Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Net or Not, Does it Matter?1
Xu JIANG, Mingzhu LI and Woody Y. WU
This paper investigates the economic consequences of banks’ disclosure of
gross derivatives position as required under Statement of Financial
Accounting Standard (SFAS) No. 161, “Disclosure about derivative
instruments and hedging activities”. Using a sample of U.S. banks that
recognize the net value of derivatives but mandatorily disclose the gross value
of derivatives in the footnotes after SFAS No. 161, we conduct the value
relevance test of gross derivatives and find that it provides significant
explanatory power for bank share price beyond that provided by the
recognized net value of derivatives. Further, using a sample of 1,672 bankquarter observations of 74 U.S. bank-holding companies during the 2006 to
2011 period, we find evidence that the mandatory disclosure of gross
derivatives required by SFAS No. 161 results in a significant decrease in
information asymmetry for banks that do netting practice towards derivatives.
We also find that the decrease in information asymmetry is more pronounced
among U.S. banks that do netting relative to European banks which have
more stringent requirements for netting. Taken together, these results suggest
that the disclosure of the gross value of derivatives is value relevant and
reduces information asymmetry, which are also consistent with the goal of
SFAS No. 161 that aims at improving the transparency of derivative
instruments.
Keywords: FAS No.161, Derivative Disclosure, Value Relevance, Information
Asymmetry
1. Introduction
The global OTC derivatives market has grown dramatically since late 1990’s and
is blamed for one of the main causes of the 2008 financial crisis (Volcker, 2011). Many
investors complain that banks' exposures are opaque, making it difficult to determine
exactly how safe a lender is as accounting treatment for derivatives differs sharply, with
netting allowed for most derivatives in the United States but not in the Europe. 2 Leaders
of the top 20 world economies (G20) have been pushing rule-makers to iron out
accounting differences (G20 Declaration, November 15, 2008), 3 and in subsequent
summits urged the International Accounting Standards Board (IASB) and Financial
Accounting Standards Board (FASB) to complete their convergence projects by June
1
We acknowledged helpful comments from Gary Biddle, Jeff Callen, Zhaoyang Gu, Xiaohong Liu, Chul Park,
Suresh Radhakrishna and workshop participants at City University of Hong Kong, Hong Kong Polytechnic
University, Jinan University, Shanghai University of Finance and Economics, Southwest Jiaotong University, and
the University of Hong Kong. Xu Jiang (xu.jiang@duke.edu) is at the Fuqua School of Business, Duke University.
Mingzhu Li (mzli@baf.cuhk.edu.hk) and Woody Y. Wu (woody@baf.msmail.cuhk.edu.hk) are at the Chinese
University of Hong Kong.
2
See section 2 for more background information.
3
Another G20 mandate is to force large users of swaps, such as banks, to process trades through clearing houses,
which guarantee trades between two parties if one defaults. It has become effective from March 11,2013 in the US
(“US and Europe launch derivatives reform”, March 10, 2013, Financial Times).
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
2011. Responding to the G20 mandate, the IASB and the FASB proposed to converge
the derivative disclosure standard and eliminate the discrepancies in the offsetting
treatment. on January 28, 2011 (FASB, 2011, IASB 2011).
The proposals were
strongly opposed by the Wall Street banks as they believe these would only
―exaggerate risks‖. Subsequently, FASB decided to be apart from IASB.
This study aims to provide some evidence about this unsettled issue by
examining the market consequences of gross value derivative disclosure required by
Statement of Financial Accounting Standard (SFAS) No. 161, ―Disclosure about
derivative instruments and hedging activities‖. Recently, Beyer (2013) theoretically
documented that netted fair value accounting numbers result in a loss of information
than the gross value. We provide initial empirical evidence on the incremental ability of
gross value disclosure to explain stock price and bid-ask spreads. Collectively, We find
that for a sample of bank-holding companies (hereafter referred to as banks) that
mandatorily disclose the gross value of derivatives, the valuation coefficients on the
netting adjustments towards gross derivatives are statistically significant, whereas the
valuation coefficients on the net value of derivatives are not significant. Furthermore, we
carry out a difference-in-difference test around the mandatory disclosure of gross
derivatives by SFAS No. 161 and find that the test reveals a significant decrease in
information asymmetry for banks that do netting practice towards derivatives.4 Overall,
these results suggest that the disclosure of the gross value of derivatives is valuerelevant and decreases information asymmetry, which are also consistent with the
objective of SFAS No. 161 to improve the disclosure transparency of derivative
instruments.
We first test the value relevance of gross value of derivatives. For banks that only
disclose and recognize the net value of their derivatives, SFAS No. 161 forces them to
disclose the gross value of derivatives in the footnotes, which is a huge amount relative
to the net value. Of primary interest is whether these new disclosures are useful to
investors in equity valuation relative to the recognized net value. Our value relevance
test differs from prior research in two aspects. First, unlike prior studies, this paper
examines the derivative assets and derivative liabilities separately rather than the
aggregate value of derivatives. We partition the gross value into two parts, the net value
and the netted amount of gross value. Second, while prior studies examine the fair
value and notional value disclosure of derivatives, their primary focus is the derivatives
used for purposes other than trading, that is, for hedging purpose. In this study, we
investigate the gross value of derivatives used for both trading and hedging. Using a
sample of banks that recognize the net value of derivatives and mandatorily disclose the
gross value of derivatives in the footnotes after SFAS No. 161, we find evidence that the
valuation coefficient on the netted amount of derivatives is statistically significant,
whereas the valuation coefficient on the net value of derivatives is not significant.
4
Please note that banks that do netting practice towards derivative instrument refer to banks that disclose and
recognize only the net value of derivatives prior to the implementation of SFAS No. 161. Such banks began to
disclose the gross value of derivatives in the footnotes after SFAS No. 161 but still continue to recognize the net
value of derivatives on their balance sheets.
2
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Furthermore, we find that the off-balance sheet derivatives for trading purpose are the
main factor of the value relevance test.
Our second test examines whether the disclosure of gross value of derivative
instruments is informative to investors in the sense that it reduces the information
asymmetry. Theoretically, more disclosure alleviates the adverse selection problem and
decreases information asymmetries between informed and uninformed investors
(Diamond et al. 1991). FASB also assert that disclosing fair value of derivatives on a
gross basis better conveys information regarding how firms’ risks are managed to
investors. Thus, we expect that the mandatory disclosure of gross amount of derivatives
reduces the information asymmetry for banks that did not disclose the gross value until
2009. Skeptics of gross value disclosure, however, argue that the gross value of
derivatives may mislead the investors and may have the unintended consequences of
making the firm appear more risky. If their argument is valid, then it is the net value of
derivatives that reflect the banks’ true underlying risk. The gross value would then at
best be very noisy information and at worst further increase information asymmetry. To
differentiate between those two opposing arguments, we do the difference-in-difference
analysis using a sample of 1,672 bank-quarter observations of 74 U.S. banks during the
2006 to 2011 period. We use U.S. banks that self-selected to disclose and recognize
the gross value of derivative instruments prior to SFAS No. 161 as a control sample. We
also use a group of large European banks as another control sample. We assume that
European banks are not able to do much netting because of the stringent requirement
of IFRS regarding netting. We find that banks that recognize the net value of derivatives
but were forced to disclose the gross value because of SFAS No. 161 have their
information asymmetry among investors significantly reduced in the post-SFAS No. 161
period. These results suggest that the gross value of derivative contains valuable
information and plays an informational role instead of a ―noisy‖ role in the capital market,
which is consistent with the goal of SFAS No. 161 to improve the transparency of
derivative instruments disclosure.
Finally, we perform sensitivity test and find that the results are robust to (1)
controlling for the gross notional amount of derivative assets and derivative liabilities; (2)
correcting for the self- selection bias that banks choose to do netting practice.
Overall, we conclude that disclosure of gross value derivatives as required by
SFAS No. 161 provides useful information to investors and mitigate the information
asymmetry problem. This paper makes three primary contributions to the literature. First,
to the best of our knowledge, this is the first study to investigate the economic
consequences of disclosure of gross value of derivatives introduced by SFAS No. 161.
We add to the debate on the disclosure of netting adjustments by providing empirical
evidence on the valuation and information asymmetry perspective. Our results suggest
that the disclosure of fair value of derivatives in a gross basis improves the financial
reporting transparency of financial institutions and helps investors to better understand
how derivatives are managed and used. This evidence, however, should be interpreted
with caution, as the above documented association can be driven by unobservable
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
characteristics of the derivatives or an imperfect control for bank characteristics, rather
than by gross value disclosure to market per se.
Second, this study contributes to the ongoing debate regarding the disparity in
the derivative instrument presentation between current IFRS and the U.S. GAAP. The
findings of this study highlight the importance of the gross value disclosure of
derivatives in both useful for valuation as well as reducing information asymmetry. To
some extent, the results in this study also provide implications for future accounting
rules regarding derivative disclosure for regulators and standard setters.
Finally, this study complements the extant value relevance literature of derivative
instruments. Prior studies only focused on the derivatives for hedging purpose and were
often based on the aggregate fair value of derivatives. This study focuses on the fair
value of all derivatives including trading and hedging purpose and disaggregates the
derivative that represents assets or liabilities position. The research design directly
reflects the different properties of each component of the derivative instruments.
The rest of the paper is organized as follows. Section 2 provides background
information regarding accounting for the practice of netting derivatives. Section 3
reviews the literature and develops the hypothesis. Section 4 defines the variables and
provides the research methodology. Section 5 describes the sample and data. Section 6
presents the empirical results. Section 7 concludes the paper.
2. Background Information and Institutional Details
2.1. Development of OTC Derivatives Market
The use of derivative instruments has increased dramatically over the past two
decades. According to the Bank for International Settlements, the nominal or notional
amounts outstanding, defined as the gross nominal or notional value of derivatives on
all types of risks concluded and not yet settled on the reporting date, have grown from
$80.3 trillion in 1998 to $647.8 trillion by 2011 and peaked at $706.9 in June, 2011
(Panel A, Table 1) .
