COMPLEXITY OR TRANSPARENCY: WHAT MAKES BANKS OPAQUE?
Tanvir Ansari
School of Economics & Finance, Queensland University of Technology
This Version: 21 February 2013
ABSTRACT
The goal of achieving transparency has become more challenging in recent years as banks’
activities have become increasingly complex and dynamic, making it harder for outsiders to
comprehend the full extent of banks’ exposures. In this study we examine various on- and offbalance sheet activities that are thought to be responsible for increased bank opacity, where
opacity is proxied by bid-ask spread and analysts surprise diversion. Using a sample of 275 U.S.
commercial banks listed on the NASDAQ/NYSE/AMEX from Q4-1999 to Q2-2012, our findings
support the argument that banks off-balance sheet trading activities, in particular derivatives, are
a significant source of opacity. This is followed by securitization, troubled loans, and secured
loans. Our results support the suggestion that assets “slipperiness”, i.e., the speed with which
banks trade in and out of their assets is the main driver of bank opacity. While we support the
proposed move of derivatives trading from over-the-counter markets to clearing houses, where
prices can be monitored, we believe that such a move is unlikely to significantly reduce bank
opacity. Reducing opacity will require more timely and detailed disclosure on banks’ volatile
trading exposures.
JEL classification: G21, G14.
Keywords: bank opacity; commercial banks; information asymmetry; loans; off-balance sheet
activities; securitization; transparency.
__________________________________________________________________________
This essay is one of three chapters of my PhD thesis. I would like to thank my supervisors, Janice
How and Peter Verhoeven. I am also thankful to Jonathan Bader, Douglas Foster, Jason Park,
Shams Pathan, Terry Walter and participants at the University of Western Australia (UWA)
Doctoral Colloquium 2012 for their helpful comments and suggestions. Errors and omissions are
solely my own.
Author: Tanvir Ansari
Address: WS5 Z701, 2 George Street, Brisbane, Queensland Australia 4000.
Email: tanvir.ansari@qut.edu.au
I.
INTRODUCTION
“... confidence had been shaken by the ‘complex and opaque’ way banks applied capital to the
various risk assets held on their balance sheets, potentially overstating their financial strength ...”
Sir Mervyn King,
Governor, Bank of England
29 November 2012 GARP Story
Whether it is the unintended consequence of deregulation or purposefully designed with intent
to deceive,1 there is no doubt that banks are informational opaque. At the heart of the problem is
a concern about the transparency of banks’ financial statements. 2 Despite enhanced risk
disclosure, e.g., via quarterly risk reporting to the regulator, banks are unable to give a full fair
and accurate statement of their risk exposure.
The problem of opacity extends well beyond the banks’ loan portfolios as it involves almost
every aspect of modern banking activity, much of which encompasses complex investments and
trading, not merely lending. One such activity is securitization which, due to its off–balance sheet
(OBS) nature, facilitates regulatory arbitrage while remaining within the letter of the law.
Securitization, where reinvestment is mostly financed by short-term money-market funds, is
thought to bring about reduced bank monitoring of loan quality, with the credit risk borne mostly
There is ample evidence supporting the proposition that banks encourage opacity to serve their own selfish ends:
helping Mexican drug dealers launder money (HSBC); funneling cash to Iran (Standard Chartered); LIBOR fraud
(Barclays and a cast of dozens); falsifying mortgage records/improperly foreclosing on borrowers (all the large
banks); routinely misleading clients (Merrill Lynch, JPMorgan Stanley, CitiCorp); selling securities known to be
“garbage” (Goldman Sachs, Merrill Lynch); and secretly betting against clients to profit from their ignorance
(Goldman Sachs). Source: http://www.ritholtz.com/blog/2013/01/big-banks-are-black-boxes-disclosure-is-woeful/
A recent list of cases in which parties disagreed about fair value or pricing practices employed can be found here:
http://expectedloss.blogspot.com.au/2011/04/contested-pricings-list.html.
2 In its report on “Enhancing Bank Transparency”, the Basle Committee on Banking Supervision (1998, page 4)
defines transparency (the opposite of opacity) as “public disclosure of reliable and timely information that enables
users of that information to make an accurate assessment of a bank’s financial condition and performance, business
activities, risk profile and risk management practices.” On page 15 of the report, the critical qualitative
characteristics of information that contribute to bank transparency are further elaborated: comprehensiveness,
relevance and timeliness, reliability, comparability, and materiality.
1
1
by outsiders—a simple case of moral hazard.3 As loan and brokerage trading margins are
increasingly squeezed, banks have progressively turned more to trading, which is an inherently
opaque and volatile business, in order to maintain profits. For the most part, banks trade in
mortgage-backed securities and other credit derivatives for which there are no active public
markets. Since market prices for these instruments in the over-the-counter (OTC) market is
seldom made available, banks employ their own model-based valuation techniques with
observed prices of similar assets or other market data as their inputs. This makes the fair
valuation of these instruments more like an educated guess. Opaque markets also encourage
price manipulation and price inflation. While exchange-traded instruments are in essence
transparent since their fair value is publicly observable, asset “slipperiness”, i.e., the speed at
which a bank trades in and out of its assets, is a potential source of bank opacity (Myers and
Rajan, 1998; Morgan, 2002; Wagner, 2007a, 2007b; O'Neil, 2012).4 The business model of banks
is perhaps best summarized by Morgan (2002, p. 874) who describes banks as “… black boxes,
where money goes in, and money goes out, and the risks taken in the process of intermediation
are difficult to observe from outside the bank”.
The aim of this paper is to examine which assets and activities cause banks to be opaque
and thus suffer from greater information asymmetry problem. We employ the (intraday) bid-ask
The U.S. Department of the Treasury (page 6) notes that “Securitization, by breaking down the traditional
relationship between borrowers and lenders, created conflicts of interest that market discipline failed to correct.
Loan originators failed to require sufficient documentation of income and ability to pay. Securitizers failed to set high
standards for the loans they were willing to buy, encouraging underwriting standards to decline. Investors were
overly reliant on credit rating agencies. Credit ratings often failed to accurately describe the risk of rated products. In
each case, lack of transparency prevented market participants from understanding the full nature of the risks they
were taking.”
4 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. 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 are taking (Hentschel and Smith, 1996).
3
2
spread and analysts surprise dispersion5 as our metric of information asymmetry and valuation
disagreement. Based on market microstructure theory, if outsiders find it difficult to value bank
assets or disagree on their value, the bid-ask spread will increase or increase in analysts surprise
dispersion reflect this (Lin et al. 1995; Agrawal et al., 2004). Hence, we make the implicit
assumption that bank activities that increase opacity are naturally associated with larger bid-ask
spreads and analysts surprise dispersion.
The bank’s on-balance sheet activities we investigate include: (i) secured loans from the
lending books; (ii) various phases of troubled loans; and (iii) loan securitization from trading
books. OBS activities examined consist of (i) notional and fair value of derivative exposures; (ii)
net use of derivatives held (hedging vs. trading purposes); (iii) exchange traded vs. OTC
derivative exposure; and (iv) swaps exposure. For each bank activity, we examine both the total
exposure and exposure by asset type to inform us which asset category augments opacity most.
We also examine the extent of banks’ involvement in short-term money-market funding through
repurchase agreements (Repos).
We conduct our tests around quarterly earnings announcements as these corporate events
spark heightened activity of informed trading (Affleck-Graves et al., 1995). Although the timing
of earnings announcements is predictable, there is a large body of literature, tracing back to the
seminal paper by Ball and Brown (1968), which shows that these corporate announcements
convey information about the results of the firm’s activities. It follows that, for banks, the
probability an outsider is able to accurately assess the banks' value must be higher around these
events. Importantly, the change in information asymmetry has been found to be greater during
Analysts surprise dispersion is computed as standard deviation of all the analysts’ surprises for last quarterly
earnings announcement.
5
3
the period surrounding the earnings announcement than in a “normal” period, suggesting a
window of opportunity for informed traders to profit on their private information. Therefore, we
anticipate that the cross-sectional variation in the level of information asymmetry around
earnings announcements is driven predominantly by informational uncertainty about banks’
assets.
We run our analyses for a sample of 275 U.S. commercial banks traded on the
NASDAQ/NYSE/AMEX from Q4-1999 to Q2-2012. Results show that loans, in particular those
secured by farmlands, and all phases of non-performing loans increase information asymmetry,
suggesting that these are a source of bank opacity. Securitization and OBS trading activities
(derivatives and swaps) also significantly intensify information asymmetry, with the latter being
a particularly important source of bank opacity. Larger banks and banks listed on the NYSE have
lower information asymmetry but the reverse is found for banks with a higher capital adequacy
ratio, greater analyst following, and a higher credit rating. Our results support the suggestion
that it is not the opaqueness of OTC markets, but rather the bank’s asset “slipperiness”, i.e., the
speed with which banks trade in and out of their assets, which drives bank opacity. While we
support the proposed move of derivatives trading from OTC to clearing houses, where prices can
be monitored in a more transparent environment, we believe that this alone is unlikely to
resolve the pervasive opacity problem in banks. Reducing opacity will require more timely
disclosure on banks’ highly volatile trading exposures.
Our study therefore contributes to the literature by identifying the specific activities that
contribute most to bank opacity. We depart from past studies in our measurement of
information asymmetry and herein rest another research contribution. Specifically, unlike past
studies which measure information asymmetry using the average (daily closing) bid-ask spread
4
computed over the full year, our information asymmetry measure concentrates on days
surrounding the day of the earnings announcement. The bid-ask spread computed this way
provides a more accurate proxy for information asymmetry due to the heightened activity of
informed trading around this corporate event where the change in information asymmetry is
greatest.
The rest of the paper is organized as follows. The next section presents the literature
review. Section III discusses the hypotheses and Section IV outlines the sample selection
procedures and research method. Empirical results are discussed in Section V, with a conclusion
provided in Section VI.
II. LITERATURE REVIEW
Since 1990, banks have traded at an average of 40 percent discount to the S&P500 Index, as
measured by the PHLX/KBW Bank Index. If bank opacity exposes investors to higher risk, then
investors would apply a higher valuation discount to opaque banks so that their stocks would
yield a higher expected return than those of more transparent banks. In this section, we
summarize the prevailing literature that examines whether banks, notwithstanding their
considerable disclosure obligations, represent an especially opaque form of business
organization.
Morgan (2002) is the first to document the opacity of banks.. Using split ratings between
Moody’s and Standard and Poor’s on nearly 8,000 new bonds issued between 1983 and 1993 to
proxy for information asymmetry, he finds that bond ratings disagree more often over bonds
issued by banks than by industrial firms. He contends that banks are like “black boxes”, with
money flowing in and out of the box but the risks taken in the process of intermediation is mostly
invisible to outsiders. Split ratings are more likely to occur when banks substitute loans for
5
securities and trading assets, while fixed assets decrease the probability of a ratings
disagreement.
