Preserving Amortized Cost within a Fair-Value-Accounting Framework:
Reclassification of Gains and Losses on Available-for-Sale Securities upon Realization
Minyue Dong†
University of Lausanne
Stephen Ryan‡
New York University
Xiao-Jun Zhang*
University of California, Berkeley
First draft: December 2008
Current draft: October 2010
(Revision in Process, for Columbia Burton Conference Participants Only:
Please Do Not Cite without Permission)
†
Universite de Lausanne, Faculty of Business and Economics, Quartier UNIL-Dorigny,
Baliment Internef Bureau 595, CH-1015 Lausanne, Switzerland, (41)216923367. ‡ Stern School
of Business, Kaufman Management Center, 44 West 4th Street, New York, NY 10012.
(01)2129980020. * 545 Student Services Building #1900, Berkeley, CA 94720, USA,
(01)5106424789. Comments and suggestions from Anne Cristine dArcy, Jonathan Glover,
Pierre Liang, Jack Stecher, Danqing Yang and seminar participants at Carnegie Mellon
University, Chinese University of Hong Kong, and University of Lausanne are gratefully
acknowledged. We thank Jialu Shan and Joseph Cadora for research and editorial assistance.
Preserving Amortized Cost within a Fair-Value-Accounting Framework:
Reclassification of Gains and Losses on Available-for-Sale Securities upon Realization
ABSTRACT: SFAS No. 115 requires firms to record available-for-sale (“AFS”) securities on
the balance sheet at fair value, with accumulated unrealized gains and losses (“AUGL”) recorded
in accumulated other comprehensive income (“AOCI”), a component of owners’ equity. Firms
reclassify AUGL to net income when they realize gains and losses either economically through
sale of AFS securities or for accounting purposes through transfer of the securities to trading or
other-than-temporary impairment write-downs. We refer to the amount of this reclassification
each period as “RECLASS.”
For a sample of 200 large U.S. commercial banks from 1998-2006, we examine the incremental
value relevance of RECLASS beyond AUGL and other components of book value of equity and
comprehensive income. We find that the market value of equity is significantly positively
associated with RECLASS, with the coefficient on RECLASS being closer to the coefficient on
the relatively permanent net income before extraordinary items and discontinued operations than
to the much lower coefficients on the remaining more transitory components of comprehensive
income. We also find that the coefficient on RECLASS is much higher than the coefficients on
AUGL and other components of book value. This result obtains despite also finding that the
coefficient on AUGL is significantly positive in a pure balance-sheet model and higher than the
coefficient on the remainder of book value in a combined balance-sheet/comprehensive-income
model, consistent with investors placing at least a normal amount of credence in AUGL.
We conduct three analyses investigating possible explanations for the incremental value
relevance of RECLASS. First, we consider the possibility that unrealized gains and losses are
unreliable, as opponents of fair value accounting often allege. Contradicting this possibility, we
find that the coefficient on RECLASS is higher and more significant for banks that hold more
liquid securities. Second, we consider the possibility that RECLASS interacts with or is an
indicator of future bank growth. Consistent with this possibility, we find that the incremental
value relevance of RECLASS is greater for higher growth banks. Third, and further supporting
this possibility, we find that RECLASS is significantly positively associated with one-year-ahead
comprehensive income, controlling for other components of current book value and
comprehensive income, more so for banks holding liquid securities and growing faster.
Overall, our findings suggest that the value relevance of RECLASS is primarily attributable to
the importance of the realization of realized gains and losses as an indicator of bank growth
rather than to the limitations of fair value accounting for AFS securities. At a minimum, these
findings suggest that the FASB should continue to require information about realized gains and
losses, an amortized cost accounting construct, within the fair value accounting framework for
AFS securities.
Key words: Available-for-sale securities; Reclassification; Fair value accounting; Realization
principle.
1. INTRODUCTION
In this paper, we examine the incremental value-relevance of realized gains and losses
beyond unrealized gains and losses and other components of book value and comprehensive
income for commercial banks’ available for sale (“AFS”) securities. We focus on AFS securities
because they are reported at fair value on the balance sheet, but amortized cost information about
realized gains and losses is preserved and reported on the income statement through the use of
“dirty surplus” accounting described below. The financial reporting for AFS securities contrasts
with the typical accounting for financial instruments under current U.S. GAAP, in which one of
fair value and amortized cost information is reported only in footnote disclosures or not at all.
Prior research generally shows that investors react more strongly to recognized than disclosed
information, either because they do not have the ability or inclination to evaluate the many
disclosures in financial reports or because they deem recognized amounts more reliable
(Schipper 2007). Hence, AFS securities constitute a relatively unambiguous setting in which to
test for the incremental value relevance of information about the fair values and amortized costs
of financial instruments.
Statement of Financial Accounting Standards (“SFAS”) No. 115, Accounting for Certain
Investments in Debt and Equity Securities, requires a hybrid fair-value-on-the-balancesheet/amortized-cost-on-the-income-statement approach to accounting for AFS securities.
Specifically, firms record AFS securities on the balance sheet at fair value, with accumulated
unrealized gains and losses (“AUGL”) recorded in accumulated other comprehensive income
(“AOCI”), a component of owners’ equity distinct from retained earnings. This is dirty surplus
accounting because changes in owners’ equity occur without corresponding changes on net
income. Subsequently, firms reclassify AUGL to net income when gains and losses are realized
economically through sale of AFS securities or for accounting purposes through transfer of the
securities to trading or other-than-temporary (“OTT”) impairment write-downs. We refer to the
amount of this reclassification each period as “RECLASS.”
Advocates of fair value accounting often criticize this accounting for AFS securities as
politically motivated and convoluted, particularly for liquid securities for which fair value is the
most relevant, comprehensive, and timely measure of the value of the securities.1 However, this
accounting has the desirable feature of preserving certain aspects of amortized cost accounting
for AFS securities—particularly the realization of gains and losses reported on the income
statement via RECLASS—within a primarily fair value accounting framework. Even if fair
values are well measured, amortized costs may be incrementally value relevant beyond fair
values for various reasons. The FASB recognizes this fact in its May 2010 Exposure Draft,
Accounting for Financial Instruments and Revisions to the Accounting for Derivative
Instruments and Hedging Activities (“the ED”), in which it observes that amortized costs may
have incremental value relevance due to their verifiability, association with contractual cash
flows, or correspondence with the firm’s business strategy. Consistent with this observation, in
the ED the FASB proposes dual presentation on the balance sheet of the amortized costs and fair
values of many financial instruments.
The FASB’s proposal is consistent with at least two positions expressed by opponents of
fair value accounting. First, most of these opponents question the reliability of fair values (e.g.,
Wallison 2008, Forbes 2009). In our view, reliability is a minor concern for most of banks’ AFS
securities, which are dominated by federal governmental, government-sponsored agency (e.g.,
Fannie Mae), and other liquid securities, although it is a significant concern for some of these
1
For example, in testimony before the Committee on Banking, Housing, and Urban Affairs, U.S. Senate, September
10, 1990, former Securities and Exchange Commission (SEC) Chairman Richard Breeden advocated a move to fair
value accounting, stating that market-based information is more relevant than cost-based information.
-2-
(e.g., structured asset-backed) securities.
More interestingly, some opponents of fair value
accounting point out that realization of gains and losses is important for various purposes, such
as contracting and stewardship assessment (Watts 1993 and Holthausen and Watts 2001), capital
regulation (Moyer 1990 and Ahmed and Takeda 1995), other aspects of firms’ business
strategies (Nissim and Penman 2008), and managerial signaling of their private information
(Abdel-Khalik 2008 and Ronen 2008).
In this study, we provide evidence regarding the benefit of preserving amortized cost
information within a fair value accounting framework in the specific context of the value
relevance of RECLASS. RECLASS constitutes a somewhat more limited and less visible
preservation of amortized costs in a fair value accounting framework than the FASB’s proposal
in the ED to require dual presentation on the balance sheet of the amortized costs and fair values
of many financial instruments. However, our examination of the value relevance of RECLASS is
meaningful because this variable embodies the realization principle that is central to amortized
cost accounting.