Bank for International Settlements defines gross market values as the sum of the
absolute values of all open contracts with either positive or negative replacement values
evaluated at market prices prevailing on the reporting date. They supply information
about the potential scale of market risk in derivatives transactions. Furthermore, gross
market value at current market prices provides a measure of economic significance that
is readily comparable across markets and products. As shown in Panel A of Table 1, the
gross market value also increased substantially from $3.2 trillion in 1998 to $27.3 trillion
in 2011. It reached $35.3 trillion in the second half of 2008, due to the sharp asset price
movements following the bankruptcy of Lehman Brothers in September 2008. Gross
market values declined quite rapidly thereafter as asset prices moved closer to their
pre-crisis values, but have increased again since the first half of 2010 as markets went
through another bout of turbulence in European crisis.
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Panel B of Table 1 indicates that most of the derivatives are foreign exchange
and interest rate contracts. A total of 78% of notional amounts outstanding are from
interest rate contracts and 10% are from foreign exchange contracts. Similarly, 73% of
gross market value is from interest rate contracts and 9% is from foreign exchange
contracts. It seems that derivatives have become a great source of revenue for banks
(Aubin 2011). Mr. Volcker, appointed by President Barack Obama as the chair of the
President's Economic Recovery Advisory Board on February 6, 2009 to advise the
Obama Administration on economic recovery matters, argued that the vast increase in
the use of derivatives, designed to mitigate risk in the system, had produced exactly the
opposite effect (Volcker, 2011).
Many investors complain that banks' derivative exposure is opaque, making it
difficult to determine exactly how safe a lender is as accounting treatment for derivatives
differs sharply, with netting allowed for most derivatives in the United States but not
under IFRS. The issue has come under scrutiny by regulators worldwide since the
global financial crisis (Basel Committee, 2009). Leaders of the top 20 world economies
have been pushing rule-makers to iron out accounting differences (G20 Declaration,
November 15, 2008). In September 2009, the G20 representatives required that global
standard setters should ―make significant progress towards a single set of high quality
global accounting standards.‖ The Financial Stability Board’s progress report stated:
―Moreover, continuing differences in accounting requirements of the IASB and FASB for
netting/offsetting of assets and liabilities also result in significant differences in banks’ total
assets, posing problems for framing an international leverage ratio .‖ (Financial Stability
Board, 2009)
2.2. The Legal Issue of Netting
2.2.1 An Introductory Example
Suppose that Bank GS has entered into a large number of derivative transactions
with Insurer AG, and expects to receive $2.45 billion from AG in respect of in-the-money
transactions and to pay $2.475 billion to AG in respect of out of-the-money transactions.
AG discovers that its trader, Dick Leeson has made unauthorized trades with
crippling exposures to third parties. Insolvency proceedings commence promptly
against AG and it is not expected that there will be any significant distribution for AG’s
creditors.
GS faces at least two possibilities. The first is that the gross amounts of $2.45
billion and $2.475 billion will be netted against each other to produce a single net
amount of $25 million which GS owes to AG. This would leave GS broadly in the same
economic position it would have been in had AG remained solvent.
The second possibility is that GS will have to pay $2.475 billion in full to AG, or to
AG’s representative in insolvency, and claim as an unsecured creditor to recover $2.45
billion in AG’s insolvency. The AG’s insolvency will have converted GS’s net liability of
$25 million into a gross exposure of $2.475 billion, which would only be reduced, if at all,
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
by whatever distribution GS ultimately receives in AG’s insolvency as an unsecured
creditor for $2.45 billion.
The second scenario could make GS insolvent. A domino effect might ensue, in
which the insolvency of one institution (AG) might lead to insolvencies of other financial
institutions such as GS.
Thus the first possibility, in which GS owes a single net amount of $25 million, is
good for the security of financial markets generally. Under what circumstance can GS
report on its balance sheet in this way?
2.2.2 Close-out Netting
The International Swaps and Derivatives Association (ISDA) is a trade
organization of participants in the market for over-the-counter derivatives. Initially
created in 1982, it has been credited for helping defeat US Congressional efforts to
regulate derivatives in 1994 and again in 1998. In 1992, the ISDA published a
standardized contract (the ISDA Master Agreement), typically used between a
derivatives dealer and its counterparty when discussions begin surrounding a
derivatives trade. In response to market difficulties in the late 1990s, a second edition
was published in 2002. It is considered ―fundamental to, and provides a template for,
the derivatives market.‖ (Ishmael 2009).
Possibly the most important aspect of the ISDA Master Agreement is that it
allows the parties to aggregate the amounts owed by each and replace them with a
single net amount payable by one party to the other. Netting, dealt with under section
2(c) of the ISDA Master Agreement, allows the parties to net out amounts payable on
the same day and in the same currency.
The more important use of netting is close-out netting under Section 6(e) of the
ISDA Master Agreement. Pursuant to this section, when an ISDA Master Agreement is
terminated (normally following a credit event), the value of each of the Terminated
Transactions is assessed and converted into the Termination Currency and any
outstanding Unpaid Amounts are taken into account. 5 The Settlement Amount and
Unpaid Amount are added up and a single figure in the Termination Currency is
determined payable by one party or the other.
In the preceding example, the successful application of close-out netting to the
open transactions between GS and AG should yield a single net amount of $25 million
payable by GS to AG.
5
Large users of swaps in the US, such as banks, will be required from March 11,2013 to process trades through
clearing houses as the industry is forced to comply with a mandate agreed by the G20 more than three years ago.
Regulators are pushing for more trades to move on to electronic trading venues and be processed through clearing
houses, which guarantee trades between two parties if one defaults. (“US and Europe launch derivatives reform”,
March 10, 2013, Financial Times)
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
2.3. Financial Reporting of Netting
2.3.1 Netting under U.S. GAAP
The netting adjustments are the amount of derivatives companies has netted
under legally enforceable master netting agreements and cash collateral receivable and
payable with the same counterparty in accordance with ASC Subtopic 210-20, Balance
Sheet – Offsetting (formerly FASB Interpretation No. 39, ―Offsetting of Amounts Related
to Certain Contracts,‖).6 FIN-39 identifies four conditions that must be met for the right of
setoff to exist: (1) Each of the two parties owns the other determinable amounts. (2) The
reporting party has the right to set off the amount owned by itself with the amount
owned by the other party. (3) The reporting party intends to set off. (4) The right of setoff
is enforceable by law.
Note that even though the right of ―setoff‖ is a ―legal‖ term, for accounting purpose,
the right of setoff does not exist unless the reporting entity intends to set off the two
amounts. Essentially, offsetting is generally an accounting option when certain
conditions are met, rather than an accounting requirement. In other words, companies
have the choice to offset assets against liabilities if companies meet the four criteria.
However, there are two important exceptions that allow companies to offset assets
against liabilities even if they don’t intend to settle on a net basis. One is for derivatives
subject to a master netting agreement with the same counterparty and the other is for
netting of repos and reverse repos that meet certain criteria. The motivation for
companies to do a netting adjustment is to reduce credit, settlement and other
counterparty risks of financial contracts by aggregating (combing) two or more
obligations to achieve a reduced net obligation. Offsetting can affect the firms’ key
financial ratios and improve firms’ perceived profitability and liquidity.
Before FASB introduced SFAS No. 161, banks selectively disclosed their
derivatives either on a net basis or a gross basis. As permitted under U.S. GAAP, banks
can net derivative assets and liabilities, and the related cash collateral received and
paid, when a legally enforceable master netting agreement exists between the firm and
the same counterparty. Derivatives are recognized on a net-by-counterparty basis when
a legal right of setoff exists under an enforceable master netting agreement (MNA). This
practice is called netting and has a significant impact on the stock price and bid-ask
spread, as examined in this study. For example, Bank A had an interest rate risk
derivative with a $100 asset fair value and a foreign currency exchange risk derivative
with a $60 liability fair value that was subject to master netting arrangements with the
same counterparty Bank B. Bank A would report those derivatives a $40 net asset on
the balance sheet in the event of bank B defaulting on the contract. Generally, some
banks voluntarily disclose and recognize the gross value of derivative, even if the
derivative instruments are subject to the MNA, e.g., Fifth Third Bank. On the other hand,
some banks that disclosed and recognized only the net amount of derivative prior to
6
Arthur Levitt, the former Chairman of SEC, raised question about the FASB interpretations in his March 9, 2007
Wall Street Journal editorial-page commentary: “FAS 133, for example, deals with the accounting for derivatives.
When it was first proposed, the standard was significantly simpler and easier to understand and -- we expect -- to
apply. Yet, as different interests asked for exceptions to the rule, it metastasized into an 800-page treatise of rules
and interpretations that continues to grow with each passing month.”
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
SFAS No. 161 mandatorily disclose the gross value after SFAS No. 161 in the footnotes,
although they still recognize the net value of derivatives after SFAS No. 161. For
example, the gross amount of derivatives of Bank of America is more than $1,000 billion
in the past two years and only 5 percent of it is recognized on its balance sheet. One
could imagine the substantial impact on the financial reports if Bank of America
recognizes the gross amount of derivatives on its balance sheet. Therefore, netting
practice could lead to systematic and significant underestimate of the size of derivatives
positions that banks hold. More importantly, there would be a lack of understanding
about the underlying type of risk being managed since the netting practice is based on
the counterparty basis. As a result, the gross amount of derivative may better convey
the information of how the derivative instrument used and managed.
This paper focuses on the netting adjustment towards derivative assets and
derivative liabilities. Generally, the fair value of derivative assets should not be offset
against derivatives liabilities unless the four criteria for offsetting are met. However,
when derivatives are entered into with the same counterparty under a master netting
agreement, even if the reporting entity does not have the intent to settle on a net basis,
the reporting entity may offset the fair value amounts recognized for derivative
instruments and the fair value amounts for the right and obligation to reclaim the cash
collateral, namely the payable or receivable. The master netting agreement provides
one party the right to terminate the entire arrangement and demand the net settlement
of all contracts, which is conditional on the event of default or bankruptcy of the other
party (FIN 39, pars. 21 and 30).
2.3.2 Netting under IFRS
Ever since the introduction of IFRS in Europe, the offsetting of financial assets
and liabilities on the balance sheet has been a controversial issue. The ability to offset
under IFRS is limited in comparison with U.S. GAAP, especially for derivatives traded
with the same counterparty under an ISDA Master Netting Agreement (MNA).
