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BANK OPACITY AND INFORMATION ASYMMETRY AROUND QUARTERLY EARNINGS ANNOUNCEMENTS Mohammad Tanvir Ansari School of Economics & Finance, Queensland University of Technology This Version: 28 September 2012 ABSTRACT This study examines the relationship between information asymmetry (bid-ask spread) and various activities that are widely thought to be responsible for bank opacity. Using a sample of 275 U.S. commercial banks listed on the NASDAQ/NYSE/AMEX from Q4-1999 to Q2-2012, I find various on- and off-balance sheet activities of banks to be positively related to information asymmetry – suggesting these are sources of bank opacity. Banks’ off-balance sheet (over-thecounter) derivative exposure stand out as particularly important – their economic impact on information asymmetry is significantly higher than for on-balance sheet activities. The evidence found in this study supports regulatory efforts to push banks into moving their on- and offbalance sheet trading activities onto clearinghouses, where prices can be monitored. JEL classification: G21, G14 Keywords: bank opacity; commercial banks; derivatives trading; information asymmetry; loans; securitization; transparency. ______________________________________________________________________________ This essay is one of three chapters of my PhD thesis. I am grateful to my supervisors, Peter Verhoeven and Janice C.Y. How for their support and feedback while developing this essay. I am also grateful to Jason Park for numerous discussions which have benefitted this essay. Errors and omissions are solely my own. 1 I. INTRODUCTION “Sovereign wealth funds should be transparent but the banks who want capital injections must also be transparent… If foreign investors do not know whether they will show the balance sheets of all the information they find it difficult to invest.” Liqun Jin, Chairman of the Board of Supervisors at China Investment Corporation 17 October 2011, Reuters Although deregulation of financial markets began in the 1970s, there are two major regulatory changes in the U.S. banking industry in the late 1990s that radically changed the operations of commercial banks. In 1994, the Riegle–Neal Interstate Banking and Branching Efficiency (RNA) Act1 allowed all national commercial banks to operate branches across state boundaries. This is followed by the 1999 the Gramm–Leach–Bliley (GLBA) Act,2 which removed business operation restrictions on all types of banking and financial institutions. In an attempt to increase profitability, the banking industry transformed itself into a more flexible commercial banking prototype – banks loaned and securitized, innovated and interconnected, swapped and reinsured, and hedged and guaranteed. Over time, this transformation has resulted in an explosion in both income sources and risks for banks, derived mostly from securitization and trading book activities (DeYoung and Rice, 2004; Allen and Santomero, 1999).3 Such activities are thought to The Riegle–Neal Act allowed banks, under certain circumstances, to acquire banks or set up branches in other states without creating a separate subsidiary. The Act streamlined banking regulation in the United States, and, for the first time, allowed out-of-state residents to set up bank accounts. It also gave federal regulators the authority to ensure that out-of-state deposits do not dominate American banking. 2 Also known as the Financial Services Modernization (FSM) Act of 1999, it repealed part of the Glass–Steagall Act of 1933 by removing barriers in the market among banking, securities, and insurance companies that prohibited any one institution from acting as any combination of an investment bank, a commercial bank, and an insurance company. With the passage of the Gramm–Leach–Bliley Act, commercial banks, investment banks, securities firms, and insurance companies were allowed to consolidate. 3 The business of banks has also been taken up by non-banks in the “shadow-banking” sector, creating unregulated and uninsured exposures. This added complexity has made the job of boards and managers difficult for many reasons. First, the number of activities to manage has multiplied. Second, the knowledge needed to understand these activities has also increased substantially. Third, techniques used to manage these activities (such as value at risk 2 1 have increasingly compromised the financial transparency of banks, resulting in a highly opaque banking sector and an erosion of trust in the financial sector as a whole. Opacity is where there is ambiguity about the profits-and-loss probability density function (risks) ex ante so that ex post, in a bad outcome, actual losses are likely to become the subject of considerable conflict and controversy. The opposite of opacity is “transparency”. A transparent investment is when the provider of the capital is well informed ex ante of the payoff distribution, and fully consents to bear the risks to which her capital is employed. This definition characterizes opacity largely in terms of ambiguity about risk ex ante. In the finance industry, opacity is more commonly understood to mean a lack of available credible information. For banking stocks, it includes a lack of information on the credit score of borrowers (loans) as well as on the trading assets of banks, especially those that are primarily traded in opaque over-the-counter (OTC) markets4. It also relates to a bank’s exposure to highly volatile capital market activities, making the bank’s position in trading assets highly liquid and hard to track (Myers and Rajan, 1998; Morgan, 2002).5,6 Last but not least, the increased connectivity between banks as a result of financial innovation has made it ever more unclear to work out where the credit risk lies. A lack of available credible information leads to information asymmetry between insiders and outsiders and a divergence in opinions between outsiders (such as investors, credit rating (VaR) in the case of risk management and credit ratings for capital requirements) have not performed well under the greater degree of complexity and duress (http://www.newyorkfed.org/research/staff_reports/sr502.pdf). 4 These include subprime mortgage-backed securities (MBS), collateralized debt obligations (CDOs), swaps, and repos. 5 Myers and Rajan (1998) call this the paradox of liquidity – the increased asset liquidity and trading shrink a bank’s debt capacity because the risk of trading banks is hard to track. 6 Trading not only creates information asymmetry between bank managers and investors, but also between bank’s traders and their managers who may have little idea of the risk the bank’s traders, particularly derivatives traders, take (Hentschel and Smith, 1996). Further, high leverage may tempt banks to take excessive risk since the risk is born more by the creditors or their insurers. 3 agencies, financial analysts, debt holders)7 about the true value of the firm. Given that increased financial disclosure lessens information opacity, this should lead to less ambiguity about the true value of the firm.8 However, based on agency theory related to adverse selection and moral hazard, bank managers are thought to encourage opacity because it assists them to hoard information about shifts in the bank’s income sources and risk-taking9 – the incentives for bank managers to take undue risks are high because of high potential payoffs and the costs are not borne by them but by equity/bond holders instead. It also creates an incentive for bank managers to corrupt regulators and to share in the proceeds, which in turn creates an incentive for regulators to encourage opacity since this makes it easier for them to claim they were trying to do their job but things got too complicated (O'Neil, 2012). Motivated by the fact that it is imperative for outsiders to precisely assess profitability and risk of banks and that opacity hinders this process, this paper examines the various on- and offbalance sheet lending and trading activities of banks as potential sources of bank opacity. Onbalance sheet activities examined include: (i) secured loans from banks’ lending book; (ii) various phases of troubled loans; and (iii) loan securitization from the trading book. Off-balance sheet activities examined consist of (i) derivative exposures; (ii) net use of derivatives held (hedging vs. trading purposes); (iii) positive and negative fair values of marked-to-market derivative Bank depositors may care less about opacity because they are almost entirely (up to US$250,000) protected through deposit insurance. Only when depositors absorb losses would they realistically care about the credit worthiness of the bank. In theory, the risk (e.g. of loan nonpayment) is borne first by bank’s equity holders, then by bank bond holders, then by uninsured depositors, and then by the complicated web of taxpayers and other-bank stakeholders who back a deposit insurance fund, and then finally on holders of inflation-susceptible liabilities (which include bank depositors). 