We hand collected RECLASS for the 200 largest publicly traded U.S. commercial banks
(based on total assets in 1998) for the period 1998-2006. We limit our sample to banks because
total (realized) gains and losses on AFS securities often constitute significant portions of their
owners’ equity (net income). Our sample period necessarily begins in fiscal year 1998, when
FAS No. 130, Reporting Comprehensive Income, first required firms to disclose RECLASS in a
visible fashion.2
Our primary findings are as follows. First, we find that banks’ market values are
significantly positively associated with AUGL in a pure balance sheet model and higher than the
2
From 1993-1997, users of financial reports generally could have inferred RECLASS from SFAS No. 115-required
AFS securities footnote disclosures of AUGL, realized gains and losses, transfers of securities between the
standard’s three categories (trading, AFS, and held-to-maturity), and OTT impairment write-downs.
-3-
coefficient on the remainder of book value in a combined balance-sheet/comprehensive-income
model, consistent with investors placing at least a normal amount of credence in AUGL. This
suggests that the incremental value relevance of RECLASS that we document in this paper is not
solely attributable to RECLASS remedying the unreliability of the fair value accounting-based
AUGL.
Second, we find that investors ascribe considerable incremental value relevance to
RECLASS, controlling for the other components of book value and comprehensive income.
Specifically, we find that the market value of equity is significantly positively associated with
RECLASS, with the coefficient on RECLASS being closer to the coefficient on the relatively
permanent net income before extraordinary items and discontinued operations (NIBEX) than to
the much lower coefficients on the more transitory components of comprehensive income. The
coefficient on RECLASS is also much higher than the coefficients on AUGL and other
components of book value. These findings imply that stock investors ascribe considerable
significance to the realization of gains and losses recorded in RECLASS. These findings obtain
even though RECLASS effectively just reclassifies one component of owners’ equity, AOCI, to
another, retained earnings. Our remaining primary findings pertain to analyses that we conduct
to investigate possible explanations for the significance of the realization of gains and losses
recorded in RECLASS.
Third, we consider the possibility that unrealized gains and losses are unreliable, as
opponents of fair value accounting often allege. This possibility is inconsistent with our
aforementioned findings for AUGL and with the high liquidity of most AFS securities. Directly
contradicting this possibility, we find that the incremental value relevance of RECLASS is
-4-
stronger for banks that hold more liquid securities. This implies that this incremental value
relevance is not primarily attributable to the lack of verifiability of fair values.
Fourth, we consider the possibility that RECLASS interacts with or is an indicator of
future bank growth. Consistent with this possibility, we find this incremental value relevance is
larger for higher growth banks. This suggests RECLASS conveys incremental information about
future valuation attributes, which have greater valuation consequences for higher growth firms.
Fifth, consistent with this suggestion, we find that RECLASS is significantly positively
associated with year-ahead comprehensive income, controlling for the other components of book
value and comprehensive income. Consistent with the valuation model results, this association is
stronger for banks holding more liquid securities and growing faster.
Sixth, we consider the possibility that investors undervalue unrealized gains and losses,
say because they fixate on reported net income. We conduct regression analyses controlling for
Fama and French’s (1992, 1993) three factors and stock return momentum (Jegadeesh and
Titman 1993) as well as portfolio analyses grouping banks into terciles each year based on the
amount of unrealized gains and losses. Both analyses yield weak statistical evidence that banks
with higher unrealized gains and losses experience economically modest higher future excess
returns. In the portfolio analysis, we find that the return drift is stronger when excess returns for
the tercile observations are value-weighted rather than equally weighted, implying that the
market under-reaction is stronger for larger banks. While there are various possible explanations
for this finding, we conjecture it may be attributable to the greater difficulty that investors face in
evaluating unrealized gains and losses for larger banks, given their more diverse holdings of AFS
securities and greater overall complexity. Given the statistically weak and economically modest
-5-
return drift, investor mispricing appears to explain at most a small portion of the value-relevance
of RECLASS.
To our best of our knowledge, our paper is the first to document the value relevance of
RECLASS or the reclassification of any other component of AOCI.
Our results have
implications for the ongoing debate about the relative and incremental usefulness of fair value
versus amortized cost accounting for financial instruments. Our findings collectively support the
FASB’s view expressed in the ED that it is incrementally value relevant to preserve amortized
cost information based on the realization principle within a fair value accounting framework.
2. Prior Research
Our study is primarily related to two areas of prior research: (1) studies on the
incremental and relative value relevance of the fair values versus amortized costs of financial
instruments and (2) studies on the incremental and relative value relevance of other
comprehensive income versus net income. We discuss these two literatures in turn.
Advocates of fair value accounting generally claim that the fair values of financial
instruments have higher or incremental value relevance compared to the amortized costs of the
instruments. Numerous empirical studies have tested this claim, typically using disclosed fair
values under SFAS No. 107, Disclosures about Fair Values of Financial Instruments. While
generally supportive of this claim, the results of these studies vary somewhat depending the type
and liquidity of the financial instruments considered, the type of firms involved, as well as
aspects of the research design such as the use of levels versus first differences valuation models.
These studies often but not always find that market values are significantly incrementally
associated with fair values controlling for amortized costs, but not vice-versa.
-6-
The most related prior study to ours, Barth (1994), examines the value relevance of
disclosures about the fair values of banks’ investment securities.3 Barth estimates levels and first
differences models in annual cross-sectional regressions and pooled regressions with fixed
effects. In her levels model, the market value of equity is regressed on the book value of equity
and the fair value and amortized costs of marketable securities. The levels model estimation
yields a highly significantly positive coefficient on the fair value of marketable securities and an
insignificant or negative significant coefficient on the amortized cost of marketable securities.
Barth concludes that the fair values of marketable securities provide significant explanatory
power beyond amortized costs, but not vice versa. In her first differences model, returns is
regressed on net income before securities gains and losses (alternatively, the change in that net
income measure) and realized gains and losses and total gains and losses. The first differences
model estimation yields a negative coefficient on realized gains and losses and a positive
coefficient on total gains and losses that usually is insignificant except for large and firms
holding liquid securities. Barth interprets the weaker result for the income statement variables in
the first differences model as attributable to greater noise in these variables.
Barth’s (1994) results suggest that RECLASS should have little value relevance, partly
because it is an amortized cost number and partly because of the aforementioned noise issues.
However, our study differs from Barth (1994) on two important dimensions. First, we use
amortized costs, fair values, and gains and losses—in particular RECLASS—that are recognized
in financial statements rather than simply disclosed. One explanation for Barth’s weak results for
gains and losses is investors put less weight on disclosures rather than recognized amounts.
Second, our levels valuation models include considerably more extensive breakdowns of book
3
Barth (1994) hand collected the fair values of marketable securities for a sample of banks from 1970-1990, a
period entirely prior to the issuance of SFAS No. 107. During this period, banks appear to have disclosed the fair
values of marketable securities under industry GAAP or practice.
-7-
value, net income, and other comprehensive income, consistent with subsequently developed
combined balance sheet and income statement valuation models (e.g., Ohlson 1995).
Barth, Beaver, and Landsman (1996), Nelson (1996), and Eccher, Ramesh, and
Thiagarajan (1996) examine the incremental value relevance of the fair values of essentially all
banks’ financial instruments—such as loans, deposits, and debt—which they disclose under
SFAS No. 107. The results of the three studies differ somewhat due to their differing model
specifications. This is particularly true for loans, for which only Barth, Beaver, and Landsman
find fair values to be incrementally value relevant. By and large, however, these studies find that
the fair value of most financial instruments are incrementally value relevant beyond the
amortized costs of those instruments.