Historically, the Europe-based International Accounting Standards Board (IASB) has
permitted significantly less balance sheet offsetting than has the FASB. Under IFRS,
assets are required to be offset against liabilities when the company has the legally
enforceable right to set-off and intends to settle on a net basis. Although seemingly
similar to that in the U.S. GAAP, there are two major differences between the two
standards regarding netting. First, offsetting is optional under U.S. GAAP while it’s
mandatory under IFRS. Second, IFRS do not provide exceptions for derivatives and
repos in which there is no intent to offset as does U.S. GAAP. As a result, most
derivatives don’t meet the criteria to be offset under IFRS and are recognized on a
gross basis on the balance sheet.
The difficulty to determine exactly how safe a lender is due to different
accounting treatment, with netting allowed for most derivatives in the United States but
not under International Financial Reporting Standards (IFRS), is best illustrated by Dr.
Josef Ackermann, Chairman of the Management Board of Deutsche Bank in his
presentation, ―Financial Transparency‖, in Montreal and Toronto during February, 19-20,
2009. Appendix B shows that, Deutsche Bank had around €2.2 trillion of assets on the
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
balance sheet at the end of 2008 under IFRS, a quarterly increase of €140 billion. But
under the US GAAP, its balance sheet would only show €1.03 trillion of assets, a
quarterly decline of 22%, €290 billion, with roughly the same level of equity. 7
2.3.3 The Convergence Project
―The fact that companies can, in some instances, report IFRS balance sheet
figures that are double the size of their US GAAP numbers is not acceptable in global
capital markets.‖ commented Sir David Tweedie, Chairman of the IASB. Responding to
the requests from the G20 and the Financial Stability Board (FSB), the IASB and the
FASB proposed on January 28, 2011 new rules for when companies would be allowed
to offset financial assets and financial liabilities on the balance sheet (FASB 2011, IASB
2011). The boards proposed that offsetting should apply only when the right of set-off is
enforceable at all times, including in default and bankruptcy, and the ability to exercise
this right is unconditional, that is, it does not depend on a future event. 8 The entities
involved must intend to settle the amounts due with a single payment or simultaneously.
Provided all of these requirements are met, offsetting would be required. The proposals
would converge IFRS and US GAAP and eliminate several industry-specific netting
practices. According to Leslie Seidman, Chairman of the FASB, ―This proposal would
change US GAAP to require netting in a narrower set of circumstances, but the effect of
other forms of credit mitigation would be disclosed in the footnotes.‖
The proposed standards would bring U.S. rules closer to existing international
rules and dramatically gross up U.S. balance sheets. The change in standard could
bloat the balance sheet figures by as much as $7 trillion (Credit Suisse 2011).The
proposals would eliminate the exception under US GAAP that allows offsetting for some
arrangements in which the ability to offset is conditional and there is no intention to
offset or the intention is conditional (KPMG 2011).
However, the top U.S. banks expressed strong opposition to this proposal as
they believed that these proposed accounting standards would only mislead the users
of financial reports as they tend to ―obscure or create‖ risks which do not necessarily
exist (Goldman Sachs, 2010). According to them, banks have a typical practice of
netting or offsetting their derivatives exposure against each other that prevents them
from being exposed to the gross amounts of losses. In a letter addressed to FASB,
Robert Traficanti, deputy controller at Citigroup, said ―the flawed offsetting model‖ in the
proposed accounting standards would only mislead the users of financial reports as
they tend to ―obscure or create‖ risks which do not necessarily exist.
7
Appendix C provides an excerpt from ISDA “Netting and Offsetting: Reporting derivatives under U.S. GAAP and
under IFRS” and this table summarizes the key differences for derivatives between current IFRS and U.S. GAAP.
8
Basel Committee for Banking Supervision (article 215, 2009) explicitly stated in its consultative document that
“Consistent with taking a non-risk based approach and international comparability the proposed measure of
exposure does not permit netting. This applies to netting of derivatives, repo style transactions, and the netting of
loans against deposits”.
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Proceedings of 8th Annual London Business Research Conference
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FASB eventually compromised with the top banks and decided to retain its
current standard for banks to continue disclosing the net position of derivatives that are
subject to master netting agreements and cash collateral. This compromise keeps the
disparity between FASB and IFRS on how they offset the financial assets and financial
liabilities, especially derivatives, on the main body of balance sheet. Given the highly
controversial nature of the requirement and the fact that FASB itself reverses its
decision, it is important to provide evidence regarding the economic importance of gross
amount disclosure of derivatives that can help both regulators and academic
researchers to evaluate the incremental usefulness of the gross value versus the net
value of derivative instruments.
2.4. SFAS No. 161
In March 2008, the FASB issued SFAS No. 161 as an amendment to SFAS No.
133, after the board received concerns that the existing legislation did not provide
adequate information about the effect of derivative contracts on the financial position
and performance. FASB was concerned that disclosing information on a net basis could
make it difficult to analyze (1) the risks being managed with derivatives and (2) the
relationship between the fair values of derivatives and the associated gains or losses
reported. FASB believe that disclosing the fair value amounts on a gross basis would
help users understand how and why an entity uses derivative instruments. SFAS 161
was established to improve the transparency of derivative instruments disclosure. Two
features of the disclosure stipulated under SFAS 161 are especially noteworthy. First,
SFAS No. 161 requires banks to disclose the fair value of derivative instruments on a
gross basis, even if the derivatives are qualified for recognized net value under FIN 39.
Second, banks need to disclose the gross fair value amounts for assets and liabilities
separately between derivatives that are designated for hedging and those that are not.
Appendix D provides an excerpt from Goldman Sachs 2010 Annual Report, Note 7
Derivatives and Hedging Activities to illustrate netting practice under SFAS 161.
SFAS No. 161 (ASC 815-10-50) requires companies to disclose the gross value
of derivatives in the footnote. It has no impact on companies that elect to report gross
value of derivatives on the balance sheet. However, it will significantly increase the
derivative assets and derivative liabilities of companies that elect to report net value of
derivatives on balance sheet. Specifically, assuming no netting, the financial companies
in the S&P 500 could bring up to $6.9 trillion and $6.8 trillion of off-balance sheet
derivatives assets and derivative liabilities onto their balance sheet. The assets and
liabilities are highly concentrated among five companies - Bank of America, Citigroup,
Goldman Sachs, J.P. Morgan, and Morgan Stanley, which accounts for 97% of the total
amount of off-balance sheet derivative assets and liabilities (Zion et al. 2011). Bringing
the huge off-balance sheet value of derivatives onto the balance sheet would increase
the reported leverage ratio while decreasing return on assets. Consequently, it may
imply that these five banks have more leverage and exposure to market risks than
previously thought, which in turn could affect how investors and analysts value them.
That probably explains why top banks strongly oppose using the gross presentation of
their balance sheets.
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Proceedings of 8th Annual London Business Research Conference
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3. Literature Review and Hypothesis Development
3.1. Value Relevance
Accounting information is considered to be value relevant when it is associated
with market value of equity (Barth et al. 2001). If a significant association is found, then
we may infer that the accounting information is relevant to investors and reliable enough
to be reflected in the share price.
In this section, we discuss the literature related to the value relevance of
derivative instruments measures. After FASB required mandatory disclosure of fair
value of financial instruments in SFAS No. 107 and No. 119, several studies examine
the value relevance of those fair value disclosures. Barth et al. (1996) and Eccher et al.
(1996) employ a cross sectional valuation framework and find that fair value disclosures
for investments and loans provide significant explanatory power beyond historical costs
in valuing firms. Nelson (1996) finds that fair value disclosure of financial assets
explains differences in market-to-book ratios. Venkatachalam (1996) documents that
the fair value of derivatives helps explain cross-sectional variation in bank share price.
These findings indicate that investors utilize fair value disclosures when valuing firms.
SFAS No. 161 introduces the mandatory disclosure of fair value of derivatives on a
gross basis. For banks that are forced to disclose the gross value but only recognize the
net value of derivatives, prior literature shows that the net value of derivatives has a
predictable association with stock price. The incremental explanatory power of gross
value of derivatives for stock price, however, has not been examined. Ahmed et al.
(2006) document that the recognized fair value of derivatives are value relevant while
the disclosed fair value of derivatives are not. This paper only investigates the
derivatives for risk management and does not distinguish whether banks practice
netting or not. We develop our first hypothesis stated in null as followings,
H1: The gross value of derivatives is not value relevant to stock price.
3.2. Information asymmetry
Financial reporting and disclosure has been identified by prior studies as an
important mechanism through which firms communicate firm-specific information to
investors with the purpose of reducing information asymmetry between management
and investors (Healy et al. (1999)). These disclosures are either mandatory or voluntary.
Prior studies suggest that disclosure alleviates the adverse selection problem as well as
information asymmetries between management and investors as well as informed and
uninformed investors (Diamond et al. 1991, Lambert et al. 2007, and Leuz et al. 2000).
Prior to SFAS No. 161, accounting standard allowed firms to disclose only their net
derivative positions. This treatment prevented investors from fully unraveling the gross
value of those positions. After SFAS No. 161, information regarding gross positions is
available to investors. Following the implications from the theoretical literature, this
mandatory disclosure requirement would result in a reduction in the information
asymmetry between banks and their investors. In this study we use the bid-ask spread
to proxy for the extent of information asymmetry. On the other hand, some practitioners
believe that the disclosure of gross amounts can mislead investors by obscuring banks’
actual riskiness (Goldman Sachs 2010). They argue that the net amount measures
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firms’ leverage and other key ratios more accurately while the gross amount serves as
pure noise and may result in investors making incorrect conjecture. Given the
preceding argument, if the disclosure of gross derivatives does provide relevant
information and does not mislead investors, information asymmetry will be reduced;
otherwise, it will be increased. Thus we develop our second hypothesis stated in null,
H2: The gross value of derivatives does not affect the information asymmetry.
