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Adverse Selection Dynamics in Privately-Produced Safe Debt Markets

Privately-produced safe debt is designed so that there is no adverse selection in trade. This is because no agent finds it profitable to produce private information about the debt’s backing and all agents know this (i.e., it is information-insensitive). But in some macro states, it becomes profitable for some agents to produce private information, and then the debt faces adverse selection when traded (i.e., it becomes information-sensitive). We empirically study these adverse selection dynamics in a very important asset class, collateralized loan obligations, a large symbiotic appendage of the regulated banking system, which finances loans to below investmentgrade firms.


Introduction
There is a primitive demand for safe debt, debt that can store value through time safely and can be traded without fear of adverse selection. The government cannot completely meet this demand (see, e.g., Krishnamurthy and Vissing-Jorgensen (2012), Caballero, Farhi and Gourinchas (2017), Gorton, Llewellen and Metrick (2012), and Gorton (2017)). The private sector cannot produce riskless debt, but it can produce a close substitute, debt such that no agent finds it profitable to produce private information about its fundamentals, and all agents know this. This debt is senior, complicated, and opaque, making the cost of understanding it high. It is information-insensitive. It provides protection against adverse selection; it is rated AAA/Aaa (S&P/Moody's) and it trades at par (minus a small bid-ask spread). But bad public news about the fundamental value of the debt's backing can prompt sophisticated investors to acquire private information creating adverse selection; see Dang, Gorton and Holmström (2019). We study the dynamics of this switch from information-insensitive to information-sensitive in the collateralized loan market and document the resulting adverse selection.
The risk of a switch from information-insensitive to sensitive, and the resulting adverse selection, is a unique risk. Because the security is designed to be information-insensitive, no investor has an incentive to produce private information about the security's fundamentals. Ex ante investors do not investigate the fundamentals of the bond and accept the debt at par. Investors are uninformed by design. In particular, the risk of a switch to adverse selection is neither investigated nor priced ex ante. Ex post in the case of bad times, they cannot price the security because they have not invested in the technology to do so. See Hanson and Sunderam (2013), and Monnet and Quintin (2017). So, privately-produced safe debt is a doubleedged sword: It serves its purpose of avoiding adverse selection until it doesn't.
A collateralized loan obligation (CLO) is a legal entity that buys loans from banks and finances them by issuing debt in the capital market.The CLO manager can subsequently buy and sell assets. The CLO liabilities, called "tranches", have ratings that range from AAA/Aaa to B. CLOs play a very significant role in financing below investment-grade firms, whose loans are called "leveraged loans". According to Fed Chair Jerome Powell Powell (2019): "Collateralized loan obligations are now the largest [nonbank] lenders, with about 62 percent of outstanding leveraged loans." The leveraged loan market is about $1.1 trillion, and is used by about 70 percent of U.S. companies, including companies like Burger King, United Airlines, Avis Rent a Car, and Equinox Fitness.
Why do banks sell their loans? The answer is that it is profitable to do so. And it reduces the cost of credit to the borrowing firms. Nadauld and Weisbach (2012) found that bank loans that are eligible to be securitized, i.e., sold to a CLO, cost borrowers 17 basis points less than otherwise (100 basis points equals one percent). The bank can make up this difference because the AAA/Aaa-rated debt sold by the CLO has a convenience yield, that is investors value it for safety in addition to the interest rate it pays, so its pecuniary coupon rate is lower than it otherwise would be. "Safety" means that it is highly likely to pay off at par at maturity and, if it is sold early, it will sell for (almost) par. It is information-insensitive.