Studies investigating the value relevance of comprehensive income and the components
of other comprehensive income items generally find that comprehensive income and its
components are less value relevant prior to effective date of SFAS No. 130 than afterwards,
consistent with the greater salience to investors of amounts recognized in financial statements
rather than simply disclosed. For a sample prior to the effective date of SFAS No. 130, Dhaliwal,
Subramanyam, and Trezevant (1999) find that comprehensive income does not have a stronger
association with stock returns than net income, except for financial firms. They also find that the
AFS securities adjustment is the only component of other comprehensive income that improves
the association between income and returns, again primarily for financial firms. O’Hanlon and
Pope (1999) report similarly negative results for other comprehensive income items for a sample
of U.K. firms.
For samples after the effective date of SFAS No. 130, Biddle and Choi (2006) find that
comprehensive income dominates other income measures in explaining equity returns. Chambers,
-8-
Linsmeier, Shakespeare, and Sougiannis (2007) find that other comprehensive income is priced
approximately dollar-for-dollar by investors.
3. HYPOTHESIS DEVELOPMENT AND RESEARCH DESIGN
3.1. RECLASS and the Mechanics of Recyling Gains and Losses
Under SFAS No. 115, firms initially record aftertax unrealized gains and losses on AFS
securities in accumulated other comprehensive income (AOCI), a component of owners’ equity.
Accumulated unrealized gains and losses (AUGL) remain in AOCI until one of the following
events occurs involving the securities:
a. Sale,
b. Transfer to trading, or
c. Other-than-temporary (OTT) impairment write-downs.
When one of these events occurs, the related AUGL is reclassified to net income (with pretax
realized gains and losses and tax effects recorded on separate line items on the income statement)
and thence to retained earnings.
We refer to the aftertax reclassification as RECLASS.
RECLASS changes the components but not the total amounts of owners’ equity and
comprehensive income. As a result, RECLASS often is referred to as “recycling” amounts
previously recognized on the balance sheet onto the income statement.
The following equations illustrate the mechanics of this recycling. All amounts are
aftertax in these equations. Net income (NI) during a period equals net income before realized
gains and losses (NIBRGL) plus RECLASS.
NIt=NIBRGLt+RECLASSt.
(1)
-9-
The change in retained earnings (RE) during a period equals NI minus dividends (DIV) during
the period, which equals NIBRGL plus RECLASS minus DIV during the period:
ΔREt = NIt – DIVt = NIBRGLt + RECLASSt – DIVt.
(2)
Hence, RE increases with RECLASS.
The change in AUGL during a period equals the unrealized gain or loss (UGL) during the
period. UGL equals the total (i.e., unrealized plus realized) gain or loss (TGL) minus RECLASS
during the period.
ΔAUGLt = UGLt = TGLt - RECLASSt.
(3)
Hence, AUGL decreases with RECLASS.
The change in owners’ equity (OE) equals ΔRE plus ΔAOCI during the period. ΔRE is
given in equation (2) and ΔAOCI equals ΔAUGL plus other comprehensive income from
sources other than AFS securities (OCIother) during the period, yielding
ΔOEt = ΔREt + ΔAOCIt
= NIBRGLt + RECLASSt - DIVt + TGLt - RECLASSt + OCIothert
(4)
= NIBRGLt + TGLt - DIVt+ OCIothert
Hence, ΔOE is unaffected by RECLASS, because the effect on RE is perfectly offset by the
effect on AOCI.
- 10 -
3.1.
Valuation Model and Related Hypotheses
Because Barth (1994) finds considerable noise in first differences valuations models, our
primary analyses employ levels valuation models.4 We estimate several variants of the following
model that includes both balance sheet and comprehensive income statement information:
MVt = α + β1BVt + β2CIt,
(5)
where MVt denotes the market value of owners’ equity, BVt denotes the book value of owners’
equity, and CIt denotes comprehensive income. Ohlson (1995) derives a similar model from
three assumptions: (1) dividend discounting with a constant cost of equity r, (2) clean surplus
accounting, and (3) a first order autoregressive process for abnormal comprehensive income.5
Ohlson shows that perfectly transitory (e.g., fair value accounting based) abnormal
comprehensive income yields a pure balance sheet model with β1=1 and β2=0. He shows that
perfectly permanent abnormal comprehensive income yields a pure income statement model with
β1=0 and, assuming a constant rate of dividend payout of comprehensive income λ, β2=(1+r)/rλ.6
Our regression models begin with equation (5) and decompose both BV and CI into
components. Specifically, we decompose BV into the aftertax book value of AFS securities
(BVafs) plus the aftertax book value of other net assets (BVother). We further decompose BVafs
4
We did estimate a returns model derived directly from equation (6) below as the change in MV plus dividends,
with all model variables deflated by beginning price. Consistent with Barth’s (1994) findings, this returns model
yields similar but weaker results than our levels valuation model. For example, we obtain a less positive and
significant coefficient on ΔRECLASS in this model—3.76 significant at the 5% level—than for the corresponding
coefficient on RECLASS in our primary levels valuation model reported in column III of Table 3—8.10 significant
at the 1% level.
5
Equation (7) in Ohlson (1995) differs in four primary respects from our equation (5). First, his comprehensive
income term is a capitalization of comprehensive income reduced by dividends. Second, the weights on the book
value and income terms are inversely related. Third, he allows for non-accounting information. Fourth, his
equation does not include an intercept.
6
If dividend payout is not constant, then dividends should be incorporated in Ohlson’s (1995) capitalized
comprehensive income term. However, prior research by Hand and Landsman (1998) finds that including dividends
in the Ohlson model empirically appears to capture dividends signaling future income rather than dividend payout.
- 11 -
into the amortized cost of AFS securities (COST) plus AUGL. We decompose CI into net
income before extraordinary items and discontinued operations (NIBEX), extraordinary items
and discontinued operations (EX), and other comprehensive income (OCI).
We further
decompose NIBEX into RECLASS and NIBEXother. We further decompose OCI into ΔAUGL
and OCIother.
Incorporating these variable decompositions into equation (5) yields our most extensive
valuation model:
MVt = α + β1COSTtafs + β2AUGLt + β3BVothert + β4RECLASSt + β5NIBEXothert
+ β6EXt + β7ΔAUGLt + β8OCItother.
(6)
In addition to the extensive model, we estimate a nested version of equation (6) that includes
only the balance sheet variables COST, AUGL, and BVother and a nested version that includes
only the comprehensive income statement variables RECLASS, NIBEXother, EX, ΔAUGL, and
OCIother. In untabulated robustness tests, we also include a large number of control variables and
further decompose the book value and comprehensive income variables by line item in equation
(6) . These robustness tests yield substantively the same results as those reported in the paper.
We develop five sets of null and alternative hypotheses to investigate whether and why
RECLASS is value relevant. The first pertains to whether RECLASS is incrementally value
relevant beyond AUGL and the other explanatory variables in equation (6). The null hypothesis
(H1N) is that RECLASS is not value relevant because the fair value of those securities and the
cash received from selling the securities conveys all relevant information, leaving no room for
RECLASS to convey incremental information. The alternative hypothesis (H1A) is that
RECLASS conveys incremental information due to the importance of realization. RECLASS
- 12 -
appears both directly as a separate variable (its effect on NI) as indirectly as a decrease in
ΔAUGLt (its effect on OCI) in equation (6). Hence, we take both the direct and indirect effects
of RECLASS into account and state these hypotheses as the restrictions on the “total” coefficient
β4- β7 on RECLASS:7
H1N: β4- β7=0.
H1A: β4-β7>0.
3.2.
Liquidity of AFS Securities
Our second set of hypotheses examines one possible reason for the value relevance of
RECLASS. Opponents of fair value accounting often allege that fair values are unreliable. If
this lack of reliability is the reason for the positive association between MV and RECLASS, then
this association should be stronger for banks that hold less liquid securities for which fair values
likely are measured less reliably. We again state null and corresponding alternative hypotheses
as restrictions on the total coefficient on RECLASS:
H2N: β4-β7 does not vary with the liquidity of banks’ AFS securities.
H2A: β4-β7 is higher for banks holding less liquid AFS securities.