8 While regulators, who police the intermediaries, may briefly pierce the veil of opacity through quarterly examinations of bank lending and trading activities, such detailed data remain largely unavailable to other outsiders (primarily bank equity holders and bond holders) who suffer most from opacity. 9 Behr, Bannier, and Guttler (2010) investigates whether bank opacity leads to bank risk taking since more opaque banks are more likely to hide their risky activities than less opaque banks. Using a cross-country sample of 199 banks from 38 countries over the period January 1996 to December 2006, he finds tentative, but not conclusive, evidence that bank opacity (proxied by split ratings) is significantly related to bank risk taking (proxied by Merton’s probability of default and bank z-score). 4 7 exposure; (iv) swaps exposure; and (v) net credit exposure.10 For each bank activity, I examine total exposure as well as exposure by asset type since this allows me to test whether opacity of banks is common to all items or driven by certain asset categories. My proxy for information asymmetry is the (intraday) bid-ask spread. Based on market microstructure theory, if outside investors find it difficult to value banks and disagree on firm value or performance, the bid-ask spread should increase to reflect this fact. I conduct my tests around quarterly earnings announcements, which are by far the most important corporate event and should therefore witness heightened activity of informed trading. Although the timing of earnings announcements is predictable, there is voluminous literature tracing back to the seminal paper by Ball and Brown (1968), which shows these corporate announcements convey price relevant information. Importantly, information asymmetry has been found to be greatest during this time of the year when compared to “normal” periods, suggesting a window of opportunity for informed traders to profit on their private information.11 Hence, unlike other studies that take the average (daily) bid-ask spread over the year, my measure of bid-ask spread taken around quarterly earnings announcements should provide a more accurate proxy of information asymmetry. Based on a large sample of 275 U.S. commercial banks from Q4-1999 to Q2-2012, I find higher information asymmetry around earnings announcements for banks that are exposed to The recent financial crisis has, however, highlighted that banks and derivatives markets deserve more reflection and reform for two reasons. First, financial innovation from banks – the design of new, customized products – typically occurs in the OBS space, where banks tailor their own risk-taking and leverage build up. However, most of these positions are OTC. This is especially true because regulatory capital requirements are not suitably adjusted to reflect all aspects of OBS or OTC derivatives exposures, such as their illiquidity, counterparty and systemic risks. The lack of such adjustment implies that risk-taking is often more attractive for banks through OBS than on-balance sheet or exchange-traded products. The second concern is about opacity and exposures in OTC derivatives. Since trades on OTC exchanges are not centrally cleared, neither regulators nor market participants have accurate knowledge of the full range of exposures and interconnections. 11 See Affleck-Graves et al. (1995); Libby et al. (2002); Agrawal et al. (2004); and Bhat and Jayaraman (2009). 5 10 opaque activities. Loans, in particular those secured by residential, farmland and commercial properties, increase information asymmetry suggesting they are a source of bank opacity. All phases of non-performing loans increase information asymmetry, implying that non-performing loans are also at the core of bank financial opacity. Securitization and off-balance sheet activities (derivatives and swaps) significantly intensify information asymmetry amongst market participants, with the latter showing up as particularly important in terms of opacity. Larger banks and banks listed on the NYSE have lower information asymmetry. Bank capital adequacy, analyst following, and credit ratings also reduce information asymmetry. My results contribute to the literature by identifying bank activities that are more opaque and, therefore, deserve a greater amount of regulatory transparency. The rest of the paper is organized as follows. The next section discusses the hypotheses and Section 3 outlines the sample selection procedures and research method. Empirical results are discussed in Section 4, with a conclusion provided in Section 5. II. HYPOTHESES My first hypothesis relates to bank’s on-balance sheet lending book. Banks are informationally opaque because of the loans they hold. Diamond (1984, 1989, 1991) and others (Campbell and Kracaw, 1980; Berlin and Loeys, 1988) argue that the role of banks is to screen and monitor borrowers so that outsiders (i.e. investors, depositors, and other lenders) do not have to. If banks are doing their job as delegated monitors, they should know more about the credit risk of their borrowers than outsiders (Morgan, 2002). The fact that investors bid up a bank’s share price after the bank loan commitment is renewed suggests that banks are better informed about their borrowers than market participants (James, 1987). Thus, I predict: 6 H1: There is a positive relationship between information asymmetry and bank’s lending book. Whether banks are better informed about the aggregate risk of their portfolio of loans, however, depends on whether banks fully diversify their loan portfolio and value correctly the various phases of troubled loans12 in the portfolio. Morgan (2002) suggests that as banks get larger and diversify the idiosyncratic risk of their loans, outsiders only have to agree on the aggregate risk that banks cannot shed. But if banks deliberately retain some of the idiosyncratic risk in the loan portfolio such as that of problematic loans, I expect greater difficulty in valuation and increased opacity. Consequently, there is greater information asymmetry among outsiders: H2: There is a positive relationship between information asymmetry and troubled loans. Since 2001, many commercial banks have moved away from the traditional deposits-loans prototype into securitization (of mortgage loans) and securities trading, in particular off-balance sheet structured derivatives. As a result, both on- and off-balance sheet trading activities have become a major source of opacity for banks. Securitization is the process by which an issuer (bank) creates a new financial instrument by combining other illiquid or doubtful assets (mostly primary or subordinated loans) into a security and then markets different tiers of the repackaged instruments to investors. Commercial banks use securitization to immediately realise the value of the loans, trade receivables, or leases.13 Securitization can increase bank opacity in several ways. First, securitization of loans is By definition, when a loan is not performing it becomes non-performing loan, and if a non-performing loan is 90days or more past-due and still non-accrual then it becomes past-due and non-accrual loan. Further if a past-due or non-accrual is still not performing then bank restructure these loans and then report under restructured loans. Finally, when a loan default occurs then banks write-off these loans to remove it from their balance sheet. 13 Securitized mortgages are known as mortgaged-backed securities, while securitized assets (non-mortgage loans or assets with expected payments streams) are known as asset-backed securities. 