1. Datos generales de la Provincia de Tungurahua
4.2 Elaboración de las rutas
SFAS 133 requires most types of hedge ineffectiveness to be measured on a fair value basis
and reported in earnings. This earnings recognition requirement was the focal point of
controversy surrounding the adoption of SFAS 133. The debate also reflects the more general
controversy over whether to recognize fair-value-based gains or losses into earnings. Using a
sample of bank holding companies, I find evidence that the newly recognized earnings
component following the adoption of SFAS 133 (i.e. the fair-value-based hedging performance
measure) improves the value and risk relevance of accounting earnings. The findings of this
study are relevant to the evaluation of SFAS 133 as well as the ongoing debate on the income
statement treatment of net asset changes due to the application of fair value accounting.
There may be concerns about whether the findings are generalizable to other sectors,
especially non-financial industries. In particular, it is likely that the information content of
derivative gains/losses is higher for bank holding companies because risk management is more
central to the core business and competitive advantage possibilities among financial than non-
financial sector firms. On the other hand, using a sample of bank holding companies can help
accurately capture the effect of SFAS 133 by focusing on a setting more likely to be
representative of the population of interest, where hedging derivatives are expected to be of
material significance to financial reporting. Future research may examine whether the inferences
from this study are valid for non-financial sectors where the impact of hedging derivatives are
also expected to be material.
Another promising avenue for future research on SFAS 133’s income statement effects is in
the area of earnings management. For example, firms may take advantage of the differential
The high-profile cases of Fannie Mae’s abuse of cash flow hedge accounting provide anecdotal
evidence for such a scenario. Further empirical evidence of SFAS 133’s impact on earnings
management behavior is important for a complete profile of the consequences of the standard.
Such studies would require the simultaneous consideration of firms’ incentive to manage
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TABLES
Table 1: Descriptive Statistics Panel A : Sample period (1995-2005)
Variables N Mean STD p5 Q1 Median Q3 p95
Total assets (book value) 11110 12,638 65,802 231.29 485.21 1,049.89 3,757.70 44,248.95 Total assets (risk-weighted) 10320 9,879 50,074 155.37 336.30 736.61 2,585.94 33,990.38 Tier1 risk-based capital ratio 10320 0.1246 0.0406 0.0797 0.1010 0.1163 0.1389 0.1913 Total risk-based capital ratio 10320 0.1408 0.0404 0.1045 0.1177 0.1314 0.1525 0.2060 Notional amount of non-trading derivatives (un-scaled) 10765 3,674.20 26,451.93 0.00 0.00 0.00 65.00 7102.00 Absolute net fair value of non-trading derivatives (un-scaled) 10760 19.84 149.61 0.00 0.00 0.00 0.39 50.12 Notional amount of non-trading derivatives (scaled) 10765 0.3792 1.5957 0.0000 0.0000 0.0000 0.1559 1.6494 Absolute net fair value of non-trading derivatives (scaled) 10760 0.0033 0.0277 0.0000 0.0000 0.0000 0.0008 0.0139 Percentage of observations with non-zero hedging derivatives 37.63%
Panel B: Pre-SFAS 133 period (1995-2000)
Variables N Mean STD p5 Q1 Median Q3 p95
Total assets (book value) 5458 10,849.66 44,860.99 215.04 448.51 1,065.97 3,984.39 43,759.52 Total assets (risk-weighted) 4668 8,884.35 38,488.62 137.66 306.91 710.88 2,837.20 34,950.05 Tier1 risk-based capital ratio 4668 0.1274 0.0438 0.0768 0.1009 0.1201 0.1452 0.1951 Total risk-based capital ratio 4668 0.1440 0.0451 0.1030 0.1188 0.1350 0.1578 0.2116 Notional amount of non-trading derivatives (un-scaled) 5178 3,474.53 22,364.39 0.00 0.00 0.00 80.00 8,464.79 Absolute net fair value of non-trading derivatives (un-scaled) 5172 13.97 87.36 0.00 0.00 0.00 0.36 41.00 Notional amount of non-trading derivatives (scaled) 5178 0.4213 1.4322 0.0000 0.0000 0.0000 0.1642 1.9198 Absolute net fair value of non-trading derivatives (scaled) 5172 0.0030 0.0296 0.0000 0.0000 0.0000 0.0008 0.0116 Percentage of observations with non-zero hedging derivatives 36.95%
Panel C: Post-SFAS 133 period (2001-2005)
Variables N Mean STD p5 Q1 Median Q3 p95
Total assets (book value) 5652 14,365.61 81,009.44 248.58 518.28 1,032.81 3,487.87 45,168.00 Total assets (risk-weighted) 5652 10,701.39 57,910.93 165.40 360.80 753.85 2,438.78 33,918.40 Tier1 risk-based capital ratio 5652 0.1223 0.0376 0.0821 0.1010 0.1142 0.1335 0.1857 Total risk-based capital ratio 5652 0.1382 0.0359 0.1055 0.1171 0.1288 0.1485 0.2009 Notional amount of non-trading derivatives (un-scaled) 5587 3,859.26 29,743.69 0.00 0.00 0.00 60.00 6,875.91
Table 1 (cont.)