Ianotta (2006) corroborates Morgan’s (2002) results. Using a sample of 2,473 bonds issued
by European firms during the 1993-2003 period, he shows that the probability of ratings
disagreement increases with financial assets, bank size, and capital ratio but decreases with fixed
assets. In a follow up study, Ianotta and Navone (2009) empirically measure bank opacity by the
frequency of “crashes”, defined as large negative market-adjusted returns. For a sample of U.S.
stocks from 1990 to 2000, and after controlling for size and leverage, they conclude that banks
are indeed more opaque, generating more frequent equity market crashes relative to non-bank
firms.
A few researchers express doubt about the relatively higher opacity in banks. Benston and
Kaufman (1988, page 48) argue that banks are easier to value than other firms: “market value
accounting is much more feasible and inexpensive for financial institutions to adopt than for
most other enterprises. Unlike nonfinancial firms, banks have relatively small investments in
assets for which current market values are difficult to measure.” Similarly, Flannery et al. (2004,
page 2) argue that “… loan illiquidity and private information about specific borrowers don’t
necessarily make banks more difficult to value than nonfinancial firms are. Just as many bank
loans do not trade in active secondary markets, neither do many assets of nonfinancial firms, e.g.,
plant and equipment, patents, managers’ human capital, or accounts receivable. How can outside
investors accurately value the public securities issued by these firms?” They argue that empirical
issues concerning opacity are more significant than the question of whether banks are more
opaque than non-bank firms. Indeed, Hauck and Neyer (2008) challenge the use of split ratings as
6
an indicator for opacity as split ratings may occur irrespective of whether an industry is
transparent or opaque.
With this in mind, Flannery et al. (2004) assess whether banks are relatively more opaque
and thus more difficult to value than matched (similarly sized and priced) non-financial firms
using bid-ask spread and analysts’ earnings forecast errors as proxies for opacity. Their sample
consists of 320 banks and bank holding companies (BHCs) traded on the NYSE/AMEX or
NASDAQ between January 1990 and December 1997. Contrary to Morgan (2002), they find no
evidence that large (NYSE/AMEX traded) banks are particularly opaque, and that investors can
evaluate large banking firms as readily as they can non-financial firms. Further, the fact that
smaller (NASDAQ traded) BHCs exhibit substantially lower return volatilities and more accurate
analyst earnings forecasts than the control sample implies that opacity is not a prominent feature
of these banking firms either. They conclude that if banks are intrinsically opaque, government
regulations and banking supervision would have reduced that opacity.
In a later study, Flannery et al. (2010) re-examine bank opacity, focusing on the information
role of supervisory stress tests during the GFC and whether such disclosures reveal new
information about bank risk exposures. Their proxies of bank opacity, based on equity trading
patterns, e.g., adverse selection component of bid‐ask spread and effective bid-ask spread,
increased dramatically during the banking crisis, implying that banks have become more opaque
during market panic even if they were not more opaque in ordinary times. Their findings also
show that the recent stress test, which the Obama government instructed banks to carry out,
provided new information to the market and succeeded in fostering financial stability.
Others support this conclusion. Bischof et al. (2012) use the 2011 EU-wide stress-testing
exercise and the concurrent Eurozone sovereign debt crisis as a setting to study the
7
consequences of supervisory disclosure of bank-specific proprietary information (i.e., stress-test
simulations and credit risk exposures). First, they analyze how one time supervisory disclosures
interact with banks’ subsequent voluntary disclosure in determining bank opacity. Second, they
analyze whether stress-test disclosures induce a change in banks’ risk-taking behavior, thus
mitigating industry-wide risk exposure and uncertainty in financial markets. They find a
substantial relative increase in stress-test participants’ voluntary disclosure of sovereign credit
risk exposures subsequent to the release of credit risk tables. Such a commitment to increased
disclosure is accompanied by a decline in bank opacity as measured by the bid-ask spread.
However, Bischof et al. (2012) note a general increase in financial market uncertainty, proxied by
liquidity volatility and its sensitivity to changes in sovereign credit risk. The increased
uncertainty is mitigated if disclosures reveal a reduction in banks’ exposure to sovereign credit
risk during the crisis. These findings indicate that negative stress-test results incentivize banks to
reduce risk exposures.
Ashcraft and Bleakley (2006) shed light on bank opacity by evaluating how access to
overnight credit is affected by changes in public and private measures of bank borrower
creditworthiness. Using plausible exogenous variation in demand for federal funds, they identify
the supply curve facing a bank borrower in the interbank market. They find evidence that lenders
respond to adverse changes in public information about credit quality by restricting access to the
market in a disciplined fashion. However, borrowers respond to adverse changes in private
information about their credit quality by increasing leverage so as to offset the future impact on
earnings. Their findings suggest that banks are able to manage the real information content of
these disclosures. In particular, public measures of loan portfolio performance have information
about future loan charge-offs, but only in quarters when the bank is examined by supervisors.
8
However, the loan supply curve is no longer sensitive to public disclosures about nonperforming
loans in an examination quarter, suggesting that investors are unaware of this information
management.
Haggard and Howe (2007) examine bank opacity (proxy by stock price informativeness (RSquare)) using the theoretical model of Jin and Myers (2006),6 which hypothesises that the stock
returns of opaque firms are more likely to reflect market information rather than firm-specific
information, and to experience stock crashes than banks with more firm-specific information in
their returns. Using a sample of 243 BHCs for the period 1993-2002, representing about half of
all assets held by BHCs at the end of 2003, their findings are consistent with banks being more
opaque than matching industrial firms. Banks have less firm-specific information in their equity
returns than matched industrial firms, and more opaque banks are more likely to experience
sudden drops in stock price. In particular, banks with a lower proportion of agricultural and
consumer loans are associated with higher opacity. Contrary to the conclusions of Flannery et al.
(2004), they find that NASDAQ banks are more opaque than matched industrial firms. As a
caveat, they admit their results are tilted towards smaller BHCs, representing just 16 percent of
all BHCs at the end of 2003.
Jones et al. (2012) examine whether share price revaluations of banks from merger
announcements convey information to the market about the value of non-merging banks, and
how it is related to non-merger bank opacity. They find evidence of information transfer as the
merger announcements indeed convey information about the value of non-merger banks. Nonmerger banks with more loans and fewer transparent assets have larger positive cumulative
The model is an extension of Roll’s (1987) model to measure stock price informativeness in terms of R 2. They
compute R2 for the returns of large stocks by assuming that returns are explained by systematic economic influences,
the returns on other stock in same industry, and public firm-specific news events.
6
9
abnormal returns (CAR) surrounding the announcement of a large merger. Other types of
potentially opaque activities, such as trading or intangible assets, appear to have little influence
on the stock price reaction. The CARs are largest for non-merger banks that hold more loans,
consistent with loans being the primary source of opacity for banks. They conclude that
contagion is a characteristic of opaque markets and that price volatility engendered by opacity
contributes to financial fragility.
Cheng et al. (2008) investigate the effect of asset securitization on BHCs’ information
opacity for the period Q2-2001 to Q2-2007. Using bid-ask spread, share turnover, and analyst
forecast dispersion as proxies of information opacity, they find BHCs that engage in securitization
have greater information opacity, which increases with the magnitude of the securitized assets.
Vallascas and Keasey (2012) examine the relationship between potential systemic risk and
opacity in European banks, and evaluate to what extent regulatory initiatives that enhance
accounting disclosure remove bank opacity and increase the amount of firm-specific information
incorporated in stock prices. They find stock price synchronicity, an indicator of potential
systemic risk, is higher in more opaque countries. Their results suggest that increased
transparency in the banking system decreases systemic risk in the financial sector. However,
additional
analysis
shows
that
enhancing
accounting
disclosure
does
not
reduce
interdependencies between banks. The impact of the disclosure on synchronicity is closely
related to the size of banking firms. In the smallest banks, more disclosure reduces stock price
synchronicity but above a certain banking size, there is a positive relationship between
disclosure and measures of banking interdependencies. Their results suggest that regulatory
initiatives motivated by the purpose of increasing the flow of information on banks are likely to
10
reduce interdependencies only for the smallest units, namely in banks that are less important
from a systemic stability perspective.
Lastly, Jeffrey et al. (2012) examine the effects of opacity on bank valuation and
synchronicity in bank equity returns over the years 2000-2006 prior to the GFC. They find that
investments in opaque assets are more profitable and, taking profitability into account, larger
valuation discounts relative to transparent assets. The valuation discounts on opaque asset
investments decline over the 2000-2006 period only to be followed by a sharp reversal in 2007.
The decline coincides with a rise in bank equity share price, a decrease in transparent asset
holdings by banks, and greater return synchronicity–all this evidence is consistent with a
feedback effect.
Their result suggests two reasons why opacity hinders the ability of financial markets to
effectively discipline bank risk taking and thereby create systemic risk. First, accounting for
profitability and other factors that impact bank equity values, investments in opaque assets
necessitate higher required rates of market return. In a perfect world, the market correctly
assesses the risks associated with opaque assets, resulting in an efficient allocation of scarce
resource to investments in opaque and transparent assets. However, if markets do not
sufficiently discount the risks imbedded in opaque assets, banks are rewarded with higher equity
values. This reward can set off a feedback effect that encourages other banks to also increase
their investment in opaque assets and thereby result in a higher concentration of risk in the
financial system (ex post) than market participants realize. Second, bank investments in opaque
assets create more systematic risk and reduce idiosyncratic risk. Opacity causes financial
markets to become less information efficient. The resulting increase in price synchronicity raises
the likelihood of systemic market failure from revaluations triggered by changes in outside
11
investors’ perceptions about risk. Their results show that opacity matters because it reduces the
effectiveness of market discipline.
In summary, whether banks are relatively more opaque than non-bank firms is an empirical
question as the findings largely depend on the proxies used for opacity. This suggests that opacity
may well depend on who is the user of the information, may it be the investor, the regulator, the
credit provider, the analysts or the credit rater. In what follows, we provide additional evidence
from the equity market to support our conjecture why banks' securitization and OBS trading
activities are significant sources of bank opacity.
III. HYPOTHESES
Our first hypothesis on bank information asymmetry relates to on-balance sheet lending
activities. 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. As delegated monitors, banks should therefore 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 a bank loan commitments are renewed suggests that banks are better
informed about their borrowers than market participants (James, 1987). Thus, we predict:
H1: There is a direct relationship between the level of information asymmetry and banks’
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 correctly value the
12
various phases of troubled loans7 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 are deliberately retaining some of the
idiosyncratic risk in their loans portfolio such as that of problematic loans and aren’t reporting it
to outsiders then it causes greater difficulty to outsiders in valuation. Troubled loans that are not
diversified away are therefore a source of opacity and increased information asymmetry:
H2: There is a direct relationship between the level of information asymmetry and troubled
loans.
