bank opacity and information asymmetry around quarterly earnings

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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-the-

counter) 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 off-

balance 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.

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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

1 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

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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.

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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 off-

balance sheet lending and trading activities of banks as potential sources of bank opacity. On-

balance 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

7 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).

http://mathbabe.org/author/mathbabe/

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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

10 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).

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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:

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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

12 By definition, when a loan is not performing it becomes non-performing loan, and if a non-performing loan is 90-days 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.

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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

14 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.

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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 high-

leverage 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-/off-

balance 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

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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 SIC code 6021 and 6022 are National Commercial Banks and State Commercial Banks, respectively. 17 NASDAQ Small-Cap (NAS), NASDAQ Global Select Market (NSM), and NASDAQ Large-Cap (NMS).

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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 bank-

quarters with missing trading or financial data, I obtain a final sample of 8,783 financial quarters

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(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 + 𝛽3𝑆𝑒𝑐𝑢𝑟𝑖𝑡𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖,𝑡 +

𝛽4𝐷𝑒𝑟𝑖𝑣𝑎𝑡𝑖𝑣𝑒𝑠 𝑇𝑟𝑎𝑑𝑖𝑛𝑔𝑖,𝑡 + 𝛽𝑗 ∗ (𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑗 )𝑖,𝑡𝑁𝑗=5 + 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).

18 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.

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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 non-

performing 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 non-

accruals 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 non-

performing 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.

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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 counter-

parties 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 counter-

parties 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).

19 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.

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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

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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

17

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 non-

performing 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 non-

performing 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

18

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 Minton et al. (2006) find that in 2003 only 19 out of 345 large US banks use credit derivatives. 21 Interest rate swaps are included in interest rate derivatives.

19

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

20

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

21

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

22 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

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 counter-

parties 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.

23

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

24

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. It can be done by pushing banks to move their trading activities onto

clearinghouses rather than OTC or privately negotiated trades, where prices can be monitored,

while demanding completer disclosure on loans, mortgages, securitization and OBS derivatives

exposures.

25

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28

TABLE 1 DESCRIPTIVE STATISTICS

Banks not involved in securitization or derivatives trading are not included in Panels D and E.

Mean MedianStandard

DeviationMin. Max.

Panel A: Dependent variable

Bid-Ask spread (cents) 13.08 9.00 11.30 1.00 46.00

Panel B: Control variables

Assets ($'billions) 25.90 8.66 50.73 1.52 424.16

Loans ($'billions) 16.40 5.82 29.68 0.96 229.17

Tobin's Q (ratio) 1.04 1.04 0.04 0.97 1.13

Sigma (%) 36.66 33.43 13.20 18.64 74.83

Volume (#'000) 1690.28 1277.17 1622.15 468.05 8842.87

Price ($) 15.68 17.45 7.36 3.20 25.40

# Analyst 6.16 4.00 6.33 1.00 38.00

Rating 4.99 5.00 1.60 8.00 1.00

CAR (%) 12.38 11.92 1.65 10.31 17.40

Panel C: Secured and troubled loans

Secured by farmland ( % of gross assets) 0.87 0.28 1.35 0.00 7.99

Secured by 1-4 residential properties ( % of gross assets) 5.01 4.90 2.98 0.00 9.99

Secured by > 4 residential properties ( % of gross assets) 1.33 0.79 1.34 0.00 4.99

Secured by commercial loans ( % of gross assets) 15.89 11.17 13.24 0.01 49.99

Net secured loans ( % of gross assets) 17.83 13.93 14.02 0.00 61.28

Secured by senior lien loans ( % of gross assets) 14.94 10.06 15.97 0.00 143.62

Secued by junior lien loans ( % of gross assets) 1.96 1.08 2.81 0.00 29.95

Non-accruals loans ( % of total loans and leases) 0.59 0.45 0.47 0.00 1.99

Past due loans ( % of total loans and leases) 0.18 0.10 0.26 0.00 1.99

Charge-offs loans ( % of total loans and leases) 0.32 0.18 0.38 0.00 2.00

FDIC Texas ratio (%) 8.68 6.36 7.43 0.25 29.99

Panel D: Securitization

Family residential loans ( % of gross managed assets) 20.63 19.42 12.74 0.00 79.85