Notional amount of non-trading derivatives (scaled) 5587 0.3402 1.7328 0.0000 0.0000 0.0000 0.1510 1.3300 Absolute net fair value of non-trading derivatives (scaled) 5588 0.0036 0.0258 0.0000 0.0000 0.0000 0.0008 0.0162 Percentage of observations with non-zero hedging derivatives 38.29%
Panel D: Hedging derivative users in the post-SFAS 133 period
Variables (scaled by beginning market value of equity) N Mean STD P5 Q1 Median Q3 P95
Earnings component attributed to non-trading derivatives 2159 0.0020 0.0111 -0.0036 -0.0002 0.0001 0.0019 0.0130 Change in fair value of non-trading derivatives 2129 -0.0004 0.0372 -0.0100 -0.0012 0 0.0010 0.0107 ABS earnings component attributed to non-trading derivatives 2159 0.0037 0.0107 0 0.0001 0.0009 0.0031 0.0157 ABS change in fair value of non-trading derivatives 2129 0.0062 0.0366 0 0.0002 0.0012 0.0041 0.0194 Income before extraordinary items 2164 0.0173 0.0167 0.0075 0.0148 0.0177 0.0206 0.0293
Table 1 reports descriptive statistics based on information from FR-Y9C filing. All dollar variables are reported in millions. Total assets (book value) are the balance sheet book value of all assets held by the bank holding company (bhck2170). Per the regulatory requirement set by the Federal Reserve Bank, bank holding companies are required to calculate and report Total risk-weighted assets, Tier 1 capital and Total risk-based capital. Tier 1 capital ratio is calculated as Tier-1 capital (bhck8274) divided by Total risk- weighted assets (bhckA223). Total risk-based capital ratio is calculated as Total risk-based capital (bhck3792) divided by Total risk- weighted assets (bhckA223). Notional amount of non-trading derivatives (un-scaled) is the sum of total notional amount of all derivatives held for non-trading purpose across all categories. Absolute net fair value of non-trading derivative (un-scaled) is the absolute value of the net fair value of all derivatives held for non-trading purpose across all categories. Notional amount of non- trading derivatives (scaled) and Absolute net fair value of non-trading derivatives (scaled) are calculated as the un-scaled amount divided by the market value of equity at the beginning of the reporting quarter. Percentage of observations with non-zero hedging derivatives is the percentage of bank-quarters with non-zero notional amount of derivatives for non-trading purpose.
Panel D of Table 1 reports the descriptive statistics for major variables of interest for the subsample of hedging derivative users in the post-SFAS 133 period. All variables reported in Panel D are scaled by the market value of equity at the beginning of the quarter. Earnings component attributed to non-trading derivatives is calculated as the sum of Schedule HI memoranda item M10(a), (b) and (c) ‘Impact on income of derivatives held for purposes other than trading’ (bhck8761+bhck8762+bhck8763). Change in fair value of non- treading derivatives is the change in the net fair value of non-trading derivative positions last quarter’s value. Each period’s net fair
value of derivative positions is calculated based on information reported in Schedule HC-L
(bhck8741+bhck8742+bhck8743+bhck8744-bhck8745-bhck8746-bhck8747-bhck8748). ABS earnings component attributed to non- trading derivatives and ABS change in fair value of non-trading derivatives are the absolute value of the two variables described above
Table 2: Forecasting Power of Hedging Performance Panel A: First stage selection model
LNTASS EQRAT NIM NOTES DIV LIQUID GAP12 NETCO
0.78 (35.72)*** 3.15 (3.15)** 8.41 (3.26)*** 2.33 (7.87)*** -59.77 (-4.09)*** -2.73 (-2.76)** 1.02 (5.99)** -10.85 (-1.09) N (population) 5382 N (censored) 3445 N(uncensored) 1937
Panel B: Second stage (Predicting future hedging performance)
HEDGE Mills (lamda)
0.5512
(29.29)*** -0.0008(-1.98)**
N 1937
Wald
chi-square 857.91 p=0.00
Panel C: Second stage (Predicting future total earnings)
EXIB HEDGE Mills (lamda)
0.5035 (26.03)*** 0.5274 (15.81)*** -0.0014 (-2.36)** N 1937 Wald chi-square 679.53 p=0.00
Table 2 reports results on the forecasting power of the fair-value-based hedging performance measure recognized into earnings under SFAS 133. Regression results are based on Heckman two-stage method to correct for potential self-selection bias. The post-SFAS 133 period observations of bank holding companies in my sample form the population sample for the two stage analysis. The population sample consists of observations that use hedging derivatives and observations that do not use hedging derivatives. The observations within the population sample that do not use hedging derivatives are referred to as the censored observations because it is impossible
SFAS 133). The observations within the population sample that use hedging derivatives are referred to as the uncensored observations because the value of the variable of interest (the fair-value-based hedging performance measure recognized into earnings under SFAS 133) can be observed.