7
RECLASS also affects AUGL and, depending on the type of realization of gains and losses, one of COST or
BVother in equation (6). For example, for economic realizations of gains, RECLASS also appears as an increase in
BVother (its effect on cash) and a decrease in AUGL (its effect on AFS securities). However, unlike RECLASS’s
direct and indirect effects discussed in the text, these two additional effects would exist in a clean surplus fair value
accounting system that does not report RECLASS or otherwise preserve amortized cost information about realized
gains and losses. We conduct our tests using the total coefficient β4-β7 because it better corresponds to our focus in
this paper. However, we have also conducted all tests using the alternative total coefficient (β3+β4)-(β2+β7), which
yields the same conclusions as for our tests using β4-β7, albeit with slightly lower significance levels (e.g., 5%
instead of 1% in our primary tests reported in column III of Table 3).
- 13 -
3.3.
Growth
Our third set of hypotheses examines the possibility that RECLASS is value relevant
because the realization of gains and losses informs about bank growth. For example, this could
occur if bank managers realize gains and losses to signal future income. Any such effect would
be enhanced to the extent that future income is more important for higher growth banks. We
again state null and corresponding alternative hypotheses in terms of the total coefficient on
RECLASS:
H3N: β4-β7 does not vary with banks’ growth.
H3A: β4-β7 is higher for higher growth banks.
3.4.
Prediction of Future Comprehensive Income
To investigate the aforementioned possibility that realized gains and losses signal future
income, we regress year-ahead comprehensive income on the same explanatory variables in
equation (6).
CIt+1 = δ + γ1COSTtafs + γ 2AUGLt + γ3BVothert + γ4RECLASSt + γ5NIBEXothert
+ γ6EXt + γ7ΔAUGLt + γ8OCItother.
(7)
We also estimate equation (7) with two components of CIt+1 as the dependent variable: (1)
NIBEXt+1, because investors likely view RECLASS as more value relevant if it indicates future
NIBEX rather than future CI; and (2) RECLASSt+1, because banks realized gains and losses are
over a fairly long period, as we show later. Regardless of the dependent variable, the null and
alternative hypotheses parallel H1N and H1A.
- 14 -
H4N: γ4-γ7=0.
H4A: γ4-γ7>0.
We also test sets of hypotheses analogous to H2 and H3 using equation (7) instead of
equation (6).
3.5.
Mispricing
Lastly, we consider the possibility that RECLASS is incrementally value relevant
because investors underprice unrealized gains and losses, say because they fixate on reported net
income. We investigate this possibility using both regression and portfolio methods described in
Section 8. We state our null and alternative hypotheses in general terms applicable to both
methods:
H5N: TGL is not associated with future excess returns.
H5A: TGL is positively associated with future excess returns.
4. SAMPLE, DATA, and DESCRIPTIVE STATISTICS
We obtain most accounting and market value data from Compustat and stock return data
from CRSP. We hand collected AUGL and RECLASS from disclosures of the components of
(accumulated) other comprehensive income in banks’ annual reports. In the Appendix we
provide several examples of these disclosures.
SFAS No. 130 required firms to report other comprehensive income items for fiscal years
beginning after December 15, 1997. For this reason, our sample period begins in 1998, and it
covers the nine fiscal years through 2006. Because of the time required to hand collect AUGL
- 15 -
and RECLASS, we restrict our sample to the 200 largest U.S. commercial banks based on total
assets in 1998. We also require the sample banks to be traded on NYSE, AMEX or NASDAQ
and to have all necessary data on the variables in equation (6). The market capitalization of the
200 banks ranges from $50 million to $230 billion, indicating that our sample selection based on
size is not excessively restrictive. Our final sample contains 1,033 bank-year observations with
complete data on the equation (6) variables.
Recall that RECLASS results from three different events: sales of AFS securities, transfer
of the securities to trading, and OTT impairment write-downs of the securities. The first event
involves economic realization and the second and third events involve realization for accounting
purposes only. Analysis of our hand-collected disclosures indicates that only 11 of the sample
reclassifications were due to AFS securities being transferred to trading or other-than-temporary
impairment write-downs, i.e., realization for accounting purposes only.8 The low frequency of
transfers to trading likely results from the proviso in paragraph 15 of SFAS No. 115 that such
transfers should be “rare.” The low frequency of OTT impairment write-downs likely results
from our sample period preceding the financial crisis and from the fact firms generally do not
record impairments for declines in value driven by movements in interest rates unless they have
decided to sell the affected securities. Hence, almost all of the variation in RECLASS reflects
actual sales of securities, i.e., economic realization.
Table 1, Panel A reports descriptive statistics for the variables AUGL, TGL, and
RECLASS for our sample banks. The mean AUGL is 10 cents per share, with a sizeable standard
deviation of 70 cents per share. The likely reason why AUGL is positive on average is that
8
Possibly this low frequency reflects nondisclosure by banks due to the immateriality of some realizations for
accounting purposes only, because SFAS No. 115’s provisions need not be applied to immaterial items.
- 16 -
interest rates decreased significantly and fairly steadily for almost two decades prior to our
sample period. Equity prices also rose considerably, albeit less steadily, over this period.
The mean of RECLASS is 3 cents per share, with a standard deviation of 16 cents per
share. The mean ratio of the absolute value of RECLASS to the absolute value of net income
equals 6%, indicating that the realization of gains and losses on AFS securities typically has a
sizeable effect on net income. The mean of TGL is -2 cent per share, with a standard deviation of
57 cents per share. The mean ratio of the absolute value of TGL to the absolute value of net
income equals 26%, over four times larger than the corresponding ratio involving RECLASS.
Hence, total gains and losses are far more variable than realized gains and losses.
Table 1, Panel B reconciles the means of the beginning and ending balances of AUGL,
TGL and RECLASS for the sample observations for which all four variables are available, as
expressed in equation (3) above. To maintain consistency, all variables are all deflated by the
end of year number of shares outstanding for the purposes of constructing this panel.
Mean
AUGL decreases by 6 cents per share per year, from 13 cents per share to 7 cents per share. This
decrease is attributable to mean TGL of negative 3 cents per share and mean RECLASS of 3
cents per share. The opposite signs of mean TGL and mean RECLASS indicate the disconnect
between total and realized gains and losses.
Table 2 reports two descriptive analyses regressing RECLASS on current NIBEXother and
either current AUGL (Panel A) or TGL for the current and three prior years (Panel B). The
purpose of these analyses is to provide insight into two issues: (1) whether banks use RECLASS
to manage income—income smoothing would yield a negative association of RECLASS with
NIBEXother, while big baths would yield the opposite—and (2) the strength and lag structure of
the relationship between realized and unrealized gains and losses.
- 17 -
The two panels of Table 2 provide consistent results. There is evidence of a low level of
income smoothing through realization of gains and losses, with a small negative coefficient
of -0.01 on NIBEXother that is significant at the 1% level in Panel A and 10% level in Panel B.
There is also evidence of gradual realization of gains and losses, with statistically significant but
relatively low positive associations between RECLASS and AUGL (coefficient=0.05, significant
at the 1% level) and between RECLASS and current and lagged TGL (coefficients smoothly
declining from 0.12 for current TGL to 0.05 for three-year-lagged TGL, all significant at the 5%
level or better).
5. VALUE RELEVANCE OF RECLASS
Table 3 presents the results of estimating equation (6). To mitigate heteroscedasticity, we
deflate all variables by number of shares outstanding.
In untabulated robustness tests, we
alternatively deflate by total assets, which yields substantively the same results. To incorporate
clustering of observations by time period and firm, we include year fixed effects and report t
statistics that incorporate firm-level clustering of observations (Petersen 2009).
As benchmarks for the estimation of the full equation (6) in column III of Table 3, we
first discuss the estimation of two nested versions of the equation: a pure balance sheet model
(column I) and a pure comprehensive income statement model (column II). We maintain the
same decompositions of BV or CI in these nested models as in the full equation.