In our sample about 65 percent of a typical CLO is rated AAA. This means that to recover the 17 basis points, the convenience yield must be at least 26 basis points. By comparison, Krishnamurthy and Vissing-Jorgensen (2012) find that the yield on U.S. Treasuries over 1926-2008 was, on average, 73 basis points lower than it otherwise would have been, due to the "moneyness" and safety of U.S. Treasury securities. Using higher frequency data, van Binsbergen, Diamond and Grotteria (2019) estimate the convenience yield on Treasuries to be about 40 basis points.
Consistent with the AAA tranches having a convenience yield, these tranches are held by insurance companies, pension funds, U.S. and foreign banks, and U.S. investment banks. The mezzanine tranches in the middle of the CLO capital structure are held by hedge funds and asset managers, among others. The equity part of the capital structure is held by private equity firms, the CLO managers, and CLO sponsors. See Liu and Schmidt-Eisenlohr (2019) and DeMarco, Liu and Schmidt-Eisenlohr (2020).
To value a CLO tranche the companies whose leveraged loans are in the underlying portfolio have to be studied and the correlations between all the loans in the CLO portfolio have to be determined. This requires credit analysts and a model to simulate outcomes. Also, as we explain below, CLOs have complicated and opaque internal structures. All these attributes make it very expensive for agents to produce private information about the value of the AAA tranche, allowing buyers of this debt to avoid adverse selection because it is very expensive to produce private information. But, in bad times this is exactly the problem! When the COVID-19 pandemic took hold in March 2020, risks to the pools of loans underpinning CLOs rose as the pandemic posed an immediate and prolonged threat to corporate profitability. Facing the prospect of widespread downgrades and possible defaults, we show below that the AAA tranches became information-sensitive. Prior to the pandemic, AAA tranches traded at (almost) par. But in the pandemic the AAA prices fan out below par, despite unprecedented actions by the Federal Reserve to improve financial intermediation. 1 The increase in AAA price dispersion, reflecting the differential pricing 1 In general, all of the Federal Reserve's actions potentially had an indirect effect on CLO markets by improving the conditions for financial intermediation. Indeed, we will show that the Federal Reserve improved market liquidity for CLOs. Nevertheless, secondary market pricing did not return to pre-pandemic levels, suggesting that trading was based on private information about the underlying pool of collateral. based on information, is shown by the boxplots in Figure 1. The boxplots in the figure show the distribution of prices for AAA-rated CLO tranches. The central boxes show the interquartile range (IQR) of prices bisected by the median as a horizontal line. The whiskers show the distribution of prices outside the IQR, up to ±1.5×IQR. For each CLO tranche, we calculate a daily weighted-average price, where the weights are transaction volumes. We then average the daily prices to obtain a weekly average weighted by CLO tranche par amount. The dashed line is the imputed round trip cost for agency trades in AAA-rated CLO tranches. Source: Authors' calculations from data provided by TRACE, Bloomberg LP., Moody's Analytics, S&P, and Fitch.
The central boxes show the interquartile range of prices bisected by the median as a horizontal line. The whiskers show the distribution of prices beyond the interquartile range, excluding outliers. The figure summarizes our argument. Prior to the pandemic there was no price dispersion. AAA tranches traded at par of $100, consistent with information-insensitivity.
When the pandemic hits, the price distribution widens out below par as shown by the box plots. Importantly, if it were an economy-wide shock that introduced a risk premium for AAA-rated CLOs, then their prices would drop uniformly and there would not be price dispersion across CLOs. The lower dotted line is similar to a bid-ask spread, a measure of averse selection (discussed in detail later). This measure increases considerably when the pandemic hits, due to adverse selection. In the analysis presented below, we will provide formal tests of the information shown in Figure 1.
To show that when the pandemic struck, some investors started producing information, we show that there is a structural break in the standard deviation of AAA-rated CLO prices with the onset of the pandemic, confirming what is shown in Figure 1 and suggesting that the prices contain tranche-specific information. We also provide the results of a difference-in-differences quantile regression of secondary market prices of AAA-rated CLO tranches on an index of the stock price volatility of industries (e.g., Oil & Gas, Leisure, Retail) that were identified by Sallerson (2020) as being especially vulnerable to the pandemic. The regression shows that the uncertainty about the vulnerable industries is driving the dispersion in AAA-rated CLO tranche prices.