Home equity lines ( % of gross managed assets) 4.30 4.24 2.41 0.00 8.99

Credit card receivables loans ( % of gross managed assets) 1.08 0.22 2.37 0.00 14.78

Auto loans ( % of gross managed assets) 4.69 2.71 4.97 0.00 19.97

Commercial and industrial loans ( % of gross managed assets) 15.47 14.56 7.79 0.03 39.86

All other loans and leases ( % of gross managed assets) 14.40 16.24 5.61 0.00 19.98

Net all loans and leases ( % of gross managed assets) 43.73 43.45 17.26 0.01 100.00

Available for sale securities ( % of gross assets) 15.49 15.48 7.06 0.01 29.98

29

TABLE 1 DESCRIPTIVE STATISTICS (CONTINUED...)

Mean MedianStandard

DeviationMin. Max.

Panel E: Off-balance sheet derivatives exposure

Gross positive fair value of equity ( % of gross assets) 0.06 0.01 0.11 0.01 0.62

Gross positive fair value of commodity and others ( % of gross assets) 0.09 0.01 0.17 0.01 0.68

Gross positive fair value of interest rate ( % of gross assets) 0.09 0.03 0.11 0.01 0.50

Gross positive fair value of foreign exchange ( % of gross assets) 0.03 0.01 0.05 0.01 0.30

Net positive fair value ( % of gross assets) 0.09 0.03 0.12 0.01 0.68

Gross negative fair value of equity ( % of gross assets) 0.01 0.01 0.02 0.01 0.10

Gross negative fair value of commodity and others ( % of gross assets) 0.01 0.01 0.01 0.01 0.05

Gross negative fair value of interest rate ( % of gross assets) 0.09 0.04 0.12 0.01 0.50

Gross negative fair value of foreign exchange ( % of gross assets) 0.04 0.01 0.07 0.01 0.40

Net negative fair value ( % of gross assets) 0.09 0.04 0.12 0.01 0.83

Equity swaps ( % of gross assets) 3.43 0.35 5.31 0.01 23.09

Commodity and other swaps ( % of gross assets) 1.46 0.70 1.89 0.01 10.00

Interest swaps ( % of gross assets) 8.46 5.27 8.36 0.01 32.15

Foreign exchange swaps ( % of gross assets) 22.50 2.96 42.65 0.01 199.33

Net OBS credit exposure ( % of gross assets) 2.10 0.12 7.40 0.01 130.90

Hedge - Equity ( % of gross assets) 0.68 0.16 1.40 0.01 6.52

Hedge - Commodity and others ( % of gross assets) 0.11 0.08 0.12 0.01 0.48

Hedge - Interest rate ( % of gross assets) 12.63 4.98 19.98 0.01 147.32

Hedge - Foreign exchange ( % of gross assets) 2.22 0.70 3.54 0.01 24.92

Trading - Equity ( % of gross assets) 4.06 0.87 5.82 0.01 24.28

Trading - Commodity and others ( % of gross assets) 28.85 5.48 62.86 0.01 478.34

Trading - Interest rate ( % of gross assets) 22.28 8.50 29.87 0.01 124.24

Trading - Foreign exchange ( % of gross assets) 21.17 1.59 59.72 0.01 301.55

Exchange traded - all options ( % of gross assets) 0.43 0.23 0.52 0.01 1.94

Exchange traded - interest rate ( % of gross assets) 7.93 3.34 10.01 0.01 49.15

Exchange traded - foreign exchange ( % of gross assets) 0.52 0.12 0.95 0.01 5.02

Over the counter - all options ( % of gross assets) 9.58 0.55 47.36 0.01 478.34

Over the counter - interest rate ( % of gross assets) 4.03 1.22 7.07 0.01 49.29

Over the counter - foreign exchange ( % of gross assets) 0.93 0.07 1.41 0.01 4.91