Panel A reports the results of the fist-stage regression that models the selection of the uncensored observations from the population sample. The predictors are: Intercept (not tabulated), LNTASS (natural logarithm of total assets), EQRAT (book value of equity scaled by total assets), NIM (net interest income scaled by total assets), NOTES (notes and debentures scaled by total assets), DIV (dividend payout scaled by total assets), LIQUID (liquid assets consisting of cash and balances, federal funds sold, and securities purchased to resell, scaled by total assets ), GAP12 (the absolute value of the difference between assets repricing or maturing within 12 months and liabilities repricing or maturing within 12 months, scaled by total assets), and NETCO (net loan charge off scaled by total assets).
Panels B and C report the results of second-stage regressions based on the following models respectively: HEDGEi,t+1=a0 + a1HEDGEi,t + εi,t
IBi,t+1=b0 + b1EXIBi,t + b2HEDGEi,t + εi,t
HEDGE is the earnings component attributed to non-trading derivatives under SFAS 133 (the fair-value-based hedging performance measure recognized into earnings under SFAS 133, scaled by the market value of equity at the beginning of the quarter). IB is income before extraordinary items. EXIB is income before extraordinary items excluding the earnings component attributed to hedging derivatives under SFAS 133, scaled by the market value of equity at the beginning of the quarter. Mills (lamda) is the inverse mills ratio generated from the first-stage estimation that is intended to correct for sample selection bias.
Table 3: Pre-and-post Comparison of Earnings Response Coefficients Panel A: Earnings response coefficient (full sample )
5-day raw return
IB LOSS IB*
LOSS
AFTER NOTIONAL IB* AFTER AFTER* NOTIONAL IB* NOTIONAL IB*AFTER* NOTIONAL 0.4109 (2.90)*** 0.0095 (1.87)* -0.2629 (-1.20) -0.0018 (-0.43) -0.0002 (-0.28) 0.1473 (1.11) -0.0032 (-2.65)*** -0.0171 (-0.86) 0.1855 (7.36)*** R-square 0.06 No. of obs. 10155 5-day market adjusted return IB LOSS IB* LOSS
AFTER NOTIONAL IB* AFTER AFTER* NOTIONAL IB* NOTIONAL IB*AFTER* NOTIONAL 0.4595 (3.35)*** 0.0088 (1.73)* -0.3498 (-1.52) 0.0009 (0.24) 0.0003 (0.41) 0.1290 (0.79) -0.0037 (-3.19)*** -0.0255 (-1.15) 0.1939 (7.10)*** R-square 0.06 No. of obs. 10155
Panel B: Earnings response coefficient (matched sample) 5-day raw
return
IB LOSS IB*
LOSS
AFTER NOTIONAL IB* AFTER AFTER* NOTIONAL IB* NOTIONAL IB*AFTER* NOTIONAL 0.4665 (2.51)** -0.0004 (-0.06) -0.8258 (-3.47)*** -0.0039 (-0.81) -0.0007 (-0.83) 0.2987 (2.07)** -0.0010 (-0.94) -0.0288 (-1.64)* 0.0909 (2.93)*** R-square 0.02 No. of obs. 6521 5-day market adjusted return IB LOSS IB* LOSS
AFTER NOTIONAL IB* AFTER AFTER* NOTIONAL IB* NOTIONAL IB*AFTER* NOTIONAL 0.5358 (2.98)*** -0.0017 (-0.28) -0.9543 (-4.16)*** -0.0012 (-0.41) -0.0004 (-0.39) 0.2671 (1.66)* -0.0012 (-0.89) -0.0369 (-1.29) 0.0957 (2.30)** R-square 0.02 No. of obs. 6521
Table 3 reports pooled regression results from the earnings-returns regressions based on the following model:
reti,t=α0 + α1IBi,t + α2LOSS + α3 AFTER + α4IBi,t*LOSS + α5IBi,t*AFTER+ α6NOTIONALi,t + α7IBi,t*NOTIONAL i,t +
α8NOTIONALi,t*AFTER + α9IBi,t*AFTER*NOTIONAL i,t + εi,t
The dependent variable reti,t is defined as bank holding company i’s 5-day cumulative return around the day when earnings for quarter
t is announced. I use both raw returns and market-adjusted returns (adjusted for CRSP value weighted market return). IB is income before extraordinary items scaled by the market value of equity at the beginning of the quarter. LOSS is a dummy variable coded as 1 when IB is negative. AFTER is a dummy variable coded as 1 for observations in the post-SFAS 133 period. NOTIONAL is the measure of hedging derivative exposure, defined as the total notional amount of non-trading derivatives scaled by the market value of equity at the beginning of the quarter (a value of zero for non- users).