Where do investors get information on the CLOs' portfolios? We look at AAA-rated CLO tranche price changes just after the release of monthly trustee reports on the CLOs (which occur at different dates during the month) that contain relevant information for investors. Prior to the pandemic, price changes were consistently near zero around trustee report dates, but not so once the pandemic hits.
Leveraged loans do not trade frequently. And they do not trade in a central location. They trade over-the-counter. Consequently, CLOs buy third-party prices to mark their loans to "market". For example, CLOs are required to mark-to-market riskier loans rated CCC and below that they hold in excess of some predetermined contractual limit. We examine the loan prices supplied by two third-party pricing agencies. A measure of the increase in ignorance of the uninformed is the difference in these prices, for the same loan on the same date, from the two pricing agencies. The difference in the distributions of these two loan prices is significant. In other words, disagreement goes up with the onset of the pandemic.
We then turn to measuring the resulting adverse selection; see Glosten and Milgrom (1985) and Dang et al. (2019). We show that a measure of the bid-ask spread, the imputed roundtrip cost (IRC) of transactions, explained later, increased dramatically, indicating a deterioration in market liquidity.
In Figure 1, the dashed line is the IRC in basis points for the AAA-rated CLO tranches from January 1, 2020. The deterioration in secondary market trading conditions began on Monday, March 9. From about 10bps at the beginning of the year, the IRC peaked at over 100bps on March 19. This sudden loss of market liquidity coincided with falling prices. By March 18, the average mid price for transacting the most-senior CLO tranches had already fallen below 95 cents.
The literature closest to our work includes Brancati and Macchiavelli (2019), Gallagher, Schmidt, Timmermann and Wermers (2020), Perignon, Thesmar and Vuillemey (2018), and Benmelech and Bergman (2018). Brancati and Macchiavelli (2019) examine the Panic of 2007-2008 and "... provide direct evidence that while in good times bank debt is largely informationally insensitive, it becomes significantly sensitive to information in bad times" (p. 99). Gallagher et al. (2020) study investor information production, money market funds (MMFs) redemptions, and MMF managers' rebalancing decisions during the Eurozone crisis. They find that there was significant selective information acquisition about fund holdings and, although there were redemptions at all funds, the reductions were largest for funds with the most sophisticated investors, who did produce information. "Under these circumstances, MMF shares become information-sensitive because MMFs' risk exposures are suddenly differentiated following the acquisition of information" (p. 1). Perignon et al. (2018) study the wholesale CD market in Europe during 2008-2014.
Lenders to banks moved their money from bad banks (those whose future performance decreases) to good banks, apparently on the basis of private information that they produced. The uninformed faced adverse selection in that they did not move their money. Benmelech and Bergman (2018) study U.S. corporate bonds. They show that when there is a drop in a bond's price, the bond becomes more illiquid, suggesting adverse selection. Other, more distantly, related literature is surveyed in Dang et al. (2019). We differ from this previous research by focusing on privately-produced safe debt. We explicitly show that when AAA-rated debt becomes informationsensitive, it coincides with the arrival of adverse selection, consistent with Dang, Gorton and Holmström (2018).
The paper proceeds as follows. In Section 2 we briefly describe the data, provide some background on CLOs, and some background on the loans in CLO portfolios. Section 3 analyzes the switch from informationinsensitive to information-sensitive. When the pandemic hit some agents became informed while others did not. Section 4 studies these two groups: the informed and the uninformed. That this switch coincided with the arrival of adverse selection is studied in Section 5. Section 7 concludes.