4. Research Design
4.1. Value Relevance Test
To test the value relevance of gross value, we estimate the association between
share price and gross value per share using a modified Ohlson (1995) model, which has
been extensively used in the literature. Barth and Clinch (2009) provide evidence that
share-deflated specifications (as opposed to equity book value-deflated, returns or
equity market value-deflated specifications) perform the best in reducing scale effects in
the modified Ohlson (1995) model.
First, we run regression only using the recognized net value of the derivative
instruments to confirm the findings in the prior literature that disclosure of net value is
relevant to investors. In model (1), we express stock price as a function of net value
derivative assets and derivative liabilities, on-balance-sheet non-derivative assets and
liabilities and net income.
Model (1):
where PRC is the per share price measured at the end of the quarter, FVA is the
aggregate fair value assets excluding the net value of derivative assets, FVL is the
aggregate fair value liabilities excluding the net value of derivative liabilities, NETBV is
the net book value of non-fair value assets and non-fair value liabilities, NDERA is the
net value of derivative assets and NDERL is the net value of derivative liabilities, and NI
is the net income. The independent variables are scaled by the outstanding shares.
Second, to test the value relevance of disclosure of gross value, we replace the
main independent variables, net value of derivative assets and liabilities, with the gross
value of derivative assets and liabilities.
Model (2):
Where GDERA is the gross value of derivative assets and GDERL is the gross value of
derivative liabilities.
Third, we partition the gross value of derivatives into two parts, the net value of
derivatives and the netted amount of derivatives. The latter part is the difference
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between the gross value and net value of derivatives, which is the off-balance sheet
amount of derivative exposure because of netting treatment.
Model (3):
+
+
(3)
Where ADERA is the netted amount of derivative assets and ADERL is the netted
amount of derivative liabilities.
Finally, we further partition the off-balance sheet derivative assets and liabilities
depending on whether the derivative instruments are designated as hedging purpose or
trading purpose.
Model (4):
+
+
(4)
Where ATDERA is the netted amount of derivatives assets for trading purpose,
ATDERL is the netted amount of derivative liabilities for trading purpose.9
We correct stand errors and related t-statistics based on two dimensional
clustering (i.e., banks and quarters) following Petersen (2009).
4.2. Information Asymmetry Test
We use the relative bid-ask spread (SPREAD) to measure information
asymmetry between informed traders and uninformed traders. Bagehot (1971) first
discussed the relation between information asymmetry and the bid-ask spread,
suggesting that more information asymmetry will result in a larger bid-ask spread.
Bagehot’s intuition was subsequently modeled by Copeland and Galai (1983), Kyle
(1985) and Glosten and Milgrom (1985). Ball et.al. (2011) define SPREAD as the
quarterly average of the difference between the ask and the bid quotes scaled by the
average of the ask and the bid, expressed in percentage terms. Specifically,
∑
Where
is the number of months in quarter q for bank i for which closing monthly
bids (
) and closing monthly asks (
) are available. We use SPREAD defined by
Ball et al. (2011) to proxy the information asymmetry variable in this paper.
9
Since hedging purpose derivatives accounts for only a tiny amount of total derivatives, we do not include them in
our main analysis. Untabulated test shows that adding the amount of derivatives for hedging purpose does not
change our results qualitatively.
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We explore the impact of mandatory disclosure of the gross value of derivatives
on information asymmetry using a difference- in- difference design. Specifically, we
regress SPREAD on an indicator variable Netting indicating whether banks do netting
practice, a dummy variable POST indicating the time period (pre-versus post SFAS No.
161), the interaction between these two indicators, and a set of control variables. This
research design allows us to investigate the change in information asymmetry in the
pre- versus post- SFAS No. 161 periods for banks that engage in netting relative to the
change for banks that do not over the same period. Using the banks that voluntarily
disclose gross value as a control group helps to isolate the effect of mandatory
disclosure of gross value of derivatives by differencing out possible confounding factors
that change around SFAS No. 161’s implementation. The specific regression model of
information asymmetry test is as follows,
+
(5)
The choice of control variables follows the research design of Ball et al. (2011).
The characteristics of banks’ balance sheet composition are associated with information
asymmetry (Morgan, 2002; Flannery et al., 2004). We control for loans and leases
(LOAN) and loan loss allowance for loan and lease (LLA), both scaled by market value
of equity. Following Fahlenbrach and Stulz (2011), we also control for Tier 1 capital ratio
(TIREONE). Firm and market characteristics such as banks size and stock liquidity are
important determinants of the bid-ask spread (Stoll, 2000). We control for size (LNMVE)
and stock liquidity using turnover (TURN). We also control for daily stock return volatility
(RETVOL) and the inverse of the quarterly end closing stock price (PRCINV).
5. Data and Sample Descriptive Statistics
Financial statement data for U.S. bank holding companies are from the Federal
Reserve’s Consolidated Financial Statements for Bank Holding Companies (FRY-9C).
Data on bid-ask spread and other microstructure variables of U.S. banks are from
CRSP. The financial data of European banks are from Bankscope database and other
microstructure data of European banks are from Datastream. Panel A of table 2
describes the sample selection process. We identify the Top 100 U.S. banks according
to the amount of total assets at the end of 2009: Q1 in FRY-9C report. Then we exclude
U.S. subsidiaries of foreign banks as well as private banks, resulting in 74 unique banks.
Panel B shows the sample selection process of the top 43 European banks. Panel C of
table 2 presents the composition of banks that do netting practice and that do not in the
pre- and post- 2009 periods. The final sample with non-missing data for all variables
covers the period from 2006: Q1 to 2011: Q4 and comprises 20 U.S. banks that do
netting practice before 2009 with 198 observations pre-SFAS No. 161 and 230
observations post-SFAS No. 161; 54 U.S. banks do not do netting practice until 2009
with 629 observation pre-SFAS No. 161 and 615 observation post-SFAS No. 161; and
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43 European banks with 316 pre- and 404 post-SFAS 161. Appendix E shows all
sample firms.
Panel A of Table 3 presents the descriptive statistics for variables used to do the
value relevance test for the sample of banks that did not report the gross value of
derivative until 2009 in the post- SFAS No. 161 period, which are those banks that
engage in netting but are forced to disclose the gross value. All variables are per share
numbers. Since the gross amounts of derivatives item at FRY-9C are available from
2009:Q2, the sample here includes 189 bank-quarter observations for 20 banks from
2009:Q2 to 2011: Q4. The mean of the share price is 32.115. The average of the fair
value assets (FVA) and the fair value liabilities (FVL) excluding derivatives are 146.310
and 47.921 respectively. The mean of the net book value of non-fair value assets
liabilities (NETBV) is -65.244. The average net value for derivative assets (NDERA) and
derivative liabilities (NDERL) are 15.901 and 12.152, respectively, while the means of
the gross values of derivative assets (GDERA) and derivative liabilities are significantly
larger, 183.935 and 167.045, respectively. The netted amount of derivatives are mostly
concentrated in the adjustments towards derivatives for trading purpose, 167.524 for
assets and 154.632 for liabilities 10 , while the adjustments towards derivatives for
hedging purpose are quite small, 0.51 for assets and 0.26 for liabilities. The mean of the
notional amount of derivatives is 9751.436, of which 9675.859 is the notional amount of
derivatives for trading.
Panel B of Table 3 presents the descriptive statistics for variables used in the
information asymmetry test. The mean (median) of bid-ask spread for the U.S. banks
that do netting is 0.174 (0.073), which is lower than the mean (median) for the U.S.
banks not practicing netting, 0.196 (0.112), but higher than the mean (median) of 0.154
(0.091) for the European banks. Loans are more prevalent on the balance sheet of the
European banks which have stringent requirements for netting. However, the U.S.
banks exhibit great turnover and tier one ratio but lower stock return volatility than the
European banks. Panel C of Table 3 shows the Pearson and Spearman Correlation
across the variables used in the information asymmetry specification.
6. Results
6.1. Value relevance Tests
To test the value relevance of the gross value of derivative assets and liabilities,
we estimate the association between stock price and related gross value. In Panel A of
Table 4, we use four model specifications to test H1 for banks that mandatorily disclose
the gross value of derivatives post-SFAS 161. Column (1) first shows the association
between the net value of derivatives and stock price. The coefficient on the net value of
derivatives assets (NDERA) is significant (1.292 with t-value 2.38) whereas the
10
The netted amounts for assets (ATDERA) and liabilities (ATDERL) consist of two parts. One is the legally
enforceable master netting agreements which have the same netted amount for assets and liabilities. The other part is
cash collateral applied which are different for netted amounts towards assets and liabilities. Thus the total netted
amount ATDERA and ATDERL do not show the same descriptive statistics in Panel A of Table 3. In the FR-Y9C
database, only the total netted amount is available. Please refer to appendix D as an example.
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coefficient on the net value of derivative liabilities (NDERL) is insignificant. In column (2),
we replace the net value of derivatives with the gross value of derivatives. The
coefficients on the gross value of derivative assets (GDERA) and liabilities (GDERL) are
both significant with the correct sign (0.703 with t-value 6.29 and -0.719 with t-value 6.38, respectively), suggesting that the gross value derivatives are value relevant and
thus H1 is rejected. Furthermore, we partition the gross value of derivatives into the net
value and the related adjustment amounts towards gross value as shown in column (3).
As expected, the netted amount of derivative assets and liabilities (ADERA and ADERL)
due to netting practice is value relevant (0.505 with t-value 3.55 and -0.555 with t-value
-4.01, respectively) and more importantly, they subsume the statistical significance of
the net value of derivative assets and liabilities. Finally, we partition the netted amount
of derivatives into these related to derivatives for trading purpose and hedging purpose.
In column (4), the coefficient on the netted amount of derivative for trading purpose
(ATDERA and ATDERL) remains significant.
Figure 1 shows the total amounts of derivatives across 20 banks that do netting
at the end of each quarter from the second quarter of 2009 to the fourth quarter of 2011.
Panel A of Figure 1 plots derivative assets’ gross value versus net value between the
full sample and the top five banks, Bank of America, Citigroup, Goldman Sachs, J.P. Morgan,
and Morgan Stanley. One feature of this figure is that the gross value of derivative assets
for the top five banks approximately accounts for 80 percent of the total gross amounts
for the 20 banks in the sample. Another feature is that there is a substantial difference
between the net value and the gross value for derivative assets, which highlights the
huge impact of netting practice on the derivative assets. Panel B of Figure 1 shows the
gross value versus net value for derivative liabilities, which depicts a similar pattern.