The results of estimating the pure balance sheet model reported in column I of Table 3
indicate that MV is significantly positively associated with AUGL at the 5% level. Moreover,
the coefficient of 3.25 on AUGL is over twice as high as the coefficients on COST and BVother;
in fact, this coefficient is too high for transitory gains and losses. As discussed below, this
- 18 -
coefficient decreases to a normal level once RECLASS and the remaining comprehensive
income variables are added to the model. These results are consistent with investors placing
credence in unrealized gains and losses.
The results of estimating the pure comprehensive income statement model reported in
column II of Table 3 indicate that MV is significantly positively associated with RECLASS. The
total coefficient on RECLASS is β4-β7=9.88-0.42=9.46, significant at the 1% level. The total
coefficient is closer to the coefficient of 13.34 on the relatively permanent NIBEXother than it is
to the coefficient on the relatively transitory comprehensive income components: 2.05 on EX,
0.42 on ΔAUGL, and 1.49 on OCIother. These results are consistent with investors putting
considerably greater valuation weight on realized than unrealized gains and losses. This likely is
attributable in part RECLASS having greater persistence than ΔAUGL, as suggested by the
results reported in Table 2.
The results of estimating the full equation (6) reported in Column III of Table 3 indicate
that the value relevance of RECLASS diminishes only slightly when AUGL and other balance
sheet variables are added to the model. In contrast, the coefficient on AUGL becomes
insignificant once RECLASS and other comprehensive income statement variables are added to
the model. Specifically, the total coefficient on RECLASS is β4-β7=7.84-0.56=7.28, significant
at the 1% level. This total coefficient is somewhat closer to the coefficient of 10.84 on the
relatively permanent NIBEXother than to the coefficients on the relatively transitory
comprehensive income components: 1.82 on EX, 0.56 on ΔAUGL, and 1.29 on OCIother. These
results are consistent with investors deeming RECLASS to have considerable incremental value
relevance beyond AUGL and the other equation (6) variables.
- 19 -
6. DETERMINANTS OF THE VALUE RELEVANCE OF RECLASS: LIQUIDITY AND
GROWTH
In this section, we examine two possible reasons for the value relevance of RECLASS.
First, we consider the possibility that unrealized gains and losses are unreliable, as opponents of
fair value accounting often allege. This possibility is inconsistent with our findings discussed
above that AUGL is value relevant in a pure balance sheet model and also with the high liquidity
of most AFS securities. Second, we consider the possibility that RECLASS interacts with or is
an indicator of future bank growth, rather than simply a reclassification of previously unrealized
but recognized gains and losses.
6.1.
Liquidity
To test the liquidity explanation for the value relevance of RECLASS, each year we
estimate the full equation (6), as in column III of Table 3, for two equal-sized subsamples formed
based on a proxy for the liquidity of AFS securities. Following Barth (1994), we use the
percentage of AFS securities that are U.S. Treasuries as our proxy. In untabulated robustness
tests, we include broader measures of liquid securities, with substantively the same results.
The results are reported in Table 4. The coefficient on RECLASS is higher and more
significant for the high liquidity subsample. Specifically, the total coefficient on RECLASS is
β4-β7=11.41-3.77=7.64 in the high liquidity subsample compared to 3.18+1.19=4.37 in the low
liquidity subsample, with the difference significant at 5% level. These results are consistent
with hypothesis H2N that the value relevance of RECLASS does not result from the unreliability
of unrealized gains and losses recorded in AUGL.
- 20 -
6.2.
Growth
To test the growth explanation for the value relevance of RECLASS, each year we
estimate the full equation (6), as in column III of Table 3, for two equal-sized subsamples formed
based on a proxy for bank growth. We use growth in net interest income as our growth proxy.
In untabulated robustness tests, we include various alternative proxies for bank growth, with
substantively the same results.
The results are presented in Table 5. The coefficient on RECLASS is higher and more
significant for the high growth subsample. Specifically, the total coefficient on RECLASS is β4β7=11.63-0.31=11.32 in the high growth subsample compared to 2.75+0.39=3.14 in the low
growth subsample, with the difference significant at the 1% level. This result is consistent with
hypothesis H3A that RECLASS interacts with or is an indicator of future bank growth.
6.3.
Other Determinants Considered
In untabulated analyses, we considered two other possible determinants of the value
relevance of RECLASS. First, as discussed above, in our sample RECLASS almost always
reflects economic realization of gains and losses. Such realization may occur because the bank
needs cash. Second, banks’ Tier 1 regulatory capital is increased by their realization of gains
(Moyer, 1990). During our sample period, however, almost all U.S. banks had ample access to
liquidity and were well capitalized, and so these possible determinants are a priori unlikely to
explain the value relevance of RECLASS. Consistent with this, we found no evidence that
RECLASS is associated with cash shortage or regulatory capital status, or that partitioning
equation (6) on these variables affects the value relevance of RECLASS. 9
9
It is possible that banks choose to realize gains and losses in conjunction with their other accounting decisions, in
particular, in setting allowances and provisions for loan losses, the major accrual estimates for most banks. Because
- 21 -
7. PREDICTING FUTURE COMPREHENSIVE INCOME USING RECLASS
To the extent the observed value relevance of RECLASS is attributable to rational market
pricing, RECLASS should be associated with future valuation attributes. We investigate whether
that is the case by regressing year-ahead CI on RECLASS and the other explanatory variables in
equation (6). Because RECLASS may be associated with many future years’ CI, to maximize
the power of the test the dependent variable ideally would be future CI summed over more than
one year in these analyses. We do not do this to avoid losing observations due to the limited
number of years of data available.
We conduct similar analyses replacing year-ahead CI as the dependent variable with year
ahead NIBEX and year-ahead RECLASS. We do this in part because RECLASS does not affect
current CI; for example, positive RECLASS increases NIBEX and reduces OCI, with no effect
on CI. We also do this in part because the descriptive analyses reported in Table 2 suggest that
banks smooth net income using RECLASS, and that RECLASS is persistent because it reflects
gradual realization of prior TGLs.
We conduct the analyses described above for the overall sample as well as for the high
and low liquidity subsamples examined in Table 4 and the high and low growth subsamples
examined in Table 5. As in the market valuation analyses, we examine the total coefficient γ4-γ7.
Because RECLASS is more value relevant for the high liquidity and growth subsamples, it
should also have higher total coefficients for those subsamples.
We report the results of the analyses described above in Table 6. Panel A (B) [C] of the
table reports the results for the overall (liquidity) [growth] sample.
In the overall sample
Beatty, Chamberlain and Magliolo (1995) provide evidence that realized gains and losses are not correlated with
these accrual estimates, we do not pursue this possibility.
- 22 -
analyses reported in Table 6, Panel A, the total coefficient on RECLASS is significantly positive
at the 5% level in the CI and NI regressions and at the 1% level in the RECLASS regression,
consistent with hypothesis H4A. The results are somewhat weaker than the levels valuation
model results reported in Table 3 due to the lower power of the tests.
In the high liquidity subsample results reported in Panel B1, the total coefficient on
RECLASS is more positive and similarly significant for this subsample as for the overall sample
in Panel A, despite the halving of the number of observations. In contrast, in the low liquidity
subsample results reported in Panel B2, the total coefficient on RECLASS is less positive and is
insignificant in the CI and NI regressions. These results are consistent with hypothesis H2A and
H4A.
In the high growth subsample results reported in Panel C1, the total coefficient on
RECLASS is more positive and at least as significant for this subsample as for the overall sample
in Panel A, despite the halving of the number of observations. In contrast, in the low growth
subsample results reported in Panel C2, the total coefficient on RECLASS is negative and
insignificant in the CI and NI regressions and less positive in the RECLASS regression than for
the overall sample. These results are consistent with hypothesis H3A and H4A.
In summary, RECLASS predicts banks’ year-ahead CI, more so for banks with more
liquid AFS securities and higher growth. These results are consistent with the results indicating
the market valuation of RECLASS reported in Tables 3-5.