Background and Data
In this section we first discuss the dataset that we constructed to assess the information sensitivity of CLOs during the pandemic. We combined several large datasets, including transactions on individual CLO debt tranches, tranche-level credit ratings, loan-level information about the underlying pools of collateral, and loan transactions by CLO managers.
The combination of these data create a window to study how information about CLO tranches can affect the trading decisions of CLO investors.
Then we provide some detail about how CLOs work, and examine the underlying loans in CLO portfolios. Finally, we summarize the loans held by CLOs.

Data
We identify CLO tranches using Bloomberg's Backoffice data on asset- as part of an effort to increase transparency in this over-the-counter market after the 2007-09 financial crisis. The limitation on data availability means that, unfortunately, we cannot compare trading during the pandemic to the financial crisis 2007-09. We follow standard procedures for cleaning these data. 3 We use confidential regulatory data with dealer bank identifiers, which allows us to match trades by buyer, seller, amount, and trade time while removing duplicates. The regulatory version of TRACE allows us to identify dealers and observe the trading behavior of every dealer.
We construct daily aggregates for each CLO tranche using the transaction-level data. The daily trading price is defined as the weighted average price on transactions, where the weights are the transaction volumes. This measure is designed to approximate the mid price for each CLO tranche as the vast majority of trades are matched in advance. We also calculate the total volume transacted.
The data we use to study the loans held by CLOs are from Moody's Analytics, which are based on trustee reports issued by each CLO each month and vastly augmented by Moody's proprietary leveraged loan analytics. These data contain details of the entire portfolio of assets held, including type of asset, credit rating, maturity, par value, market value, creditor information, as well as multiple individual loan prices provided by Markit, Reuters, and the CLO trustees. Moody's also provides information about the performance of the CLO, such as details of the internal triggers for cash flows, discussed below. the unit of observation is CLO tranche i on day t, conditional on at least one transaction occurring. Our sample data cover the period from January 1, 2020 onwards. We separate the period into two subsamples: The dates prior to March 1, 2020 are in the "pre-pandemic period" and the dates thereafter are in the "pandemic period." The tests of equality of the means and standard deviations between the two subsamples are reported in the columns labeled "p-value". During the pandemic there was a drop in the average transaction prices, together with an increase in the dispersion of transaction prices, while there was no change in aggregate trading volumes. These statistics suggest that investors' trading behaviour changed significantly as investors sought information about the underlying risks across CLO tranches. We investigate the role of information production below. Dang et al. (2018) have no prediction about the trading volume once there is a switch to information-sensitive. While the market might collapse, as in Akerlof (1970), uninformed agents may choose to trade anyway, accepting the adverse selection. And, in reality, fearing that the AAArated tranches may be downgraded may motivate some institutions to sell.

CLO Background
CLOs are securitization vehicles created typically by large asset managers to finance pools of leveraged loans extended to below investment grade firms. These loans are predominantly originated by banks as part of a syndication (Bruche, Malherbe and Meisenzahl, 2020).  funds, hedge funds, asset managers, banks, or insurance companies. These firms have large teams of credit analysts and loan traders, which contribute to the CLO managing and trading its portfolios.
CLOs are an important way for banks to offload large amounts of risky loans that would otherwise reside on their balance sheets. Figure 2 compares the amounts of leveraged loans and CLOs outstanding. As of 2019, according to the Loan Syndications and Trading Association (LSTA), there were more than $1.1 trillion of U.S. leveraged loans outstanding. U.S.
CLOs, which are the largest nonbank investors in U.S. leveraged loans, amounted to over $500 billion. 4

CLO Loan Portfolios
We use Moody's data largely drawn from CLO trustee reports to examine the typical CLO loan portfolio characteristics. We then use the same data to describe how prices of leveraged loans evolved during the pandemic. We show that variation in the distributions of loan prices depend on whether the firms belonged to industries that were more vulnerable to the pandemic shock. 370 loans and a total market value of roughly $420 million. Bonds account for less than 3 percent of the principal value of the typical portfolio. The average loan in the portfolio is worth $1-1.5 million and has a residual maturity of 4.5-5 years. The last line of describes how we map these industries to other data. We use these sectors throughout our analysis to tease out a differential impact of the pandemic. There was no change in the average value of the loan transactions per CLO.
As a robustness check on the summary statistics reported in the table, we excluded loan transactions that took place during a CLO's ramp-up period and report the results in Table 8 in appendix 9.
The trustee reports also reveal greater deterioration in the market value of leveraged loans in the seven vulnerable sectors. CLO managers provide monthly updates through the trustee reports on the market value of the leveraged loans in their portfolios. 5 We use these data to construct distributions of loan market values over time. For each month, we plot the distributions separately for loans to firms in the vulnerable and not vulnerable industries. Figure 3 shows that the values of loans to firms 5 We compared the loan market values in the trustee reports to the bid prices provided by Reuters and Markit. These pricing services obtain their data using a combination of polling traders and modelling. We found that the vast majority of CLO managers report market values identical to Markit bid prices. in the vulnerable industries (shaded boxplots) fell further and were more variable than the values of loans to firms in other industries.