Since 2001, many commercial banks have moved away from the traditional deposits-loans
business model to securitized banking8 (of mortgage loans) and highly leveraged securities
trading, in particular OBS structured derivatives. Securitization can increase bank opacity in
three ways. First, securitization of loans is thought to be a means of “arbitraging” regulatory
capital requirements by keeping risky assets on the balance sheet of so-called “special purpose
vehicles” (SPV) instead of their own (Calomiris, 2009, 2010; Calomiris and Mason, 2004). 9 By
transferring risky capital OBS, banks are able (on paper) to maintain lower regulatory capital and
appear less risky and more healthy than they actually are. Calomiris and Mason (2004) find that
while securitization results in some transfer of risk out of the originating bank, the risk remains
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 classified as past-due and non-accrual loan. Further if a past-due
or non-accrual is still not performing then bank restructures these loans and then reports under restructured loans.
Finally, when a loan default occurs then banks write-off these loans to remove it from their balance sheet.
8 Since 1989, commercial banks have been able to securitize loans, when an appeals court ruled that securitization
does not violate the Glass-Steagall Act that separated commercial and investment banking
(http://openjurist.org/885/f2d/1034andSecuritizeGlasssSteagall.pdf).
9 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).
7
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in the securitizing bank as a result of implicit recourse.10 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 provide a misleading picture about bank
capital, performance, and underlying risk.11
Second, banks rely on “soft” information12 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 monitor 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 can create severe market and credit risk exposures for banks. To
balance liquidity with bank exposure to market and credit risk from securitization, banks engage
in highly liquid and volatile trading activities. While these trading activities offset the liquidity
risk exposures of securitization, they come at the cost of additional market risk exposure as well
as adding additional performance pressure on trading managers which may result in even riskier
and aggressive decisions (Keoun, 2010). The increased risk-return appetite leads banks to the
Recourse refers to guarantees promised to securitized securities investors allowing the transfer of losses to the
bank if the performance of the underlying portfolio of receivables deteriorates. Therefore, if explicit recourse terms
are present, the transaction is not considered a 'true sale' under FASB 140, since some risk remains with the seller,
and the protected assets would remain on the bank’s balance sheet. While opposite of it is implicit recourse in which
a bank maintains credit support beyond contractual obligations to the securitized assets. Sometimes implicit
recourse referred to as moral recourse, the originator may choose to provide support to distressed asset pools,
although this behavior would violate the true sale conditions.
11 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.
12 Soft information is like what the borrower plans to do with the loan proceeds, generally obtained by a loan officer
taking a prospective borrower’s loan application. The use of soft information allows the lender to tailor the contract
to the borrower’s risk. If a loss occurs, the lender is fairly compensated for it. Soft information reduces credit losses
overall and increase profits for banks.
10
14
extensive use of short-term money market funding, complex OBS derivatives, and swaps
trading.13
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 leads to
the classic agency problem of asset substitution in two ways. First, traders can change their
position without the knowledge of owners/management, let alone 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) influential 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 hidden by complex financial arrangements. These
make it increasingly difficult, if not impossible, for outsiders to accurately assess the true value of
bank assets. Therefore, we predict:
H3: There is a direct relationship between the level of information asymmetry and banks’
securitization activities.
H4: There is a direct relationship between information asymmetry and banks’ on- and offbalance sheet trading activities.
Lack of data may well have prevented ratings agencies to assign correct rating to structured securities. Apparently
even the rating firms learn from the sub-prime crisis that having access to all relevant information and in a timely
manner is necessary for rating structured financial products.
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15
IV. DATA AND RESEARCH METHOD
Data
Our focus is on those financial institutions which are insured and supervised by Federal Deposit
Insurance Corporation (FDIC) and Office of the Comptroller of the Currency (OCC). The initial
sample consists of all commercial banks with SIC codes 14 6021 and 6022 that are listed on the
three major U.S. exchanges: New York Stock Exchange (NYSE), American Stock Exchange (ASE),
and National Association of Securities Dealers Automated Quotations (NASDAQ).15 The sample
period is from Q4-1999 to Q2-2012. Banks with at least 50 percent of shares outstanding owned
by another domestic bank holding company or foreign bank are excluded and so are banks
trading on pink slips. This results in a sample of 330 commercial banks, of which 54 are traded on
NYSE/ASE and 276 on NASDAQ.
Table 1 shows the selection of our sample from the U.S. banking system. Panel A presents
the banking industry by type (commercial or savings) and assets concentration. There are 7,436
banks, of which 85.42 percent (6,352) are commercial banks and 14.58 percent (1,084) are
savings institutions. Over 50 percent of financial institutions (3,954) are commercial lenders, and
20 percent (1,152) are agricultural banks. Panel B indicates that 70.28 percent of commercial
banks remained active during our sample period. The banking industry is top heavy, as Panel C
shows, with the top 353 banks (or 6.31 percent of 5,592 active commercial banks) representing
90 percent of the banking industry in terms of total assets. Panel D shows that 330 commercial
banks (or 6.31 percent of 5,592 active commercial banks) are traded on NYSE, ASE or NASDAQ.
Although not reported in detail, our sample captures the bulk of the commercial banking industry
14
15
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).
16
in terms of total asset value. For example, the 42 commercial banks trading on the NYSE make up
69.62 percent of the total asset value of the U.S. commercial banking industry as at 25th August
2012.
For the final sample of 330 banks, quarterly earnings announcement dates and times are
collected from I/B/E/S, Capital IQ Compustat, Thomson Reuters Global News, Thomson Reuters
Ticker History (TRTH) supplied by Securities Industry Research Centre of Asia-Pacific (SIRCA), and
Worldscope. Table 2 presents the step-by-step verification process on the date and time of the
earnings announcements across the five databases. We start with a sample of 9,484 quarterly
earnings announcement dates and times from I/B/E/S. We eliminate banks with missing
quarterly earnings announcements dates, and with dates that could not be verified by the other
databases.
Panel B shows there is a high degree of inconsistency in the reported dates of quarterly
earnings announcements among the databases. For example, just 71.34 percent of the
announcement dates from I/B/E/S agree with those from Worldscope. I/B/E/S and Compustat
databases have the highest degree of agreement at 88.55 percent. Panel C shows that when
I/B/E/S and Compustat disagree on the reporting dates, there is a mean difference of up to 30
days in 53.04 percent of the cases, which is quite significant by any standard.
We obtain intra-day trading data for the 275 banks from TRTH. Quarterly financial data are
collected from Worldscope, Bloomberg, and Federal Financial Institutions Examination Council's
(FFIEC) data repository website. Call Reports containing data on banks’ loan, securitization and
trading activities are sourced from Call Report Agencies (CRAs) and verified with The Uniform
Bank Performance Report (UBPR) through Central Data Repository (CDR) and WRDS Data
Repository. Some financial data from FFIEC are verified using Worldscope and Bloomberg. After
17
removing all acquiring banks from the quarter in which the acquisition occurred and removing
bank-quarters with missing trading or financial data, we obtain a final sample of 8,783 financial
quarters (51-quarterly periods) for 275 U.S. commercial banks from Q1-1999 to Q2-2012. This
final sample accounts for 87 percent of the total assets of the U.S. commercial banking industry as
at 25th August 2012.
Research Method
We use the fixed-effects panel regression model with residuals clustered at the firm level to
estimate the relationship between the test variables and the level of information asymmetry. The
model specification is as follows:
∑
(1)
The dependent variable is our measure of information asymmetry (Info. Asymm) surrounding the
earnings announcement, which we proxy by the bid-ask spread (
), 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,
we take the next trading day to be earnings announcement day, consistent with Berkman and
Truong (2009).
Following Bagehot (1971), we 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 stock, the
smaller the number of liquidity creators (uninformed traders) trading the stock. While the
market maker loses to informed traders, she recoups these losses from uninformed traders by
18
increasing the bid-ask spread. Thus, the level of information asymmetry is directly related to the
bid-ask spread (buyers-sellers stock price disagreement).
The first source of information asymmetry we examine is loans secured by the following
properties as a percentage of total assets: (i) farmland; (ii) 1-4 family residential; (iii) multi-family
(>4) residential; and (iv) commercial. The second is the various phases of troubled loans. The first
phase is non-accrual loans placed into the bank's non-accruals as a percentage of total loans and
leases. The next phase is past due loans measured by the ratio of all loans that are more than 90days past due and non-accruals as a percentage of total loans and leases. Rather than summing
up the two phases of troubled loans, we use the FDIC guided Texas ratio to proxy the bank’s
overall troubled loans. According to the FDIC call report, Texas ratio is defined as bank nonperforming assets (excluding government sponsored non-performing loans) divided 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.
Another driver of bank opacity is bank securitization activities (Net securitization),
measured by total securitized assets available for sale as a percentage of gross managed assets.
Since all banks are not involved in securitization, we create a dummy variable D-Securitization,
which takes the value of 1 if the bank is involved in securitization and zero otherwise. To
determine which type of securitization contributes most to bank opacity, we categorize the banks
according to how the securitized securities are backed: (i) family residential loans; (ii) home
equity lines; (iii) credit card receivables loans; (iv) auto loans; and (v) commercial and industrial
loans, all as a percentage of gross managed assets. Finally, we examine banks’ activities in moneymarket short-term funding as Repos, measured by federal funds sold and securities purchased
under repurchase agreement as percentage of total assets.
19
The remaining opacity drivers are bank OBS trading activities, measured by net exposure to
(i) exchange (or OTC) traded derivatives;16 (ii) interest rate derivatives; and (iii) foreign exchange
rate derivatives, all as a percentage of total assets. These activities are expected to be directly
related to the level of information asymmetry because OTC contracts are privately negotiated
with very lax regulatory supervision requiring no disclosure to or monitoring by the
clearinghouse.
We also examine marked-to-market gross notional amount of (i) equity contracts; (ii)
commodities and other contracts; (iii) interest rate contracts; and (iv) foreign exchange rate
contracts, all expressed as a percentage of total assets. In addition, we examine marked-tomarket derivative exposures, i.e., gross positive and negative fair values of derivatives contracts.
Fair value simply means the market price of the security. Gross positive fair value (GPFV) is the
sum of the fair values of the contract where the bank is owed money by its counterparties,
without taking into account netting. This represents an initial measurement of credit risk, i.e., the
maximum loss a bank could incur if all its counterparties were to default at the same time and
there is no netting of contracts, and the bank holds no counterparty collateral. Conversely, gross
negative fair value (GNFV) is the sum of the fair values of contracts where the bank owes money
to its counterparties without taking into account netting. This represents the maximum credit
risk the bank poses to its counterparties, i.e., the maximum loss to counterparties would incur if
the bank were to default and there is no netting of contracts, and no bank collateral was held by
the counterparties.
