised the interesting possibility that the actual default of a corporation and thus the prices of its securities are themselves affected by activities in credit derivatives markets, in particular, by insider or strategic trading. Of course, insider trading problems potentially exist in most markets. But the credit derivatives market may be especially vulnerable since, almost by definition, most of the
Such events have been acknowledged often in press. See, for example, the article in The Economist, “Pass the Parcel – Credit Derivatives,” January 18, 2003. A similar observation has also been made with respect to potentially a predatory form of trading by hedge funds in the credit derivatives market: “It was the hedge funds creating a credit event by forcing the bond price down and trying to get the rating agencies to downgrade the company to benefit themselves: they were scaring everyone into selling. (Henry Snedel, December 5, 2002, in Deals and Deal Makers, Wall Street Journal).
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major players are insiders. A large number of banks and financial institutions act as intermediaries: they quote prices for credit derivatives written on corporations to which they have loan exposures.4 From an academic standpoint, this renders the credit derivatives market a particularly attractive laboratory for testing of hypotheses pertaining to insider-trading risk: first, the potentially informed players are well-identified, namely the banks; second, the nature of private information is unambiguous, specifically the identification of credit risk; and, third, unlike the cash market, daily data on prices of most widely traded credit derivatives is available for the past four years. Given this motivation, we study empirically the market for trading in credit default swaps (CDS), the most active credit derivative instrument, in order to answer the following question: Is there evidence of actual insider trading in credit derivatives markets? We use data on the CDS levels and bid-ask spreads for a cross-section of North American firms over the period January 2001 through October 2004, and on the banking relationships of these firms. Using news reflected in the stock market as a benchmark for public information, we report evidence of significant incremental information revelation in the CDS market, consistent with the occurrence of insider trading. We show that the degree of this activity increases with the number of banks having lending (monitoring) relations with a given firm. Furthermore, this finding is robust to controls for non-informational trade. Crucially, information revelation in the CDS market is asymmetric, consisting exclusively of bad news, consistent with hedging activity by banks with loan exposure and private information. Finally, we show that the information flow from the CDS market to the stock market is robust when studied over sub-samples where we expect a priori that insider trading would be a more significant issue: entities that experience credit deterioration during the sample
The fact that banks make money from their customers by reselling the default swaps to other customers, including other banks, is now well-recognized. For example, Bloomberg Markets Credit Swaps, High Risks Few Rules, reports that on June 6, 2003 J.P.Morgan was offering $209,000 to buy credit default protection on a $10 million loan to Altria Group Inc.’s Philip Morris tobacco unit and was offering to sell that same contract to anyone for $221,000 – a difference of 5 percent - according to Bloomberg data. On the same day, J.P.Morgan was offering to pay $9,000 to sellers of protection on $10 million of newspaper publisher Gannett Co.’s debt while charging $21,000 to anyone who wanted to buy the same protection, a difference of more than 100 percent.
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period, whose CDS levels are generally high, and whose credit ratings are low. If insider trading does exist in these markets, then it is possible that market makers will be less willing to make prices in these derivatives in situations where they perceive the likelihood of private information to be high. In particular, in volatile conditions or when default risks rise, the risk of insider trading may rise, resulting in a loss of liquidity precisely when hedging needs are greatest. To investigate this, we also study whether liquidity providers in the CDS markets fear insider trading, and, in turn, charge greater bid-ask spreads for credits and in times where insider-trading risk is greater? That is, should insider-trading risk in the CDS markets be a significant concern for liquidity and orderly functioning of these markets? Our answer to these questions are not in the affirmative. We find no evidence that the degree of insider activity adversely affects prices or liquidity in either the equity or credit markets. If anything, the reverse appears to be true: CDS markets for corporate entities that have a large number of banking relationships tend to have smaller bid-ask spreads on average. Though we find this result puzzling, we provide a few candidate explanations in the paper. These explanations are based on the potential endogeneity of insider trading to the liquidity of the CDS market, and on the possibility that insiders play a liquidity-provision role in relatively opaque CDS market in order to learn about the order flow (in the spirit of Bloomfield and O’Hara, 1999, 2000). The institutional response to complaints of insider trading in CDS markets has been to issue voluntary guidelines on information sharing inside banks (issued by agencies such as the Joint Market Practices Forum and the Financial Stability Forum in North America), and on how “material non-public information should be handled by credit portfolio managers in European Union member states” (issued by International Swap Dealers’ Association (ISDA), the Bond Market Association, and the Loan Market Association).5 Our results suggest that while the complaints against insider trading in CDS markets may
As cited earlier, some of the recent debate has explicitly referred to insider-trading risk as proxied in our paper: “Use of material non-public information by securities and credit derivative market participants will be embraced by these new laws on insider dealing. Knowledge gleaned from bank lending relationships is the main area of scrutiny, so far as the use of non-public information in these markets is concerned.” (Creditflux, January 2005, www.creditflux.com)
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be well-founded, it is unclear whether there should be any regulatory concern and action to curb insider trading. In particular, we do not find evidence to support the hypothesis that insider trading in CDS markets is harmful per se to the overall liquidity of these markets. Indeed, our results suggest that regulatory restrictions on the activity of commercial banks, the potentially informed players in these markets, may result in a reduction in the overall liquidity of these markets: for example, insiders may withdraw their liquidity-provision role if they do not stand to reap any benefits from their being informed. The remainder of the paper is organized as follows. Section 2 summarizes the related literature. Section 3 describes the data sets we pool together for our analysis. Section 4 presents our tests to detect evidence of insider trading in CDS markets and the results. Section 5 examines the effect of insider-trading risk in the CDS market of a corporate entity on the liquidity of CDS and equity markets for that entity. Section 6 concludes.
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Related Literature
To date, we are not aware of a study that has examined the set of issues we study concerning insider trading in credit markets and its effect on liquidity of these markets. Insider trading has been the focus of a large body of literature on equity markets which has found that insider trading lowers liquidity and increases trading costs (Easley, Kiefer, O’Hara, Paperman (1996), Chun & Charoenwong (1998), Bettis, Coles & Lemmon (2000), Brockman & Chung (2003), and Fishe & Robe (2004), among others), that insider trading raises cost of equity capital (Bhattacharya & Daouk (2002)), and that insider trading increases volatility (Du & Wei (2003)). There has been somewhat of a surge in studying liquidity in credit markets (Fleming & Remolana (1997, 1999), Fleming (2001), Schultz (2001), Hotchkiss & Ronen (2002), and Chordia, Sarkar, Subrahmanyam (2003)), but this has been contained on studying liquidity in the cash (bond) market and not the credit derivatives markets. The few existing studies on credit derivatives (e.g., Blanco, Brennan and Marsh, 2003, and Longstaff, Mithal and
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Neis, 2003) focus primarily on explaining the basis between the CDS and the cash (bond) markets. Crucially, most of the papers studying liquidity in credit markets focus on how quickly the information in equity markets gets incorporated into credit (cash) markets, but not the reverse channel of information flow. Examining the flow of information from the credit (derivatives) markets to the equity markets is key to detecting insider trading in the context of our paper, and also constitutes the most important contribution of our paper. Finally, our study is also unique in its integration of two different strands of literature (and indirectly in its synthesis of the respective datasets): banking – the specialness of banks in possessing private information about their borrowers; and, market microstructure – the strategic use of private information by insider potentially affecting prices and bid-ask spreads.