7 12 thought to be a means of “arbitraging” regulatory capital requirements by keeping risky assets on the balance sheet of the so-called “special purpose vehicles” (SPV) instead of their own (Calomiris, 2009, 2010; Calomiris and Mason, 2004).14 By transferring risky capital off the balance sheet, banks are able (on paper) to maintain lower regulatory capital and appear less risky. Calomiris and Mason (2004) find securitization results in some transfer of risk out of the originating bank, and that the risk remains in the securitizing bank as a result of implicit recourse. Based on these results, they suggest that securitization with implicit recourse provides an important means of avoiding minimum capital requirements for banks. The additional equity capital and earnings gained from securitization may exacerbate opacity in financial reporting and provides a misleading picture about bank capital, performance, and underlying risk.15 Second, banks rely on “soft” information to grant and manage loans. Since this information cannot be credibly transmitted to the market when loans are securitized, banks may lack the incentives to screen borrowers at origination or to keep monitoring them once the loan has been securitized (Morrison, 2005; Parlour and Plantin, 2008). Third, although securitization of loans is a major source of non-interest income against illiquid loan portfolios, it may create severe market and credit risk exposures for banks. To balance the originated liquidity with bank exposure to market and credit risk from securitization, banks engage in highly liquid and volatile trading activities. These trading activities offset the liquidity risk exposures originated from securitization but at the cost of additional market risk exposure and pressure of performance by trading managers which in turn result in them taking on aggressive and additional risk. To offset Several capital requirements for the treatment of securitized assets originated by banks and for debts issued by those conduits and held or guaranteed by banks were specifically and consciously designed to permit banks to allocate less capital against their risks if they had been held on their balance sheets (Calomiris, 2008). 15 In July 2012, Goldman Sachs paid $550 million to settle SEC accusations that the firm gave incomplete information about a mortgage-linked investment sold in 2007 that caused buyers at least $1 billion in losses. 8 14 the originated liquidity, credit, and market risk, their risk-return appetite further involves banks in extensive use of complex off-balance sheet derivatives and swaps trading. A series of spectacular losses by rogue traders has highlighted the risk associated with highleverage trading by banks, as exemplified by Barings Bank, Daiwa Bank, Merrill Lynch & Co., UBS, J.P. Morgan Chase, and more recently Citigroup’s $45 billion taxpayer bailout. Trading in general leads to the classic agency problem of asset substitution in two ways. First, traders can change their position without owners/management knowing, much less so for outsiders like creditors and regulators (Hentshel and Smith, 1996). Second, trading causes severe agency problems between owners/management and creditors. Myers and Rajan’s (1998) model illustrates how increased liquidity and volatile trading positions can reduce bank debt capacity. In short, while securitization and leveraged trading exposures create new sources of cash flow, they come at the cost of excessive risk and complex financial arrangements. These have the consequence of making it increasingly difficult (or say practically impossible) to accurately assess the true value bank assets, performance, and risk. Therefore, I predict: H3: There is a positive relationship between information asymmetry and banks’ securitization activities. H4: There is a positive relationship between information asymmetry and banks’ on-/offbalance sheet trading activities. III. DATA AND RESEARCH METHOD My focus is on those financial institutions which are insured or supervised by Federal Deposit Insurance Corporation (FDIC) and Office of the Comptroller of the Currency (OCC) because these two regulatory bodies conduct regular inspection of banks and requires them to do 9 extensive risk reporting. The initial sample consists of all commercial banks with SIC codes16 6021 and 6022 that are listed on the three major U.S. exchanges: the New York Stock Exchange (NYSE), the American Stock Exchange (ASE), and the National Association of Securities Dealers Automated Quotations (NASDAQ).17 The sample period is from Q4-1999 to Q2-2012. Banks which are major subsidiaries of foreign banks, defined as those with at least 50% of shares outstanding owned by another domestic bank holding company or foreign bank, are excluded. This results in a sample of 330 commercial banks, of which 54 banks are traded on NYSE/ASE and 276 banks are traded on NASDAQ. Appendix A (available on request) presents detailed statistics of the selection of my sample from the U.S. banking system. Panel A presents the banking industry by type (commercial or savings) and assets concentration. The U.S. financial system consists of 7,436 banks, of which 85% (6352) are commercial banks and 15% (1084) are savings institutions. Over 50% of financial institutions (3954) are commercial lenders, while 20% (1152) are agricultural banks. Panel B indicates that 70% of commercial banks remained active during my sample period. Panel C shows that the banking industry is top heavy, with the top 353 banks (or 6.31% of 5,592 active commercial banks) representing 90% of the banking industry in terms of total assets. Finally, Panel D shows that 330 commercial banks (or 5.92% of 5,592 active commercial banks) are traded on NYSE, ASE or NASDAQ. Although not reported in detail, my sample captures the bulk of the commercial banking industry in terms of total asset value. For example, the 42 commercial banks trading on the NYSE make up to 70% of the total asset value of the U.S. commercial banking industry as at 25th August 2012. 16 17 SIC code 6021 and 6022 are National Commercial Banks and State Commercial Banks, respectively. NASDAQ Small-Cap (NAS), NASDAQ Global Select Market (NSM), and NASDAQ Large-Cap (NMS). 10 For my sample of 330 banks, quarterly earnings announcements dates and times are collected from I/B/E/S, Capital IQ Compustat, Thomson Reuters Global News, TRTH, and WorldScope. Appendix B (available on request) presents the step-by-step verification process on the date and time of the earnings announcements across the five databases. I start with a sample of 9,484 quarterly earnings announcement dates and times from I/B/E/S. After eliminating banks with missing quarterly earnings announcements dates or with dates that could not be verified by other database, I am left with a sample of 9,089 financial quarters for 275 banks. Panel B shows there is a high degree of inconsistency among the databases in terms of the reported dates of quarterly earnings announcements. For example, just 71% of the announcement dates from I/B/E/S agree with those in WorldScope. I/B/E/S and Compustat databases have the highest degree of agreement at 89%. Panel C shows that when I/B/E/S and WorldScope disagree on the reporting dates, there is a mean difference of 31-90 days in 43% of the cases, which is quite significant by any standard. For this sample of 275 banks, I obtain intra-day trading data from Thomson Reuters Ticker History (TRTH) supplied by Securities Industry Research Centre of Asia-Pacific (SIRCA). Quarterly financial data are collected from WorldScope, Bloomberg, and Federal Financial Institutions Examination Council's (FFIEC) data repository website. Call Reports containing banks’ loan, securitization and trading activities data are sourced from Call Report Agencies (CRAs) and verified with The Uniform Bank Performance Report (UBPR) through Central Data Repository (CDR). Financial data from FFIEC are verified using WorldScope and Bloomberg. Details about the selected data fields are provided in Appendix C (available on request). After eliminating bankquarters with missing trading or financial data, I obtain a final sample of 8,783 financial quarters 11 (51-quarterly periods) for 275 banks from Q1-1999 to Q2-2012. This final sample accounts for 87% of the total assets of U.S. commercial banking industry as at 25th August 2012. I use the random effects panel regression model clustered at the firm level to estimate the effect of the test variables on information asymmetry.18 The model specification is as follows: 𝐼𝑛𝑓𝑜. 