Net Exposure - Equity ( % of total assets) 10.66 2.17 38.90 0.01 478.34

Net Exposure - Commodities and others ( % of total assets) 28.04 3.21 90.34 0.01 956.68

Net Exposure - Interest rate ( % of total assets) 28.69 10.75 49.66 0.01 1023.40

Net Exposure - Foreign exchange ( % of total assets) 22.30 1.43 57.73 0.01 390.46

Net Exposure - All together ( % of total assets) 40.29 10.51 100.50 0.01 2460.82

30

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) (5) (6) (7) (8) (9) (10) (11)

Opacity measurements

Secured by farmlands 0.720 * 0.147 ***

Secured by 1-4 residential properties 0.264 *** 0.465 ***

Secured by > 4 residential properties 0.343 * 0.847

Secured by commercial properties 0.052 ** 0.228 ***

Net secured loans 0.018 *

Non-accruals loans 2.421 *** 1.196

Past due loans 3.125 *** 0.717

FDIC Texas ratio 0.188 *** 0.234 ***

Net troubled loans 0.422 ***

Control variables

Size -5.824 *** -5.737 *** -6.523 *** -5.800 *** -5.844 *** -5.585 *** -6.167 *** -5.750 *** -6.131 *** -6.229 *** -6.137 ***

Tobin's Q -0.819 -5.869 0.769 -1.061 -5.564 -0.944 2.008 1.104 2.323 2.812 1.867

Sigma 0.096 *** 0.080 *** 0.096 *** 0.095 *** 0.079 *** 0.097 *** 0.133 *** 0.096 *** 0.131 *** 0.142 *** 0.147 ***

Volume -0.108 *** -0.109 ** -0.102 *** -0.104 *** -0.105 ** -0.167 *** -0.281 *** -0.107 *** -0.641 *** -0.778 * -0.263 **

Price 0.253 *** 0.251 *** 0.245 *** 0.239 *** 0.257 *** 0.242 *** 0.300 ** 0.279 *** 0.286 *** 0.306 *** 0.301 ***

# Analyst -0.280 ** -0.302 ** -0.179 * -0.218 * -0.304 ** -0.269 ** -0.585 *** -0.415 *** -0.539 *** -0.609 *** -0.672 ***

Rating -1.251 * -0.040 -1.362 ** -1.342 ** -0.021 -1.432 ** -1.222 * -1.213 * -0.978 -1.266 * -1.241 *

CAR -0.352 *** -0.472 *** -0.377 *** -0.364 *** -0.476 *** -0.335 *** -0.429 *** -0.351 *** -0.419 *** -0.375 *** -0.422 ***

Dummy variables

NYSE dummy -5.669 * -0.365 -4.337 -5.756 * -0.318 -5.939 -3.305 -5.606 * -3.085 -2.576 -2.527

News 1.515 *** 1.097 1.424 *** 1.173 ** 1.072 1.743 *** 1.578 *** 1.574 *** 1.434 ** 1.316 ** 1.590 ***

Constant 112.46 *** 104.68 *** 120.89 *** 111.97 *** 105.99 *** 107.62 *** 114.98 *** 108.63 *** 116.50 *** 116.38 *** 113.67 ***

R - Sqr.

Within: 0.063 0.056 0.059 0.054 0.060 0.067 0.059 0.061 0.062 0.064 0.061

Between: 0.339 0.307 0.322 0.334 0.312 0.324 0.373 0.360 0.350 0.377 0.374

Overall: 0.244 0.223 0.235 0.245 0.230 0.242 0.256 0.248 0.257 0.261 0.254

Obs (# Quarters) 4808 2536 4497 4464 2486 4021 3841 4839 3923 3573 3714

*, **, *** Significant at 10%, 5% and 1% respectively.