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Data
Our primary data set for this study comes from daily closing quotes for the most widely traded CDS names from January 1, 2001 through October 20, 2004. The bid-ask quotes are obtained from CreditTrade, an online trading platform for credit derivatives, that attracts a significant portion of trade and quotes in these markets and has participation from all the top players. The CDS levels are calculated as mid prices based on the bid-ask quotes. By and large, the quotes are for standard trades, that is, for 10 million USD notional transaction for a CDS of five year maturity with the underlying reference credit of the firm being of Senior Unsecured category. In a few quotes, the reference credit is of Subordinated category. The CreditTrade data has also been employed by Blanco, Brennan and Marsh (2003) in their study of the basis between CDS and the cash markets. We focus on a total of 79 North American corporate credits, the so called “benchmarks” in CreditTrade terminology. The names of these entities are listed in Appendix A. Matching information for stock market data and balance-sheet data is obtained for these firms from CRSP and Compustat. While limited in a time-series and a cross-sectional sense, this is the best data available on CDS
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prices. Furthermore, the sample period is in fact one of the most interesting ones in the history of corporate defaults since it witnessed defaults of large U.S. corporations such as WorldCom, Enron and Sprint, and significant credit deterioration for others such as Tyco. Our next data set is that on banking relationships for these firms. This data was obtained using the Loan Pricing Corporation (LPC)’s DealScan database on loans made to large publicly traded firms (as all the firms we study are). For each borrower on a given date, we look back as far as 1996 for any outstanding syndicated loan facilities undertaken by this borrower, as well as its affiliated and predecessor companies. Based on the lead arrangers retained for these deals, we identify the number of unique banks with which the borrower has an on-going relationship. Our approach to calculating the number of bank relationships is similar in spirit to the one described in detail in Bharath, Dahiya, Saunders and Srinivasan (2004). As acknowledged by these authors, LPC data does not give detailed bank-by-bank break-up for a loan facility. It is thus not possible to come up with a precise loan-size-weighted measure of the number of banking relationships that would incorporate actual exposure data (without making some ad-hoc assumption such as assigning the entire amount of the loan facility to each lead bank). While calculating the number of bank relationships for a firm, we focus on the top 100 large, commercial and investment banks which together account for most of the market share in syndicated lending in the LPC database over our sample period. These banks are listed in Appendix B. An important reason for focusing on these largest banks is linked to the specific question of insider trading in CDS markets that we wish to explore. The small commercial banks are not perceived to be major players in the CDS markets. Hence, accounting for relationships of a borrower with small banks would overstate the actual number of potential insiders for the borrower. Finally, an additional advantage of focusing on large banks is that most of these banks are also underwriters (who may also have access to private information about the borrowers) whereby the need to compute separate underwriting relationships is obviated. The large amount of merger and acquisition activity in the banking sector over our sample
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period, and to some extent in the corporate sector, requires us to make careful adjustments while employing the LPC data. The data on bank merger and acquisition activity is obtained from the data employed and generously provided to us by Bharath et al. (2004), Sufi (2004), and from the web-site of Federal Reserve Bank of Chicago. The data on corporate merger and acquisition activity is obtained from the SDC Platinum database. Bank relationships are computed at the level of parent banks, and are assumed to merge whenever a merger takes place at the bank level or at the level of the borrowing firms. Similarly, at the level of each firm, the relationships of the parent and the subsidiary entities are also included in our relationship measure. Table 1 provides summary statistics for the credits in our sample. The median CDS level is 81 basis points (bps) with a high of 2400 bps (for Enron close to its bankruptcy). Interestingly, though the median bid-ask spread in CDS markets is 20 bps, the high is 2000 bps representing a trading cost that is comparable in magnitude to the highest observed CDS level. The median firm in our sample has a Moody’s (S&P) credit rating of Baa1 (BBB+), a market capitalization of 15.82 billion USD, book debt of 8.88 billion USD, and book debt to assets ratio of 21%. The median number of firms for which quotes are observed on a day is 46, about 60% of the total number of firms in our sample. In terms of banking relationships, the median number of lead banking relationships is 16 with the median for all banking relationships being 29. There is substantial cross-sectional variation: there are firms with no banking relationships (only public debt) and firms with as many as 50 lead-bank relationships. Most relationships arise from loan facilities, the median number of facilities being 4 and the median amount per firm being about 4 billion USD. The average relationship length across various banking relationships has a median of 4.1 years in the sample. Since most firms in our sample are reasonably large, trading in their equity market is by and large liquid: the median volume is 2.65 million shares per day and the median turnover is 5.6% of the outstanding shares per day. The median of average (annualized) stock return volatility based on daily stock returns is 39% with a high of 83% and a low of
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24%. Interestingly, while there are firms with no banking relationships, there are also firms with no public bond issues outstanding, that is, our sample includes firms that are entirely bank-financed as far as debt financing is concerned. The median number of public bond issues for firms in our sample is 9 with a high of 71.6
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Is there evidence of actual insider trading?
In this section, we discuss our primary tests aimed at understanding the nature of insidertrading risk in the credit default swap market.
4.1
Preliminary evidence
In order to address the question of whether the claims of insider trading in CDS markets are actually valid, we conduct some preliminary tests. Specifically, we examine the crosscorrelation structure of information revelation in CDS and equity markets. Our maintained assumption is that stock market reaction is coincident with public release of information about the firm. Hence, if there is in fact insider trading in CDS markets, then we should at least sometimes observe a flow of information from CDS markets to equity markets. Examining this possibility also offers a nice counterpart to the much-studied response of bond prices to stock moves. The literature here has generally found a slow adjustment of bond prices to stock moves in that bond returns have a negative cross-correlation with up to several days’ lag of stock returns.7 We provide a graphical representation of cross-correlation structure between daily stock returns and CDS changes in Figure 1. Specifically, the figure shows the cross-correlation between percent changes in CDS prices at time t and stock returns at time t + k as a
Our data on corporate bonds comes from the data employed and generously provided to us by Schaefer and Strebulaev (2003). The data includes corporate bonds that are included either in the Merrill Lynch Corporate Master index or the Merrill Lynch Corporate High Yield index. These indices include most rated publicly issued U.S. corporate bonds. 7 This finding of prior literature is however most likely due to poor quality of bond data. A recent study by Hotchkiss and Ronen (2002) which employs daily data on corporate bond prices from NASDAQ finds the bond market to be informationally as efficient as the stock market.
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Figure 1: Cross Correlation of Stock Returns and CDS Changes.
Whole Sample 0.05 0 −0.05 −0.1 −0.15 −0.2 −5 −4 −3 −2 −1 0 1 2 3 4 5
WorldCom, Enron, Sprint, Tyco 0.1 0 −0.1 −0.2 −0.3 −0.4 −5 −4 −3 −2 −1 0 1 lead/lag (days) 2 3 4 5
The figure shows the cross-correlation between percent changes in CDS prices at time t and stock returns at time t + k as a function of k. In each panel the cross-correlations for individual firms are averaged across firms.
function of k = −5, −4, . . . , +5. In the first panel, results are shown for the whole sample, whereas in the second panel, results are shown for the four large cases of corporate distress in our sample: WorldCom, Enron, Sprint, and Tyco. In each panel, the cross-correlations for individual firms are averaged across firms. In each of the two panels, there is a negative cross-correlation between CDS changes and lagged equity returns, reflecting a flow of information from equity market to CDS market: when stock returns are positive, there is a decrease in contemporaneous and subsequent price of default protection. In the panel where the entire sample is employed, the cross-correlation between CDS changes and future equity returns is essentially zero. In striking contrast, in the panel with four firms of our sample that experienced significant deterioration in credit quality, this cross-correlation structure is different: there is on average a negative cross-
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correlation between CDS changes and future equity returns, the correlation being highest in magnitude for future date t + 1. In other words, for firms for which there was adverse credit information during our sample, we do find evidence of an information flow from today’s CDS price changed to future stock returns. Indeed, for these firms the flow of information is both ways between these markets. This preliminary evidence is consistent with the hypothesis that if insiders are active in our sample, then firms that experienced severe credit deterioration should exhibit significant cross-correlation of CDS changes with future stock returns. In other words, insiders appear to be exploiting information in the CDS market only when there is significant negative information. Since studying the “pure” effect of a CDS market innovation at time t on the stockmarket return at time t + k should also control for CDS innovations between t and t + k, we now conduct more rigorous econometric tests of the hypotheses concerning insider trading.