𝐴𝑠𝑦𝑚𝑚𝑖,𝑡 = 𝛽1 ∗ 𝐿𝑜𝑎𝑛𝑠𝑖,𝑡 + 𝛽2 ∗ 𝑇𝑟𝑜𝑢𝑏𝑙𝑒𝑑 𝐿𝑜𝑎𝑛𝑠𝑖,𝑡 + 𝛽3 𝑆𝑒𝑐𝑢𝑟𝑖𝑡𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖,𝑡 + 𝛽4 𝐷𝑒𝑟𝑖𝑣𝑎𝑡𝑖𝑣𝑒𝑠 𝑇𝑟𝑎𝑑𝑖𝑛𝑔𝑖,𝑡 + 𝑁 𝑗 =5 𝛽𝑗 ∗ (𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑗 )𝑖,𝑡 + 𝜀𝑖,𝑡 . (1) The dependent variable is information asymmetry (Info.Asymm) surrounding the earnings announcement, and is proxied by the bid-ask spread (BAS): 𝐵𝐴𝑆 = |𝐵𝑖𝑑 − 𝐴𝑠𝑘| (2) where Bid and Ask are the average value of the 5-minute bid and ask quotes from nine days before to nine days after the announcement day. When the earnings announcement is after the close of trading, I take the next trading day to be day 0, consistent with Berkman and Truong (2009). Following Bagehot (1971), I propose that market makers trade with two kinds of traders, informed traders and liquidity traders. The higher the bid-ask spread of a bank’s equity, the smaller the number of liquidity creators (uninformed traders) trading the stock. While the market maker loses to informed traders, he recoups these losses from uninformed traders by increasing the bid-ask spread. Thus, the higher the level of information asymmetry, the greater the bid-ask spread (buyers-sellers stock price disagreement). To test whether there is any correlation between the error term and the explanatory variables, the Hausman specification test is performed upon running the fixed effects and random effects regression models (Baltagi, 2008). Variation in independent variables and errors across the years is rejected by Hausman test which produces insignificant p-values; thus the null hypothesis of fixed effects being the appropriate model is rejected. 12 18 The first source of bank opacity is loans secured by, (i) farmland properties; (ii) 1-4 family residential properties; (iii) multi-family (>4) residential properties; and (iv) commercial properties; as a percentage of total assets. The second source of bank opacity is the various phases of troubled loans. The first stage is when loan placed into bank's non-accruals as a nonperforming loan which will default as a percentage of total loans and leases. The next phase is past due loans and is measured by the ratio of all loans that are 90-days plus past due and nonaccruals to as a percentage of total loans and leases. Rather than summing up the two phases of problematic loans, I use the FDIC guided Texas ratio as a proxy for bank overall troubled loans. According to the definition by FDIC call reports, Texas ratio is determined by dividing bank nonperforming assets (excluding government sponsored non-performing loans) by tangible common equity and loan loss reserves. As an early indicator of bank trouble, the higher this ratio, the more precarious the bank's financial situation. To examine bank securitization activities as a source of bank opacity, total securitized assets available for sale as a percentage of gross managed assets is computed. Since not all banks are involved in securitization, a dummy variable is created which takes the value of 1 if the bank is involved in securitization and zero otherwise. I also compute securitization by category in order to determine which type contributes most to bank opacity. I employ bank's securitized securities backed by: (i) family residential loans; (ii) home equity lines; (iii) credit card receivables loans; (iv) auto loans; and (v) commercial and industrial loans as a percentage of gross managed assets. 13 The remaining opacity drivers are off-balance sheet (OBS) activities, measured by (i) net exposure to exchange (or OTC) traded derivatives;19 (ii) interest rate derivatives; and (iii) foreign exchange rate derivatives; as a percentage of total assets. These activities are expected to be positively related to information asymmetry because OTC contracts are privately negotiated contracts with very lax regulatory supervision requiring no disclosure to or monitoring by the clearinghouse. I also examine marked-to-market gross notional amount of (i) equity contracts; (ii) commodities and others contracts; (iii) interest rate contracts; and (iv) foreign exchange rate contracts as a percentage of total assets. In addition, I examine marked-to-market derivative exposures to positive (and negative) fair value of derivatives contracts. Gross negative fair value is the sum of the fair values of contracts where the bank owes money to its counter-parties without taking into account netting. This represents the maximum losses the bank’s counterparties would incur if the bank were to default and there is no netting of contracts, and no bank collateral was held by the counter-parties. Conversely, the gross positive fair value is the sum of the fair values of contracts where the bank is owed money by its counter-parties, without taking into account netting. This represents the maximum losses a bank could incur if all its counterparties were to default and there is no netting of contracts, and the bank holds no counter-party collateral. The final set of off-balance sheet derivatives used as a source of bank opacity is equity, commodities and others, interest rate, and foreign exchange rate, swaps written/purchased and net OBS credit derivatives exposure, as a percentage of total assets. Detailed descriptions of the variables used in the regressions are summarized in Appendix C (available on request). Net position refers to the difference between gross notional amount of equity and commodity (except interest rate and foreign exchange rate) derivatives contracts written minus purchased. 14 19 A number of variables that have been shown to impact information asymmetry in past studies are also controlled for in the tests. The first control variable is regulatory capital quality enforcements in the form of capital adequacy ratio (CAR), a ratio specified by the Basel Committee (2008). Banks which maintain higher regulatory capital ratios are expected to be safer and are therefore associated with lower information asymmetry. I use total capital requirement reported to FDIC as it is the most stringent capital adequacy ratio. S&P credit quality rating (Ratings) is used as a proxy for banks overall credit health. Banks with a lower credit rating have a higher probability of default which should result in greater information asymmetry. The qualitative credit ratings are converted to numerical values, with the highest credit rating (AAA) assigned with a score of 7 and credit ratings at or below “C” are assigned a value of one. The information environment is expected to impact on information asymmetry and is thus also controlled for in the regression. The information environment is proxied by analyst following and bank size. Larger banks (Lang and Lundholm, 1996; Johnson, Kasznik, and Nelson, 2001) and banks that are followed by more analysts (O’Brien and Bhushan, 1990; Lang and Lundholm, 1996) have a richer information environment and thus lower information asymmetry. Analyst Followings is computed as the natural logarithm of the total number of analysts covering a bank. Firm size is the natural logarithm of total assets. As a long-run performance measure, Tobin’s Q is the ratio of the market value of bank assets (as measured by the market value of outstanding stock and debt) to the replacement cost of bank assets (Tobin, 1969). If Tobin’s Q is greater (less) than 1, it implies that the bank is over (under) valued in the market. A higher Tobin’s Q indicates either outsiders are not able to value the bank assets and underlying risks correctly or the bank is performing very well. However, the probability of wrong valuation is higher because of banks’ reporting opacity and risky business 15 lines. Therefore, a positive relationship between Tobin’s Q and information asymmetry is expected. Stock price (Price), return volatility (Sigma), and trading volume (Volume) control for outsiders’ equity valuation, risk, and liquidity respectively. I expect a positive relationship between information asymmetry and stock price volatility, and a negative relationship between information asymmetry and trading volume. Stock price controls for the fact that higher priced stocks tend to have higher bid-ask spreads. I also include a Bad News dummy since investors respond to bad news more aggressively relative to good news and the effect of their reaction remains in the market for a longer period compared to good news (Lakhal, 2008). Bad News is equal to 1 if this quarter EPS is less than last quarter EPS, and zero otherwise. Finally, an NYSE dummy is included to control for the relatively higher disclosure