8. MARKET MISPRICING OF UNREALIZED GAINS AND LOSSES
In this section, we investigate the possibility that the value relevance observed for
RECLASS could result in part from market mispricing of TGL. In particular, investor fixation
- 23 -
on reported net income might result in the market’s reaction to gains and losses being delayed
until they are realized. Prior research provides considerable evidence consistent with such
functional fixation (e.g., see Lakonishok, Shleifer, and Vishny 1992, Sloan 1996, Teoh, Welch,
and Wong 1998, and Penman and Zhang 2002).
To detect possible investor mispricing, we regress stock returns for the year beginning
with the fifth month after fiscal year end (to ensure that the annual financial reports are available
to investors, but the following first quarter financial reports are not) on TGL and the four-factor
model variables documented in the literature to explain the cross-section of future stock returns
(e.g., Fama and French 1992, 1993 and Jegadeesh and Titman 1993). These variables are: (1)
systematic risk (BETA) measured estimating the market model on monthly data for the 60
months ending with the fourth month after the fiscal year end; (2) book-to-market ratio (BTM)
measured as book value of equity divided by market value of equity at the end of the fourth
month after fiscal year end; (3) firm size (SIZE) measured as the natural logarithm of market
value of equity at the end of the fourth month after fiscal year end; and (4) stock return
momentum (MOM) measured as the cumulative stock return for the past 12 months up to the
fourth month after the end of the fiscal year. To minimize the impact of the skewness/extreme
observations of the explanatory variables, we transform these variables into decile ranks,
maintaining the variable names indicated above.
The results of this regression are reported in Panel A of Table 7. Consistent with the
findings of prior studies, the estimated coefficient on BTM is significantly positive at the 5%
level, the coefficient on SIZE is significantly negative at the 1% level, and the coefficient on
MOM is marginally significantly positive at the 10% level. The estimated coefficient on TGL is
- 24 -
marginally significantly positive at 10% level, providing weak evidence of incomplete market
reaction to unrealized gains and losses.
To gauge the magnitude of this excess return, we perform a portfolio return analysis.
Each year we divide our sample firms into equal-sized terciles based on the ratio of TGL to the
absolute value of reported net income. For the observations in each tercile, we calculate both the
equally-weighted and value-weighted average excess stock return for the year beginning with the
fifth month after fiscal year end. To mitigate potential hindsight bias, we eliminate 11 (out of
1,177) observations for which the sample banks do not have December fiscal year ends.
The results of this analysis are reported in Panel B of Table 7. For the high TGL group,
the subsequent equal-weighted (value-weighted) average excess stock return equals 2.79%
(4.11%). The equal-weighted (value-weighted) excess return significantly exceeds the
corresponding excess return for the low TGL group at the 10% (5%) level. The fact that the
magnitude and significance is larger with value-weighting implies that any mispricing of TGL
exists mostly for larger firms. While there are various possible explanations for this finding, we
conjecture it may be attributable to the greater difficulty that investors face in evaluating TGL
for larger banks, given their more diverse holdings of AFS securities and overall greater
complexity.
Untabulated supplemental analysis partitioning on various measures of firm size confirms
that market under-reaction to TGL is observed only for large firms. This firm-size-related
mispricing does not appear to explain the value relevance of RECLASS, however, because
supplemental analysis also indicates that RECLASS is value-relevant both for large and small
firms but somewhat more so for small firms. For this reason, we conclude that while investor
- 25 -
mispricing may contribute to the observed value relevance of RECLASS, it does not appear to be
the main reason for this phenomenon.
9. Conclusion
To be written.
- 26 -
APPENDIX
SFAS 130 requires U.S. companies to disclose the components of other comprehensive
income and the information about reclassifications of accumulated other comprehensive income
in financial reports. This appendix provides representative examples of banks’ disclosures.
1. Other comprehensive income components disclosures
Banks typically disclose the components of other comprehensive income either in the
statement of owners’ equity or in a separate table. We provide an example of each of these
formats below.
Statement of Owners’ Equity Example:
(Source: Amcore Financial Inc., 2005 Annual Report)
- 27 -
Separate Table Example:
(Source: National City Corp., 2007 Annual Report, p. 107)
2. Reclassification Disclosures
Banks typically disclose reclassifications in a separate table, as shown in the following
example.
- 28 -
(Source: JPMorgan Chase & Co., 2007 Annual Report, p.163)
Some banks also provide this reclassification information along with a rollforward of the
balances of accumulated other comprehensive income in a table.
(Source: Fifth Third Bancorp, 2007 Annual Report, p. 75)
As evidenced in both of these sample disclosures, most banks report both the pretax and
aftertax reclassifications. For those that report only the pretax reclassifications, we use the
standard federal tax rate of 35% to calculate the aftertax reclassifications.
- 29 -
Table 1
Descriptive Statistics
The sample includes the 200 largest (based on total assets in 1998) U.S. commercial banks
traded on NYSE, AMEX and NASDAQ for the years 1998-2006. Stock return data are obtained
from CRSP and most financial data are obtained from COMPUSTAT. Accumulated unrealized
gains and losses on AFS securities (AUGL) and reclassification of AUGL upon realization of
gains and losses (RECLASS) are hand collected from banks’ annual Form 10-K filings. Sample
observations must have non-missing AUGL, RECLASS, and total gains and losses (TGL) for
AFS securities. SIZE denotes the natural logarithm of the market value of equity at the end of
the fourth month after fiscal year end. NI denotes net income. AUGL is deflated by end of year
shares outstanding, while RECLASS, TGL, and NI are deflated by the shares outstanding used in
calculating earnings per share. Panel B reconciles the change in mean AUGL during the year
with the means of TGL and RECLASS for the year; to maintain consistency in this panel all
variables are deflated by end of year shares outstanding.
Panel A: Statistics (1,033 observations)
Variable
SIZE
AUGL per share
RECLASS per share
TGL per share
|RECLASS|/|NI|
|TGL|/|NI|
Mean
6.95
0.08
0.03
-0.02
0.06
0.26
STDEV
1.49
0.68
0.16
0.57
0.33
0.95
Q1
5.84
-0.16
0.00
-0.25
0.01
0.05
Median
6.71
0.04
0.01
0.01
0.01
0.13
Q3
7.70
0.30
0.05
0.23
0.05
0.26
Panel B: Reconciliation of Mean AUGL, TGL, and RECLASS (1,004 observations)
beginning mean
AUGL per share
0.13
plus mean TGL per
share
-0.03
minus mean
RECLASS per share
0.03
- 30 -
ending mean AUGL
per share
0.07
Table 2
Reclassifications, Income Smoothing, and Gradual Realization of Total Gains and Losses
This table reports the results of regressing RECLASS on other net income before extraordinary
items (NIBEXother) and accumulated unrealized gains and losses (AUGL) for the current year
(Panel A), and on current NIBEXother and total gains and losses (TGL) for the current and prior
three years. AUGL is deflated by the number of shares outstanding at fiscal year end.
NIBEXother and UGL are deflated by the number of shares used in calculating earnings per share.
Year dummies are included in all regression, with t-statistics adjusted for clustering among
observations for the same firm (Petersen 2009). *, **, and *** indicate statistical significance at 10,
5, and 1 percent levels in two-tailed tests.