4.2
Econometric analysis
We seek to establish rigorously whether or to what extent credit markets actually acquire information prior to its revelation to the market as a whole. As already discussed, our identifying assumption is that “the market as a whole” means the stock market. Since our sample consists entirely of liquid, large, and well-followed companies, there is every reason to suspect that public information is rapidly incorporated in their equity prices. Hence any predictable flow of information from credit markets to stocks is highly likely to stem from the availability of non-public information to participants in the credit markets. The section proceeds as follows. First we describe our methodology for isolating innovations in the CDS prices. Then we establish the basic findings that, while there is little unconditional spillover of these innovations to the stock market, there are strong conditional effects linked to certain firms at certain times. We examine alternative explanations, particularly those based on the extent of uninformed trade. We then employ a variety of econometric techniques to test the robustness of these conditional effects and further refine 11
our understanding of where they are coming from. 4.2.1 Constructing innovations in CDS market
Since credit and equity markets are highly dependent, the first step in our analysis is to regress changes in our CDS prices on contemporaneous stock returns in order to extract the residual component. We do this by means of separate time-series regressions for each firm, also including five lags of both the CDS changes and the stock returns to absorb any lagged information transmission within the credit market. Two points about this step are worth clarifying. First, the CDS changes are defined as log differences in credit spread, or, implicitly, percentages of percentages. Second, we anticipate that the relationship between these changes and stock returns should be inherently nonlinear. This can be seen in the context of any structural model of credit spreads. To illustrate, Figure 2 plots the elasticity of credit spreads with respect to stock prices under the Merton (1973) model of risky debt. This elasticity represents the theoretical relationship that should, under that model, relate percentage changes in stock prices to percentage changes in credit spreads. The figure shows that there is a roughly linear relationship between this elasticity and the inverse level of the credit spread itself. Guided by this, our specification of expected CDS returns includes interactions of the stock returns (both contemporaneous and lagged) with the inverse CDS level. We could impose the functional form of the Merton (1973) or any other structural model in this stage. But we choose to remain agnostic about the degree of the nonlinearity. As a result, we are, in effect purging the credit market innovations of any level-dependent dynamics. This affects our ability to separately identify such level-dependent effects in the cross-market dynamics studied below. To summarize, for each firm, a regression is run of CDS percentage changes on a constant, five lags of CDS percentage changes, the contemporaneous stock return, the product of that return and the inverse CDS level, and five lags of the latter two terms. We view the residuals from each of these regressions as independent news arriving in the credit markets, which is 12
Figure 2: Merton (1973) model credit-spread elasticity.
0 sigma = 0.125 sigma = 0.25 sigma = 0.375
CDS return elasticity w.r.t. Stock return
−0.5
−1
−1.5 10
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30 35 1 / CDS Level
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The figure plots the elasticity of the credit spread s with respect to equity value E under the Merton (1973) model. Specifically, zero-coupon debt with face value of 100 maturing in 5 years is considered, with risk-free interest rate set to 4%. Three values for firm-value volatility are considered: 12.5%, 25% and 37.5%. The firm value V is varied in order to generate different values of the credit spread s and equity value E. The ds dE credit-spread elasticity is calculated as ( dV /s) divided by ( dV /E). This elasticity is plotted against the reciprocal of the credit spread s for different values of V .
either not relevant or simply not appreciated by the stock markets at the time. It is worth noting that these credit innovations are not small. Although the debt and equity returns are highly correlated, and although our specification errs on the side of imposing too few restrictions, the R2 from our regressions are mostly in the single digits.8 4.2.2 Information flows from credit markets to stock markets
We now present tests of our main hypothesis: that the degree of insider trading in the CDS market is a function of the number of informed participants in that market. To do so, we
This, in itself, is a somewhat surprising finding which perhaps points to the inherent limitations on the explanatory power of structural models.
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run regressions of the form: stock returnt = a0 + [b0 + b1 (number of insiders)](CDS innovation)t−1 + where the CDS innovation is constructed as described above. As outlined in the data section, our primary candidate measure for the number of informed players is the number of banks having outstanding syndicated loan commitments to each firm.9 This number varies substantially across firms and over time. To exploit this, we start by estimating the model in a panel data setting. Table 2 show several versions of the specification. Column (A) shows that, unconditionally, there is a statistically significant spillover of yesterday’s CDS innovation to today’s stock return. In column (B) we see that this unconditional effect is completely absorbed by the inclusion of the number of banking relationships. For a firm with 10 relationships there is essentially no spillover, whereas for one with 50 relationships the spillover is around 4%. The tests in columns (C) and (D) distinguish between the effect of positive and negative CDS innovations. In (C) there is no difference in the unconditional response: both are about the same as that found in (A). However, the conditional responses are quite different. Only positive CDS shocks (i.e., bad news) are now found to have a significant impact on future stock prices. The coefficient b+ is around eight times larger than b− and implies approximately 1 1 6% spillover for a firm with 50 relationships. The last two columns augment the specification with lagged CDS innovations, so that the response term looks like
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ak [b0 + b1 (number of insiders)t−k ](CDS innovation)t−k .
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This specification allows us to check whether the leading relationship we have identified is merely a transient effect, possibly due to some short-term price pressure from the hedging activity of debt-market participants in the stock market. Indeed there is some evidence that
The definition used in the main tests considers only lead-bank relationships and restricts attention to the top 100 banks by market share in overall syndicated lending. Recall that the measure also includes relationships of parent and subsidiary entities.
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the unconditional effect is partially reversed: the coefficients a2 to a5 imply that around a quarter of the effect dissipates within a week (although the coefficients are estimated imprecisely). By contrast, the conditional effect appears to be robust to the inclusion of lags, with day-two and day-three reversals being more than outweighed by additional continuation effects from days four and five.10 Because the lag effects are poorly estimated, we will continue to check below for the impact of weekly effects in exploring alternative hypotheses. We conclude from the results in Table 2 that there is, indeed, prima facie evidence that informed trading exists in the credit derivatives markets. The fact that the information flow occurs primarily for negative news is also consistent with the interpretation of hedging activity by asymmetrically informed banks with positive loan exposures. We now explore alternative specifications designed to further test and refine this interpretation.
4.3
Alternative hypotheses
We have suggested that the evidence uncovered so far is consistent with banks using their monitoring role to uncover relevant credit information about borrowers and then engaging in informed hedging when negative information arises. We now consider some alternative potential explanations and, in the process, establish more clearly how the lead-lag relationship operates. Table 3 shows panel regressions, similar to those in Table 2, which model the CDS innovation component as a function of other controls. That is, (stock return)t = a0 + [b0 + b1 (number of banks) + b (other controls)](CDS innovation)t−1 +
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t.
This specification is estimated by non-linear least squares and the ak terms are measured relative to a1 which is set equal to one for purposes of identification. We calculate p values for all specifications (in square brackets) using a within-period bootstrap which accounts for any cross-sectional autocorrelation. For the non-linear specification, the ak coefficients are not identified under the null of no effect. For these terms the p-values measure the bootstrap probability that an unrestricted lag term exceeds the sample estimates.
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We limit the specification here to one lag of the CDS innovation, but, as before, include five own lags of each stock’s return. A first natural concern is that our measure of bank relationships is simply picking up the quantity of a firm’s borrowing. That is, debt markets may, as a whole, be better informed if there are more participants of every kind. If this is so, the role of bank relationships may have nothing to do with their differential ability to gather information. Specifications (A) - (D) in Table 3 include other measures of the size of firms’ debt market. We include the overall equity value of the firm and the book value of its debt (both in logs). Also we count the number of public bond issues of each firm at each date, and include this count along with an indicator variable if any of these issues is a convertible bond. We also employ the total number of syndicated loan facilities and the face value of these. None of these variables is significant, nor does any affect the size of the bank relationship coefficient. The coefficient on the number of bond issues is economically large, but its sign is positive, meaning that there is less lagged information flow for firms with more non-bank debt. We interpret this as indicating that cross-market arbitrage is probably more active in these names, i.e., that any informational advantage by debt markets is quickly exploited. Specification (E) includes a refinement of our definition of bank relationships. Hitherto, following Sufi (2004), we have viewed lead arrangers as the most likely to acquire nonpublic information about borrowers. But, as stressed by Lee and Mullineaux (2004), each participating bank has full rights and responsibilities for monitoring, and is entitled to share any information acquired by any delegated member.11 When we add the number of nonlead participating banks, we actually find that their influence is apparently larger than that of leads. Both are in the same direction, and the lead effect is not much diminished by the inclusion of non-leads. A firm with 20 lead banks and 20 participants would have an information spillover effect of around 7%. Another hypothesis about our results is that the lead-lag relationship is due to the relative liquidities of debt and equity markets for each firm. Under this view, slower information
Further, there is a regulatory requirement that each participating bank perform independent duediligence for each loan.