requirements on NYSE, which suggests lower information asymmetry for NYSE-listed banks. Table 1 shows the descriptive statistics of the test variables. In Panel A, the average (median) bid-ask spread around the earnings announcement is 13.08 cents (9.00 cents), with a standard deviation of 11.30 cents. Although similar to those reported by Flannery, Simon, and Nimalendran (2004), these numbers are much higher than those for non-banking firms, consistent with bank stocks suffering substantially higher information asymmetry. Panel B shows the control variables. The average (median) bank size is US$25.90 (US$8.66) billion, with a standard deviation of US$50.73 billion. The average (median) loan size is US$16.40 (US$5.82) billion, with a standard deviation of US$29.68 billion. The average (median) capital adequacy ratio (CAR) is 12.38% (11.92%), with a standard deviation of 1.65%. This value is close to the minimum 12% required by Basel II. The minimum CAR is 10.31% and the maximum is 17.40%. The average (median) S&P credit rating score is 5 out of a maximum 7, with a standard deviation 16 of 2. The average (median) Tobin’s Q ratio is 1.04 (1.04), with a standard deviation of 0.04, implying that banks are marginally overpriced. The average bank is followed by six analysts, with analyst following ranging from 1 to 38. The average (median) stock price volatility is 36.66% (33.43%), ranging between 13.20% and 74.83%. Panel C shows secured and non-performing loans by category. Net secured loans make up 17.83% of total assets (on average) whereas average loans and leases make up 63.34% of total assets (on average). Of the four categories of secured loans, the largest category is commercial loans (15.89% of total assets), followed by 1-4 residential properties backed loans (5.01% of total assets) and >4 residential properties backed loans (1.33% of total assets). Lastly, loans secured by farmland properties make up just 0.87% of total assets. Of the three stages of nonperforming loans, non-accruals loans, past due loans and charge-off loans make up less than 1% of total loans, respectively. The maximum value of troubled loans is 2.00%. These statistics suggest that banks have few troubled loans. The average (median) percentage value of nonperforming loans divided by tangible common equity and loan loss reserves (FDIC Texas ratio) is 8.68% (6.36%), with a maximum of 29.99%. Panel D shows the descriptive statistics of bank on-balance sheet securitization activities. Banks with zero securitization activities are excluded. Family residential loans backed securitized assets is the largest category by far (20.63% of gross managed assets), followed by commercial and industrial loans backed securitized assets (15.47% of gross managed assets) and all other loans and leases backed securitized assets (14.39% of gross managed assets). Credit card loans, auto loans, and home equity loans backed securitized assets each makes up less than 5% of gross managed assets each. Net securitized loans and leases make up 43.73% of gross 17 managed assets, with a standard deviation of 17.26%. These values are similar to those reported by Cheng, Dhaliwal, and Neamtiu (2008) for their sample of BHCs. Panel E provides descriptive statistics on the off-balance sheet derivatives trading activities of banks. Banks with zero derivatives exposures are excluded.20 It provides details on the notional and fair values of (equity, commodity, foreign exchange, and interest rate21) derivatives used for hedging and trading purposes, the bank’s net notional position in derivatives as well as whether banks primarily use exchange-traded or OTC traded derivatives. Bank’s activities in derivatives for hedging purposes mainly extend to interest rates derivatives (12.63% of gross assets). Bank’s activities in derivatives for trading purposes mostly include commodity derivatives (28.85% of gross assets), interest rate derivatives (22.28% of gross assets), and foreign exchange rate derivatives (21.17% of gross assets). Interest rate derivatives are used twice as much for trading (dealer) than for hedging activities, consistent with the findings of Minton et al. (2006). The notional value of OTC traded derivatives is substantially higher than exchange-traded derivatives (9.58% vs. 0.43% of gross assets). Finally, the net notional value of derivates exposure is 40.29% of gross assets. Net notional value of interest rate and commodity derivatives around 28% of gross assets each, followed by foreign exchange derivatives (22.30%) and equity derivatives (10.66%). Banks have high average (median) exposure to foreign exchange rate swaps, with a notional value of 22.50% (2.96%) of gross assets. The highest notional value exceeds bank total assets by a factor of 2. Interest rate swaps are less popular with banks, with an average (median) notional value of 8.46% (5.27%) of gross assets. Equity and commodity swaps are used even less 20 21 Minton et al. (2006) find that in 2003 only 19 out of 345 large US banks use credit derivatives. Interest rate swaps are included in interest rate derivatives. 18 frequently, with a notional value below 5% of gross assets. The net positive (negative) fair value of derivatives used is 0.09% (0.09%) of gross assets and much smaller than the notional amounts. Primarily this is because derivatives involve a future exchange of payments and fair value is the net present value of the exchange (for forwards, futures, and swaps, contracts are set so that values are initially zero). In contrast, notional amounts relate to payment obligations based on one side of the contract. Difference between positive and negative fair value is net fair value and are even smaller. One is that institutions substantially hedge their derivatives exposures, holding long and short positions on the same market exposures. This would be expected to be typical of bank dealers in derivatives whose income is generated mainly from market-making activity. A second hedging reason is that undertaking a hedge on an outstanding derivatives position provides the bank with a way of closing out a market exposure without having to sell the instrument. Also, different derivatives will have exposures to different markets, which may move in different directions and thus create both positive and negative market values among different exposures. In sum, the descriptive statistics show that those banks that are involved in securitization and derivatives activities have high exposures, on average. IV. EMPIRICAL RESULTS Table 2 presents the panel regression results of the impact of the first two sources of opacity, secured loans from the bank’s lending book and various stages of troubled loans, on the proxy for information asymmetry – the bid-ask spread. From policy prospective, banks as delegated monitors, are supposed to screen and monitor borrowers so that outsiders do not have to (Diamond, 1984). If banks are doing their jobs, they should know more about the credit risk of their borrowers than outsiders. This makes loans opaque to outsiders. Consistent with the 19 hypotheses, there is statistically significant positive relationship between the secured loans and bid-ask spread (regressions 1 to 5) and non-performing loans and bid-ask spread (regressions 6 to 10). These results confirm that bank's loans are at the core of bank’s opacity, irrespective whether loans are performing or non-performing. The results are also economically significant. For example, a one percent increase in loans secured by 1-4 residential properties increases the bid-ask spread by 0.26 cents (regression 2), while a one percent increase in past due loans increases the bid-ask spread by 2.42 cents (regression 8). All secured loans categories are significant positively related to the bid-ask spread. These results are contrary to the findings of Haggard and Howe (2007) for their sample of BHCs for the period 1993-2002, who find that banks with a lower proportion of agricultural (and consumer) loans are associated with higher opacity. The relationship between the control variables and the bid-ask spread is in line with predictions. In particular, larger banks, banks with a higher capital adequacy ratio, larger number of analysts following, higher credit ratings, and higher trading volume have smaller bid-ask spreads in line with expectations. In contrast, return volatility, price and bad news dummy are significantly positively related to the level