Download bank opacity and information asymmetry around quarterly earnings

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.
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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
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).
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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 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).
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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
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
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.
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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
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 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
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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.
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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).
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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 bankquarters 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 + 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.
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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 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.
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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 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.
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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
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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
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. 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.
24
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27
TABLE 1
DESCRIPTIVE STATISTICS
Banks not involved in securitization or derivatives trading are not included in Panels D and E.
Standard
Mean Median
Min.
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
**
***