The final set of OBS derivatives examined are equity, commodities and others, interest rate,
and foreign exchange rate swaps written/purchased and net OBS credit derivatives exposure, all as
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.
16
20
a percentage of total assets. Detailed descriptions of the variables used in the regressions are
summarized in Appendix A.
A number of variables that have been shown to impact on 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). We use total capital requirement reported to FDIC as it is the most stringent
capital adequacy ratio. Banks which maintain higher regulatory capital ratios are expected to
hold quality assets than complex and risky assets. Therefore higher the CAR will show lower
information asymmetry. We also control for the bank’s overall credit health, as proxied by the
S&P credit quality rating (Credit Ratings). Banks with a lower credit rating have a higher
probability of default. We expect such banks are associated with greater information asymmetry
hide/obscure their actual financial position. The qualitative credit ratings are converted to
numerical values, with the highest credit rating (AAA) being assigned a score of 7 and credit
ratings at or below “C” a score of 1.
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 et al., 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 following is the natural
logarithm of the total number of analysts following a bank and bank size is the natural logarithm
of total assets.
Stock price (Price) controls for the fact that higher priced stocks tend to have higher bid-ask
spreads. We control for stock price volatility (Sigma) which is expect it to be positively related to
21
information asymmetry. We also include a 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 when compared to good news (Lakhal, 2008). 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. Quarterly time dummies are also included to
control for numerous market microstructure changes over the sample period.
Table 3 shows the descriptive statistics of the test variables. In Panel A, the average
(median) bid-ask spread around the earnings announcement is 13.08 (9.00) cents, with a
standard deviation of 11.30 cents. These numbers are much higher than those reported for nonbanking firms by Flannery et al. (2004), consistent with bank stocks suffering from substantially
higher information asymmetry. Our alternative proxy for information asymmetry, analysts
(earnings) surprise divergence, has an average (median) of 6.30 (0.60) cents per share, with a
standard deviation of 11.86 cents.
Panel B shows the descriptive of 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, reflecting the presence
of a few very large banks in our sample. The average (median) capital adequacy ratio (CAR) is
12.38 (11.92) percent, close to the minimum 12 percent required by Basel II, with a standard
deviation of 1.65 percent. The CAR ranges between 10.31 percent and 17.40 percent. The average
(median) S&P credit rating score is 5 out of a maximum 7, with a standard deviation of 2. The
average bank is followed by six analysts, with analyst following ranging from 1 to 38 analysts.
The average (median) stock price volatility is 36.66 (33.43) percent, ranging between 18.64 and
74.83 percent.
22
Panel C shows that, on average, net secured loans make up 17.83 percent of total assets
whereas average loans and leases make up 63.34 percent of total assets. Of the four categories of
secured loans, the largest is commercial loans (15.89 percent of total assets), followed by 1-4
residential properties backed loans (5.01 percent of total assets) and >4 residential properties
backed loans (1.33 percent of total assets). Loans secured by farmland properties make up just
0.87 percent of total assets. Of the two stages of troubled loans, non-accrual loans and past due
loans make up less than 1 percent of total loans on average. The maximum value of troubled
loans is 2 percent of total loans. These statistics indicate that banks have few troubled loans,
suggesting that they are vigilant in monitoring the credit worthiness of their borrowers. The
average (median) value of non-performing loans as a percentage of tangible common equity and
loan loss reserves (FDIC Texas ratio) is 8.68 (6.36) percent, with a maximum of 29.99 percent.
Descriptive statistics of bank OBS securitization activities in Panel D show that on average,
79% of bank-quarters have securitized assets. Banks with zero securitization activities are
excluded in computing the remaining variables. Family residential loans backed securitized
assets is the largest category by far (20.63 percent of gross managed assets), followed by
commercial and industrial loans backed securitized assets (15.47 percent of gross managed
assets). Credit card loans, auto loans, and home equity loans backed securitized assets each
makes up less than 5 percent of gross managed assets. Net securitized loans and leases make up
43.73 percent of gross managed assets, with a standard deviation of 17.26 percent. These values
are similar to those reported by Cheng et al. (2008) for their sample of BHCs. Repos make up an
average (median) of 2.46 (0.98) percent of total assets, and has a maximum of 65.14 percent.
23
Panel E provides descriptive statistics of OBS derivatives trading activities. Banks with zero
derivatives exposures are excluded.17 Details on the notional and fair values of (equity,
commodity, foreign exchange, and interest rate)18 derivatives used for hedging and trading
purposes, the bank’s net notional position in derivatives as well as whether banks use exchangeor OTC traded derivatives are reported. As expected, banks’ derivatives used for hedging
purposes mainly extend to interest rates (12.63 percent of gross assets). In contrast, banks’
derivatives used for trading purposes extend to commodities (28.85 percent of gross assets),
followed by interest rates (22.28 percent of gross assets) and foreign exchange rate (21.17
percent of gross assets).19 Most of these derivative activities take place in OTC markets, with the
mean notional value of OTC-traded derivatives outstripping that of exchange-traded derivatives
by more than 20-fold (9.58 percent vs. 0.43 percent of gross assets).
Banks have a high average (median) exposure to foreign exchange rate swaps, with a
notional value of 22.50 (2.96) percent of gross assets. The maximum notional value exceeds total
assets by a factor of 2. Interest rate swaps are less popular, with an average (median) notional
value of 8.46 (5.27) percent of gross assets. Equity and commodity swaps have a mean notional
value below 5 percent of gross assets. The net positive (negative) fair value of derivatives is 0.09
(0.09) percent of gross assets and much smaller than their notional amount. This is primarily
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 initial values are close
to zero).
Minton et al. (2009) find that in 2003 only 19 out of 345 large US banks use credit derivatives.
Interest rate swaps are included in interest rate derivatives.
19 Interest rate and foreign exchange trading are closely aligned, as dealers often use interest rate contracts to hedge
FX risk.
17
18
24
In contrast, the notional amount relates to the payment obligation based on one side of the
contract (the reference amount from which contractual arrangements will be derived), but is
generally not an amount at risk. Differences between positive and negative fair value, i.e., net fair
value, are even smaller.20 One reason for this finding is that institutions substantially hedge the
market risk of their derivative portfolio, holding both long and short positions on the same
market exposure. This is typical of banks whose income is generated mainly from market-making
activities in derivatives. A second reason is that undertaking a hedge on an outstanding
derivative position provides the bank with a way of closing out a market exposure without
having to sell the instrument. Further, different derivatives 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 banks that are involved in securitization and
OBS derivatives activities tend to have high exposures to these assets, on average.
V. EMPIRICAL RESULTS
Table 4 presents the panel regression results of the relationship between the bid-ask spread, our
proxy for information asymmetry, and the first two sources of opacity, secured loans from the
bank’s lending book and various stages of troubled loans. Consistent with our prediction, there is
a statistically significant positive relationship between the bid-ask spread and secured loans
(regressions 1 to 5) and non-performing loans (regressions 6 to 10). Therefore, bank loans are at
the core of bank opacity, irrespective of whether the loans are performing or not. All secured loan
categories are significant positively related to the bid-ask spread. Our results for loans secured by
For a portfolio with a single counterparty where the bank has legally enforceable bilateral netting agreement,
contracts with negative fair value may be used to offset contracts with positive fair value.
20
25
farmlands are contrary to Haggard and Howe (2007) who find, for their sample of BHCs for the
period 1993-2002, that banks with a lower proportion of agricultural (and consumer) loans are
associated with higher information asymmetry.
The relationship between the control variables and the bid-ask spread is in line with
predictions. In particular, larger banks and banks with a higher CAR, greater analyst following,
and a higher credit rating have a smaller bid-ask spread. In contrast, return volatility, price, and
the (bad) news dummy are significantly positively related to the bid-ask spread, as expected.
Banks that are traded on the NYSE have a lower average bid-ask spread (by 5.67 cents in
regression 1), presumably due to the more stringent requirements there as compared to NASDAQ
or AMEX.
Table 5 shows the results for securitization activities as a source of bank opacity. The types
of assets involved in securitization transactions are primarily bank receivables, i.e., banks convert
their 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 for
outsiders.21 We find a positive relationship between the net securitization dummy and the bidask spread, as predicted, suggesting banks that are involved in securitization have significantly
higher information asymmetry (by 1.543 cents, on average). For a reduced sample of banks that
are active in securitization, we find a positive relationship between net securitization and the
bid-ask spread. That is, banks with a greater exposure to securitized assets (as a percentage of
total managed assets) have higher information asymmetry. Focusing on the five securitization
categories, family residential loans and auto loans are statistically significant. Family residential
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.
21
26
loans, claimed to be a major cause of the GFC, are the most commonly securitized asset by banks.
Commercial & industrial loans backed securitization is not significantly related to the bid-ask
spread, suggesting that this is not a major source of bank opacity.
Available for sale securities, which represent the total fair value of all available securities
for sale, is significantly positively related to the bid-ask spread. This suggests that the bank’s
asset “slipperiness”, i.e., the speed with which banks trade in and out of their securities on the
trading book, is an important driver of bank opacity.
Surprisingly, we find a negative relationship between bank activities in Repos and the bidask spreads. This suggests that bank’s exposure to short-term money-market funds decreases
bank information asymmetry, contrary to the popular belief that repos play a significant role in
financing the bank’s opaque OBS trading activities.
Table 6 displays the results for the notional value of banks’ OTC and exchange traded
derivative exposures. As shown in the descriptive statistics, banks use derivatives (particularly
interest rate, foreign exchange, and commodity and other) mostly for trading (market making)
purposes. Overall, our results suggest that banks’ OBS derivative exposure is a significant
determinant of information asymmetry, irrespective of whether it is used for hedging or trading
purposes. Although theoretically hedging reduces firm risk (and therefore the bid-ask spread),
banks with higher exposure to derivatives for hedging purposes are likely to be (i) more involved
in market-making; and (ii) have higher risk exposures to the assets that are being hedged.
Finally, the notional value of foreign exchange rate derivative activity, whether traded on the
exchange or OTC, increases bank opacity.
Table 7 presents evidence on the relationship between positive and negative fair values of
banks’ (marked-to-market) derivative exposures and the bid-ask spread. Gross negative fair
27
value represents the maximum loss the bank’s counterparties would incur if the bank defaults,
while gross positive fair value represents the maximum loss a bank would incur if all its
counterparties default. Both positive and negative gross fair value of equity, commodities and
others, and foreign exchange rate derivatives are positively relative to the bid-ask spread. The
fair value of interest rate derivative exposure of banks is not significantly related to the bid-ask
spread. The net OBS derivative exposure is positively related to the bid-ask spread, suggesting
these are a main source of bank opacity.