Information Asymmetry (Bid-Ask Spread)

31

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) (4) (5) (6) (7) (8) (9)


Securitization (dummy) 1.543 ***

Family residential loans 0.130 ** 0.030 *

Home equity lines 0.044 0.067

Credit card receivables loans -0.216 -0.234

Auto loans 0.296 *** 0.290 ***

Commercial and industrial

loans

-0.082 -0.060

Net securitization 0.071 *

Available for sale securities 0.243 ***

Control variables

Size -5.388 *** -5.174 *** -5.468 *** -5.760 *** -5.292 *** -5.443 *** -5.338 *** -5.466 *** -5.263 ***

Loans -0.006 0.031 -0.016 0.052 0.001 -0.019 0.003 -0.009 0.121 *

Tobin's Q 1.310 -2.933 1.323 0.568 0.581 0.942 0.568 0.206 2.801

Sigma 0.091 *** 0.091 *** 0.099 *** 0.068 *** 0.096 *** 0.095 *** 0.096 *** 0.095 *** 0.088 ***

Volume -0.107 *** -0.104 *** -0.111 *** -0.122 ** -0.109 *** -0.112 *** -0.113 *** -0.109 *** -0.103 ***

Price 0.260 *** 0.291 *** 0.250 *** 0.165 ** 0.257 *** 0.257 *** 0.260 *** 0.257 *** 0.225 ***

# Analyst -0.360 *** -0.445 *** -0.368 *** -0.129 -0.309 *** -0.371 *** -0.330 *** -0.334 *** -0.332 ***

Rating -1.193 * -1.062 -1.361 ** -2.238 ** -1.032 -1.315 * -1.008 -1.103 -1.445 **

CAR -0.321 *** -0.377 ** -0.394 ** -0.339 -0.312 -0.376 ** -0.289 *** -0.337 ** -0.327 **

Dummy variables

NYSE dummy -6.252 ** -6.745 * -5.888 * -5.088 -7.216 ** -5.669 -6.563 ** -7.099 ** -5.037

News 1.575 *** 1.505 *** 1.581 *** 0.886 1.546 *** 1.742 *** 1.561 *** 1.562 *** 1.466 ***

Constant 101.84 *** 99.21 *** 106.35 *** 114.98 *** 99.00 *** 107.73 *** 99.31 *** 101.77 *** 89.01 ***

R - Sqr.

Within: 0.061 0.060 0.060 0.036 0.062 0.059 0.062 0.060 0.060

Between: 0.347 0.353 0.336 0.335 0.336 0.344 0.344 0.341 0.324

Overall: 0.247 0.248 0.244 0.286 0.249 0.244 0.249 0.248 0.236

Obs (# Quarters) 4908 4463 4661 1834 4867 4677 4908 4905 4365


32

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) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)


Equity derivatives 3.659 ** 0.511 **

Commodities and others derivatives 8.961 * 0.011 ***

Net derivatives (written - purchased) 0.018 0.012 ***

Interest rate derivatives 0.005 0.014 0.054 ** 0.003 ***

Foreign exchange rate derivatives 0.915 * 0.266 *** 0.187 * 0.005 *

Control variables

Size -5.554 *** -5.552 *** -5.550 *** -5.572 *** -5.560 *** -5.565 *** -5.580 *** -5.538 *** -5.399 *** -5.584 *** -5.817 *** -5.586 *** -5.843 *** -5.685 ***

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 -0.012

Tobin's Q 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 1.033

Sigma 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 ***

Volume -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 ***

Price 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 ***

# Analyst -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 **

Dummy variables

NYSE dummy -6.142 ** -6.156 *** -6.156 ** -6.150 ** -6.122 ** -6.093 ** -6.234 ** -6.108 ** -5.371 ** -6.144 ** -7.027 ** -6.137 ** -7.165 ** -6.263 **

News 1.575 *** 1.574 *** 1.574 *** 1.578 *** 1.577 *** 1.575 *** 1.559 *** 1.588 *** 1.594 *** 1.581 *** 1.640 *** 1.581 *** 1.616 *** 1.607 ***

Constant 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 ***

R - Sqr.