Panel A:
Intercept
NIBEXother
AUGL
N
R2
Dependent Variable: RECLASS
0.02*
-0.01***
0.05***
1,158
0.08
Panel B:
Intercept
NIBEXother
TGL, year
TGL, year-1
TGL, year-2
TGL, year-3
N
R2
Dependent Variable: RECLASS
0.06**
-0.01*
0.12***
0.10***
0.07**
0.05***
679
0.18
- 31 -
Table 3
Value Relevance of Reclassifications
This table reports the results of pooled estimations of various nested versions of equation (6), in
which the market value of owners’ equity (MV) is regressed on the aftertax components of book
value of owners’ equity (BV) and comprehensive income (CI). The components of BV are: the
amortized cost of AFS securities (COST); the accumulated unrealized gains and losses on AFS
securities at the end of each fiscal year (AUGL); the book value of available-for-sale securities
(BVafs=COST + AUGL); and the net book value of assets and liabilities other than AFS
securities (BVother=BV-BVafs). The components of CI are net income before extraordinary items
and discontinued operations (NIBEX); reclassification of previously unrealized gains and losses
on AFS securities upon realization (RECLASS); other net income before extraordinary items
(NIBEXother); extraordinary items (EX); other comprehensive income (OCI); the change in
AUGL; and other comprehensive income other than ΔAUGL (OCIother). All stock variables (e.g.,
book value) are deflated by the number of shares outstanding at fiscal year end. All flow
variables (e.g., RECLASS) are deflated by the number of shares used in calculating earnings per
share. β4-β7 denotes the difference of the coefficients on RECLASS and ΔAUGL. Year fixed
effects are included in all regressions and t-statistics are adjusted for clustering of observations
by firm (Petersen 2007). *, **, and *** indicate two-tailed statistical significance at 10, 5, and 1
percent levels, respectively.
Intercept
BV
BVafs
COST
AUGL
BVother
CI
NIBEX
RECLASS
NIBEXother
EX
OCI
ΔAUGL
OCIother
N
R2
β4-β7
I
9.09***
Dependent Variable: MV
II
4.39*
1.44***
3.25**
1.37***
III
2.35
0. 40***
0.72
0.37***
1,033
0.55
--
- 32 -
9.88***
13.34***
2.05
7.84***
10.84***
1.82
0.42
1.49***
1,033
0.72
9.46***
0.56
1.29***
1,033
0.75
7.28***
Table 4
Effect of AFS Security Liquidity (Reliability of Fair Values) on the
Value Relevance of Reclassifications
This table reports the results of estimating equation (6) (the model in column III of Table 3) for
banks with above and below median liquid AFS securities (more and less reliable fair values,
respectively). See Table 3 for description of the model and variables. Liquidity is measured as
the percentage of available-for-sale securities invested in U.S. Treasury securities. β4-β7 denotes
the difference of the coefficients on RECLASS and ΔAUGL. Year fixed effects are included in
all regressions and t-statistics are adjusted for clustering of observations by firm (Petersen 2007).
*, **, and *** indicate two-tailed statistical significance at 10, 5, and 1 percent levels.
Intercept
BV
BVafs
COST
AUGL
other
BV
CI
NIBEX
RECLASS
NIBEXother
EX
OCI
ΔAUGL
OCIother
N
R2
β4-β7
(β4-β7)High - (β4-β7)Low
High Liquidity Low Liquidity
3.89***
4.10*
0.64***
1.90
0.62***
0.66***
2.21*
0.65***
11.41***
7.64***
-2.84
3.18*
8.17***
3.48
3.77**
0.85
462
0.84
7.64***
-1.19
1.64**
509
0.75
4.37
3.27**
- 33 -
Table 5
Effect of Bank Growth on the Value Relevance of Reclassifications
This table reports the results of estimating equation (6) (the model in column III of Table 3) for
banks with above and below median growth in net interest income. See Table 3 for description
of the model and variables. β4-β7 denotes the difference of the coefficients on RECLASS and
ΔAUGL. Year fixed effects are included in all regressions and t-statistics are adjusted for
clustering of observations by firm (Petersen 2007). *, **, and *** indicate two-tailed statistical
significance at 10, 5, and 1 percent levels.
Intercept
BV
BVafs
COST
AUGL
BVother
CI
NIBEX
RECLASS
NIBEXother
EX
OCI
ΔAUGL
OCIother
N
R2
β4-β7
(β4-β7)High - (β4-β7)Low
High Growth Low Growth
4.44
1.12
0.39***
0.94
0.32*
0.43***
0.63
0.43***
11.63***
11.10***
-3.91
2.75*
10.35***
8.14*
0.31
1.69**
516
0.72
11.32***
7.18***
-0.39
0.37
516
0.80
3.14
- 34 -
Table 6
Association between Year-Ahead Comprehensive Income and Reclassifications
This table reports the results of regressions of year-ahead comprehensive income (CI) and two
components of year-ahead CI—net income before extraordinary items and discontinued
operations (NIBEX) and reclassification of gains and losses on AFS securities upon realization
(RECLASS)—on the same explanatory variables as in equation (6) (the model in column III of
Table 3). See Table for description 3 of the model and explanatory variables. γ4-γ7 denotes the
difference of the coefficients on RECLASS and ΔAUGL. Panel A reports results for the overall
sample, Panel B1 (B2) reports results for the high (low) AFS security liquidity subsamples, and
Panel C1 (C2) reports result for the high (low) growth in net interest income subsamples.
Liquidity is measured as the percentage of AFS securities invested in U.S. Treasuries. Year fixed
effects are included in all regressions and t-statistics are adjusted for clustering of observations
for the same firm. (Petersen 2007) *, **, and *** indicate two-tailed statistical significance at 10, 5,
and 1 percent levels, respectively.
Panel A: Overall Sample
Intercept
BV
BVafs
Costafs
AUGL
BVother
CI
NIBEX
RECLASS
NIBEXother
EX
OCI
ΔAUGL
OCIother
N
R2
γ4-γ7
CIt+1
-0.15
Dependent variable
NIt+1
RECLASSt+1
*
-0.35
0.00
0.01
-0.23*
0.01
0.03***
0.03
0.03***
-0.00
0.06***
-0.00
0.61*
0.90***
-0.08
0.29*
0.78***
-0.35
0.39***
0.01
0.11**
-0.23
0.24**
973
0.54
0.84**
-0.12
0.03
973
0.69
0.41*
0.05**
-0.01*
959
0.19
0.34***
- 35 -
Table 6 (Continued)
Panel B1: High AFS Security Liquidity Sub-sample
Dependent variable
CIt+1
NIt+1
RECLASSt+1
Intercept
-0.25
-0.44
0.01
BV
BVafs
Costafs
0.01
0.02***
-0.00
*
AUGL
-0.26
0.05
0.04**
BVother
0.00
0.02***
-0.00
CI
NIBEX
RECLASS
0.58*
0.72*
0.66***
NIBEXother
1.11***
0.92***
-0.00
EX
-0.66
-1.20
0.15*
OCI
ΔAUGL
0.11
0.05
0.08*
other
**
OCI
0.33
0.01
-0.00
N
430
430
424
R2
0.63
0.74
0.37
γ4-γ7
0.47*
0.67*
0.58***
Panel B2: Low AFS Security Liquidity Sub-sample
Dependent variable
CIt+1
NIt+1
RECLASSt+1
Intercept
0.11
-0.19
0.02
BV
BVafs
Costafs
0.01
0.03***
-0.01**
AUGL
-0.11
0.13
0.07***
BVother
0.01
0.03***
-0.00
CI
NIBEX
RECLASS
0.25
0.22
0.24***
NIBEXother
0.75***
0.66***
0.02
EX
-0.53
-0.53
0.10*
OCI
ΔAUGL
-0.47*
-0.25*
-0.00
other
**
**
OCI
0.22
0.07
-0.01
N
484
484
477
R2
0.54
0.69
0.13
γ4-γ7
0.72*
0.47
0.24***
- 36 -
Table 6 (Continued)
Panel C1: High Interest Income Growth Sub-sample
Dependent variable
CIt+1
NIt+1
RECLASSt+1
Intercept
-0.22
-0.17
0.04
BV
BVafs
Costafs
0.02
0.02**
-0.00
*
AUGL
-0.36
0.00
0.07**
BVother
0.01
0.02**
-0.01*
CI
NIBEX
RECLASS
1.13*
0.79**
0.57***
NIBEXother
0.99***
0.81***
0.01
EX
-0.59
-0.97*
0.07*
OCI
ΔAUGL
0.11
-0.15
0.07**
other
**
**
OCI
0.29
0.10
-0.01
N
487
487
482
R2
0.59
0.70
0.27
γ4-γ7
1.02*
0.94***
0.64***
Panel C2: Low Interest Income Growth Sub-sample
Dependent variable
CIt+1
NIt+1
RECLASSt+1
***
Intercept
-0.05
-0.42
-0.00
BV
BVafs
Costafs
0.02
0.03***
-0.00
AUGL
-0.12
0.04
0.05**
BVother
0.02
0.03***
0.00
CI
NIBEX
RECLASS
-0.11
-0.11
0.22***
NIBEXother
0.75***
0.75***
0.01
EX
0.42
-0.39
0.26*
OCI
ΔAUGL
-0.47*
-0.14
0.01
other
**
OCI
0.14
-0.08
-0.01
N
485
485
476
R2
0.54
0.71
0.17
γ4-γ7
0.36
0.03
0.21***
- 37 -
Table 7
Earnings Fixation
Panel A reports the association between stock returns for year beginning with the fifth month
after fiscal year end and total gains and losses (TGL) during the year, controlling for four factors
known to explain the cross-section of future stock returns. Systematic risk (BETA) is estimated
using the market model over the 60 months up to the fourth month after fiscal year end. Firm
size (SIZE) is the logarithm of the market value of equity at the end of the fourth month after
fiscal year end. The book-to-market ratio (BTM) is the book value of owners’ equity at the end
of the fiscal year divided by market capitalization measured at the end of the fourth month after
fiscal year end. Stock return momentum (MOM) is calculated as the cumulative stock return
during the past 12 months up to the fourth month after fiscal year end. Panel B reports the
average portfolio returns for firms sorted based on the reclassification of gains and losses on
AFS securities (RECLASS) divided by the absolute value of the reported net income. Each year
firms are grouped into equal-sized terciles (Low, Medium, and High) based on TGL divided by
the absolute value of net income. The mean portfolio size-adjusted returns are then calculated
for each sample year. The following tables report the average of the mean excess portfolio
returns over all sample years. t statistics in panel A are based on standard errors clustered by
firm with year fixed effects. t statistics in Panel B are based on the distribution of the mean
portfolios returns across sample years. *, **, and *** indicate two-tailed statistical significance
at 10, 5, and 1 percent levels.