Figure 1 not only points to the substantial amount of gross value relative to net value but
also shows that the netting practice is mostly concentrated in the top five banks. It is
thus possible that the results in Panel A of Table 4 are driven by the top five banks,
rather than by the rest 15 banks. Therefore, we exclude the top five banks to check the
robustness of results in Panel A. The main results remain unchanged, as shown in
Panel B of Table 4, and further strengthen the value relevance results for the mandatory
disclosure of gross value derivative instruments.
6.2. Information Asymmetry Test
First, panel A of figure 2 presents the change in the bid-ask spread around the
introduction of SFAS 161 from the first quarter of 2006 to the fourth quarter of 2011,
where the solid line denotes spreads for U.S. banks that do netting practice (netting=1)
and the dotted line plots spreads for U.S. banks that do not do netting practice
(netting=0). There are three noteworthy points in this Figure. First, the solid line lies
above the dotted line in the pre period (and significantly so), indicating that banks use
netting practice had larger spreads than banks that did not. Second, there is a
downward spike in both the solid line and the dotted line in the post-SFAS 161 period.
However, the downward trend is more significant for banks that do netting practice.
Third, the solid and dotted lines overlap significantly in the post-SFAS 161 period,
indicating that the pre period difference in spreads between two groups disappears
once SFAS No. 161 is introduced. These results imply that the change in spread is due
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to the change in accounting treatment rather than other confounding factors. Second,
using the same U.S. banks that do netting as those of panel A, panel B of figure 2
presents the change in bid-ask spread around the introduction of SFAS 161 between
U.S. banks that do netting and European banks. U.S banks (netting=1) which are
represented by solid lines shows similar pattern as what it has in figure 2(a), while
European banks (netting=0) have relatively smooth pattern except for a sharp
downward at the third quarter of 2008 due to the financial crisis.
Table 5 presents the results of pooled regression analysis of the impact of SFAS
No. 161 on bid-ask spread. We compare U.S. banks that do netting versus those do not
as well as U.S. banks that do netting versus European banks. The European banks are
assumed to be not doing netting because of the stringent requirements of netting
practice by IFRS. Panel A reports the coefficients, t-statistics and two tailed p values of
the regression model for the full sample period (2006:Q1 to 2011:Q4). To examine the
relation between the spread and the netting practice (banks affected by the mandatory
disclosure of gross value derivatives), we first combine some of the coefficients in Panel
A and test the significance of the aggregated coefficients. Panel B and Panel C
presents the restricted coefficients and the significance levels in a two by two analysis
for the full sample period. The columns in Panel B and Panel C partition the sample by
the pre- and post-SFAS 161 and the rows partition by Netting and Non-Netting groups.
Panel B of Table 5 shows that U.S. banks that do netting practice experience a
significant reduction in the spread after the mandatory disclosure of gross value of
derivative instruments (0.376 versus 0.498, two tailed p<0.01). For U.S. banks that
voluntarily disclose the gross value of derivatives in the pre-SFAS No. 161, their
spreads also change significantly after the imposition of the mandatory gross value
disclosure in 2009 (0.350 versus 0.396, two tailed p<0.01). One possible interpretation
is that SFAS 161 highlights the importance of gross value disclosure and attracts
greater attention from investors. More importantly, the change in spreads resulting from
the mandatory accounting changes is significantly stronger for the Netting group versus
the Non-Netting group (-0.122 versus -0.046, two tail p<0.01). Consistent with these
results, the Netting group have a significantly higher spread than Non-Netting group in
the pre-SFAS 161 (0.498 versus 0.396, two tailed p<0.01). Further, these cross
sectional differences between the Netting and the Non-Netting group in the post-SFAS
No. 161 become insignificant (0.376 versus 0.350, two tailed p>0.1). Panel C reports
similar results when comparing U.S. banks that do netting with European banks that are
subject to more stringent requirement to practice netting.
To sum up, the results in Table 5 suggest that the mandatory disclosure is
associated with a significant reduction in the spread for both the Netting and the NonNetting group, which suggests that the gross value of derivatives is informative to
investors rather than noise and thus H2 is rejected. Moreover, the mandatory disclosure
requirement levels the playing field among banks that do netting versus those that do
not as the bid-ask spread between the two groups becomes insignificant in the postSFAS 161 period.
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6.3. Sensitivity Tests
The FASB also introduced mandatory disclosure of notional amount of
derivatives. The empirical evidence is mixed on the usefulness of notional amount. 11
Venkatachalam (1996) finds that the notional amount of derivatives to be negatively
related to the price and inferior to the explanatory power of fair values. On the contrary,
Riffe (1997) reported that notional amounts of derivatives are positively related to the
market value of equity. Wang et al. (2005) indicates that the notional amounts of
derivatives are economically significant and provide incremental information beyond
earnings and book value. Eccher et al. (1996) and Nelson (1996) control for notional
principal amounts, but provide limited results pertaining to the information content of fair
value disclosures for 1992-1993. Using a longer time series, Seow and Tam (2002)
show that the notional amount of derivatives are not value relevant after including the
other derivative disclosures for 1990-1996. In this section, we provide initial empirical
evidence on the value relevance of the gross notional amounts. Using the preceding
framework of the value relevance test, we add the gross notional amount of derivatives
to the original regression model. Specifically, we partition the gross notional amounts of
derivatives into those for derivatives of trading purpose and those for derivatives of
hedging purpose.
Panel A of Table 6 reports the value relevance results of gross notional amounts
of derivatives. In columns (1), (2) and (3), the coefficients on the gross notional amounts
of derivatives for trading and hedging purpose (NPTDER and NPHDER) are significant,
indicating the value relevance of gross notional amounts of derivatives. More
importantly, the significant value relevance of gross value derivatives remains the same
as in prior main tests. Similarly, the Panel B of Table 6 excludes the top 5 banks and the
results are also robust.
Secondly, firms’ selection of netting practice is endogenous and this raises a selfselection problem. The netting practice enables banks to hide a substantial amount of
the gross value of derivatives and the banks recognize a tiny portion of this gross value
as net value on the balance sheet. As a result, the netting practice is adopted by banks
that have a relatively substantial gross value. We correct for this self -selection problem
using a two-stage approach. In the first stage, we estimate a probit model of banks
choosing netting practice. In the second stage, we add the inverse Mills ratio from the
first stage to the original spread model.
In table 7, we only use the U.S. banks that do netting and U.S. banks that do not
due to the data availability of European banks. Panel A of Table 7 reports the results of
11
According to the Bank for International Settlements, the nominal amounts outstanding provide a measure of
market size and a reference from which contractual payments are determined in derivatives markets. However, such
amounts are generally not those truly at risk. The amounts at risk in derivatives contracts are a function of the price
level and/or volatility of the financial reference index used in the determination of contract payments, the duration
and liquidity of contracts, and the creditworthiness of counterparties. They are also a function of whether an
exchange of notional principal takes place between counterparties. Gross market values provide a more accurate
measure of the scale of financial risk transfer taking place in derivatives markets.
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the first stage. The coefficient on the notional amounts of derivatives (DERIV) is
significantly positive, indicating that banks with more derivatives are more likely to use
netting practice. Panel B of Table 7 presents the second stage results of changes in the
bid-ask spreads around SFAS 161 with the inverse mills ratio (MILLS) from the first
stage. We find that the coefficient on MILLS is significantly negative but does not
subsume the statistical significance of the indicator variable Netting, which implies that
the probit model may not fully capture the differences in economic characteristics
between these two groups. However, the coefficient on the Netting*POST is significantly
negative, which is consistent with H2 and suggests that U.S. banks that do netting
experience an decrease in spread relative to banks that do not. Generally, the results
are robust after controlling for the self-selection problem.
7. Conclusion
Using data disclosed by the top U.S. bank holding companies we find that the
mandatory disclosure of the gross value derivative is value relevant and reduces the
information asymmetry as evidenced by the decrease in spread. These results indicate
that the gross value of derivatives provide incremental power of value relevance tests
and are informative to investors. As the first empirical study to compare the gross
versus the net value of derivatives, this paper not only contributes to the recent debate
over the gross versus net value of derivative disclosure and but also have significant
policy implications for regulators and standard setters.
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Nelson, K. K. 1996. Fair Value Accounting for Commercial Banks: An Empirical Analysis of
SFAS No. 107. The Accounting Review 71 (2):161-182.
Ohlson, J. A. 1995. Earnings, Book Values, and Dividends in Equity Valuation. Contemporary
Accounting Research 11 (2):661-687.
Petersen, M. A. 2009. Estimating Standard Errors in Finance Panel Data Sets:
Comparing Approaches. The Review of Financial Studies 22 (1):435-480.
Riffe, S. 1996. The valuation of off-balance-sheet financial instrument disclosures in the
banking industry. In Derivatives, Regulation and Banking: Advances in Finance,
Investment and Banking Series Amsterdam, The Netherlands: North-Holland.
Ryan, S. G. 2007. Financial Instruments and Institutions: Accounting and Disclosure Rules
New York, NY: John Wiley & Sons, Inc.
Seow, G. S., and T. Kinsun. 2002. The Usefulness of Derivative-related Accounting
Disclosures. Review of Quantitative Finance & Accounting 18 (3):273.
Venkatachalam, M. 1996. Value-relevance of banks' derivatives disclosures. Journal of
Accounting and Economics 22 (1–3):327-355.
Zion, D., Varshney A., and Burnap N. 2011. Grossing up the balance sheet. Credit Suisse.