Within: 0.059 0.059 0.059 0.059 0.059 0.059 0.059 0.059 0.062 0.059 0.061 0.059 0.061 0.060

Between: 0.344 0.344 0.344 0.344 0.344 0.344 0.346 0.344 0.337 0.345 0.360 0.344 0.360 0.348

Overall: 0.245 0.245 0.245 0.245 0.245 0.245 0.246 0.245 0.241 0.246 0.254 0.245 0.257 0.247

Obs (# Quarters) 4908 4908 4908 4908 4908 4908 4894 4894 4863 4908 4813 4908 4877 4876


Exchange traded Over the counter Hedge Trading


33

TABLE 5 FAIR VALUE OF DERIVATIVES ACTIVITY AS SOURCE OF BANK OPACITY


(1) (2) (3) (4) (5) (6) (7) (8) (9)


Equity derivatives 10.206 * 120.167 ***

Commodities and others derivatives 5.936 *** 40.094

Interest rate derivatives 2.215 0.167

Foreign exchange rate derivatives 13.063 * 14.028 **

Net OBS trading exposure 0.001 ***

Control variables

Size -5.563 *** -5.549 *** -5.517 *** -5.576 *** -5.569 *** -5.581 *** -5.549 *** -5.550 *** -5.626 ***

Loans -0.023 -0.022 -0.012 -0.019 -0.025 -0.019 -0.023 -0.014 0.032

Tobin's Q 0.949 0.912 -0.428 0.909 0.812 0.983 0.876 0.908 -4.306

Sigma 0.094 *** 0.094 *** 0.104 *** 0.095 *** 0.095 *** 0.095 *** 0.094 *** 0.095 *** 0.073 ***

Volume -0.108 *** 0.108 *** -0.110 ** -0.108 ** -0.108 *** -0.108 *** -0.108 *** -0.108 *** -0.086 **

Price 0.259 *** 0.258 *** 0.314 *** 0.259 *** 0.264 *** 0.258 *** 0.258 *** 0.262 *** 0.225 ***

# Analyst -0.34 *** -0.340 *** -0.539 *** -0.347 *** -0.349 *** -0.340 *** -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 *

Dummy variables

NYSE dummy -6.109 ** -6.154 ** -5.532 * -6.147 ** -6.179 ** -6.054 ** -6.125 ** -6.097 ** -5.606 *

News 1.575 *** 1.571 *** 1.667 *** 1.599 *** 1.557 *** 1.606 *** 1.574 *** 1.574 *** 0.260

Constant 107.01 *** 106.92 *** 106.10 *** 106.96 *** 107.27 *** 107.06 *** 106.94 *** 106.26 *** 112.97 ***

R - Sqr.

Within: 0.059 0.059 0.065 0.059 0.060 0.060 0.059 0.060 0.042

Between: 0.343 0.344 0.355 0.346 0.346 0.344 0.344 0.348 0.432

Overall: 0.245 0.245 0.245 0.246 0.246 0.246 0.245 0.247 0.289

Obs (# Quarters) 4908 4908 4591 4886 4894 4896 4908 4886 3214


Positive fair value Negative fair value


34

TABLE 6 NOTIONAL VALUE OF SWAP ACTIVITY AS SOURCE OF BANK OPACITY


(1) (2) (3) (4) (5) (6)