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transmission may be an artifact of relatively less liquid stock markets for companies which have many banks. Specifications (F) and (G) include proxies for stock and CDS market activity and liquidity. For the stock market we include share volume and turnover, as well as Amihud’s (2002) measure of market impact. The only direct proxy for credit market liquidity we have is the bid/ask spread in our CDS data, which we measure as a percentage of the mid-market CDS level.12 The inclusion of these quantities again fails to diminish the role of banks in explaining the lead-lag effect, and even increases its statistical significance. Stock volume alone appears to be an additional significant conditioning variable. Curiously, its negative sign implies that bond lags matter more for more highly traded stocks. This may be due to unmodeled heteroscedasticity: volume is known to be positively associated with volatility. Both are driven by the quantity of news released about a given stock. The result here then could simply be telling us that there is more information flow when there is more information.
4.4
4.4.1
Robustness
Sub-samples
In this section, we check the robustness of the information flow from the CDS to equity markets to different sub-samples based on credit conditions and on bank relationships. In addition, the specifications in this section allow the own-lag effects on stock returns to vary across the subsamples, to ensure that any apparent CDS-lag effects are not artifacts of unmodeled dynamics in the share price itself. First, we allow for the unconditional information flow to vary between sub-samples formed on the basis of whether (i) firms experienced significant credit deterioration on some day during our sample period; (ii) firms experienced a general widening of credit spread during our sample period; and, (iii) credit rating of firms is low.
The spreads are close to a linear function of CDS levels as they should be, mechanically, since a matched purchase and sale in the CDS markets is still itself exposed to default risk.
12
17
Specifically, we estimate the panel specification
5
(stock return)t = a0 +
5
[b0,k + bD · (Credit-condition Dummy)t ](CDS innovation)t−k 0,k
k=1
+
[c0,k + cD · (Credit-condition Dummy)t ](stock return)t−k + t . 0,k
k=1
We estimate this specification for three dummies in Table 4. In specification (A), the dummy is one if the firm experienced a one-day decline in credit spread level exceeding 50 basis points between time t and end of the sample period. In specification (B), the dummy is one whenever the firm’s credit spread level remained at a level greater than 100 basis points between time t and end of the sample period. Finally, in specification (C), the dummy is one if the credit rating of the firm at time t was low (A3/A- or worse). Table 4 shows that the evidence is consistent with there being greater information flow from the CDS to equity markets for those firms which experienced, or were more likely to experience, “credit” events in future. In each of the three specifications, there is no unconditional flow from CDS to equity markets (
5 k=1 b0,k
is essentially zero). However, conditional
on the credit-condition dummy, the flow is present. For firms that actually experienced credit deterioration (specifications A and B), the sum of the coefficients on lagged CDS innovations,
5 k=1 (b0,k
+ bD ), is negative, and the flow measure ( 0,k
5 D k=1 b0,k ),
is both negative
and statistically significant. For firms that are more likely to experience credit deterioration (the low-rated firms), the effect is negative but not as statistically significant. Overall, this reinforces the earlier finding that CDS markets reveal more information about adverse credit developments than about improvement in credit conditions. Note that examining the sum of the coefficients on the five lags also confirms that the information flow we have detected is permanent. Next, we test if the conditional effect of bank relationships identified in Tables 2 and 3 is robust across sub-samples of firms with high and low number of relationships.
5
(stock return)t = a0 +
[b0,k + bD · (Relationship Dummy)t ](CDS innovation)t−k 0,k
k=1
18
5
+
[c0,k + cD · (Relationship Dummy)t ](stock return)t−k + t . 0,k
k=1
We estimate three specifications in Table 5, defining the dummy to be one when the number of banks is above median (specification A), and when in addition the one-lagged CDS innovation is positive (specification B) and negative (specification C). While the first specification merely checks whether the effect of CDS innovations on stock markets is negative only for firms with a large number of banks, the next two specifications also check whether the effect of large number of banks is restricted to days with adverse credit news (as in Table 2). These hypotheses are supported in this non-linear conditional tests as well. Estimates in specification (A) clarify that the negative flow from CDS innovations to stock markets arises for firms with number of bank relationships that are above the sample median, but not for the remaining firms. In particular,
5 k=1 (b0,k
+ bD ) is negative, and the flow ( 0,k
5 D k=1 b0,k )
is
negative and statistically significant. This is consistent with the panel estimates of Table 2 where the information flow was found to be close to zero for firms with around 10 banking relationships. Somewhat surprisingly, the flow (
5 k=1 b0,k )
is positive (albeit small) and sta-
tistically significant for these remaining firms. In specification (B), we see that the negative flow for firms with above-median relationships is in fact twice as large on days with positive changes in CDS level. In contrast, specification (C) reveals that there is no significant flow for these firms on days with negative changes in CDS level. 4.4.2 Cross-sectional analysis
A feature of the panel estimates described so far is that they force all firms to have the same dynamic properties, except in so far as captured by the conditioning introduced in the lagged-response terms. In this section, we estimate separate dynamics for each firm and then study the cross-firm variation in response to credit market information. This analysis addresses the possibility that our previous finding of a significant conditional effect from the CDS innovations was actually driven by uncaptured variation in the other terms (the 19
intercept and stock lag coefficients). Here we follow the methodology of Hou and Moskowitz (2005) who study cross-firm variation in lagged response to market news. Specifically, for each firm i we run the timeseries regression (stock return)i,t = af i
5
+
k=1
bf (CDS innovation)i,t−k + t , i,k
continuing to include five lags of stock return on the right hand side. We then define a measure of the information flow from CDS market to the stock market for firm i as
5
θi =
k=1
bf . i,k
For firms for which the information flow is large and permanent, θ should be large and negative; if the information flow is partially reversed within five days, then θ should be less negative; and, θ should be close to zero for firms for which there is not much information flow in the first place. Panel A of Table 6 shows the summary statistics for the estimated θi ’s. The mean is 0.0043 and statistically insignificant. That is, there is not much of an unconditional effect once the full dynamics are allowed to be firm specific. Next we sort our firms into quintiles based on their lagged response and examine the average firm characteristics of each. Panel B of Table 6 reveals that the main evidence of insider activity is confined to the lowest θ quintile. Firms in this set are on average larger, more actively traded, and somewhat more volatile than the sample as a whole. (Note that the table reports medians, and is thus not sensitive to individual outliers.) Neither credit rating nor leverage varies much across quintiles. However credit risk, as measured by CDS level, does rise monotonically as θ falls, echoing the observation above that acquisition of non-public information may respond to the incentive represented by increased risk to bank portfolios. Finally, in line with our primary findings, number of bank relationships also varies monotonically with θ. To check that the cross-firm dynamic characteristics do indeed preserve our previous panel results, we run a single cross-sectional regressions of θi ’s on time-series averages of 20
firm-specific characteristics, most notably bank relationships. Specification (A) in Panel C confirms that banks with more lead-bank relationships have a more negative θi . The estimated coefficient on lead banks implies that a firm with 50 lead-bank relationships has 13% information spillover from CDS innovations to stock returns. Specifications (B) through (E) show that the effects of CDS level and firm rating are insignificant, irrespective of whether lead banks are included in the specification or not. The failure of CDS level in explaining the cross-sectional variation of θi ’s is at odds with the median summaries in Panel B. However, this can be rationalized once the within-quintile variation in CDS levels is examined. For the lowest quintile firms, the standard deviation of mean CDS levels (across firms) is 123 bps, and for the highest quintile, this figure is 169.35. These variability measures are 75% and 250% of the median CDS levels for these quintiles, respectively. In contrast, the within-quintile variation in the number of bank relationships is much smaller. For quintile 1, the variability in number of banks is 10.4 (40% of median banks), and for quintile 5, the corresponding variability is 11.7 (95% of median banks). In particular, the lowest quintile firms, for which thetai ’s are the most negative, are primarily firms with large number of bank relationships. This confirms that the number of insiders, proxied by the number of lead bank relationships, is the singular critical determinant of the cross-sectional variation in the permanent information flow from CDS markets to equity markets, as measured by θ. Put together, these robustness checks provide convincing evidence of information flow from CDS markets to stock markets, in times when the potential of inside information with relationship banks is likely to be high, and for firms where the number of such relationships is large.