of information asymmetry, as expected. Finally, banks that are trade over the NYSE have lower bid-ask spreads (by 5.67 cents in regression 1) probably because of better disclosures requirement as compared to NASDAQ or AMEX. Table 3 shows the results for securitization activities as a source of bank opacity. The types of assets involved in securitization transactions are primarily bank's receivables, i.e., banks convert its illiquid assets (primarily loans) to highly liquid trading assets (e.g. residential mortgage backed securities and CDOs) by pooling them together to create investment tranches 20 for outsiders.22 While securitization results in some transfer of risk out of the originating bank, risk remains in the securitizing bank as a result of implicit recourse (Calomiris and Mason, 2004). Securitization provides an important means of avoiding minimum capital requirements for banks, and may exacerbate opacity in financial reporting. Banks may lack the incentives to screen borrowers at origination or to keep monitoring them once the lending has been securitized. Further, securitization leads banks into extensive use of complex off-balance sheet derivatives and swaps trading. There is a positive relationship between the net securitization dummy and the bid-ask spread, suggesting that banks that are involved in securitization have significantly higher information asymmetry (by 1.54 cents, on average). The continuous variable for net securitization is also significantly positively related to the bid-ask spread – banks with higher exposure to securitized assets (as a percentage of total managed assets) have higher information asymmetry. Focusing on each of the five securitization categories, there is a statistically significant coefficient for securitization of family residential loans and auto loans only. As noted in the descriptive statistics, family residential loans are the most common securitized assets that banks are involved with. Commercial and industrial loans backed securitization has no statistically significant impact on the bid-ask spread suggesting that these are not a source of bank opacity. Table 4 shows the results for the impact of OBS derivatives trading activities on information asymmetry. A series of spectacular losses by rogue traders has highlighted the risk associated with high-leverage trading by banks. Therefore, the more banks are involved in derivatives activities, the higher the bid-ask spread. This is particular so for derivatives that are The practice of securitization originated with the sale of securities backed by residential mortgages. However, nowadays a wide variety of assets are securitized, including lease, auto loan, credit card receivables, and commercial loans. 22 21 traded OTC. As expected, net exposure and foreign exchange rate exposure to OTC traded derivatives is significantly positively related to the bid-ask spread, consistent with its opaque nature. Irrespective whether derivatives are used for hedging or trading purposes, there is a significant positive relationship between the bank’s (equity, commodity, interest and foreign exchange) derivatives exposure and the bid-ask spread. For example, a one percent increase in exposure to equity derivatives for hedging (trading) purposes, increases the bid-ask spread by 3.66 (0.511) cents. While theoretically, hedging reduces risk (and therefore the bid-ask spread), banks with higher exposure to derivatives for hedging purposes are also likely to be (1) more involved in market-making; and (2) have higher risk exposures to the assets that are being hedged. Both these activities should results in larger bid-ask spreads.23 Overall, the results suggest that OBS derivatives exposure is a significant source of bank opacity. Table 5 presents evidence on the relationship between positive and negative fair value (marked-to-market) derivatives exposure and the bid-ask spread. Gross negative fair value represents the maximum losses the bank’s counter-parties would incur if the bank defaults, while gross positive fair value represents the maximum losses a bank could incur if all its counterparties default. Both gross positive and negative fair value of equity, commodity and foreign exchange derivatives are positively relative to the bid-ask spread. That the regression coefficients for positive fair value are much larger than for negative fair value is because with positive fair values banks act as guarantors. There is no significant relationship between positive or negative fair value of interest rate derivatives and the bid-ask spread. Table 6 presents the results for bank’s exposure to OBS swaps. Net swap exposure is significantly positively related to the bid-ask spread, with every one percent increase in bank 23 The hedging activity of banks may give outsiders an indication of the trading exposure of banks. 22 exposure increasing the bid-ask spread by 0.05 cents. For the individual categories, only the coefficients for interest rate swaps and foreign exchange rate swaps are significantly positive. Since the results may be driven by a few large banks, which have very significant exposure to securitization and OBS trading, I rerun the robustness tests by dividing the sample of banks into two categories. The first category includes banks who are supposed to be too big to fail with total assets exceeding US$100 billion. The second category belongs to those banks with total assets less than US$100 billion. Consistent with Adrian et al. (2009a, 2009b), Table 7 shows that the results are not driven by big commercial banks, with all commercial banks involved in highly opaque activities such as loans and OBS derivatives exposure. V. CONCLUSION This study combines the literature on bank opacity and market microstructure to assess whether on- and off-balance sheet sources of bank opacity are positively related to level of information asymmetry. I use quarterly earnings announcements as the event to capture the effects on information asymmetry. Using a large sample of 275 U.S. commercial banks listed on the NASDAQ/NYSE/AMEX for Q4-1999 to Q2-2012, I find bank sources of bank opaqueness such as secured and troubled loans, securitization, and OBS derivatives exposure are positively related to the level of information asymmetry. In particular, OBS derivatives exposures held for hedging purposes has a higher economic significance with information asymmetry compared to derivatives held for trading purposes. This result implies that outside investors are uncertain on true value of banks underlying loans and hedging position. As expected, OTC derivatives exposure significantly contributes to information asymmetry. It confirms that the proposed move of derivatives trading from OTC onto clearinghouses, where prices can be monitored, is a good initiative as it should reduce bank opacity. Interestingly, banks gross negative fair value exposure 23 to derivatives held for hedging purpose is economically more significantly related with bid-ask spread compared to gross positive fair value exposure to derivatives held for hedging purpose. Of significance, this result re-affirms that banks are informationally more opaque than their counter parties and outsiders are probably more uncertain to scale banks’ exposure to default. In general, OBS interest rate and foreign exchange derivatives exposures stand out as particularly important – their economic impact on information asymmetry is higher as compared to other derivatives exposures. Regulatory capital requirements provide a cushion to investors as it reduces the level of information asymmetry. An important policy implication that flows from my results is that bank regulators and lawmakers should develop risk reporting standards that contribute to a more transparent information environment for market participants. Of economic significance, bank’s trading activities needs to be better regulated and requires additional screening. Greater information transparency may also have a positive impact on market discipline, which may further help to reduce bank failures. 