Finally, Table 8 presents the results for banks’ OBS swaps activities. As shown in the
descriptive statistics, bank exposures to interest rate and foreign exchange rate swaps have a
high notional value. Consistent with this, we find that interest rate and foreign exchange rate
swap exposures are significantly positively related to the bid-ask spread. This is consistent with
our earlier results for banks’ exposures to interest rate and foreign exchange rate derivatives.
Robustness
Since the results may be driven by a few very large banks, which are heavily involved in
securitization and OBS trading activities, we rerun the tests using two sub-groups of banks −
banks that are “too big to fail” with total assets exceeding US$100 billion and the rest. Table 9
shows that net OBS derivatives exposure is particularly significant for the subsample of large
banks. Therefore, minimising exposures to net OBS derivatives is more effective in curbing
information problem in larger banks than in smaller ones.
We further test robustness of our findings to an alternative proxy for information
asymmetry, which is the divergence in analysts’ surprises. This divergence measure is computed
as the standard deviation of the earnings surprise across analysts. Greater divergence in earnings
28
surprises across analysts shows greater information asymmetry among analysts due to banks
opacity. The results, presented in Table 10, are robust to this alternative proxy.
Economic Value
Table 11 ranks the economic significance of the test variables, obtained by multiplying the
regression coefficient ( ) with the standard error of the test variable (se(X)). Hence, the
economic value is interpreted as the impact of a one standard deviation increase in the test
variable on the bid-ask spread (in cents). Clearly, available for sale securities and OBS trading
activities (in particular foreign exchange rate and equity derivatives) rank highest in terms of
opacity, followed by securitization of family residential loans, troubled loans (as measured by the
FDIC Texas ratio), and loans secured by farmlands. A one standard deviation increase in banks’
exposure to available for sale securities, securitized family residential loans, the FDIC Texas ratio,
and loans secured by farmland increases the bank’s bid-ask spread by 1.715, 1.656, 1.398, and
0.972 cents respectively. Banks involved in securitization have a 1.543 cents higher bid-ask
spread, on average. Interestingly, banks’ exposure to exchange-traded foreign exchange rate
derivatives and equity derivatives has a larger economic impact on the bid-ask spread than
banks’ exposure to OTC-traded derivatives. Finally, banks’ negative gross fair value of derivative
exposure is more strongly related to the bid-ask spread than positive gross fair value of
derivative exposure. This suggests that banks are informationally more opaque than their
counterparties in derivative contracts, and outsiders are uncertain about banks’ exposure to
default. Overall, our results suggest that it is the bank’s asset “slipperiness”, i.e., the speed with
which a bank trades in and out of its assets, which drives bank opacity, consistent with the
argument of Morgan (2002).
29
VI. CONCLUSION
This study combines the literature on bank opacity and market microstructure to assess which
sources of bank opacity are directly related to the level of information asymmetry. We use
quarterly earnings announcements as the event to capture the effects of bank’s trading activities
on information asymmetry. Using a sample of 275 U.S. commercial banks listed on the
NASDAQ/NYSE/AMEX for Q4-1999 to Q2-2012, we find trading activities of banks (in particular
available for sale securities, foreign exchange rate and equity derivatives) rank highest in terms
of opacity, followed by securitization of family residential loans, troubled loans as measured by
the FDIC Texas ratio, and loans secured by farmlands. Our results suggest that it is not the
opaqueness of the OTC market but rather the bank’s asset “slipperiness”, i.e., the speed with
which a bank trades in and out of its assets, which drive bank opacity. While we support the
proposed move of derivatives trading from the OTC market to clearing houses, where prices can
be monitored, it is unlikely to reduce bank opacity significantly. More timely disclosure on banks’
highly volatile trading exposures will perhaps do more in reducing opacity.
30
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33
TABLE 1
DISTRIBUTION OF U.S. BANKING INDUSTRY
This table shows the distribution of bank firms as at 25 August 2012.
PANEL A: By the type of financial institution
Financial institution type
Commercial banks
Saving institutions
Total
Insured by FDIC
6352
1084
7436
(85.42%)
(14.58%)
(100.00%)
Supervised by FDIC
4193
448
4641
(90.35%)
(9.65%)
(100.00%)
By assets concentration type
Credit card International Agricultural Commercial Mortgage Consumer specialized All other
lenders
banks
banks
lenders
lenders
lenders <$1 Billion <$1 Billion
Insured by FDIC
18
5
1552
3854
713
71
363
801
(0.24%)
(0.07%)
(20.87%)
(51.83%)
(9.59%) (0.95%) (4.88%) (10.77%)
Supervised by FDIC
9
0
1091
2481
311
44
212
471
(0.19%)
(0.00%)
(23.51%)
(53.46%)
(6.70%) (0.95%) (4.57%) (10.15%)
PANEL B on distribution of commerical banks
Active
National
Charter &
Fed
Member
(OCC)
1925
(24.19%)
State
Charter &
Fed
Member
(FRB)
1121
(14.09%)
Top 75% by assets
31
(0.55%)
90% by assets
353
(6.31%)
Inactive
Commercial banks
5592
2365
(70.28%)
(29.72%)
PANEL C on active commercial banks (N = 5592)
Top 25% by assets
Top 50% by assets
Number of Banks
3
6
Percentage of Active Banks (0.05%)
(0.11%)
All other
>$1 Billion
59
(0.79%)
22
(0.47%)
State Charter
& Fed Nonmember
(FDIC)
4910
(61.71%)
95% by assets Remaining 5%
Total
1227
3972
5592
(21.94%)
(71.03%) (100.00%)
PANEL D on commerical banks trading in U.S. stock markets (N = 353)
NAS
Number of Banks
+
NSM
276
+
NMS
34
ASE
12
Total
7436
(100.00%)
4641
(100.00%)
NYS
42
TABLE 2
VERIFICATION OF I/B/E/S EARNINGS ANNOUNCEMENT DATES
This table shows the verification of the I/B/E/S earnings announcement dates. The sample is based on a panel of 275 U.S.
commercial banks from Q1-1999 to Q2-2012.
PANEL A: Total number of banks & bank's quarterly earnings announcements
Bank's quarters
Date & time availability
IBES
9484
Both date and time available
Worldscope
9493
Only date available
TRTH
11260
Only date available
COMPUSTAT
11160
Only date available
GlobalNews
4645
Both date and time available
Number of banks
326
323
182
324
171
PANEL B: Matching distribution of date of quarterly earnings announcements with I/B/E/S
I/B/E/S vs. Worldscope
I/B/E/S vs. TRTH
N
Pct
N
Pct
Number of agreements
6766
71.34%
4833
50.96%
Number of disagreements
2718
28.66%
4651
49.04%
9484
100.00%
9484
100.00%
I/B/E/S vs. GlobalNews
N
Pct
3690
38.91%
5794
61.09%
9484
100.00%
I/B/E/S vs. COMPUSTAT
N
Pct
8398
88.55%
1086
11.45%
9484
100.00%
PANEL C: Day-difference distribution of unmatched (PANEL B) quarterly earnings announcements
I/B/E/S vs. Worldscope
I/B/E/S vs. TRTH
Up to 30-days
583
21.45%
137
2.95%
Between 31-days to 90-days
1172
43.12%
221
4.75%
More than 90-days or missing
963
35.43%
4293
92.30%
2718
100.00%
4651
100.00%
I/B/E/S vs. GlobalNews
95
1.64%
64
1.10%
5635
97.26%
5794
100.00%
I/B/E/S vs. COMPUSTAT
576
53.04%
383
35.27%
127
11.69%
1086
100.00%
PANEL D: Overall matching distribution of I/B/E/S quarterly earnings announcements
PANEL D.1:
I/B/E/S vs. All Others
N
Pct
Number of agreements
9089
95.84%
Number of disagreements
395
4.16%
9484
100.00%
35
PANEL D.2:
Number of agreements
Number of disagreements
I/B/E/S vs. FFIEC
N
Pct
8783
96.63%
306
3.37%
9089
100.00%
TABLE 3
DESCRIPTIVE STATISTICS
The sample is based on a panel of 275 U.S. commercial banks from Q1-1999 to Q2-2012. Banks
not involved in securitization or derivatives trading are not included in Panels D and E.
Observations outside the 99th percentile are removed.
Mean Median
Standard
Min.
Deviation
Max.
Panel A: Dependent variable
Bid-Ask spread (cents)
Analysts surprise divergence (cents)
13.08
6.30
9.00
0.60
11.30
11.86
1.00
0.00
46.00
60.27
25.90
36.66
15.68
6.16
4.99
12.38
8.66
33.43
17.45
4.00
5.00
11.92
50.73 1.52 424.16
13.20 18.64 74.83
7.36 3.20 25.40
6.33 1.00 38.00
1.60 8.00
1.00
1.65 10.31 17.40
0.87
5.01
1.33
15.89
17.83
0.59
0.18
8.68
0.67
0.28
4.90
0.79
11.17
13.93
0.45
0.10
6.36
0.57
1.35
2.98
1.34
13.24
14.02
0.47
0.26
7.43
0.48
0.00
0.00
0.00
0.01
0.00
0.00
0.00
0.25
0.00
0.79
20.63
4.30
1.08
4.69
15.47
43.73
15.49
2.46
1.00
19.42
4.24
0.22
2.71
14.56
43.45
15.48
0.98
0.41
12.74
2.41
2.37
4.97
7.79
17.26
7.06
4.14
0.00
1.00
0.00 79.85
0.00
8.99
0.00 14.78
0.00 19.97
0.03 39.86
0.01 100.00
0.01 29.98
0.00 65.14
Panel B: Control variables
Size ($'billions)
Sigma (%)
Price ($)
Analyst following
Credit Rating
CAR (%)
Panel C: Secured and troubled loans
Secured by farmland ( % of total assets)
Secured by 1-4 residential properties ( % of total assets)
Secured by > 4 residential properties ( % of total assets)
Secured by commercial properties ( % of total assets)
Net secured loans ( % of total assets)
Non-accrual loans ( % of total loans and leases)
Past due loans ( % of total loans and leases)
FDIC Texas ratio (%)
Net troubled loans ( % of total loans and leases)
7.99
9.99
4.99
49.99
61.28
1.99
1.99
29.99
2.00
Panel D: Securitization
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)
Net securitization ( % of gross managed assets)
Available for sale securities ( % of total assets)
Repos ( % of total assets)
36
TABLE 3
DESCRIPTIVE STATISTICS (CONTINUED...)
Mean Median
Standard
Min.
Deviation
Max.