21
Proceedings of 8th Annual London Business Research Conference
Appendix A. Key Variable Definitions
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
FVA
Aggregate fair value assets excluding net value of derivative assets, scaled by
total shares outstanding
FVL
Aggregate fair value liabilities excluding net value of derivative liabilities, scaled
by total shares outstanding
NETBV
Net book value of non-fair value assets and non-fair value liabilities, scaled by
total shares outstanding
NDERA
Net value of derivative assets, scaled by total shares outstanding
NDERL
Net value of derivative liabilities, scaled by total shares outstanding
GDERA
Gross value of derivative assets, scaled by total shares outstanding
GDERL
Gross value of derivative liabilities, scaled by total shares outstanding
ADERA
Netting adjustments towards derivative assets, scaled by total shares outstanding
ADERL
Netting adjustments towards derivative liabilities, scaled by total shares
outstanding
ATDERA
Netting adjustments towards derivative assets for trading purpose, scaled by total
shares outstanding
ATDERL
Netting adjustments towards derivative liabilities for trading purpose, scaled by
total shares outstanding
AHDERA
Netting adjustments towards derivative assets for other than trading purpose,
scaled by total shares outstanding
AHDERL
Netting adjustments towards derivative liabilities for other than trading purpose,
scaled by total shares outstanding
NPDER
Gross notional amount of all derivatives, scaled by total shares outstanding
NPTDER
Gross notional amount of derivatives for trading purpose, scaled by total shares
outstanding
NPHDER
Gross notional amount of derivatives for other than trading purpose, scaled by
total shares outstanding
SPREAD
The quarterly average of the monthly difference between the closing ask and the
closing bid quotes, scaled by the average of the ask and the bid
NI
The quarterly income before extraordinary ,scaled by total shares outstanding
NETTING
An indicator variable equal to 1 if a bank does not report gross value of derivative
until 2009, and 0 otherwise
NON
An indicator variable equal to 1 if a bank report gross value of derivative before
NETTING
2009
POST
An indicator variable equal to 1 if a bank-quarter observation falls in or after 2009,
and 0 otherwise
LOAN
Total loans and leases (2122) scaled by the market value of equity
LLA
Loan loss allowance (3123) scaled by the market value of equity
TIREONE
The ratio of Tier 1 capital (8274) to total assets (2170)
LNMVE
The log of market value of equity at the end of the quarter
TURN
The log of the total number of shares traded (VOL) during the quarter divided by
total shares outstanding (SHROUT) at the end of the quarter
RETVOL
The standard deviation of daily returns over the quarter
PRCINV
The inverse of the average stock price during the quarter
DERIV
The notional amount of derivatives during the quarter scaled by total assets
B/M
Book value of equity divided by market value of equity over the quarter
SIZE
The log of the total assets over the quarter
LEV
The ratio of total liabilities to total assets over the quarter
22
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Appendix B
A slide from the presentation by Dr. Josef Ackermann, Chairman of the Management
Board of Deutsche Bank, ―Financial Transparency‖, in Montreal and Toronto during
February, 19-20, 2009.
23
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Appendix C:
An excerpt from ISDA ―Netting and Offsetting: Reporting derivatives under U.S. GAAP
and under IFRS‖. This table summarizes the key differences for derivatives between
current IFRS and U.S. GAAP.
24
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Appendix D:
An Excerpt from Goldman Sachs 2010 Annual Report, Note 7 Derivatives and Hedging
Activities
25
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Appendix E: Sample firms
U.S. banks that do netting practice
1. AMERICAN EXPRESS CO
2. REGIONS FINANCIAL CORP NEW
3.
4.
5.
U S BANCORP DEL
HUNTINGTON BANCSHARES INC
KEYCORP NEW
6.
BANK OF AMERICA CORP
7.
8.
NORTHERN TRUST CORP
P N C FINANCIAL SERVICES GRP
INC
9. STATE STREET CORP
10. W HOLDING CO INC
11. T C F FINANCIAL CORP
12. NEW YORK COMMUNITY BANCORP
INC
13. BANK NEW YORK INC
14. JPMORGAN CHASE & CO
15. CITIGROUP INC
16. MORGAN STANLEY DEAN WITTER &
CO
17. WELLS FARGO & CO NEW
18. SUNTRUST BANKS INC
19. GOLDMAN SACHS GROUP INC
20. NEWALLIANCE BANCSHARES INC
U.S. banks that do not take netting practice
1. ASSOCIATED BANC CORP
28. BANCORPSOUTH INC
2.
3.
POPULAR INC
BANK OF HAWAII CORP
4.
5.
COMMERCE BANCSHARES INC
SYNOVUS FINANCIAL CORP
6.
7.
8.
CULLEN FROST BANKERS INC
CITY NATIONAL CORP
COMERICA INC
29. F N B CORP PA
30. FIRST CITIZENS BANCSHARES INC
NC
31. SOUTH FINL GROUP INC
32. WEBSTER FINL CORP WATERBURY
CONN
33. FIRST BANCORP P R
34. STERLING FINANCIAL CORP WASH
35. UNITED BANKSHARES INC
9.
10.
11.
12.
13.
14.
TRUSTMARK CORP
M & T BANK CORP
FIFTH THIRD BANCORP
FIRST HORIZON NATIONAL CORP
CORUS BANKSHARES INC
MARSHALL & ILSLEY CORP
36.
37.
38.
39.
40.
41.
15. B B & T CORP
16. U M B FINANCIAL CORP
S V B FINANCIAL GROUP
CATHAY GENERAL BANCORP
B O K FINANCIAL CORP
M B FINANCIAL INC NEW
PACIFIC CAPITAL BANCORP NEW
WINTRUST FINANCIAL
CORPORATION
42. UMPQUA HOLDINGS CORP
43. U C B H HOLDINGS INC
17. VALLEY NATIONAL BANCORP
18. WILMINGTON TRUST CORP
44. PROSPERITY BANCSHARES INC
45. EAST WEST BANCORP INC
26
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
19.
20.
21.
22.
23.
24.
25.
26.
27.
ZIONS BANCORP
FIRSTMERIT CORP
FULTON FINANCIAL CORP PA
FIRST MIDWEST BANCORP DE
46.
47.
48.
49.
PRIVATEBANCORP INC
WHITNEY HOLDING CORP
CAPITAL ONE FINANCIAL CORP
INTERNATIONAL BANCSHARES
CORP
FRANKLIN RESOURCES INC
50. SANTANDER BANCORP
NATIONAL PENN BANCSHARES INC 51. METLIFE INC
SUSQUEHANNA BANCSHARES INC 52. UNITED COMMUNITY BANKS INC GA
PA
OLD NATIONAL BANCORP
53. C I T GROUP INC NEW
CITIZENS BANKING CORP MI
54. INVESTORS BANCORP INC
European Banks
1. Allied Irish Banks PLC
2. BNP Paribas
3. Banca Monte DEI Paschi
4. Banca Popolare DI Milano
23.
24.
25.
26.
Dexia
Erste Group Bank AG
HSBC Holdings PLC
ING Groep NV
5.
6.
7.
8.
9.
10.
11.
12.
13.
Banca popolare Emilia Romagna
Banco Bilbao Vizcaya Argentaria SA
Banco Comercial Portugues
Banco Espanol de Credito SA
Banco Espirito Santo SA
Banco Popolare
Banco Popular Espanol SA
Banco Santander SA
Banco de Sabadell SA
27.
28.
29.
30.
31.
32.
33.
34.
35.
Intesa Sanpaolo
KBC Groep NV
Lloyds Banking Group PLC
Natixis
Nordea Bank AB
Raiffeisen Bank International AG
Royal Bank Of Scotland Group PLC
SEB 'A' SA
SNS Reaal NV
14.
15.
16.
17.
18.
19.
20.
21.
Bankinter SA
Barclays PLC
Commerzbank AG
Credit Industriel Et Commerical
Credit Agricole SA
DNB ASA
Danske Bank A/S
Deutsche Bank AG
36.
37.
38.
39.
40.
41.
42.
43.
Societe Generale
Standard Charterted PLC
Svenska Handelsbanken AB
Swedbank AB
UBS AG
Unicredit Spa
Unione DI Banche Italian
Yapi Ve Kredi Bankasi AS
22. Deutsche Postbank AG
27
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Figure 1: Plot of Derivative Instrument Gross value versus Net value across full
sample versus Top 5 banks
This figure depicts the total amounts of derivatives across 20 banks that do netting at
the end of each quarter from the second quarter of 2009 to the fourth quarter of 2011.
Panel A plots derivative assets’ gross value versus net value between the full sample
and the top five banks. Panel B shows the gross value versus net value for derivative
liabilities, which depicts a similar pattern.
Panel A: Derivative Assets (in billions)
Derivative Assets : Gross vs. Net
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
GDERA_Total
GDERA_Top 5
NDERA_Total
NDERA_Top 5
09Q2
5787
4644
480
369
09Q3
5742
4707
459
349
09Q4
5666
4785
391
296
10Q1
5495
4637
370
280
10Q2
6639
5659
413
308
10Q3
7349
6282
435
324
10Q4
5606
4745
370
276
11Q1
4943
4161
345
255
11Q2
5215
4401
345
251
11Q3
7793
6639
464
338
11Q4
7085
6024
401
295
11Q3
7537
6515
464
338
11Q4
6866
5921
401
295
Panel B: Derivative Liabilities (in billions)
Derivative Liabilities : Gross vs. Net
8000
7000
6000
5000
4000
3000
2000
1000
0
GDERL_Total
GDERL_Top 5
NDERL_Total
NDERL_Top 5
09Q2
5485
4488
480
369
09Q3
5450
4559
459
349
09Q4
5411
4665
391
296
10Q1
5275
4544
370
280
10Q2
6399
5547
413
308
28
10Q3
7107
6169
435
324
10Q4
5405
4662
370
276
11Q1
4762
4093
345
255
11Q2
5028
4332
345
251
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Figure 2: Spread changes around the introduction of SFAS No. 161
This figure depicts the spread changes around the introduction of SFAS No. 161. The xaxis plots quarters from 2006: Q1 to 2011: Q4 around the introduction of SFAS 161.
The y-axis plots average residual spreads, orthogonalized w.r.t controls and year effects.
The solid (dotted) line denotes banks that do (do not) take netting practice. Panel A
presents the change in the bid-ask spread around the introduction of SFAS 161 from
the first quarter of 2006 to the fourth quarter of 2011, Panel B presents the change in
bid-ask spread around the introduction of SFAS 161 between U.S. banks that do netting
and European banks.