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Deviation Panel A: Dependent variable Bid-Ask spread (cents) 13.08 9.00 11.30 1.00 Max. 46.00 Panel B: Control variables Assets ($'billions) Loans ($'billions) Tobin's Q (ratio) Sigma (%) Volume (#'000) Price ($) # Analyst Rating CAR (%) 25.90 8.66 16.40 5.82 1.04 1.04 36.66 33.43 1690.28 1277.17 15.68 17.45 6.16 4.00 4.99 5.00 12.38 11.92 50.73 1.52 424.16 29.68 0.96 229.17 0.04 0.97 1.13 13.20 18.64 74.83 1622.15 468.05 8842.87 7.36 3.20 25.40 6.33 1.00 38.00 1.60 8.00 1.00 1.65 10.31 17.40 Panel C: Secured and troubled loans Secured by farmland ( % of gross assets) Secured by 1-4 residential properties ( % of gross assets) Secured by > 4 residential properties ( % of gross assets) Secured by commercial loans ( % of gross assets) Net secured loans ( % of gross assets) Secured by senior lien loans ( % of gross assets) Secued by junior lien loans ( % of gross assets) Non-accruals loans ( % of total loans and leases) Past due loans ( % of total loans and leases) Charge-offs loans ( % of total loans and leases) FDIC Texas ratio (%) 0.87 5.01 1.33 15.89 17.83 14.94 1.96 0.59 0.18 0.32 8.68 0.28 4.90 0.79 11.17 13.93 10.06 1.08 0.45 0.10 0.18 6.36 1.35 2.98 1.34 13.24 14.02 15.97 2.81 0.47 0.26 0.38 7.43 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.25 7.99 9.99 4.99 49.99 61.28 143.62 29.95 1.99 1.99 2.00 29.99 20.63 4.30 1.08 4.69 15.47 14.40 43.73 15.49 19.42 4.24 0.22 2.71 14.56 16.24 43.45 15.48 12.74 2.41 2.37 4.97 7.79 5.61 17.26 7.06 0.00 0.00 0.00 0.00 0.03 0.00 0.01 0.01 79.85 8.99 14.78 19.97 39.86 19.98 100.00 29.98 Panel D: Securitization Family residential loans ( % of gross managed assets) Home equity lines ( % of gross managed assets) Credit card receivables loans ( % of gross managed assets) Auto loans ( % of gross managed assets) Commercial and industrial loans ( % of gross managed assets) All other loans and leases ( % of gross managed assets) Net all loans and leases ( % of gross managed assets) Available for sale securities ( % of gross assets) 28 TABLE 1 DESCRIPTIVE STATISTICS (CONTINUED...) Mean Median Standard Deviation Min. Max. Panel E: Off-balance sheet derivatives exposure Gross positive fair value of equity ( % of gross assets) Gross positive fair value of commodity and others ( % of gross assets) Gross positive fair value of interest rate ( % of gross assets) Gross positive fair value of foreign exchange ( % of gross assets) Net positive fair value ( % of gross assets) Gross negative fair value of equity ( % of gross assets) Gross negative fair value of commodity and others ( % of gross assets) Gross negative fair value of interest rate ( % of gross assets) Gross negative fair value of foreign exchange ( % of gross assets) Net negative fair value ( % of gross assets) Equity swaps ( % of gross assets) Commodity and other swaps ( % of gross assets) Interest swaps ( % of gross assets) Foreign exchange swaps ( % of gross assets) Net OBS credit exposure ( % of gross assets) Hedge - Equity ( % of gross assets) Hedge - Commodity and others ( % of gross assets) Hedge - Interest rate ( % of gross assets) Hedge - Foreign exchange ( % of gross assets) Trading - Equity ( % of gross assets) Trading - Commodity and others ( % of gross assets) Trading - Interest rate ( % of gross assets) Trading - Foreign exchange ( % of gross assets) Exchange traded - all options ( % of gross assets) Exchange traded - interest rate ( % of gross assets) Exchange traded - foreign exchange ( % of gross assets) Over the counter - all options ( % of gross assets) Over the counter - interest rate ( % of gross assets) Over the counter - foreign exchange ( % of gross assets) Net Exposure - Equity ( % of total assets) Net Exposure - Commodities and others ( % of total assets) Net Exposure - Interest rate ( % of total assets) Net Exposure - Foreign exchange ( % of total assets) Net Exposure - All together ( % of total assets) 29 0.06 0.09 0.09 0.03 0.09 0.01 0.01 0.09 0.04 0.09 3.43 1.46 8.46 22.50 2.10 0.68 0.11 12.63 2.22 4.06 28.85 22.28 21.17 0.43 7.93 0.52 9.58 4.03 0.93 10.66 28.04 28.69 22.30 40.29 0.01 0.01 0.03 0.01 0.03 0.01 0.01 0.04 0.01 0.04 0.35 0.70 5.27 2.96 0.12 0.16 0.08 4.98 0.70 0.87 5.48 8.50 1.59 0.23 3.34 0.12 0.55 1.22 0.07 2.17 3.21 10.75 1.43 10.51 0.11 0.17 0.11 0.05 0.12 0.02 0.01 0.12 0.07 0.12 5.31 1.89 8.36 42.65 7.40 1.40 0.12 19.98 3.54 5.82 62.86 29.87 59.72 0.52 10.01 0.95 47.36 7.07 1.41 38.90 90.34 49.66 57.73 100.50 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.62 0.68 0.50 0.30 0.68 0.10 0.05 0.50 0.40 0.83 23.09 10.00 32.15 199.33 130.90 6.52 0.48 147.32 24.92 24.28 478.34 124.24 301.55 1.94 49.15 5.02 478.34 49.29 4.91 478.34 956.68 1023.40 390.46 2460.82 TABLE 2 NOTIONAL VALUE OF SECURED AND NON-PERFORMING LOANS AS A SOURCE OF BANK OPACITY Year dummies are included in the regressions. (1) (2) (3) (4) Information Asymmetry (Bid-Ask Spread) (5) (6) (7) (8) (9) (10) (11) Opacity measurements Secured by farmlands 0.720 * Secured by 1-4 residential properties 0.264 *** Secured by > 4 residential properties 0.343 * Secured by commercial properties 0.147 *** 0.465 *** 0.847 0.052 ** 0.228 *** Net secured loans 0.018 * Non-accruals loans 2.421 *** Past due loans 1.196 3.125 *** FDIC Texas ratio 0.717 0.188 *** 0.234 *** Net troubled loans 0.422 *** -6.137 *** Control variables Size -5.824 Tobin's Q -0.819 *** -5.737 *** -5.869 0.080 *** -0.109 0.253 *** -0.280 ** Rating -1.251 * -0.040 CAR -0.352 *** -0.472 NYSE dummy News -5.669 1.515 * -0.365 1.097 Constant R - Sqr. 112.46 Volume Price # Analyst 0.096 -0.108 *** 0.769 *** Sigma *** -6.523 -5.800 *** -1.061 -5.844 -5.564 0.096 *** ** -0.102 0.251 *** 0.245 -0.302 ** -0.179 * -0.218 * -1.362 ** -1.342 ** -0.021 -0.377 *** -0.364 *** -0.476 -5.756 1.173 * -0.318 1.072 *** *** 0.095 *** *** -0.104 *** 0.239 -5.585 *** -0.944 0.079 *** *** -0.105 *** *** 2.008 0.097 *** ** -0.167 0.257 *** -0.304 ** *** -6.167 -5.750 *** 1.104 0.133 *** *** -0.281 0.242 *** -0.269 ** -1.432 ** -0.335 *** -6.131 *** 2.323 0.096 *** *** -0.107 0.300 ** -0.585 *** -1.222 * -0.429 *** -6.229 *** 2.812 0.131 *** 0.142 *** -0.641 *** -0.778 0.279 *** 0.286 *** 0.306 -0.415 *** -0.539 *** -1.213 * -0.978 -0.351 *** -0.419 -5.606 1.574 * -3.085 1.434 *** 1.867 *** 0.147 *** -0.263 ** *** 0.301 *** -0.609 *** -0.672 *** -1.266 * -1.241 * -0.375 *** -0.422 *** * Dummy variables Within: Between: Overall: Obs (# Quarters) *** *** 0.063 0.339 0.244 4808 104.68 0.056 0.307 0.223 2536 -4.337 1.424 *** 120.89 0.059 0.322 0.235 4497 *** *** 111.97 ** *** 0.054 0.334 0.245 4464 105.99 0.060 0.312 0.230 2486 *, **, *** Significant at 10%, 5% and 1% respectively. 30 -5.939 1.743 *** 107.62 0.067 0.324 0.242 4021 *** *** -3.305 1.578 114.98 0.059 0.373 0.256 3841 *** *** 108.63 0.061 0.360 0.248 4839 *** *** 116.50 0.062 0.350 0.257 3923 ** *** -2.576 1.316 116.38 0.064 0.377 0.261 3573 ** *** -2.527 1.590 113.67 0.061 0.374 0.254 3714 *** *** TABLE 3 NOTIONAL VALUE OF SECURITIZATION AS A SOURCE OF BANK OPACITY Banks not involved in securitization are not included. Year dummies are included in the regressions. (1) (2) (3) Information Asymmetry (Bid-Ask Spread) (4) (5) (6) (7) (8) (9) Opacity measurements Securitization (dummy) 1.543 *** 0.130 Family residential loans ** Home equity lines 0.030 0.044 * 0.067 Credit card receivables loans -0.216 -0.234 Auto loans 0.296 *** Commercial and industrial loans Net securitization 0.290 -0.082 *** -0.060 0.071 * Available for sale securities 0.243 *** -5.263 *** Control variables Size -5.388 Loans -0.006 *** -5.174 *** 0.031 -5.468 *** -0.016 *** 0.052 1.310 Sigma 0.091 *** 0.091 *** 0.099 *** 0.068 -0.107 *** -0.104 *** -0.111 *** -0.122 ** 0.260 *** 0.291 *** 0.250 *** 0.165 ** # Analyst -0.360 *** -0.445 *** -0.368 *** -0.129 Rating -1.193 * -1.062 -2.238 -0.321 *** -6.252 1.575 ** Price CAR 1.323 -5.292 *** 0.001 Tobin's Q Volume -2.933 -5.760 0.568 ** *** -0.019 0.581 *** -5.443 -5.338 *** 0.003 0.942 -5.466 *** -0.009 0.568 0.121 0.206 * 2.801 0.096 *** 0.095 *** 0.096 *** 0.095 *** 0.088 *** -0.109 *** -0.112 *** -0.113 *** -0.109 *** -0.103 *** 0.257 *** 0.257 *** 0.260 *** 0.257 *** 0.225 *** -0.309 *** -0.371 *** -0.330 *** -0.334 *** -0.332 *** -1.315 * -1.008 -0.376 ** -1.361 ** -0.377 ** -0.394 ** -0.339 -0.312 -6.745 1.505 * -5.888 1.581 * -5.088 0.886 -7.216 1.546 -1.032 -1.445 ** -0.337 ** -0.327 ** -7.099 1.562 ** -5.037 1.466 -1.103 -0.289 *** -6.563 1.561 ** Dummy variables NYSE dummy News Constant R - Sqr. 101.84 Within: Between: Overall: Obs (# Quarters) 0.061 0.347 0.247 4908 *** *** 99.21 0.060 0.353 0.248 4463 *** *** 106.35 0.060 0.336 0.244 4661 *** *** 114.98 0.036 0.335 0.286 1834 31 *** 99.00 0.062 0.336 0.249 4867 ** *** *** -5.669 1.742 107.73 0.059 0.344 0.244 4677 *** *** 99.31 0.062 0.344 0.249 4908 *** *** 101.77 