Panel E: Off-balance sheet derivatives exposure
Exchange traded vs. Over the counter traded derivatives
ET Net derivatives (written - purchased) ( % of total assets)
Exchange traded - interest rate ( % of total assets)
Exchange traded - foreign exchange ( % of total assets)
OTC Net derivatives (written - purchased) ( % of total assets)
Over the counter - interest rate ( % of total assets)
Over the counter - foreign exchange ( % of total assets)
Hedge vs. Trading derivatives
Hedge - Equity ( % of total assets)
Hedge - Commodity and others ( % of total assets)
Hedge - Interest rate ( % of total assets)
Hedge - Foreign exchange ( % of total assets)
Trading - Equity ( % of total assets)
Trading - Commodity and others ( % of total assets)
Trading - Interest rate ( % of total assets)
Trading - Foreign exchange ( % of total assets)
Positive fair value vs. Negative fair value derivatives
Gross positive fair value of equity ( % of total assets)
Gross positive fair value of commodity and others ( % of total assets)
Gross positive fair value of interest rate ( % of total assets)
Gross positive fair value of foreign exchange ( % of total assets)
Gross negative fair value of equity ( % of total assets)
Gross negative fair value of commodity and others ( % of total assets)
Gross negative fair value of interest rate ( % of total assets)
Gross negative fair value of foreign exchange ( % of total assets)
Net OBS derivatives exposure ( % of total assets)
OBS Swaps
Equity swaps ( % of total assets)
Commodity and other swaps ( % of total assets)
Interest swaps ( % of total assets)
Foreign exchange swaps ( % of total assets)
Net OBS credit exposure ( % of total assets)
Net OBS swaps exposure ( % of total assets)
Robustness test
Net secured loans ( % of total assets)
Net troubled loans ( % of total loans and leases)
Net securitization ( % of gross managed assets)
Net OBS derivatives exposure ( % of total assets)
Net OBS swaps exposure ( % of total assets)
37
0.43
7.93
0.52
9.58
4.03
0.93
0.23
3.34
0.12
0.55
1.22
0.07
0.52
10.01
0.95
47.36
7.07
1.41
0.01
1.94
0.01 49.15
0.01
5.02
0.01 478.34
0.01 49.29
0.01
4.91
0.68
0.11
12.63
2.22
4.06
28.85
22.28
21.17
0.16
0.08
4.98
0.70
0.87
5.48
8.50
1.59
1.40
0.12
19.98
3.54
5.82
62.86
29.87
59.72
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
6.52
0.48
147.32
24.92
24.28
478.34
124.24
301.55
0.06
0.09
0.09
0.03
0.01
0.01
0.09
0.04
5.27
0.01
0.01
0.03
0.01
0.01
0.01
0.04
0.01
1.27
0.11
0.17
0.11
0.05
0.02
0.01
0.12
0.07
11.42
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
0.10
0.05
0.50
0.40
77.25
3.43
1.46
8.46
22.50
2.10
8.96
0.35
0.70
5.27
2.96
0.12
2.32
5.31
1.89
8.36
42.65
7.40
14.55
0.01 23.09
0.01 10.00
0.01 32.15
0.01 199.33
0.01 130.90
0.01 66.14
17.83
0.67
43.73
5.27
8.96
13.93
0.57
43.45
1.27
2.32
14.02
0.48
17.26
11.42
14.55
0.00 61.28
0.00
2.00
0.01 100.00
0.01 77.25
0.01 66.14
TABLE 4
NOTIONAL VALUE OF SECURED AND TROUBLED LOANS AS A SOURCE OF BANK OPACITY
This table shows results of bank-level regressions of information asymmetry on bank are lending activities. The dependent
variable is the average bid-ask spread around quarterly earnings announcements. The sample is based on a panel of 275 U.S.
commercial banks from Q1-1999 to Q2-2012. All regressions include bank and quarter fixed-effects. The standard errors are
clustered at the bank level, with *, **, and *** indicating significance at 10%, 5%, and 1% respectively. To reduce the adverse
impacts of outliers, observations outside the 99th percentile are removed.
(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-accrual loans
2.421
***
Past due loans
1.196
3.125
***
FDIC Texas ratio
0.717
0.188
***
0.234
***
Net troubled loans
0.422
***
Control variables
-5.824
***
-5.737
***
-6.523
***
-5.800
***
-5.844
***
-5.585
***
-6.167
***
-5.750
***
-6.131
***
-6.229
***
-6.137
***
Sigma
0.096
***
0.080
***
0.096
***
0.095
***
0.079
***
0.097
***
0.133
***
0.096
***
0.131
***
0.142
***
0.147
***
Price
0.253
***
0.251
***
0.245
***
0.239
***
0.257
***
0.242
***
0.300
**
0.279
***
0.286
***
0.306
***
0.301
***
-0.539
***
-0.609
***
-0.672
***
-1.266
*
-1.241
*
Ln(Size)
-0.280
**
Credit rating
-1.251
*
-0.040
CAR
-0.352
***
-0.472
-5.669
1.515
Yes
*
***
-0.365
1.097
Yes
***
104.68
ln(Analyst following)
-0.302
**
***
-0.179
*
-0.218
*
-1.362
**
-1.342
**
-0.021
-0.377
***
-0.364
***
-0.476
-4.337
1.424
Yes
-5.756
1.173
Yes
*
***
-0.318
1.072
Yes
120.89
***
111.97
-0.304
**
***
-0.269
**
-0.585
***
-0.415
***
-1.432
**
-1.222
*
-1.213
*
-0.978
-0.335
***
-0.429
***
-0.351
***
-0.419
***
-0.375
***
-0.422
***
-5.939
1.743
Yes
-3.305
1.578
Yes
***
-5.606
1.574
Yes
*
***
***
-3.085
1.434
Yes
**
-2.576
1.316
Yes
**
-2.527
1.590
Yes
***
107.62
***
114.98
***
108.63
***
116.50
113.67
***
Dummy variables
NYSE
News
Quarter
Constant
R - Sqr.
112.46
Within:
Between:
Overall:
Obs (# Quarters)
0.063
0.339
0.244
4808
0.056
0.307
0.223
2536
***
0.059
0.322
0.235
4497
0.054
0.334
0.245
4464
38
**
***
105.99
0.060
0.312
0.230
2486
***
0.067
0.324
0.242
4021
0.059
0.373
0.256
3841
0.061
0.360
0.248
4839
0.062
0.350
0.257
3923
***
116.38
0.064
0.377
0.261
3573
***
0.061
0.374
0.254
3714
TABLE 5
NOTIONAL VALUE OF SECURITIZATION AS A SOURCE OF BANK OPACITY
This table shows the results of bank-level regressions of information asymmetry on bank’s notional value of securitization
activities. The dependent variable is the average bid-ask spread around quarterly earnings announcements. The sample is
based on a panel of 275 U.S. commercial banks from Q1-1999 to Q2-2012. All regressions include bank and quarter fixedeffects. The standard errors are clustered at the bank level, with *, **, and *** indicating significance at 10%, 5%, and 1%
respectively. Banks not involved in securitization are not included in their individual regressions. To reduce the adverse
impacts of outliers, observations outside the 99th percentile are removed.
(1)
(2)
Information Asymmetry (Bid-Ask Spread)
(4)
(5)
(6)
(7)
(3)
(8)
(9)
(10)
Opacity measurements
D - Securitization
1.543
***
Family residential loans
0.130
**
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
0.290
-0.082
***
-0.060
Net securitization
0.071
*
Available for sale securities
0.243
***
Repos
-0.245
**
Control variables
-5.388
***
0.091
***
0.260
***
Ln(Analyst following)
-0.360
***
Credit rating
-1.193
*
-1.062
-0.321
***
NYSE
News
Quarter
-6.252
1.575
Yes
**
Constant
R - Sqr.
101.84
Ln(Size)
Sigma
Price
CAR
-5.174
***
0.091
***
0.291
***
-0.445
***
-5.468
***
0.099
***
-5.760
***
0.068
***
0.250
***
0.165
**
-0.368
***
-0.129
-2.238
**
-5.292
***
0.096
***
0.257
***
-0.309
***
-1.361
**
-0.377
**
-0.394
**
-0.339
-0.312
-6.745
1.505
Yes
*
***
-5.888
1.581
Yes
*
***
***
-5.088
0.886
Yes
-7.216
1.546
Yes
***
99.21
***
106.35
***
114.98
-5.443
***
0.095
***
0.257
***
-0.371
***
-1.315
*
-1.008
-0.376
**
***
-5.669
1.742
Yes
***
107.73
-1.032
-5.338
***
0.096
***
0.260
***
-0.330
***
-5.466
***
0.095
***
0.257
***
-0.334
***
-5.263
***
-5.157
***
0.088
***
0.090
***
0.225
***
0.229
***
-0.332
***
-0.287
**
*
-1.445
**
-0.686
-0.337
**
-0.327
**
-0.234
-7.099
1.562
Yes
**
***
***
-5.037
1.466
Yes
***
-7.204
1.140
Yes
***
101.77
***
89.01
***
105.05
-1.103
-0.289
***
-6.563
1.561
Yes
**
***
***
99.31
Dummy variables
***
99.00
**
Within:
0.061
0.060
0.060
0.036
0.062
0.059
0.062
0.060
0.060
0.048
Between:
0.347
0.353
0.336
0.335
0.336
0.344
0.344
0.341
0.324
0.341
Overall:
Obs (# Quarters)
0.247
4908
0.248
4463
0.244
4661
0.286
1834
0.249
4867
0.244
4677
0.249
4908
0.248
4905
0.236
4365
0.241
3300
39
*
**
***
TABLE 6
NOTIONAL VALUE OF DERIVATIVES ACTIVITY AS SOURCE OF BANK OPACITY
This table shows the results of bank-level regressions of information asymmetry on bank’s notional value of derivatives
activities. The dependent variable is the average bid-ask spread around quarterly earnings announcements. The sample is
based on a panel of 275 U.S. commercial banks from Q1-1999 to Q2-2012. All regressions include bank and quarter fixedeffects. The standard errors are clustered at the bank level, with *, **, and *** indicating significance at 10%, 5%, and 1%
respectively. Banks not involved in derivatives trading are not included. To reduce the adverse impacts of outliers,
observations outside the 99th percentile are removed.