Panel A: for U.S. banks that do netting and U.S. banks that do not
0.4
Netting
0.3
No Netting
0.2
0.1
-0.1
06Q1
06Q2
06Q3
06Q4
07Q1
07Q2
07Q3
07Q4
08Q1
08Q2
08Q3
08Q4
09Q1
09Q2
09Q3
09Q4
10Q1
10Q2
10Q3
10Q4
11Q1
11Q2
11Q3
11Q4
0.0
-0.2
-0.3
Panel B: for U.S. banks that do netting and European banks that do not
0.4
Netting
0.3
No Netting
0.2
0.1
-0.1
06Q1
06Q2
06Q3
06Q4
07Q1
07Q2
07Q3
07Q4
08Q1
08Q2
08Q3
08Q4
09Q1
09Q2
09Q3
09Q4
10Q1
10Q2
10Q3
10Q4
11Q1
11Q2
11Q3
11Q4
0
-0.2
-0.3
29
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Panel A. Size in trillions of US dollars
1998/12
1999/06
1999/12
2000/06
2000/12
2001/06
2001/12
2002/06
2002/12
2003/06
2003/12
2004/06
2004/12
2005/06
2005/12
2006/06
2006/12
2007/06
2007/12
2008/06
2008/12
2009/06
2009/12
2010/06
2010/12
2011/06
2011/12
Notional Amounts
Outstanding
80317
81458
88201
94037
95199
99755
111115
127564
141665
169658
197167
220058
257894
281493
297670
369906
414845
516407
595341
638725
598147
594495
603900
582655
601046
706884
647762
Gross Market Value
3231
2628
2813
2581
3183
3045
3788
4450
6360
7896
6987
6394
9377
10605
9749
10074
9691
11140
15813
20353
35281
25314
21542
24673
21296
19518
27285
Panel B. Types of derivatives, in trillions of US dollars, at the end of 2011
Foreign exchange contracts
Interest rate contracts
Equity-linked contracts
Commodity contracts
Credit default swaps
Unallocated
Grand Total
Notional
Amounts
63349
504098
5982
3091
28633
42609
647762
% of Grand
Total
10%
78%
1%
0%
4%
7%
100%
30
Gross Market
Value
2555
20001
679
487
1586
1977
27285
% of Grand
Total
9%
73%
2%
2%
6%
7%
100%
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Table 2: Description of sample selection
This table reports the sample selection for our sample of 74 U.S. banks and 43 European
banks.
# of
firms
Panel A: U.S. bank
1. Top 100 bank holding company sorted by total assets
as of 2009Q1 in FRY-9C
2. Foreign bank subsidiaries in U.S. and private
banks
Total
100
(26)
74
# of
firms
Panel B: European bank
1. Top 100 European banks under IFRS sorted by total
assets as of 2009Q1 in Bankscope Database
100
2. Private banks and banks with missing data
(57)
Total
43
Panel C: Composition of sample
3. U.S. Banks that not report gross value of derivative until
2009
(Netting = 1)
4. U.S. Banks that report gross value of derivative before
2009 (Netting = 0)
5. European Banks that report gross value of derivative
before 2009 (Netting=0)
Total
31
# of
firms
# of
Obs.
Pre2009
# of Obs.
Post2009
20
198
230
54
629
615
43
316
404
117
1,143
1,249
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Table 3: Sample Descriptive Statistics and Correlation Matrix
Panel A: Descriptive Statistics for the sample of 20 U.S. Netting Banks from 2009:Q2 to 2011:Q4
25th
50th
Variables(Thousand)
N
Mean
Std. Dev.
Percentile
Percentile
Price/Share
189
32.115
32.886
11.500
25.620
75th
Percentile
41.640
FVA/Share
189
146.310
260.261
23.350
64.121
145.373
FVL/Share
189
47.921
142.710
0.226
2.336
16.147
NETBV/Share
189
-65.244
112.555
-60.185
-24.100
-9.343
NDERA/Share
189
15.901
36.651
0.880
4.655
9.232
NDERL/Share
189
12.152
26.903
0.556
2.958
8.531
GDERA/Share
189
183.935
439.497
1.645
13.540
97.835
GDERL/Share
189
167.045
385.357
1.426
13.046
61.024
ADERA/Share
189
168.034
403.849
0.621
7.346
57.166
ADERL/Share
189
154.892
359.342
0.789
7.374
31.937
ATDERA/Share
189
167.524
402.989
0.130
5.921
57.166
ATDERL/Share
189
154.632
359.403
0.639
6.773
31.938
AHDERA/Share
189
0.510
3.174
0.000
0.000
0.028
AHDERL/Share
189
0.260
0.797
0.000
0.000
0.097
NPDER/Share
189
9751.436
23503.050
76.497
701.936
6507.194
NPTDER/Share
189
9675.859
23459.870
53.362
588.562
6277.938
NPHDER/Share
189
75.578
105.766
15.495
28.429
91.598
NI/Share
189
-0.279
11.133
0.118
0.489
0.846
32
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Table 3 (Continued)
Panel B: Descriptive Statistics of main variables in bid-ask spread test among U.S. banks that do netting
practice, U.S. banks that don’t do netting, and European banks.
Netting=1
U.S. banks
Stat.
SPREAD
LOAN
LLA
TIREONE
LNMVE
TURN
RETVOL
PRCINV
Obs.
Netting=0
U.S. banks
Std. Dev.
Mean
Median
0.460
27.551
0.881
0.018
1.704
0.718
0.026
0.133
428
0.174
9.289
0.254
0.076
9.749
4.160
0.031
0.072
0.073
4.290
0.054
0.074
9.950
4.147
0.023
0.037
Std.
Dev.
0.258
25.188
1.330
0.063
1.159
0.867
0.025
0.289
1244
Netting=0
European banks
Mean
Median
Std. Dev.
Mean
Median
0.196
10.006
0.304
0.094
7.547
3.837
0.032
0.107
0.112
5.206
0.074
0.084
7.402
3.919
0.024
0.043
0.293
20.785
16.730
0.016
1.100
1.352
0.166
0.425
720
0.154
16.970
2.560
0.043
9.744
-1.729
0.182
0.177
0.091
12.132
0.252
0.042
9.697
-1.431
0.129
0.079
Panel C: Simple correlation of key variables in bid ask spread test, with Parson correlation below the diagonal
and Spearman correlation above the diagonal.
(1) Between U.S. banks that do netting and U.S. banks that don’t do netting.
SPREAD
Netting LOAN
LLA
TIREONE LNMVE
SPREAD
-0.179
0.474
0.378
0.068
-0.475
Netting
-0.030
-0.132
-0.103
-0.250
0.554
LOAN
0.468
-0.012
0.909
0.203
-0.468
LLA
0.386
-0.018
0.945
0.231
-0.356
TIREONE
-0.058
-0.141
-0.103
-0.100
-0.202
LNMVE
-0.335
0.589
-0.403
-0.348
0.074
TURN
0.102
0.167
0.240
0.209
-0.014
0.009
RETVOL
0.456
-0.014
0.487
0.453
-0.044
-0.307
33
TURN
0.204
0.156
0.460
0.469
0.109
0.006
0.562
RETVOL PRCINV
0.522
0.463
-0.043
-0.063
0.594
0.690
0.557
0.639
0.109
0.211
-0.329
-0.541
0.679
0.353
0.505
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
PRCINV
0.398
-0.060
0.700
0.778
-0.090
-0.377
0.194
(2) Between U.S. banks that do netting and European banks that don’t do netting.
SPREAD
Netting LOAN
LLA
TIREONE LNMVE TURN
-0.063
0.412
0.314
0.023
-0.506
-0.058
SPREAD
0.027
-0.542
-0.413
0.711
0.041
0.838
Netting
0.452
-0.156
0.803
-0.411
-0.413
-0.396
LOAN
-0.008
-0.084
0.022
-0.157
-0.279
-0.306
LLA
0.033
0.691
-0.202
-0.038
-0.213
0.631
TIREONE
-0.402
0.002
-0.485
0.139
-0.220
0.074
LNMVE
-0.002
0.927
-0.131
-0.079
0.686
0.018
TURN
0.094
-0.483
0.355
0.000
-0.359
-0.135
-0.428
RETVOL
0.299
-0.146
0.210
0.009
0.041
-0.216
-0.139
PRCINV
34
0.457
RETVOL
0.254
-0.759
0.708
0.583
-0.532
-0.182
-0.541
0.284
PRCINV
0.322
-0.284
0.522
0.527
0.015
-0.410
-0.066
0.404
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Table 4: Value Relevance Test of U.S. Netting Banks
This table reports results of value relevance tests of U.S. banks that do netting practice
(Netting=1) in the post-SFAS No. 161 period (from 2009:Q2 to 2011:Q4). Panel A
includes 189 bank-quarter observations of 20 banks. Panel B excludes the Top 5
banks, which includes 134 bank-quarter observations of 15 banks. Column (1) presents
the OLS regression results of share price on net value of derivative assets and
liabilities. Column (2) replaces the net value of derivatives with the gross value of
derivatives. Column (3) partitions the gross value of derivatives into the net value and
the netted amounts. Column (4) partitions the netted amounts of derivatives by
derivatives for trading and hedging purpose. See the Appendix A for variable definitions.
T-statistics are presented in parentheses. *, **, and *** denote significance at the 10%,
5%, and 1% levels, respectively, based on two-sided p-values.