0.060 0.341 0.248 4905 *** *** 89.01 0.060 0.324 0.236 4365 *** *** TABLE 4 NOTIONAL VALUE OF DERIVATIVES ACTIVITY AS SOURCE OF BANK OPACITY Banks not involved in derivatives trading are not included. Year dummies are included in the regressions. (1) Exchange traded (2) (3) (4) Information Asymmetry (Bid-Ask Spread) Over the counter Hedge (5) (6) (7) (8) (9) (10) (11) (12) Trading (13) (14) Opacity measurements Equity derivatives 3.659 ** Commodities and others derivatives Net derivatives (written - purchased) 0.511 8.961 0.018 0.012 Interest rate derivatives * 0.014 0.915 0.011 *** *** 0.005 Foreign exchange rate derivatives ** 0.054 * 0.266 *** -5.565 *** ** 0.003 0.187 *** * 0.005 * Control variables *** -5.550 *** -5.572 *** -5.560 *** -5.580 *** -5.538 *** -5.399 *** -5.584 *** -5.817 *** -5.586 *** -5.554 Loans -0.022 -0.022 -0.024 -0.023 -0.023 -0.021 -0.024 -0.022 -0.028 -0.018 -0.006 -0.023 -0.002 0.881 0.884 0.820 0.939 0.920 0.970 0.825 0.901 0.438 1.031 0.498 0.933 0.617 Tobin's Q -5.552 *** Size -5.843 *** -5.685 *** -0.012 1.033 0.094 *** 0.094 *** 0.095 *** 0.095 *** 0.094 *** 0.095 *** 0.094 *** 0.094 *** 0.095 *** 0.094 *** 0.097 *** 0.094 *** 0.096 *** 0.095 *** -0.108 *** -0.108 *** -0.108 *** -0.108 *** -0.108 *** -0.107 *** -0.108 *** -0.109 *** -0.108 *** -0.108 *** -0.108 *** -0.108 *** -0.111 *** -0.108 *** 0.258 *** 0.258 *** 0.260 *** 0.259 *** 0.258 *** 0.259 *** 0.263 *** 0.256 *** 0.276 *** 0.257 *** 0.272 *** 0.258 *** 0.268 *** 0.260 *** -0.339 *** -0.339 *** -0.342 *** -0.337 *** -0.341 *** -0.339 *** -0.343 *** -0.349 *** -0.362 *** -0.344 *** -0.358 *** -0.337 *** -0.351 *** -0.340 *** Rating -1.238 * -1.237 ** -1.231 * -1.235 * -1.241 * -1.239 * -1.209 * -1.247 * -1.156 * -1.243 * -1.243 * -1.235 ** -1.251 * -1.238 * CAR -0.313 *** -0.312 *** -0.316 *** -0.319 *** -0.315 *** -0.313 *** -0.316 *** -0.311 *** -0.326 *** -0.311 *** -0.302 ** -0.319 *** -0.310 ** -0.320 ** NYSE dummy News -6.142 1.575 ** -6.156 1.574 *** -6.156 1.574 ** -6.150 1.578 ** -6.122 1.577 ** -6.093 1.575 ** -6.234 1.559 ** -6.108 1.588 ** -5.371 1.594 ** -6.144 1.581 ** -7.027 1.640 ** -6.137 1.581 ** -7.165 1.616 ** -6.263 1.607 ** Constant R - Sqr. 107.00 *** 106.93 *** 107.07 *** 107.26 *** 107.14 *** 106.97 *** 107.30 *** 106.84 *** 105.24 *** 106.96 *** 109.52 *** 107.48 *** 109.73 *** 107.96 *** Sigma Volume Price # Analyst Dummy variables Within: Between: Overall: Obs (# Quarters) *** 0.059 0.344 0.245 4908 0.059 0.344 0.245 4908 *** 0.059 0.344 0.245 4908 *** 0.059 0.344 0.245 4908 *** 0.059 0.344 0.245 4908 *** 0.059 0.344 0.245 4908 *, **, *** Significant at 10%, 5% and 1% respectively. 32 *** 0.059 0.346 0.246 4894 *** 0.059 0.344 0.245 4894 *** 0.062 0.337 0.241 4863 *** 0.059 0.345 0.246 4908 *** 0.061 0.360 0.254 4813 *** 0.059 0.344 0.245 4908 *** 0.061 0.360 0.257 4877 *** 0.060 0.348 0.247 4876 *** TABLE 5 FAIR VALUE OF DERIVATIVES ACTIVITY AS SOURCE OF BANK OPACITY Banks not involved in derivatives trading are not included. Year dummies are included in the regressions. Positive fair value (2) (3) (1) Information Asymmetry (Bid-Ask Spread) Negative fair value (4) (5) (6) (7) (8) (9) Opacity measurements Equity derivatives 10.206 * Commodities and others derivatives 120.167 5.936 *** *** Interest rate derivatives 40.094 2.215 0.167 Foreign exchange rate derivatives 13.063 * 14.028 ** Net OBS trading exposure 0.001 *** -5.626 *** Control variables Size -5.563 Loans -0.023 Tobin's Q *** 0.094 -0.108 Price # Analyst -5.517 *** -0.012 0.912 *** Volume *** -0.022 0.949 Sigma -5.549 0.094 *** 0.108 0.259 *** -0.34 *** -0.019 -0.428 *** -5.576 0.104 *** -0.110 0.258 *** *** -0.340 *** -0.025 0.909 *** -5.569 0.095 ** -0.108 0.314 *** *** -0.539 *** -0.019 0.812 *** -5.581 0.095 ** -0.108 0.259 *** *** -0.347 *** -0.023 0.983 *** -5.549 0.095 *** -0.108 0.264 *** *** -0.349 *** -0.014 0.876 *** -5.550 0.032 0.908 0.094 *** *** -0.108 0.258 *** *** -0.340 -4.306 0.095 *** 0.073 *** *** -0.108 *** -0.086 ** 0.258 *** 0.262 *** 0.225 *** *** -0.340 *** -0.365 *** -0.235 ** Rating -1.238 * -1.242 * -1.117 * -1.236 * -1.211 * -1.241 * -1.238 * -1.234 * -1.594 ** CAR -0.311 *** -0.311 *** -0.362 *** -0.310 *** -0.317 *** -0.315 *** -0.311 *** -0.312 *** -0.358 * NYSE dummy News -6.109 1.575 ** -6.154 1.571 ** -5.532 1.667 * -6.147 1.599 ** -6.179 1.557 ** -6.054 1.606 ** -6.125 1.574 ** -6.097 1.574 ** -5.606 0.260 * Constant R - Sqr. 107.01 *** 106.92 *** 106.96 *** 107.27 *** 107.06 *** 106.94 *** 106.26 *** Dummy variables Within: Between: Overall: Obs (# Quarters) *** 0.059 0.343 0.245 4908 0.059 0.344 0.245 4908 *** 106.10 0.065 0.355 0.245 4591 *** *** 0.059 0.346 0.246 4886 *, **, *** Significant at 10%, 5% and 1% respectively. 33 *** 0.060 0.346 0.246 4894 *** 0.060 0.344 0.246 4896 *** 0.059 0.344 0.245 4908 *** 0.060 0.348 0.247 4886 *** 112.97 0.042 0.432 0.289 3214 *** TABLE 6 NOTIONAL VALUE OF SWAP ACTIVITY AS SOURCE OF BANK OPACITY Banks not involved in derivatives trading are not included. Year dummies are included in the regressions. Information Asymmetry (Bid-Ask Spread) (1) (2) (3) (4) (5) (6) Opacity measurements Equity swaps 0.315 Commodity and other swaps Interest rate swaps 0.012 0.006 ** Foreign exchange rate swaps Net OBS credit exposure 0.159 *** 0.050 * OBS - net swaps exposure 0.003 *** -5.125 *** * Control variables *** -5.579 *** -0.021 -0.021 0.000 -0.005 -0.019 -0.086 Tobin's Q 0.872 0.851 0.106 0.762 0.828 1.692 Sigma 0.094 *** 0.094 *** 0.098 *** 0.096 *** 0.094 *** 0.055 *** -0.108 *** -0.108 *** -0.108 *** -0.112 *** -0.108 *** -0.053 ** 0.257 *** 0.259 *** 0.281 *** 0.267 *** 0.259 *** 0.138 *** -0.340 *** -0.355 *** -0.367 *** -0.345 *** -0.337 *** -0.049 Rating -1.247 * -1.249 * -1.276 * -1.257 * -1.230 * -1.323 ** CAR -0.311 *** -0.312 *** -0.304 *** -0.312 *** -0.315 *** -0.320 ** NYSE dummy News -6.301 1.579 ** -6.174 1.607 ** -7.237 1.658 ** -7.099 1.606 ** -6.260 1.577 ** -4.306 -0.068 * Constant R - Sqr. 107.63 *** 106.73 *** 111.21 *** 109.89 *** 107.19 *** # Analyst -5.845 *** Loans Price -5.940 *** -5.601 Volume -5.532 *** Size Dummy variables Within: Between: Overall: Obs (# Quarters) 0.059 0.348 0.248 4908 *** *** 0.060 0.345 0.246 4847 0.062 0.363 0.256 4810 *, **, *** Significant at 10%, 5% and 1% respectively. 34 *** 0.061 0.359 0.256 4864 *** 0.059 0.345 0.246 4908 *** 104.17 0.064 0.398 0.288 2103 *** TABLE 7 ROBUSTNESS TESTS FOR OPACITY: LARGE VS. SMALL BANKS Banks not involved in securitization or derivatives trading are not included in their individual regressions. Year dummies are included in the regressions. Information Asymmetry (Bid-Ask Spread) Bank's Asset Size <= US$100 billion Bank's Asset Size > US$100 billion (2) (3) (4) (5) (6) (7) (8) (9) (1) (10) Opacity measurements Net secured loans 0.001 * Net troubled loans 0.006 0.422 * ** Net securitization 0.165 ** 0.049 0.024 Net derivatives (written - purchased) 0.008 * Net OBS credit exposure 0.065 ** -0.005 0.010 *** -2.319 *** Control variables Size -6.576 Loans Tobin's Q *** -6.133 *** -7.570 0.002 -0.015 0.457 -0.668 -2.216 -14.477 0.106 *** 0.111 -0.105 *** -0.087 0.320 *** 0.300 *** 0.188 -0.472 *** -0.444 *** -0.356 Rating -1.107 ** -1.055 CAR -0.326 *** -0.283 -6.856 1.701 ** Sigma Volume Price # Analyst *** ** 0.053 -0.160 *** ** ** *** ** -7.127 *** -0.382 -0.226 -0.297 -4.650 0.034 -0.092 ** -0.004 0.001 0.000 0.229 -5.881 -3.196 -1.865 -5.325 -6.909 0.084 -0.078 0.281 *** *** ** *** -0.308 ** -1.866 ** -1.474 * ** -0.810 ** -0.379 -8.364 1.726 *** -0.339 1.966 115.99 *** 0.067 -0.040 0.211 *** ** *** -0.039 0.019 *** -0.056 0.043 -0.039 *** -0.014 -1.249 ** 0.147 -0.354 ** -0.149 0.012 0.023 -0.069 ** *** ** *** ** -0.202 ** -0.125 -3.072 0.019 -0.054 0.041 *** -0.176 0.005 0.019 ** -0.099 ** 0.032 ** *** -0.030 ** -0.066 -2.177 ** 0.013 3.290 -0.158 0.118 *** ** -0.287 * -0.020 *** -0.041 *** -0.139 -0.568 0.006 Dummy variables NYSE dummy News Constant R - Sqr. 119.59 Within: Between: Overall: Obs (# Quarters) *** *** 0.068 0.373 0.263 4622 0.070 0.367 0.262 4622 *** 125.71 0.051 0.311 0.250 964 * *** -4.949 0.311 131.48 0.049 0.465 0.309 2970 *, **, *** Significant at 10%, 5% and 1% respectively. 35 * *** -3.337 -0.031 137.49 0.081 0.434 0.307 1825 -0.361 -0.486 *** 12.07 0.172 0.208 0.259 286 *** * -0.373 -0.311 15.94 0.232 0.117 0.234 286 *** ** -0.529 -0.505 11.74 0.157 0.239 0.260 286 *** ** 4.058 -0.303 *** 81.50 *** 0.329 0.499 0.475 177 0.325 -0.548 43.12 0.192 0.353 0.357 172 ** ***