(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
3.659 **
Equity derivatives
8.961 *
Commodities and others derivatives
Net derivatives (written - purchased)
0.511 **
0.011 ***
0.012 ***
0.018
Interest rate derivatives
0.005
0.054 **
0.014
0.915 *
Foreign exchange rate derivatives
0.266 ***
0.003 ***
0.187 *
0.005 *
Control variables
Ln(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 ***
***
***
***
***
***
***
***
***
***
***
***
***
***
0.095 ***
Sigma
0.094
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 ***
Ln(Analyst following)
-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 ***
Credit 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
***
0.094
-0.312
***
0.095
-0.316
***
0.095
-0.319
***
0.094
-0.315
***
0.095
-0.313
***
0.094
-0.316
***
0.094
-0.311
***
0.095
-0.326
***
0.094
-0.311
***
0.097
-0.302
**
0.094
-0.319
***
0.096
-0.310
**
-0.320 **
Dummy variables
NYSE
News
Quarter
-6.142 **
1.575 ***
Yes
-6.156 ***
1.574 ***
Yes
-6.156 **
1.574 ***
Yes
-6.150 **
1.578 ***
Yes
-6.122 **
1.577 ***
Yes
-6.093 **
1.575 ***
Yes
-6.234 **
1.559 ***
Yes
-6.108 **
1.588 ***
Yes
-5.371 **
1.594 ***
Yes
-6.144 **
1.581 ***
Yes
-7.027 **
1.640 ***
Yes
-6.137 **
1.581 ***
Yes
-7.165 **
1.616 ***
Yes
-6.263 **
1.607 ***
Yes
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 ***
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
0.346
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
Within:
Between:
Overall:
Obs (# Quarters)
0.059
0.344
0.245
4908
0.059
0.344
0.245
4908
40
TABLE 7
FAIR VALUE OF DERIVATIVES ACTIVITY AS SOURCE OF BANK OPACITY
This table shows the results of bank-level regressions of information asymmetry on bank’s fair value of derivatives activities.
The dependent variable is the average bid-ask spread around quarterly earnings announcements. The sample is based on a
panel of 275 U.S. commercial banks from Q1-1999 to Q2-2012. All regressions include bank and quarter fixed-effects. The
standard errors are clustered at the bank level, with *, **, and *** indicating significance at 10%, 5%, and 1% respectively.
Banks not involved in derivatives trading are not included. To reduce the adverse impacts of outliers, observations outside the
99th percentile are removed.
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 derivatives exposure
0.001
***
Control variables
Ln(Size)
Sigma
Price
Ln(Analyst following)
-5.563
***
-5.549
***
-5.517
***
-5.576
***
-5.569
***
-5.581
***
-5.549
***
-5.550
***
-5.626
***
0.094
***
0.094
***
0.104
***
0.095
***
0.095
***
0.095
***
0.094
***
0.095
***
0.073
***
0.259
***
0.258
***
0.314
***
0.259
***
0.264
***
0.258
***
0.258
***
0.262
***
0.225
***
-0.34
***
-0.340
***
-0.539
***
-0.347
***
-0.349
***
-0.340
***
-0.340
***
-0.365
***
-0.235
**
Credit 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
*
-6.109
1.575
Yes
**
-6.154
1.571
Yes
**
***
-5.606
0.260
Yes
*
***
-6.097
1.574
Yes
**
***
-6.125
1.574
Yes
**
***
-6.054
1.606
Yes
**
***
-6.179
1.557
Yes
**
***
-6.147
1.599
Yes
**
***
-5.532
1.667
Yes
*
***
***
106.92
***
106.10
***
106.96
***
107.27
***
107.06
***
106.94
***
106.26
***
112.97
Dummy variables
NYSE
News
Quarter
Constant
R - Sqr.
107.01
Within:
Between:
Overall:
Obs (# Quarters)
0.059
0.343
0.245
4908
0.059
0.344
0.245
4908
0.065
0.355
0.245
4591
0.059
0.346
0.246
4886
41
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
0.042
0.432
0.289
3214
***
TABLE 8
NOTIONAL VALUE OF SWAP ACTIVITY AS SOURCE OF BANK OPACITY
This table shows the results of bank-level regressions of information asymmetry on bank’s
swap activities. The dependent variable is the average bid-ask spread around quarterly
earnings announcements. The sample is based on a panel of 275 U.S. commercial banks
from Q1-1999 to Q2-2012. All regressions include bank and quarter fixed-effects. The
standard errors are clustered at the bank level, with *, **, and *** indicating significance at
10%, 5%, and 1% respectively. Banks not involved in swap trading are not included.
(1)
Information Asymmetry (Bid-Ask Spread)
(2)
(3)
(4)
(5)
(6)
Opacity measurements
Equity swaps
0.315
Commodity and other swaps
0.012
0.006 **
Interest rate swaps
0.159 ***
Foreign exchange rate swaps
0.050 *
Net OBS credit risk exposure
0.003 ***
Net OBS swaps exposure
Control variables
-5.601 ***
-5.532 ***
-5.940 ***
-5.845 ***
-5.579 ***
-5.125 ***
Sigma
0.094 ***
0.094 ***
0.098 ***
0.096 ***
0.094 ***
0.055 ***
Price
0.257 ***
0.259 ***
0.281 ***
0.267 ***
0.259 ***
0.138 ***
Ln(Analyst following)
-0.340 ***
-0.355 ***
-0.367 ***
-0.345 ***
-0.337 ***
-0.049
Credit 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
News
Quarter
-6.301 **
1.579 ***
Yes
-6.174 **
1.607 ***
Yes
-7.237 **
1.658 ***
Yes
-7.099 **
1.606 ***
Yes
-6.260 **
1.577 ***
Yes
-4.306 *
-0.068
Yes
Constant
R - Sqr.
107.63 ***
106.73 ***
111.21 ***
109.89 ***
107.19 ***
104.17 ***
Ln(Size)
Dummy variables
Within:
Between:
Overall:
Obs (# Quarters)
0.059
0.348
0.248
4908
0.060
0.345
0.246
4847
42
0.062
0.363
0.256
4810
0.061
0.359
0.256
4864
0.059
0.345
0.246
4908
0.064
0.398
0.288
2103
TABLE 9
ROBUSTNESS TESTS FOR OPACITY BY BANK SIZE
This table shows the results of bank-level regressions of information asymmetry on bank activities. The dependent variable is
the average bid-ask spread around quarterly earnings announcements. The sample is based on a panel of 275 U.S. commercial
banks from Q1-1999 to Q2-2012. The columns reflect the stratification of the data into two subsamples of banks based on their
size: medium to small (total assets <= $100 billion), and large (total assets >$100 billion. All regressions include bank and
quarter fixed-effects. The standard errors are clustered at the bank level, with *, **, and *** indicating significance at 10%, 5%,
and 1% respectively. Banks not involved in securitization or derivatives trading are not included in their individual regressions.
To reduce the adverse impacts of outliers, observations outside the 99th percentile are removed.
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 OBS derivatives exposure
0.008
*
Net OBS swaps exposure
0.065
**
-0.005
0.010
***
-2.319
***
Control variables
-6.576
***
-6.133
***
-7.570
Sigma
0.106
***
0.111
***
0.053
Price
0.320
***
0.300
***
0.188
-0.472
***
-0.444
***
-0.356
-1.107
**
-0.326
***
-6.856
1.701
Yes
**
Ln(Size)
Ln(Analyst following)
Credit rating
CAR
***
-6.909
***
-7.127
***
-0.382
**
0.084
***
0.067
***
0.019
***
0.012
***
0.019
***
0.005
0.019
**
**
0.281
***
0.211
***
0.043
***
0.023
***
0.041
***
-0.030
0.032
**
-1.866
**
-0.283
**
-0.810
**
-8.364
1.726
Yes
***
***
***
-0.339
1.966
Yes
*
***
115.99
***
125.71
-1.055
-0.308
**
-1.474
*
-0.379
-0.039
-0.226
-0.014
-1.249
**
-0.354
**
-0.069
0.147
-0.297
**
-0.202
-0.149
**
-0.361
-0.486
Yes
***
-4.650
-0.020
-0.066
0.118
-0.125
***
-0.373
-0.311
Yes
***
***
-2.177
-0.041
***
-0.176
**
-0.139
-0.529
-0.505
Yes
4.058
-0.303
Yes
***
***
81.50
***
-0.568
0.006
Dummy variables
NYSE
News
Quarter
Constant
R - Sqr.
119.59
Within:
Between:
Overall:
Obs (# Quarters)
0.068
0.373
0.263
4622
0.070
0.367
0.262
4622
0.051
0.311
0.250
964
***
-4.949
0.311
Yes
131.48
*
***
0.049
0.465
0.309
2970
43
-3.337
-0.031
Yes
137.49
0.081
0.434
0.307
1825
***
12.07
0.172
0.208
0.259
286
*
15.94
0.232
0.117
0.234
286
**
11.74
0.157
0.239
0.260
286
**
0.329
0.499
0.475
177
0.325
-0.548
Yes
43.12
0.192
0.353
0.357
172
**
***
TABLE 10
ROBUSTNESS TESTS USING ALTERNATIVE MEASURES OF INFORMATION
ASYMMETRY
This table shows the results of bank-level regressions of information asymmetry on bank
activities. The dependent variable is the analyst surprise standard deviation around
quarterly earnings announcements. The sample is based on a panel of 275 U.S. commercial
banks from Q1-1999 to Q2-2012. All regressions include bank and quarter fixed-effects.
The standard errors are clustered at the bank level, with *, **, and *** indicating
significance at 10%, 5%, and 1% respectively. Banks not involved in securitization or
derivatives trading are not included in their individual regressions. To reduce the adverse
impacts of outliers, observations outside the 99th percentile are removed.
Information Asymmetry
Analysts surprise standard deviation
(1)
(2)
(3)
(4)
(5)
Opacity measurements
Net secured loans
1E-05
Net troubled loans
0.004
***
Net securitization
0.001
***
Net OBS derivatives exposure
2E-04
***
Net OBS swaps exposure
4E-04
***
0.025
***
Control variables
***
Ln(Size)
0.024
0.022
Sigma
0.000
0.000
Price
0.001
0.008
Ln(Analyst following)
0.049
***
Credit rating
0.004
CAR
0.001
***
0.018
***
0.016
***
0.000
0.000
0.000
**
-0.001
0.006
0.029
***
0.049
***
0.045
***
0.058
***
0.058
***
**
0.004
**
0.003
*
0.005
**
0.006
*
0.000
0.005
**
-0.052
-0.014
Yes
***
0.000
0.002
**
-0.025
-0.010
Yes
***
-0.134
-0.049
0.085
0.568
0.212
3004
0.129
0.486
0.204
1973
Dummy variables
NYSE
News
Quarter
-0.023
-0.010
Yes
***
Constant
R - Sqr.
-0.193
Within:
Between:
Overall:
Obs (# Quarters)
***
***
-0.023
-0.010
Yes
***
-0.008
-0.007
Yes
***
-0.203
***
-0.181
0.098
0.607
0.248
5306
0.101
0.595
0.252
5306
44
0.137
0.567
0.266
5306
**
**
**
TABLE 11
RESULTS SUMMARY: SIGN, STATISTICAL AND ECONOMICAL SIGNIFICANCE
This table shows summary results of bank-level regressions of information asymmetry on
bank activities. The dependent variable is the average bid-ask spread around quarterly
earnings announcements. The sample is based on a panel of 275 U.S. commercial banks
from Q1-1999 to Q2-2012.