Panel A: Future Return Regression
Dep. Variable: RETt+1
Intercept
BETA
BTM
SIZE
MOM
TGL
-0.163***
0.003
0.006**
-0.009***
-0.005*
0.005*
N
R-square
1,150
0.39
Panel B: Average Excess Returns to TGL Terciles
Equal-weighted Return Value-weighted Return
Low
-0.79%
-1.96%
Medium
-1.08%
2.67%
High
2.79%
4.11%*
High-Low
3.58%*
6.07%**
- 38 -
REFERENCES
Abdel-Khalik, R. 2008. The Case Against Fair Value Accounting. SEC Comment Letter 4-573.
Ahmed, A. and C. Takeda. 1995. Stock Market Valuation of Gains and Losses on Commercial
Banks’ Investment Securities: An Empirical Analysis. Journal of Accounting and
Economics 20: 207-225
Barth, M. 1994. Fair Value Accounting: Evidence from Investment Securities and the Market
Valuation of Banks. The Accounting Review 69 (January): 1-25.
Barth, M, W. Beaver and W. Landsman. 1996. Value-relevance of Banks’ Fair Value
Disclosures under SFAS No. 107. The Accounting Review 71 (October): 513-537.
Beatty, A., S. Chamberlain, and J. Magliolo. 1995. Managing Financial Reports of Commercial
Banks: the Influence of Taxes, Regulatory Capital and Earnings. Journal of Accounting
Research 33: 231-261.
Biddle, G. and J. H. Choi, 2006. Is Comprehensive Income Useful? Journal of Contemporary
Accounting and Economics 2: 1-32.
Chambers, D., T. Linsmeier, C. Shakespeare, and T. Sougiannis. 2007. An Evaluation of SFAS
No. 130 Comprehensive Income Disclosures, Review of Accounting Studies 12: 557-593.
Dhaliwal, D., K. Subramanyam, and R. Trezevant. 1999. Is Comprehensive Income Superior to
Net Income as a Measure of Firm Performance? Journal of Accounting and Economics
26: 43-67.
Eccher, A., K. Ramesh, and S. Thiagarajan. 1996. Fair Value Disclosures by Bank Holding
Companies. Journal of Accounting and Economics 22: 79-117.
- 39 -
Fama, E. and K. R. French. 1992. The Cross-Section of Expected Stock Returns. Journal of
Finance 47: 427-465.
Fama, E. and K. R. French. 1993. Common Risk Factors in the Returns on Stocks and Bonds.
Journal of Financial Economics 33: 3-56.
Financial Accounting Standards Board. 1991.
Disclosures about Fair Value of Financial
Instruments. Statement of Financial Accounting Standards No. 107. Stanford, CT.
Financial Accounting Standards Board. 1993. Accounting for Certain Investments in Debt and
Equity Securities. Statement of Financial Accounting Standards No. 115. Stanford, CT.
Financial Accounting Standards Board (FASB). 1997. Reporting Comprehensive Income.
Statement of Financial Accounting Standards No. 130. Stanford, CT.
Financial Accounting Standards Board (FASB). 2010. Accounting for Financial Instruments and
Revisions to the Accounting for Derivative Instruments and Hedging Activities. Exposure
Draft. Stanford, CT.
Forbes, S. 2009. End market-to-market. Forbes.com, March 29, 2009.
Hand, J. and W. Landsman. 1998. Testing the Ohlson Model: v or not v, that is the Question.
Working paper, University of North Carolina at Chapel Hill.
Holthausen, R., and R. Watts. 2001. “The Relevance of the Value-Relevance Literature for
Financial Accounting Standard Setting,” Journal of Accounting and Economics
September: 3-75.
Jegadeesh, N., and S. Titman. 1993. Returns to buying winners and selling losers: implications
for stock market efficiency. Journal of Finance 48: 65-91.
Lakonishok, J., A. Shleifer, and R. Vishny. 1994. Contrarian Investment, Extrapolation, and Risk.
Journal of Finance 49: 1541-1578.
- 40 -
Moyer, S. 1990. Capital adequacy ratio regulations and accounting choices in commercial banks.
Journal of Accounting and Economics 13: 123-154.
Nelson, K. 1996. Fair Value Accounting for Commercial Banks: An Empirical Analysis of
SFAS No. 107. The Accounting Review 71: 161-182.
Nissim, D., and S. Penman. 2008. Principles for the Application of Fair Value Accounting.
Columbia Business School Center for Excellence in Accounting & Security Analysis White
Paper No. 2, July.
Ohlson, J. 1995. Earnings, Book Values, and Dividends in Equity Valuation. Contemporary
Accounting Research 11: 661-687.
O’Hanlon, J. and P. Pope. 1999. The Value Relevance of U.K. Dirty Surplus Accounting Flows,
British Accounting Review 31: 459-482.
Penman, S., and X. Zhang. 2002. Accounting conservatism, the quality of earnings, and stock
returns. The Accounting Review 77(2): 237-264.
Petersen, M. A. 2009. Estimating Standard Errors in Finance Panel Data Sets: Comparing
Approaches. Review of Financial Studies 22:435-480.
Ronen, J. 2008. To Fair Value or Not to Fair Value: A Broader Perspective. Abacus 44: 181-208.
Schipper, K. 2007. Required Disclosure in Financial Reports. The Accounting Review (82): 301326.
Sloan, R. 1996. Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about
Future Earnings? The Accounting Review July: 289-315.
Teoh, S., I. Welch, and T. Wong. 1998. Earnings management and the underperformance of
seasoned equity offerings. Journal of Financial Economics 50, 63-99.
Wallison, P. 2008. Judgment too Important to be Left to the Accountants. Financial Times,
May 1, 2008.
- 41 -
Watts, R., 1993. A Proposal for Research on Conservatism, University of Rochester Financial
Research and Policy Working Paper Series FR 93-13, May.
- 42 -