The Switch to Information-Sensitive Debt
In this section we study the switch from information-insensitive debt to information-sensitive debt. First, we show that the onset of the pandemic coincided with a regime switch in the standard deviation of AAA tranche prices. Then we adopt a quantile regression approach to show that uncertainty about the vulnerable industries is responsible for differentiation in transaction prices across AAA-rated CLO tranches. The lowest transaction prices for AAA CLOs became correlated with an index of the volatility of the vulnerable industries' stock prices, while the highest transaction prices remained relatively uncorrelated with the same index.

Quantile Regression
We delved deeper into the relationship between the dispersion of AAA CLO tranche prices and uncertainty about vulnerable industries using quantile regression with CUSIP fixed effects. The dependent variable Trading price it is the weighted-average price of CLO tranche i on day t, where the weights are the transaction volumes. The variable Covid t takes the value 0 before March 1, 2020 and 1 thereafter. Volatility t is the volumeweighted average daily difference between the high and low log prices on seven vulnerable industries (Sallerson, 2020). We estimate the conditional quantile functions Q Trading price it (τ |Covid t , Volatility t ) of the response of the t-th observation on the i-th CLO tranche's Trading price it given by with quantile τ ∈ {0.25, 0.5, 0.75} and where α i is the CUSIP fixed effect.
The CUSIP fixed effects absorb all time-invariant cross-sectional differences in the CLOs that were traded during the period. The quantile fixed effect regressions are implemented using the penalized fixed-effects estimation method proposed by Koenker (2004).  Looking at the AAA tranches, the difference in the coefficients between the 25 th and the 75 th percentile is economically meaningful.
The counterfactual price of a AAA CLO tranche that moved from the 75 th percentile to the 25 th percentile would have been 180 bps lower, given  That investors are distinguishing between different CLO tranches means that they are distinguishing between different loans. We can also look at the loans in CLO portfolios. Panel (a) of Figure 7 shows loan prices by quartile. It is clear that, indeed, investors were differentiating been good and bad loans. And this was at the root of differential AAA tranche pricing.
But note that it is not the case that the price dispersion is simply due to the pandemic shock to the vulnerable industries. Panel (b) of Figure 7 shows vulnerable loan prices by quartile.

The Informed and the Uninformed
According to a Bloomberg article, on May 20, 2020, J.P. Morgan sent an extraordinary email to its clients warning them of "information asymmetries" in the fast-moving CLO market. 9 The cited cause for concern was originators, sponsors, and even managers that were interested in buying their own CLO tranches based on nonpublic information about "the potential effects of Covid-19 on the portfolio" that "may constitute insider information". In this section we examine the risk to investors from such information asymmetries.
Investors produce information about investment-grade debt securities when it is profitable to do so. After a corporate bond has been issued there are few subsequent analyst reports. Resources are only devoted to information production when the bond starts to deteriorate. Johnson  In the case of AAA-rated CLO tranches there are never analysts' reports. But there are monthly CLO trustee reports. In the first subsection, we ask whether the information in those reports was used once the pandemic started. Then we look at a measure of disagreement between third-party loan price suppliers. These agents see the trustee reports but have no direct knowledge of the loan market. They proxy for uninformed agents.
To be clear, informed agents are those who have produced private The trustee reports are informative but, as mentioned above, they are only the starting point. The second step is credit analysis of the firms which borrowed money and whose loans are in the portfolio. Credit analysts are necessary to take the information in the trustee reports and make it meaningful. We do not observe this second step except insofar as the AAA-tranche price changes. Further, and perhaps most importantly, the correlations between loans in the portfolio have to be studied.

The Uninformed
Bonds and loans are traded over-the-counter so there is no price discovery in the sense that a price aggregates many agents' information sets. Like bond mutual fund managers, CLO managers engage a third-party pricing service to track the value of their loans, that is obtain a "price" for each loan. Loans do not trade frequently so these prices are essentially informed guesses. Pricing services use models and rely heavily on communication This difficulty has been shown by Cici, Gibson and Merrick (2011) who study bonds. In the case of bonds, the "prices", called "marks", are supplied by dealers using different methodologies. These are not transaction prices. Cici et al. (2011) study the dispersion of month-end mark-to-market prices for identical bonds held by many bond mutual funds. The marks from different dealer banks differ substantially (in normal times), even for AAArated bonds and the dispersion increases the lower the rating.
In the case of loans, Markit and Reuters provide loan marks, "prices".
The basis for these prices is proprietary, but likely involve anecdotal evidence from loan traders and models. These companies do not have large teams of credit analysts. Because these companies also see the trustee reports, they can proxy for uninformed traders who cannot analyze or understand the reports. To measure the degree to which the pandemic created problems and/or disagreements between these essentially uninformed agents we can measure the percentage difference between Markit and Reuters prices for the same vulnerable leveraged loan on the same date and compare that distribution to the distribution of the mark difference in pre-pandemic times. Figure 9 shows that disagreement increased significantly once the pandemic started. But there is no statistical difference between the disagreement about vulnerable and non-vulnerable industries.