Panel A reports regression results for the full sample. Panel B reports regression results for the matched sample representing bank holding companies that have observations in both the pre-and post-SFAS 133 periods. Two-way clustered t statistics are reported in parentheses (clustered by firm and quarter). *, **, and *** indicate significance (two-tailed test) at 0.1, 0.05, and 0.01 level
Table 4: Relative Explanatory Power of Alternative Income Measures
Panel A: Exposure to hedging derivatives Mean exposure level Median exposure level Average notional amount outstanding (in thousands of dollars) Percentage of total notional amount outstanding Number of observations Sample 0.8783 0.2518 9,963,812 100.00% 2164 Quintile 1 0.0251 0.0220 31,519 0.06% 432 Quintile 2 0.1164 0.1139 245,788 0.49% 433 Quintile 3 0.2547 0.2518 1,063,962 2.14% 433 Quintile 4 0.5468 0.5161 3,392,628 6.81% 433 Quintile 5 3.4467 1.7580 45,062,224 90.49% 433
Panel B: Relative explanatory power over concurrent stock returns R-Squared (Model: Earnings) R-Squared (Model: Earnings Adj. for hedging derivatives)
Vuong’s Z statistic p-value Number of observations Sample 0.0938 0.0485 4.42 0.00 2133 Quintile 1 0.0324 0.0283 1.36 0.17 429 Quintile 2 0.1255 0.1053 1.13 0.26 431 Quintile 3 0.1091 0.0887 1.69 0.09 421 Quintile 4 0.1280 0.0955 1.97 0.05 426 Quintile 5 0.0972 0.0296 3.42 0.00 426
Panel C: Relative explanatory power over future earnings R-Squared (Model: Earnings) R-Squared (Model: Earnings Adj. for hedging derivatives)
Vuong’s Z statistic p- value Number of observations Sample 0.2628 0.1638 2.13 0.03 1940 Quintile 1 0.1558 0.1729 -1.52 0.13 372 Quintile 2 0.1194 0.0947 0.97 0.33 385 Quintile 3 0.3977 0.3567 2.62 0.00 387 Quintile 4 0.3445 0.2821 1.73 0.08 399 Quintile 5 0.3351 0.0910 1.64 0.10 397
Table 4 reports results of tests comparing the explanatory power for two alternative income measures (earnings and earnings excluding the earnings component attributed to hedging derivatives under SFAS 133) based on the subsample of hedging derivative users in the post SFAS 133 period. This sample is further sorted into quintiles based on the level of hedging derivative exposure, measured as the total notional amount of non-trading derivatives scaled by the market level of equity at the beginning of the quarter.
Panel B reports results comparing the following two models: Ri,t=β0 + β1IBi,t + β2LOSS i,t + β3 IBi,t _LOSS i,t + εi,t Model (Earnings Adj. for hedging derivatives):
Ri,t=β0 + β1EXIBi,t + β2LOSSE i,t + β3 EXIBi,t _LOSSE i,t + εi,t
The dependent variable is the bank holding company’s cumulative stock returns during the same quarter. IBi,t is bank holding company’s income before extraordinary items during quarter t, scaled by the market value of equity and the beginning of the quarter. EXIB is quarterly income before extraordinary items excluding the earnings component attributed to hedging derivatives under SFAS 133. LOSS is a dummy variable coded as 1 if IB is negative and LOSSE is a dummy variable coded as 1 EXIB is negative. Panel B reports R2 for each model and Vuong’s (1989) Z-statistic comparing the explanatory power of the two models for the overall sample and for each hedging derivative exposure quintile within the sample.
Panel C reports results comparing the following two models: IBi,t+1=β0 + β1IBi,t + β2LOSSi,t + β3 IBi,t _LOSSi,t + εi,t Model (Earnings Adj. for hedging derivatives):
IBi,t+1=β0 + β1EXIBi,t + β2LOSSEi,t + β3 EXIBi,t _LOSSEi,t + εi,t