5
The impact of information asymmetry
Does insider trading in the credit derivatives markets matter? As discussed in the introduction, there is abundant evidence that participants, regulators, and industry bodies all regard 21
it as a serious threat to the integrity of the market. Having isolated some strong predictors of relative insider activity, we are now in a position to offer evidence on this topic. To do so, we need to first consider what is actually perceived as being under threat. Our interpretation of the industry view is that the situation is analogous to the classic moral hazard problem in any other insurance market. The threat of informed purchase of insurance leads to a lemon’s problem in which insurance premia are set too high and the quantity of insurance written in equilibrium is too low. Since we have no information on the amount of credit risk insured for any of our names and, anyway, cannot hope to gauge the amount of such transfer that is efficient, our approach is to try to detect the effect of information asymmetry on bid and ask prices in the CDS market. If the threat of informed trading drives a wedge between the reservation prices of buyers and sellers, then the effect is the same as in classic microstructure models in finance (where uninformed market makers face potentially informed buyers and sellers). Unlike most microstructure settings, the one-sided-ness of the threat in the CDS market, further implies that we should potentially see an effect in the levels of prices, i.e., insurance may be too expensive. It is worth noting, at this point, that the evidence of informed trading uncovered above is not necessarily evidence of asymmetric information within the credit markets. An alternative interpretation is that the CDS market as a whole is better informed, in certain circumstances, than other investors. Under this view, the threat of asymmetric information, if it exists, might be to the liquidity of the stock market. Accordingly, we test for such effects as well. Table 7 shows regressions of stock and CDS liquidity measures on some standard control variables as well as on bank relationships, our measure of the prevalence of non-public information in the credit market. Results are shown for both panel regressions with time fixed effects, and for Fama-MacBeth (1973) regressions. In the latter case, standard errors are corrected for autocorrelation of up to six months; in the former case, the reported t statistics are clustered at the firm level. Both methodologies lead to the same conclusions. In the first two columns, stock liquidity
22
is shown to be essentially insensitive to the bank relationship variable. More interestingly, the second two columns indicate that these relationships do affect credit market illiquidity – but with a negative sign. Hence, to the extent that more banks implies more informed players, the evidence suggests that this leads to, or is associated with, narrower spreads and greater liquidity provision. In unreported tests, we find this result to be robust for inclusion of the proxies employed in Table 3 for overall activity in a firm’s debt, suggesting that there is a possibly endogenous connection between being informed and choosing to provide liquidity, or between prevalence of insider-trading activity with availability of liquidity. Table 8 investigates whether the risk of informed trading shows up in the cost of credit insurance, i.e. the CDS levels themselves. In principle, the moral hazard effect could raise these prices even if there is no direct effect on liquidity. However the first two columns establish that, controlling for known determinants of credit spreads, bank relationships play no additional role. Finally we check whether our one direct measure of liquidity, the bid/ask spreads of the default swaps, influence prices directly. This test allows for the possibility that, in using bank relationships, we have simply failed to isolate a valid proxy for asymmetric information. However, the regressions in the rightmost columns show no influence of spreads on levels.13 To summarize, returning to the policy question at issue, we find no evidence that the presence of informed insiders adversely affects liquidity provision or raises the price of credit insurance.
5.1
Possible explanations
If insider-trading risk does not have an effect on liquidity in a market, then it is not surprising that it does not affect the level of prices in that market either. Hence, it suffices in our context to try and understand why insider-trading risk in CDS markets, proxied by the number of bank relationships, does not increase illiquidity (as measured by bid-ask spreads) and perhaps
We have replicated these results using numerous additional controls, including non-linear terms, structural estimates of credit risk, accounting predictors of default, and firm transparency ratings.
13
23
is even associated with greater liquidity. We provide a few candidate explanations that are consistent with our findings and discuss their relative merits. The first candidate explanation is based on the observation that even informed players in markets need to know the nature of the order flow (the pattern of uninformed trades) in order to strategically time their information release and reap rewards thereof. CDS markets are relatively opaque. In particular, quotes posted at CreditTrade, the on-line brokerage service we have obtained our data from, are anonymous: until a trade hits a quote, the counterparty information is not revealed to the two involved parties. Bloomfield and O’Hara (1999, 2000) have shown through trading experiments that in opaque markets, the informed players emerge as liquidity-providers and post narrower bid-ask spreads (compared to other agents and more transparent markets). This is consistent with the informed players learning about liquidity for a strategic reason and in the process providing liquidity to the market.14 Banks having relationships with the corporate entities we have examined are also the intermediaries of the CDS markets. The proposed explanation can thus rationalize simultaneously the greater information release for CDS names that have more relationship banks, and their enjoying greater liquidity compared to names with fewer relationship banks. The second candidate explanation relies on the possibility that the informed are perhaps aware of the nature of uninformed trading, but merely time their trades or choose the location of their trades so as to minimize their price-impact and trading costs. If having more relationship banks also induces greater uninformed trading on average (e.g., due to portfolio-rebalancing or regulatory-arbitrage reasons), then there would be greater liquidity for CDS names with more bank relationships, and simultaneously a greater information release. While this simple endogeneity argument is appealing at first blush, it is at odds with our panel results in Table 3 that the bid-ask spread in the CDS market has no effect on the extent of information flow from the CDS to the stock markets. Nevertheless, a complete test would recognize the endogeneity of the bid-ask spread and instrument for it suitably. The final explanation we propose is based on the idea of competition amongst informed
See also Hong and Rady (2002) for strategic timing of trades by the informed when they are uncertain about the exact nature of uninformed trading.
14
24
agents. Holden and Subrahmanyam (1992) show that if a large number of informed agents receive the same piece of information about an asset’s value, then they trade aggressively, revealing instantaneously most of this information into prices. The depth of the market in the limiting case becomes infinite as the informed compete and erode each other’s profits. It is possible that all relationship banks receive the same quantum of credit information about the underlying CDS name, and in an attempt to capitalize on this information, reveal all of this information into the market. This would also generate greater information release and greater liquidity being associated with CDS names that have more bank relationships. Note, however, that in our specific setting, we would expect the competing informed banks to also avail of trading in the stock markets, an outcome of which would be a coincident release of information in the CDS and stock markets. Since our CDS innovations are orthogonal to contemporaneous stock-market innovations, the information flow from CDS innovations to future stock-market changes is necessarily due to information revealed only in the CDS markets.15 The data and tests we have employed so far cannot shed conclusive light on the relative merit of these candidate explanations. The general finding of liquidity rising with information asymmetry runs counter to much of accepted wisdom in market microstructure. So further investigation of this topic is certainly warranted.