Information-Sensitivity, Adverse Selection
The preceding analysis showed that AAA CLO tranches became informationsensitive. In this section we argue that the switch from insensitive to sensitive coincided with the arrival of adverse selection or the fear of adverse selection. Prior to the pandemic, investors had not been producing information. There was no need to and it would not pay to produce information. In fact, they may not know how to produce information. It is not sufficient to just look at the portfolio. An investor must understand In this section we look at evidence of adverse selection. In the first subsection below we look at the imputed roundtrip trading cost (IRC), a measure of the bid-ask spread. Glosten and Milgrom (1985) show that the bid-ask spread widens when there is an increase in adverse selection. 12 Then in the second subsection we look at the evidence for adverse selection using the IRC. The bid-ask spread is a common measure of adverse selection in fixed income markets. For example, Benmelech and Bergman 12 There are also interest costs and regulatory costs associated with holding an inventory. But agency trades are intraday. And, in any case, these did not change over our sample period.

Trading Costs
We measure trading costs in the secondary trading market for CLO tranches using the method proposed by Feldhütter (2012). The imputed roundtrip trading cost (IRC) is the difference in the price paid by a dealer to purchase a bond from a client and the price charged by a dealer to sell the same amount of the same security to a client. 13 Measuring the time between trades is important for markets with relatively thick trading and where the dealer is willing to carry risk in so-called "principal" trades. When trading CLO tranches, dealers almost always match clients in advance, in "agency" trades, and make very few principal trades. For this reason, the liquidity measure based on principal trades proposed by Choi and Huh (2017) is uninformative in our setting. The ratio of agency trades to principal trades, both by number and by volume, is close to one. 14 We identify agency trades as those with two or three trades in a given CLO tranche with the same trade size that take place within a calendar day. 15 13 We also estimated the realized bid-ask spread, calculated as the difference between the volume-weighted average prices paid to dealers by their clients and the volumeweighted average prices paid by dealers to clients. The two measures of liquidity are highly correlated.
14 This measure underestimates the number and volume of agency trades because it treats as principal trades those transactions that the dealer split over two or more clients.    Figure 11 shows that imputed roundtrip costs were differentially affected by the pandemic. We separate the CLOs in our sample into two groups:

Adverse Selection
Those above and those below the median holdings of loans to firms in vulnerable industries identified by Sallerson (2020). To avoid any potential confounding effect of secondary loan market trading, we calculate each CLO's exposure to vulnerable industries from the last trustee report prior to the declaration of the pandemic.
We find generally higher and more variable imputed roundtrip costs of trading CLOs that were more exposed to vulnerable industries during the pandemic. Nonparametric tests confirm that the two distributions are statistically different. We implemented both Anderson-Darling and Kruskal-Wallis rank tests using the trade data after the WHO declared the pandemic. In both cases, we reject with a p-value less than 5 percent the null hypothesis that the samples are drawn from a common distribution.
Full details and the results of the tests are provided in appendix 9.
We provide further evidence consistent with an increase in adverse thereafter. The specification is given by: (2) with quantile τ ∈ {0.25, 0.5, 0.75} and where α i is the CUSIP fixed effect. Table 5 shows the results.
Column 1 of table 5 shows that the negative correlation between the IRC and the transaction price for AAA-rated CLOs is consistent with adverse selection and the quantile regression reveals that the negative correlation is stronger for more illiquid (higher IRC quantile) CLOs. Moreover, the χ 2 test of the null hypothesis of equality between the 25 th and 75 th percentile coefficients on the interaction terms (Covid t ×Trading price it ) indicates that the negative correlation became stronger for those illiquid CLOs during the pandemic. By contrast, column 2 of the same table shows that the information sensitive E-class tranches did not experience the same shift. Of course, these results cannot be interpreted causally because the pandemic likely affected the liquidity of CLOs through several channels, not only through the decline in prices. Nevertheless, the findings are consistent with AAA-rated CLOs becoming information-sensitive during the pandemic. was an increase in the negative correlation between the imputed roundtrip cost (IRC) and the price of AAA-rated CLOs during the pandemic. The dependent variable is the weighted average price of CLO tranche i on day t, where the weights are the transaction volumes. Covid t takes the value 0 before March 1, 2020 and 1 thereafter. Column 1 includes only the CLO tranches rated AAA by at least one of the three main credit rating agencies. The quantile fixed effect regressions are implemented using the penalized fixedeffects estimation method proposed by Koenker (2004). Percentiles are indicated in the square parentheses. Clustered bootstrapped standard errors (1,000 replications) are implemented using the generalized bootstrap of Chatterjee and Bose (2005) with unit exponential weights sampled for each individual CUSIP, and reported in parentheses. Source: Authors' calculations from data provided by TRACE, Bloomberg LP., Moody's Analytics, S&P, and Fitch. * * * p<0.01, * * p<0.05, * p<0.1 (1) AAA During the pandemic, CLOs have continued to be issued. But their structure has been altered significantly to avoid adverse selection. A key issue is how much discretion the portfolio manager has to trade the portfolio, meaning that loans go in and out (see Table 3). More trading would require constant monitoring of new loans. As reported by Millar (2020) "New issues were largely either static or had one-year reinvestment periods in April..." In other words, the managerial discretion to alter the portfolio has been significantly reduced. This shift is shown in Figure 12.
The dark shaded part of a bar is the number of CLOs that had a five-year reinvestment period, i.e., the manager could trade the portfolio during the first five years.
Another key issue is how incentive compatible the manager's stake is in the CLO. Deals have been restructured to decrease managerial discretion and also to increase the incentives of managers not to engage in insider trading of the sort J.P. Morgan warned about. Also, new deals are being driven by affiliated equity (equity from elsewhere in the firm that is not consolidated with the CLO managers). Small CLO managers do not have access to affiliated equity and so cannot issue new CLOs. Only those with reputation risk are able to issue, using affiliated equity. shows the monthly count of newly-issued CLOs by the period over which managers have reinvestment discretion. Source: Authors' calculations from data provided by S&P Global Market Intelligence.

Concluding remarks
We traced the adverse selection dynamics in the CLO market, focusing on AAA tranches, before and after the pandemic. When securities are information-insensitive, it is not profitable to produce private information about their fundamentals ex ante and everyone knows this. AAA CLO tranches trade at par. Adverse selection is avoided. When AAA-rated CLO tranches become information-sensitive, as they did in the pandemic, uninformed traders are not prepared to produce information (see Hanson and Sunderam (2013)). They had not invested in the technology to do so (e.g., buying data, hiring analysts). The price of the security plummeted in part because uninformed investors do not know what the price should be. They face adverse selection. But informed investors do know what the price should be. The price goes down but the dispersion of prices goes up because of the informed traders. In this paper we documented these dynamics of adverse selection in the CLO market when the pandemic hit.
Note that if it were an economy-wide shock that introduced a risk premium for AAA-rated CLOs, then their prices would drop uniformly and there would not be dispersion across CLOs. The information about each CLO's exposure to vulnerable industries in the trustee reports is not sufficient to explain the dispersion in prices. Importantly, it is one thing to know the exposure and another thing to know how loans/industries will perform. We showed in Figure 7 that there is dispersion in loan prices within the vulnerable/not industries and we showed in Figure 9 that there is price disagreement within the vulnerable/not industries. For these reasons, we believe that investors are producing private information about the loans held by CLOs. In normal times, investors don't produce this private information.
We used the variation in vulnerable/not industries to tease out further evidence for adverse selection. The quantile regression (Table 4) showed that only the lowest transaction prices for AAA-rated CLOs became correlated with uncertainty about the vulnerable industries during the pandemic. At the same time, the IRC increased more for those CLOs that were heavily exposed to vulnerable industries. The combination of these results is difficult to rationalize outside an environment where investors are producing private information.
When adverse selection sets in, the market becomes less liquid in the precise sense that the securities are information-sensitive; there is adverse selection. Such price drops have often been referred to as "fire sales" or "selling pressure". These notions seem to imply that there are no buyers with available cash to invest, though no evidence has been shown that this is the case. It would also seem to imply that all the prices should drop more or less uniformly. We showed, contrary to these ideas, that the prices drop and become more dispersed due to adverse selection.