Panel A: Value relevance tests of 20 U.S. Netting Banks (Dependent Variable =
Price)
Independent
(1)
(2)
(3)
(4)
Variables
FVA
0.163*
0.156**
0.203***
0.197***
(1.95)
(2.03)
(2.64)
(2.60)
FVL
-0.175
-0.215**
-0.311***
-0.295***
(-1.63)
(-2.21)
(-3.09)
(-2.99)
NETBV
0.193*
0.175*
0.251**
0.243**
(1.68)
(1.67)
(2.39)
(2.33)
NDERA
1.292**
0.708
0.720
(2.38)
(1.35)
(1.41)
NDERL
-0.702
0.389
0.332
(-1.15)
(0.66)
(0.57)
GDERA
0.703***
(6.29)
GDERL
-0.719***
(-6.38)
ADERA
0.505***
(3.55)
ADERL
-0.555***
(-4.01)
ATDERA
0.496***
(4.02)
ATDERL
-0.544***
(-4.55)
NI
0.155
0.175
0.128
0.128
(1.24)
(1.50)
(1.12)
(1.14)
CONSTANT
17.198***
21.793***
18.874***
18.943***
(8.21)
(11.94)
(9.32)
(9.53)
Observations
189
189
189
189
Adj. R-squared
0.71
0.75
0.76
0.76
35
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Table 4 (Continued)
Panel B: Value relevance tests of 15 U.S. Netting Banks (Dependent Variable =
Price)
Independent
(1)
(2)
(3)
(4)
Variables
FVA
FVL
NETBV
NDERA
NDERL
0.254***
(2.69)
-0.276**
(-2.32)
0.337**
(2.60)
-0.087
(-0.11)
1.365
(1.51)
GDERA
0.109
(1.25)
-0.136
(-1.26)
0.126
(1.05)
0.239***
(3.10)
-0.328***
(-3.36)
0.339***
(3.18)
2.045***
(2.96)
0.775
(1.03)
1.163***
(5.91)
-1.264***
(-5.81)
GDERL
ADERA
0.761***
(4.21)
-1.020***
(-5.34)
ADERL
ATDERA
ATDERA
NI
CONSTANT
Observations
Adj. R-squared
0.239***
(3.07)
-0.296***
(-3.01)
0.339***
(3.15)
1.879***
(2.69)
0.969
(1.28)
0.076
(0.58)
18.985***
(7.84)
134
0.76
0.135
(1.09)
23.956***
(11.17)
134
0.78
36
0.011
(0.10)
15.738***
(7.18)
134
0.84
0.525***
(3.72)
-0.759***
(-5.19)
0.013
(0.12)
15.491***
(6.99)
134
0.84
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
TABLE 5
Change in bid-ask spread around the implementation of SFAS No. 161
Panel A reports the pooled regression results of the impact of SFAS No.161 on bidask spread from 2006:Q1 to 2011:Q4. Column (1) includes 1,672 bank-quarter
observations of 20 U.S. Netting banks (Netting=1) and 54 U.S. Non-Netting banks
(Netting=0). Column (2) includes 1,148 bank-quarter observations for 20 U.S.
Netting banks (Netting=1) and 43 European Non-Netting banks (Netting=0). Panel B
presents two-by-two way analysis of U.S. Netting banks versus U.S. Non-Netting
banks by aggregating the coefficients in Column (1) of Panel A. Panel C uses the
coefficients in Column (2) of Panel A. See the Appendix A for variable definitions. *,
**, and *** denote significance at the 10%, 5%, and 1% levels, respectively, based
on two-sided p-values.
Panel A : Pooled regression (Dependent Variable = Spread)
(1)
(2)
U.S. Netting banks
U.S. Netting banks vs.
vs.
European Non-Netting banks
U.S. Non-Netting
banks
Independent
Variables
tptpCoeff.
stat
value
Coeff.
stat
value
Constant
0.396
7.38
0.000
0.520
4.70
0.000
Netting(1)
0.102
4.24
0.000
0.218
4.08
0.000
POST(2)
-0.046
-3.03
0.002
-0.015
-0.63
0.531
Netting*POST(3) -0.076
-2.65
0.008
-0.129
-3.39
0.001
LOAN
0.011
12.94
0.000
0.006
11.10
0.000
LLA
-0.194 -10.45
0.000
0.000
0.62
0.534
TIREONE
0.105
0.91
0.365
0.076
0.12
0.908
LNMVE
-0.021
-3.66
0.000
-0.050
-5.73
0.000
TURN
-0.064
-6.67
0.000
-0.016
-1.96
0.051
RETVOL
4.478
2.93
0.000
-0.195
-2.68
0.007
PRCINV
0.292
7.10
0.000
0.227
8.14
0.000
Test: (2) + (3)=0
0.000
0.000
Test: (1) + (3)=0
0.281
0.126
Observations
1,672
1,148
2
Adj. R
0.38
0.31
37
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
TABLE 5(Continued)
Panel B: Two-by-Two Analysis of U.S. Netting banks vs. U.S. Non-Netting
banks, by Period, Using the coefficients in Column (1) of Panel A.
Netting
No Netting
Diff.
Pre-SFAS No. 161
(2006-2008)
Post-SFAS No. 161
(2009-2011)
0.498
N=198
0.396
N=629
0.102***
0.376
N=230
0.350
N=615
0.026
Diff.
-0.122***
-0.046***
-0.076***
Panel C: Two-by-Two Analysis of U.S. Netting banks vs. European Non-Netting
banks, by Period, Using the coefficients in Column (2) of Panel A.
Netting
No Netting
Diff.
Pre-SFAS No. 161
(2007-2008)
Post-SFAS No. 161
(2009-2011)
0.738
N=198
0.520
N=316
0.218***
0.594
N=230
0.505
N=404
0.089
38
Diff.
-0.144***
-0.015
-0.129***
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
TABLE 6
Sensitivity Test: value relevance
This table reports sensitivity test results for main findings of value relevance tests in table
4 by controlling for the gross notional amount of derivative, other things being equal. See
the Appendix A for variable definitions. T-statistics are presented in parentheses. *, **,
and *** denote significance at the 10%, 5%, and 1% levels, respectively, based on twosided p-values.
Panel A: Value relevance tests of 20 U.S. Netting Banks (Dependent Variable =
Price)
Independent
(1)
(2)
(3)
(4)
Variables
FVA
-0.072
-0.031
-0.040
-0.046
(-0.75)
(-0.37)
(-0.47)
(-0.53)
FVL
0.164
-0.067
-0.052
-0.005
(1.36)
(-0.61)
(-0.46)
(-0.04)
NETBV
-0.122
-0.073
-0.078
-0.085
(-0.93)
(-0.63)
(-0.66)
(-0.73)
NDERA
0.026
-0.704
-0.532
(0.05)
(-1.28)
(-0.97)
NDERL
0.856
1.554**
1.407**
(1.28)
(2.54)
(2.29)
GDERA
0.555***
(5.11)
GDERL
-0.608***
(-5.52)
ADERA
0.551***
(4.05)
ADERL
-0.621***
(-4.62)
ATDERA
0.482***
(3.37)
ATDERA
-0.544***
(-3.82)
AHDERA
-0.322
(-0.66)
AHDERL
2.906
(1.16)
NPTDER
-0.0006*
0.0010***
0.0008**
0.0005
(-1.92)
(2.71)
(2.01)
(1.22)
NPHDER
0.0758***
0.0679***
0.0782***
0.0608***
(4.72)
(4.85)
(5.42)
(2.81)
NI
0.126
0.135
0.091
0.096
(1.05)
(1.22)
(0.84)
(0.90)
Constant
17.050***
19.941***
18.463***
18.679***
39
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Observations
Adj. R-squared
(8.45)
189
0.83
(11.19)
189
0.84
(9.66)
189
0.86
(9.69)
189
0.86
TABLE 6 (Continued)
Panel B: Value relevance tests of 15 U.S. Netting Banks (Dependent Variable = Price)
Independent
(1)
(2)
(3)
(4)
Variables
FVA
-0.024
-0.161
-0.069
-0.073
(-0.23)
(-1.48)
(-0.72)
(-0.75)
FVL
-0.192
0.037
0.109
0.153
(-1.30)
(0.19)
(0.63)
(0.87)
NETBV
-0.043
-0.233
-0.079
-0.081
(-0.30)
(-1.58)
(-0.61)
(-0.62)
NDERA
-3.190***
1.654
2.697**
(-3.99)
(1.49)
(2.09)
NDERL
3.636***
1.013
0.259
(4.14)
(1.01)
(0.23)
GDERA
0.708*
(1.77)
GDERL
-0.826**
(-1.98)
ADERA
1.030***
(2.70)
ADERL
-1.301***
(-3.19)
ATDERA
1.440***
(3.08)
ATDERA
-1.767***
(-3.47)
AHDERA
2.166**
(2.49)
AHDERL
-0.327
(-0.12)
NPTDER
0.0032***
0.0017
-0.0011
-0.0022
(3.96)
(1.16)
(-0.72)
(-1.28)
NPHDER
0.1001***
0.0894***
0.0761***
0.0554**
(5.73)
(5.45)
(4.94)
(2.08)
NI
0.014
0.103
0.017
0.027
(0.12)
(0.89)
(0.17)
(0.27)
Constant
20.471***
23.081***
17.090***
17.167***
(9.46)
(10.91)
(8.40)
(8.44)
Observations
Adj. R-squared
134
0.82
134
0.82
40
134
0.87
134
0.87
Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
TABLE 7
Sensitivity test: self-selection problem
This table presents the sensitivity test results for main findings of information
asymmetry tests in table 5 by correcting the self-selection problem. Due to data
availability of European banks, here we only test the U.S. netting banks versus U.S.
Non-netting banks. Using Heckman (1979) procedure, in the first stage, we examine
the determinant of netting practice, as reported in Panel A. In the second stage, we
include the Inverse Mills Ratio generated from the first stage in the regression model of
table 5, as reported in Panel B. See the Appendix A for variable definitions. *, **, and
*** denote significance at the 10%, 5%, and 1% levels, respectively, based on twosided p-values.
Panel A: First Stage - probit model (Dependent Variable = Netting)
Coeff.
B/M
0.017*
SIZE
0.408***
ROA
-0.863
LEV
-0.670
DERIV
1.039***
Constant
-4.882***
Observations
1,672
2
Pseudo R
0.47
z-stat
1.84
9.61
-0.22
-0.88
8.61
-6.44
Panel B: Second stage – change in bid-ask spread around SFAS No.161
(Dependent Variable = Spread)
Coeff.
t-stat
Constant
0.355***
6.26
Netting
0.389***
2.89
POST
-0.051***
-3.36
Netting*POST
-0.077***
-2.70
LOAN
0.010***
12.20
LLA
-0.188***
-10.01
TIREONE
0.199
1.61
LNMVE
-0.038***
-3.95
TURN
-0.064***
-6.62
RETVOL
4.266***
11.87
PRCINV
0.278***
6.68
MILLS
-0.188**
-2.16
Observations
1,672
Adj. R-squared
0.39
41