6
Conclusion
In this paper, we provided empirical evidence that there is information flow from the credit default swaps markets to equity markets and this flow is permanent and more significant for entities that have a greater number of bank relationships. This information flow is concentrated on days with negative credit news, and for entities that experience or are more likely to experience adverse credit events. These findings are consistent with the existence of insider trading in these markets. However, we do not find evidence that this form of
Examining this last explanation makes it clear that the first two explanations implicitly rely on imperfect competition between informed players in the CDS markets.
15
25
insider-trading risk affects adversely the liquidity provision in the credit derivatives or the equity markets of these entities. Our study is the first in the literature to examine insider trading in credit markets and its effect on liquidity of these markets. Our findings also constitute a first step towards understanding whether there is a case for the current regulatory response to complaints of insider trading in these markets: the response has been to limit the use of material nonpublic information gleaned from bank lending relationships. Our view is that such limits on trading by banks need to be cautiously re-examined since such limits may potentially endanger the liquidity-provision role that banks seem to play in the CDS markets. Future work employing better proxies of insider-trading risk, perhaps using intra-day data on actual transactions in the CDS market, and enlarging the sample to include nonUS and sovereign credits would be valuable in shedding further light on these issues. We are currently in the process of acquiring intra-day data on actual CDS transactions with information on whether the involved counterparties have banking relationships with the underlying corporate entity. This data should help us enrich our analysis by providing more direct tests of the effect of actual insider trades on market prices and liquidity.
26
References
Bettis, Coles and Lemmon, Corporate policies restricting trading by insiders, 2000 Journal of Financial Economics, 57, 2, 191-220. Bhattacharya and Daouk, The World Price of Insider Trading, 2002 Journal of Finance, 57, 1, 75 - 108 Bharath, Sreedhar, Sandeep Dahiya, Anthony Saunders, and Anand Srinivasan, So What Do I Get? The Banks View of Lending Relationships, Working Paper, University of Michigan, 2004. Blanco, Robert, Simon Brennan and Ian W. Marsh, An Empirical Analysis of the Dynamic Relationship between Investment Grade Bonds and Credit Default Swaps, 2003, forthcoming, Journal of Finance. Bloomberg Markets, Credit Swaps High Risks, Few Rules, August 2003. Bloomfield, Robert and Maureen O’Hara, “Market Transparency: Who Wins and Who Loses?,” Review of Financial Studies, 1999, 12(1), pp. 5–35. Bloomfield, Robert and Maureen O’Hara, “Can Transparent Markets Survive?,” Journal of Financial Economics, 2000, 55, pp. 425–59. Brockman and Chung, Investor Protection and Firm Liquidity, 2005, forthcoming, Journal of Finance Chordia, Sarkar, Subrahmanyam, An Empirical Analysis of Stock and Bond Market Liquidity, 2003, Review of Financial Studies, 18, 85 - 129. Chun and Charoenwong, Insider Trading and the Bid-Ask Spread, 1998, Financial Review Du and Wei, Does Insider Trading Raise Market Volatility?, 2004 Economic Journal, 114, 498, 916-942. 27
Duffee, G. and C. Zhou, Credit Derivatives in Banking: Useful Tools for Managing Risk?, 2001 Journal of Monetary Economics, 48, 25–54. Easley, Kiefer, and O’Hara, One Day in the Life of a Very Common Stock, 1997, Review of Financial Studies, 10, 3, 805-835. Easley, Kiefer, O’Hara, and Paperman (1996); Liquidity, Information, and Infrequently Traded Stocks, 1996, Journal of Finance, 51, 4, 1405–1436. Fama, Eugene, and James D. MacBeth (1973), Risk, Return and Equilibrium: Empirical Tests, Journal of Political Economy, 81, 607–636. Fishe and Robe, The impact of illegal insider trading in dealer and specialist markets: evidence from a natural experiment, 2004, Journal of Financial Economics, 71, 3, 461-488 Fleming, Michael J., and Eli M. Remolona, Price Formation and Liquidity in the U.S. Treasury Market: The Response to Public Information, 1999 Journal of Finance, 54, 5, 1901 - 1915. Fleming, Michael J., Measuring Treasury Market Liquidity, 2001, Federal Reserve Bank of New York Staff Paper 133. Holden, Craig and Avanidhar Subrahmanyam, Long-Lived Private Information and Imperfect Competition, Journal of Finance, March 1992. Hong, Harrison and Sven Rady, Strategic Trading and Learning About Liquidity, Journal of Financial Markets, 2002. Hotchkiss and Ronen, The Informational Efficiency of the Corporate Bond Market: An Intraday Analysis, 2002, Review of Financial Studies, 15, 1325 - 1354. Hou, K. and T. Moskowitz, Market Frictions, Price Delay, and the Cross-Section of Expected Returns, 2005, forthcoming, Review of Financial Studies. 28
Lee, Sang Whi, and Donald J. Mullineaux, Monitoring, Financial Distress, and the Structure of Commercial Lending Syndicates, Financial Management, 2004, 33(3). Longstaff, Francis A., Sanjay Mithal and Eric Neis, Corporate Yield Spreads: Default Risk or Liquidity?, 2003, forthcoming, Journal of Finance. Merton, Robert C., 1974, ”On the Pricing of Corporate Debt: The Risk Structure of Interest Rates”, Journal of Finance, Vol. 29, pp. 449-470. Sneder, Henny, A Market Backfires, and Investors Pay Though Greenspan praised idea, Credit-Derivative Trading Mutates; Instead of Stability Instability, December 5, 2002, in Deals and Deal Makers, Wall Street Journal. Schaefer, Stephen and Ilya Strebulaev, Structural Models of Credit Risk are Useful: Evidence from Hedge Ratios on Corporate Bonds, 2003 Working Paper, London Business School. Schultz, Paul, Corporate Bond Trading Costs: A Peek Behind the Curtain, 2001 Journal of Finance, 56, 2, 677 - 698. Sufi, Amir, Information Asymmetry and Financing Arrangements: Evidence from Syndicated Loans, 2004, Working Paper, MIT Department of Economics. The Economist, Whos Carrying the Can? August 14, 2003.
29
Table 1: Summary Statistics Low CDS level (mid price, BP) CDS bid-ask spread (BP) Credit rating Firm size (equity mkt val, $mm) Firm debt (book val, $mm) Firm leverage (debt at book val) Average stock volume (mm shrs/day) Average stock turnover (pct/day) Average stock volatility (ann std dev) Number of Bond Issues Number of Bank Relationships (leads) Number of Bank Relationships (all) Average Relationship Length (yrs.) Number of Active Facilities Amount of Active Facilities ($mm) Observations/day Median High
13 81 2400 1 20 2000 Ba3/BB- Baa1/BBB+ AAA/Aaa2 720 9 0.00 0.14 2.0 0.24 0 0 0 0.3 0 0 9 15820 8874 0.21 2.65 5.6 0.39 9 16 29 4.1 4 4019 46 412900 312684 0.65 76.1 26.8 0.83 71 50 69 7.2 36 66099 62
The table describes the firm characteristics for our sample of credit derivatives. Sample statistics are computed across all observations, except average stock trading statistics which are computed across firms.
30
Table 2: Regressions of stock returns on CDS innovations (A) b0 -0.0080
(2.75) [.003]
(B) 0.0094
(1.64) [.206]
(C)
(D)
(E) -0.0056
[.018]
(F) 0.0055
[.080]
b1
-0.00093
(3.55) [.001]
-.00033
[.028]
b+ 0
-0.0088
(1.98) [.138]
0.0216
(2.63) [.037]
b+ 1
-0.0016
(4.38) [.000]
b− 0
-0.0073
(1.61) [.107]
-0.0033
(0.39) [.650]
b− 1
-0.0002
(0.58) [.600]
a1 a2 a3 a4 a5
1.0
[.018]
1.0
[.024]
-0.076
[.718]
-0.470
[.150]
-0.116
[.884]
-0.076
[.794]
-0.137
[.754]
1.108
[.146]
0.067
[.744]
0.194
[.398]
The table shows the results of regressing daily stock returns on lagged values of CDS innovations. The lag coefficient is modelled as b0 + b1 · (number of bank relationships) in the first two columns. The second two allow the coefficients to differ depending on the sign of the lagged CDS innovation. The final two columns include five lags, with each constrained to have the same values of b0 and b1 . All regressions also include an intercept and five lags of the dependent variable. OLS t-statistics appear in parentheses. Bootstrap p-values using within-period randomization are shown in square brackets.