Data appendix -For Online Publication
This appendix provides the details on data construction to replicate the analysis in the paper.

Bloomberg
We begin our data construction using the asset-backed security Backoffice data from Bloomberg. We identify the universe of collateralized loan obligations (CLOs) using the identifier "mtg_deal_typ" column in the Backoffice data. Bloomberg provides cross-sectional information for every tranche issed by each CLO, including CUSIP identifiers.

Regulatory TRACE
We use the CUSIP variable from the Bloomberg data to identify individual transactions on CLO tranches in the Trade Reporting and Compliance Engine (TRACE), created by the Financial Regulatory Authority (FINRA).

Ratings
We also use the CUSIP variable from the Bloomberg data to identify ratings actions by Moody's, S&P, and Fitch. Our rating information comes from direct daily feed from Moody's, S&P, and Fitch that are added to the initial historical baseline refresh that cover every security in our sample.
The rating actions include when an agency places a tranche on watch to forewarn investors of a potential change in rating.

Moody's loan-level data
We combine loan-level information about the CLOs in our analysis from Moody's. The majority of this information is obtained from monthly trustee reports prepared by each CLO and processed by Moody's. In addition, Moody's provides further information from pricing specialists (e.g. Loanx and Reuters) and in-house assessments. We link the loan-level data to the other datasets using a cross-walk provided by Moody's to the Bloomberg identifier "mtg_deal_name".

Barchart data
Download the seven sectors listed in the table below from the website (https://www.barchart.com/stocks/sectors/rankings) and stack the files in a single .csv file. Our measure of volatility is the difference between the intra-day high and low log prices. We combine the seven sectors into a single weighted-average measure of volatility in the vulnerable sectors, where the weights are the volumes.  Figure 11 showed that the imputed roundtrip cost (IRC) of trading a CLO depends on whether that CLO is more or less exposed to the industries identified by Moody's as vulnerable to the pandemic shock. We formally test this hypothesis using the Anderson-Darling and Kruskal-Wallis rank tests for whether k samples are drawn from a common distribution. We divide all the IRC observations in the month following the declaration of the pandemic into two samples: Above and below the median market value of loans in the CLO collateral pool. Table 7 reports that there were roughly equal number of observations in the two samples.
The left-hand panel and right-hand panels of Table 7 report the results from the Anderson-Darling and Kruskal-Wallis tests, respectively. The tables report both the asymptotic and simulated p-values, as well as two versions of the Anderson-Darling test that differ in how they treat "ties" i.e.
identical values in a sample. In all cases, we can reject the null hypothesis that the two samples are drawn from a common distribution at less than 5 percent. Table 7: Testing for significant differences between distributions of imputed roundtrip costs by vulnerability. Panels A and B show the results from Anderson-Darling and Kruskal-Wallis rank tests for differences in two distributions of imputed roundtrip costs (IRC). The two distributions are formed by separating CLOs into those above and those below the median share of their market value that is exposed to the industries identified by Moody's as vulnerable to the pandemic shock. We calculate the exposure from the last trustee report prior to the pandemic. Source: Authors' calculations from data provided by TRACE, Bloomberg LP, and Moody's.   Table 3 excluding the transactions that occurred during the ramp-up period between the CLO closing date and the completion of the initial portfolio purchases. Because the data do not include a date for the end of the ramp-up period, we excluded all transactions that occurred in the two months after a CLO's closing date. The ramp-up period typically lasts one or two months, so this is a conservative approach. 16  Table 4 showed that Class E tranches were information-sensitive prior to the pandemic.