Expected Sign Actual Sign
Statistical Significance
Co-efficient se(X) Economic Value
Opacity drivers
Secured & Troubled loans
FDIC Texas ratio
Non-accrual loans
Secured by farmlands
Past due loans
Secured by 1-4 residential properties
Secured by commercial properties
Secured by > 4 residential properties
Net secured loans
Net troubled loans
Securitization
Available for sale securities
Family residential loans
D - Securitization
Auto loans
Net securitization
Home equity lines
Credit card receivables loans
Commercial and industrial loans
Repos
ET vs. OTC Derivatives
ET: Foreign exchange rate derivatives
OTC: Net (written - purchased)
OTC: Foreign exchange rate derivatives
OTC: Interest rate
ET: Interest rate
ET: Net (written - purchased)
Hedge vs. Trade Derivatives
Hedge: Equity
Trade: Equity
Hedge: Interest rate
Hedge: Commodity and other
Trade: Commodity and other
Hedge: Foreign exchange
Trade: Foreign exchange
Trade: Interest rate
Positive vs. Negative fair value derivatives
Negative: Equity derivatives
Positive: Equity derivatives
Positive: Commodities and others derivatives
Negative: Foreign exchange rate derivatives
Positive: Foreign exchange rate derivatives
Negative: Commodities and others derivatives
Positive: Interest rate derivatives
Negative: Interest rate derivatives
Net OBS derivatives exposure
OBS Swaps
Foreign exchange rate swaps
Equity swaps
Net OBS credit exposure
Interest rate swaps
Net OBS swaps exposure
Commodity and other swaps
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(√
(√
(√
(√
(√
(√
(√
(√
(√
)
)
)
)
)
)
)
)
)
0.188
2.421
0.720
3.125
0.264
0.052
0.343
0.018
0.422
7.43
0.47
1.35
0.26
2.98
13.24
1.34
14.02
0.48
1.398
1.147
0.972
0.804
0.788
0.688
0.458
0.252
0.202
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(-)
(-)
(-)
(√)
(√)
(√)
(√)
(√)
(⤫)
(⤫)
(⤫)
(√)
0.243
0.130
1.543
0.296
0.071
0.044
-0.216
-0.082
-0.245
7.06
12.74
1.00
4.97
17.26
2.41
2.37
7.79
4.14
1.715
1.656
1.543
1.471
1.226
0.106
-0.511
-0.639
-1.014
(-)
(+)
(+)
(+)
(-)
(-)
(+)
(+)
(+)
(+)
(+)
(+)
(√)
(√)
(√)
(⤫)
(⤫)
(⤫)
0.915
0.012
0.266
0.014
0.005
0.018
0.95
47.36
1.41
7.07
10.01
0.52
0.870
0.568
0.375
0.099
0.050
0.009
(-)
(+)
(-)
(-)
(+)
(-)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(√
(√
(√
(√
(√
(√
(√
(√
3.659
0.511
0.054
8.961
0.011
0.187
0.005
0.003
1.40
5.82
19.98
0.12
62.86
3.54
59.72
29.87
5.126
2.974
1.079
1.069
0.691
0.662
0.299
0.090
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(√)
(√)
(√)
(√)
(√)
(⤫)
(⤫)
(⤫)
(√)
120.167
10.206
5.936
14.028
13.063
40.094
2.215
0.167
0.001
0.02
0.11
0.17
0.07
0.05
0.01
0.11
0.12
11.42
2.606
1.103
1.028
0.972
0.675
0.553
0.254
0.019
0.011
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(+)
(√)
(⤫)
(√)
(√)
(√)
(⤫)
0.159
0.315
0.050
0.006
0.003
0.012
42.65
5.31
7.40
8.36
14.55
1.89
6.782
1.674
0.370
0.050
0.044
0.023
(+)
(+)
(-)
(-)
(-)
(-)
(+)
(+)
(-)
(-)
(-)
(-)
(√
(√
(√
(√
(√
(√
0.092
0.255
-0.320
-0.335
-1.247
-5.534
13.195
1.996
1.648
1.845
1.599
3.927
1.220
0.508
-0.528
-0.619
-1.993
-21.730
)
)
)
)
)
)
)
)
Control variables
Sigma
Price
CAR
Ln(Analyst following)
Credit Rating
Ln(Size)
45
)
)
)
)
)
)
APPENDIX A
DEFINITION OF VARIABLES
This table shows the descriptive statistics of the variables used in the regressions.
Panel A: Independent variable
Bid-Ask Spread (BAS)
The difference in price between the highest price that a buyer is willing to pay for a share and the lowest price for
which a seller is willing to sell it (Boyd and Graham, 1986).
Analysts surprise standard Standard deviation in analysts surprise where analysts surprise is difference of acutal earnings per share and
deviation
forecasted earnings per share.
Panel B: Secured and troubled loans
Secured by farmlands
Loans secured by farmlands as a percentage of total assets.
Secured by 1-4 residential
Loans secured by 1-4 residential properties as a percentage of total assets.
properties
Secured by >4 residential
Loans secured by multi-family residential (>4) properties as a percentage of total assets.
properties
Secured by commercial
Loans secured by commercial loans as a percentage of total assets.
properties
Net secured loans
Sum of all loans secured by farmlands, 1-4 residential, >4 residential, and commercial properties as a percentage of
total assets.
Non-accruals loans
Total non-accruals loans and lease finance receivables as a percentage of gross loans and leases.
Past due loans
Total past due 90-days or more and still accruing loans and lease finance receivables as a percentage of gross loans
and leases.
FDIC Texas ratio
The Texas ratio is an early warning system to identify banks potential lending problems. It is calculated by dividing
the value of the bank's non-performing assets (Non-performing loans + Real Estate Owned)*100 by the sum of its
tangible common equity capital and loan loss reserves.
Net troubled loans
Sum of all non-accrual and past due loans as a percentage of gross loans and leases.
Panel C: Securitization
D-Securitization
A dummy variable which is equals to one if bank is involved in securitization otherwise zero.
Family residential loans
Family residential loans securitization as a percentage of total on balance sheet managed assets.
Home equity lines
Home equity lines loans securitization as a percentage of total on balance sheet managed assets.
Credit card receivables
Credit card receivables loans securitization as a percentage of total on balance sheet managed assets.
loans
Auto loans
Auto loans securitization as a percentage of total on balance sheet managed assets.
Commercial and industrial
Commercial and industrial loans securitization as a percentage of total on balance sheet managed assets.
loans
Net securitization
Sum of family, home, credit card, auto, and commercial and industrial loans securitization as a percentage of total
managed assets.
Available for sale securities Includes the total fair value of available-for-sale securities as a percentage of total assets.
Repos
Net Fed fund sold and securities purchased under repurchase agreements to total assets.
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APPENDIX A (CONTINUED …)
Panel D: Off-balance sheet derivatives exposure
Net derivatives (written Exchange (or OTC) traded equity, commodities and others derivative contracts written minus purchased as a
purchased)
percentage of total assets.
Equity derivatives
1.
Gross notional amount of equity derivative contracts held for purposes other than trading (or for the
purposes held for trading) as a percentage of total assets.
2.
Gross positive (or negative) fair value of equity derivative contracts as a percentage of total assets.
Commodities and others
1.
Gross notional amount of commodities and others derivative contracts held for purposes other than trading
derivatives
(or for purposes held for trading) as a percentage of total assets.
2.
Gross positive (or negative) fair value of commodities and others derivative contracts as a percentage of total
assets.
Interest rate derivatives
1.
Exchange (or OTC) traded interest rate derivatives contracts written minus purchased as a percentage of total
2.
Gross notional amount of interest rate derivative contracts held for purposes other than trading (or for
purposes held for trading) as a percentage of total assets.
3.
Gross positive (or negative) fair value of interest derivative contracts as a percentage of total assets.
Foreign exchange rate
1.
Exchange (or OTC) traded foreign exchange rate derivatives contracts written minus purchased as a
2.
Gross notional amount of foreign exchange rate derivative contracts held for purposes other than trading (or
derivatives
for purposes held for trading) as a percentage of total assets.
3.
Gross positive (or negative) fair value of foreign exchange rate derivative contracts as a percentage of total
assets.
Net OBS derivatives
Sum of all type equity, commodities, interest rate, foreign exchange rate and others derivatives to total assets.
exposure
Equity swaps
Includes the notional principal value of all outstanding equity or equity index swaps, whether the swap is
undertaken by the reporting entity to hedge its own equity-based risk, in an intermediary capacity, or to hold in
inventory as a percentage of total assets.
Commodity and other swaps Includes the notional principal value of all other swap agreements that are not reportable as either interest rate,
foreign exchange rate, or equity derivative contracts, whether the swap is undertaken by the reporting entity to
hedge its own equity-based risk, in an intermediary capacity, or to hold in inventory as a percentage of total assets.
Interest swaps
Includes the notional principal value of all outstanding interest rate and basis swaps whose predominant risk
characteristic is interest rate risk, whether the swap is undertaken by the reporting entity to hedge its own interest
rate risk, in an intermediary capacity, or to hold in inventory as a percentage of total assets.
Foreign exchange rate
Includes the notional principal value (stated in U.S. dollars) of all outstanding cross-currency foreign exchange rate
swaps
swaps, whether the swap is undertaken by the reporting entity to hedge its own exchange rate risk, in an
intermediary capacity, or to hold in inventory as a percentage of total assets.
Net OBS credit exposure
Includes all off-balance-sheet interest rate, foreign exchange, equity derivative, and commodity and other contracts
in a single current credit exposure amount for off-balance-sheet derivative contracts covered by the risk-based
capital standards as a percentage of total assets.
Net OBS swaps exposure
Sum of all type equity, commodities, interest rate, and foreign exchange rate swaps to total assets.
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APPENDIX A (CONTINUED …)
Panel E: Control variables
Size
Sigma
Price
Analyst following
Credit rating
CAR
NYSE
News
Quarter
Bank’s total assets.
The sigma is 90-day volatility of stock market returns.
Closing share price on announcement day.
Total numbers of analysts following a bank.
S&P credit quality rating.
Following the Basel accords, banks minimum risk-based capital requirements in terms of capital adequacy ratio (CAR)
(calculated as Tier-I capital plus Tier-II capital to total risk-weighted assets).
A dummy variable which is equals to one if bank is trading over NYSE otherwise zero.
A dummy variable which is equal to one if actual EPS is negative or lower than last quarter EPS.
A quarter dummy variable
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