Equity swaps 0.315

Commodity and other

swaps

0.012

Interest rate swaps 0.006 **

Foreign exchange rate

swaps

0.159 ***

Net OBS credit exposure 0.050 *

OBS - net swaps exposure 0.003 ***

Control variables

Size -5.601 *** -5.532 *** -5.940 *** -5.845 *** -5.579 *** -5.125 ***

Loans -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 ***

Volume -0.108 *** -0.108 *** -0.108 *** -0.112 *** -0.108 *** -0.053 **

Price 0.257 *** 0.259 *** 0.281 *** 0.267 *** 0.259 *** 0.138 ***

# Analyst -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 **

Dummy variables

NYSE dummy -6.301 ** -6.174 ** -7.237 ** -7.099 ** -6.260 ** -4.306 *

News 1.579 *** 1.607 *** 1.658 *** 1.606 *** 1.577 *** -0.068

Constant 107.63 *** 106.73 *** 111.21 *** 109.89 *** 107.19 *** 104.17 ***

R - Sqr.

Within: 0.059 0.060 0.062 0.061 0.059 0.064

Between: 0.348 0.345 0.363 0.359 0.345 0.398

Overall: 0.248 0.246 0.256 0.256 0.246 0.288

Obs (# Quarters) 4908 4847 4810 4864 4908 2103



35

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.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)


Net secured loans 0.001 * 0.006 *

Net troubled loans 0.422 ** 0.165 **

Net securitization 0.049 0.024

Net derivatives (written - purchased) 0.008 * 0.065 **

Net OBS credit exposure -0.005 0.010 ***

Control variables

Size -6.576 *** -6.133 *** -7.570 *** -6.909 *** -7.127 *** -0.382 -0.226 -0.297 -4.650 *** -2.319 ***

Loans 0.002 -0.015 0.457 ** 0.034 -0.092 ** -0.004 0.001 0.000 0.229 ** 0.013

Tobin's Q -0.668 -2.216 -14.477 -5.881 -3.196 -1.865 -5.325 ** -3.072 -0.287 3.290

Sigma 0.106 *** 0.111 *** 0.053 ** 0.084 *** 0.067 *** 0.019 *** 0.012 *** 0.019 *** 0.005 0.019 **

Volume -0.105 *** -0.087 ** -0.160 *** -0.078 ** -0.040 ** -0.056 -0.039 ** -0.054 * -0.158 ** -0.099 **

Price 0.320 *** 0.300 *** 0.188 ** 0.281 *** 0.211 *** 0.043 *** 0.023 *** 0.041 *** -0.030 0.032 **

# Analyst -0.472 *** -0.444 *** -0.356 -0.308 ** -0.039 -0.014 -0.069 ** -0.020 -0.066 -0.041

Rating -1.107 ** -1.055 -1.866 ** -1.474 * -1.249 ** 0.147 -0.202 0.118 -2.177 *** -0.568

CAR -0.326 *** -0.283 ** -0.810 ** -0.379 -0.354 ** -0.149 ** -0.125 *** -0.176 ** -0.139 0.006

Dummy variables

NYSE dummy -6.856 ** -8.364 *** -0.339 -4.949 * -3.337 -0.361 -0.373 -0.529 4.058 *** 0.325

News 1.701 *** 1.726 *** 1.966 * 0.311 -0.031 -0.486 *** -0.311 *** -0.505 *** -0.303 -0.548 **

Constant 119.59 *** 115.99 *** 125.71 *** 131.48 *** 137.49 *** 12.07 * 15.94 ** 11.74 ** 81.50 *** 43.12 ***

R - Sqr.

Within: 0.068 0.070 0.051 0.049 0.081 0.172 0.232 0.157 0.329 0.192

Between: 0.373 0.367 0.311 0.465 0.434 0.208 0.117 0.239 0.499 0.353

Overall: 0.263 0.262 0.250 0.309 0.307 0.259 0.234 0.260 0.475 0.357

Obs (# Quarters) 4622 4622 964 2970 1825 286 286 286 177 172



Bank's Asset Size <= US$100 billion Bank's Asset Size > US$100 billion

bank opacity and information asymmetry around quarterly earnings

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