31
Table 3: Controls for Uninformed Trade (A) b0 banks size debt bonds CB ind loans loan amt nonleads volume turnover ILLIQ CDS b/a
(0.05) (2.93)
(B)
(0.11) (3.05)
(C) -0.0022
(0.05)
(D) -0.0062
(0.14)
(E) -0.0224
(0.51)
(F) 0.0448
(0.92)
(G) 0.0126
(0.27)
-0.0022 -0.0049 -0.0010 -0.0011
-0.0011
(2.11)
-0.0008
(1.73)
-0.0009
(2.55)
-.0009
(3.26)
-.0009
(2.39)
0.0003 -0.0004 -0.0003
(0.08) (0.11) (0.08)
0.0006
(0.18)
0.0013
(0.37)
0.0063
(1.38)
0.0057
(1.48)
0.0009
(0.24)
0.0031
(0.75)
0.0009
(0.23)
0.0014
(0.32)
0.0038
(0.92)
0.0021
(0.56)
0.0128
(1.43)
0.0145
(1.60)
0.0128
(1.42)
0.0134
(1.49)
0.0161
(1.76)
0.0132
(1.46)
-0.0005
(1.39)
0.0001
(0.08)
-0.0030
(0.58)
-0.0018
(3.55) (2.15)
-0.0017
(3.39)
-0.0098 -0.0066
(2.16)
0.0024
(0.40)
-0.0063
(0.03)
0.0071
(0.35)
The table shows the results of regressing daily stock returns on one lag of CDS innovations. The lag coefficient is modelled as b0 + b1 · (number of bank relationships) + b (other controls). The controls are: size (log of market capitalization); debt (log of compustat book value of debt); bonds (number of public dollar denominated bonds of parent company); CB inf (indicator = 1 if any of the bonds counted in the previous variable is convertible and = 0 otherwise); loans (number of active syndicated loan facilities); loan amt (notional value of active syndicated loan facilities); nonleads (number of paericipant banks in active syndicated loans); volume (daily stock market volume in millions); volume (daily stock market turnover); ILLIQ (average absolute value of stock returns divided by average volume ); CDS b/a (percentage bid/ask spread of credit default swap). All regressions also include an intercept and five lags of the dependent 32 variable. OLS t-statistics appear in parentheses.
Table 4: Robustness to credit-condition sub-samples. (A) a0
5 k=1 b0,k
(B) 0.0003 (2.21) 0.0050 (0.66) −0.0465 (2.52) −0.0399 (3.00) 0.1338 (4.68)
(C) 0.0003 (2.04) 0.0111 (0.91) −0.0224 (1.51) 0.0183 (0.97) −0.0488 (2.02)
0.0003 (2.19) 0.0033 (0.44) −0.0492 (2.36) −0.0413 (3.22) 0.1917 (5.90)
5 D k=1 b0,k
5 k=1 c0,k
5 D k=1 c0,k
The table shows OLS estimates and t-statistics for the coefficients of a regression of daily stock returns on a constant, lagged CDS innovations, and lagged stock returns, as follows:
5
(stock return)t
= +
a0 +
5
[b0,k + bD · (Credit-condition Dummy)t ](CDS innovation)t−k 0,k
k=1
[c0,k + cD · (Credit-condition Dummy)t ](stock return)t−k + t . 0,k
k=1
That is, the regression also includes interaction terms of the lagged CDS innovations and stock returns with an indicator equal to one for firm i at date t if (i) the firm experiences a credit deterioration of more than 50 basis points between date t and the end of the sample (specification A); (ii) the firm’s credit spread level remained at a level greater than 100 basis points between time t and end of the sample period (specification B); and, (iii) the credit rating of the firm at time t was low, that is, A3/A- or worse (specification C).
33
Table 5: Robustness to relationship sub-samples. (A) a0
5 k=1 b0,k
(B) 0.0005 (3.35) 0.0146 (1.79) −0.0714 (4.17) −0.0211 (1.56) 0.0343 (1.23)
(C) 0.0003 (1.81) −0.0035 (0.43) −0.0006 (0.03) −0.0008 (0.06) −0.0584 (1.91)
0.0003 (2.10) 0.0192 (2.01) −0.0475 (3.41) −0.0583 (3.46) 0.0909 (3.86)
5 D k=1 b0,k
5 k=1 c0,k
5 D k=1 c0,k
The table shows OLS estimates and t-statistics for the coefficients of a regression of daily stock returns on a constant, lagged CDS innovations, and lagged stock returns, as follows:
5
(stock return)t
= +
a0 +
5
[b0,k + bD · (Relationship Dummy)t ](CDS innovation)t−k 0,k
k=1
[c0,k + cD · (Relationship Dummy)t ](stock return)t−k + t . 0,k
k=1
That is, the regression also includes interaction terms of the lagged CDS innovations and stock returns with an indicator equal to one for firm i at date t if (i) the firm has above-median number of bank relationships at time t (specification A); (ii) in addition to (i), the CDS innovation at date t − 1 is positive (specification B); and, (iii) in addition to (i), the CDS innovation at date t − 1 is negative (specification C).
34
Table 6 Panel A: Properties of θ Mean = t-stat = Min = Max = 0.0043 0.4600 −0.1961 0.3262
Panel A shows univariate properties of θ, the firm-specific measure of permanent information flow from CDS innovations to stock markets. In the first stage, we run for each firm i the time-series regression
5
(stock return)i,t = af + i
k=1
bf (CDS innovation)i,t−k + t , i,k
continuing to include five lags of stock return on the right hand side. Then, θi is the measure of permanent 5 information flow from CDS market to the stock market for firm i, defined as θi = k=1 bf . i,k
Panel B: Properties (medians) of firms in different θ-quintiles Q1 Average θ CDS level (mid price, BP) CDS bid-ask spread (BP) Credit rating Firm size (equity mkt val, $mm) Firm debt (book val, $mm) Firm leverage (debt at book val) Average stock volume (mm shrs/day) Average stock turnover (pct/day) Average stock volatility (ann std dev) Number of Bond Issues Number of Bank Relationships (leads) Amount of Active Facilities ($mm) −11% 185 26 27 28021 12785 0.28 8.07 5.4 0.40 12.6 26.1 3085 Q2 −2% 108 21 27 Q3 1% 101 20 26 Q4 4% 79 18 27 12677 6864 0.33 1.71 5.0 0.33 12.0 10.6 1358 Q5 8% 68 17 26 13862 6380 0.27 2.49 5.6 0.33 5.8 12.1 1050
12477 9663 7178 4136 0.32 0.33 2.10 6.4 0.37 7.1 18.1 1429 1.37 4.8 0.33 5.2 14.5 978
For Panel B, firms are ranked into quintiles based on the first-stage estimates of θ, Q1 being the quintile with the smallest (most negative) estimates, and Q5 being the quintile with the largest estimates. The summary statistics reported for each quintile are the medians (across firms) of the time-series means of the characteristics for each firm.
35
Panel C: Second-stage cross-sectional determinants of θ (A) constant 0.0511 (2.95) −0.0026 (3.17) −4.8555 (0.56) −0.0021 (0.46) (B) 0.0110 (0.72) (C) 0.0608 (0.49) (D) 0.0513 (2.65) −0.0026 (3.12) −0.1008 (0.01) 0.0020 (0.456) (E) -0.0017 (0.01) −0.0027 (3.17)
banks
CDS (∗10−5 ) rating
Panel C shows the OLS estimates and t statistics from second-stage regressions in which the first-stage estimates of θ for different firms are regressed on firm-specific characteristics. The characteristics employed for a given firm are the time-series averages for that firm.
36
Table 7: Illiquidity Regressions Stock Illiq FM size volume r1mo σ1mo banks obs R2 -0.0016
(3.38)
CDS % b/a FM 0.0480
(8.59)
Panel -0.0021
(2.93)
Panel 0.0499
(3.55)
-0.0086
(5.88)
-0.0083
(2.88)
-0.0414
(10.59)
-0.0429
(6.02)
0.0028
(1.95)
0.0002
(0.17)
-0.0144
(0.48)
-0.0364
(1.42)
0.0130
(5.01)
0.0071
(2.28)
0.0561
(1.71)
0.0780
(2.42)
-0.00004
(1.50)
-0.00002
(0.37)
-0.0029
(8.18)
-0.0026
(2.75)
667 0.5266
39109 0.3822
947 0.2646
44932 0.1365
Stock and credit market illiquidity measures are the dependent variables in daily regressions using both Fama-MacBeth (1973) regressions and panels. Stock Illiq is the lagged monthly average of absolute returns divided by volume (c.f. Amihud (2002)). CDS % b/a is the bid-ask spread as a percentage of the midmarket quote for our sample of credit default swaps. The controls are log market capitalization, stock volume, one month stock return, and one month stock standard deviation. Bank relationships are as described in the text. For the Fama-MacBeth regressions, obs is the number of cross-sections, R2 is the arithmetic average of the R2 s from the individual regressions, and the t statistics have been corrected for six months autocorrelation. For the panels, the specification includes time fixed-effects, and the reported t statistics are adjusted for clustering at the firm level.
37
Table 8: Credit Spread Regressions (A) FM r6mo σ6mo debt leverage tangible rating banks bid/ask obs R2 891 0.5867 39988 0.5167 -88.76
(3.60)
(B) Panel FM -84.75
(3.49)
Panel -102.8
(5.50)
-101.4
(5.57)
390.7
(9.32)
542.2
(7.15)
370.6
(7.70)
524.4
(6.98)
16.75
(2.50)
11.31
(1.07)
14.13
(1.64)
14.04
(1.73)
93.78
(4.77)
138.4
(2.18)
103.3
(3.88)
132.4
(2.21)
64.89
(2.08)
47.29
(1.24)
68.63
(2.41)
45.04
(1.27)
-21.42
(6.17)
-17.14
(3.72)
-20.65
(5.38)
-17.75
(4.20)
-0.040
(0.11)
0.407
(0.40)
-27.50
(0.71)
2.67
(0.05)
891 0.6079
39988 0.5161
The table shows regressions of credit default swap levels on proxies for asymmetric information. The controls are lagged 6-month equity return and standard deviation, log book value of debt, leverage using market value of equity, tangible asset ratio, and credit rating. Bank relationships are as described in the text. The CDS bid/ask spread is expressed as a percentage of the CDS level. For the Fama-MacBeth regressions, obs is the number of cross-sections, R2 is the arithmetic average of the R2 s from the individual regressions, and the t statistics have been corrected for six months autocorrelation. For the panels, the specification includes time fixed-effects, and the reported t statistics are adjusted for clustering at the firm level.
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Appendix A: Corporate entities with CDS and Stock market data in our sample from Jan 2001 till Oct 2004
ALBERTSONS INC AMR CORP AMERICAN INTERNATIONAL GROUP AOL TIME WARNER INC AT&T CORP AT&T WIRELESS SERVICES INC BELLSOUTH CORPORATION BOEING CO BURLINGTON NORTHERN SANTA FE CORP CAMPBELL SOUP CO CARNIVAL CORP CATERPILLAR INC CENDANT CORP CENTEX CORP CITIZENS COMMUNICATIONS CO. COCA-COLA ENTERPRISES INC COMCAST CABLE COMMUNICATIONS INC COMPAQ COMPUTER CORP COOPER TIRE & RUBBER COX COMMUNICATIONS INC CSX CORP CVS CORP DANA CORP DEERE AND CO DELL INC DELPHI CORP DELTA AIRLINES INC DOW CHEMICAL CO EASTMAN KODAK CO ELECTRONIC DATA SYSTEMS CORP ENRON CORP FEDERATED DEPARTMENT STORES INC FEDERAL EXPRESS CORP FORD MOTOR CREDIT CO GENERAL ELECTRIC CAPITAL CORP GENERAL MOTORS ACCEPTANCE CORP GEORGIA-PACIFIC CORP GOODYEAR TIRE AND RUBBER CO HARRAHS OPERATING CO INC HEWLETT-PACKARD CO
HILTON HOTELS CORP INTERNATIONAL BUSINESS MACHINES CORP INTERNATIONAL PAPER CO INTERPUBLIC GROUP COS. INC LIBERTY MEDIA CORP LOCKHEED MARTIN CORP LUCENT TECHNOLOGIES INC MARRIOTT INTERNATIONAL INC MAY DEPARTMENT STORES CO MAYTAG CORP MGM MIRAGE INC MOTOROLA INC NEIMAN MARCUS GROUP INC NEWS AMERICA INC NORDSTROM INC NORFOLK SOUTHERN CORP NORTHROP GRUMMAN CORP OMNICOM GROUP PARK PLACE ENTERTAINMENT CORP PHILIP MORRIS COS INC QWEST CAPITAL FUNDING INC RAYTHEON CO RJ REYNOLDS TOBACCO HOLDINGS INC SAFEWAY INC SBC COMMUNICATIONS INC SEARS ROEBUCK ACCEPTANCE SOUTHWEST AIRLINES CO SPRINT CORP SUN MICROSYSTEMS INC TARGET CORP TOYS R US INC TRW INC TYCO INTERNATIONAL LTD VERIZON GLOBAL FUNDING CORP VIACOM INC VISTEON CORP WAL-MART STORES INC WALT DISNEY CO WORLDCOM INC
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Appendix B: Syndicated Loan Originating/Participating Banks
ABN AMRO Bank Allfirst Bank ANZ Banking Group Banca Commerciale Italiana Banca di Roma Banca Nazionale del Lavoro Banca Popolare di Milano Banco Bilbao Vizcaya Argentaria Bank Brussels Lambert Bank of America Bank of Boston Bank of Hawaii Bank of Montreal Bank of New York Bank of Nova Scotia Bank of Tokyo-Mitsubishi BANK ONE Corp. Bankers Trust Co. Barclays Bank Bayerische Hypo-und Vereinsbank Bayerische Landesbank BNP Paribas CIBC CIC Banques Citicorp Comerica Bank Commerzbank CoreStates Bank Credit Agricole Credit Lyonnais Credit Suisse Crestar Bank Dai-Ichi Kangyo Bank Danske Bank
Deutsche Bank DG Bank Dresdner Bank Fifth Third Bank First Chicago Corp. First Hawaiian Bank First Tennessee Bank First Union Corp. Firstar Bank Fleet Bank Fortis Bank Fuji Bank Goldman Sachs & Co Hibernia National Bank HSBC HypoVereinsbank Industrial Bank of Japan ING Bank IntesaBci J.P. Morgan JP Morgan-Chase KBC Bank KeyCorp Kredietbank International Lehman Brothers Lloyds Bank Long Term Credit Bank Mellon Bank Merrill Lynch & Co Mitsubishi Trust & Banking Mizuho Bank Morgan Stanley National Australia Bank
National City Corp. NationsBank NatWest Bank Norddeutsche Landesbank Northern Trust Corp. PNC Bank Rabobank Regions Bank Royal Bank of Canada Royal Bank of Scotland Sakura Bank Salomon Smith Barney San Paolo IMI Santander Central Hispano Sanwa Bank Societe Generale Standard Chartered Bank State Street Bank & Trust Sumitomo Bank Suntrust Swiss Bank Corp. Tokai Bank Toronto Dominion Bank U.S. Bancorp UFJ Bank Union Bancorp Union Bank of Switzerland Wachovia Bank Wells Fargo Bank Westdeutsche Landesbank WestLB Westpac Banking Corp. William Street
40