- Email: [email protected]
Discussion of “Evidence on the efficacy of interest rate risk disclosures by commercial banks”
The International Journal of Accounting 39 (2004) 257 – 262
Discussion
Discussion of ‘‘Evidence on the efficacy of interest rate risk disclosures by commercial banks’’ Daniel B. Thornton * Queen’s University, School of Business, 99 University Avenue, Kingston, ON, Canada K7L 3N6
1. Introduction Canadian researchers and policymakers, who can count the number of domestic banks on their fingers, surely envy the research opportunity afforded by the availability of data for more than 10,000 U.S. banks. Ahmed, Beatty, and Bettinghaus (2004) exploit this opportunity in a novel way. Rather than testing a hypothesis, they describe the extent to which banks’ maturity-gap disclosures ‘‘indicate their net income that is exposed to interest-rate risk.’’ This is an interesting exercise because although it is well known that customer-relationship issues complicate banks’ exposures (Begley, Chamberlain, and Li, 2003), there is little descriptive evidence relating banks’ maturity-gap disclosures to their actual net income sensitivity to interest-rate changes. This paper provides such evidence. The first section of my discussion addresses its interpretation. The authors see associations between gap and future net interest income as evidence justifying the disclosure requirements of SEC Financial Reporting Release No. 48 (FRR No. 48, SEC, 1997). A better basis for such a conclusion, beyond the present study’s scope, would be to reestimate the regression coefficients using actual FRR No. 48 data and see if the magnitude of the new estimates were more consistent with expectations than the ones obtained using pre-FRR No. 48 data. The second section of the discussion offers suggestions along these lines for future studies and some proposals for further exploiting pre-FRR No. 48 data.
2. Interpretation of results To facilitate the discussion, I simplify the authors’ model as follows. At time t, let a bank’s maturity gap be Gt = At Lt, where At represents the bank’s interest-earning assets
* Tel.: +1-613-533-6194. E-mail address: [email protected] (D.B. Thornton). 0020-7063/$30.00 D 2004 University of Illinois. All rights reserved. doi:10.1016/j.intacc.2004.06.004
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and Lt its interest-bearing liabilities. In a base case, the difference between the bank’s net interest income for the year ending at Time 2 and its net interest income for the year ending at Time 1 can be represented as DNI1,2 = G1 Dr. In this expression, Dr is the beginning-of-year change in an interest rate applicable to both assets and liabilities. The gap, G1, is the same as it was during Year 1 and is constant during Year 2 because either the bank is not growing or any increases in financial assets (loans) are coincidental with increases in liabilities (deposits). Although it is unrealistic, I find this base case helpful in organizing my thinking about the authors’ analyses. The authors perform cross-sectional regressions of DNI1,2 on G1 and other variables meant to capture some complexities that the base case omits, finding that the coefficient of G1 is less than Dr. In a sense, this is like regressing current tax expense on net income across a sample of firms and finding that the slope coefficient is less than the statutory tax rate (s). Such results are not surprising. Yet, they are interesting because they raise questions about why the estimated coefficients are not equal to Dr and s, respectively. We know that banks can dampen their interest-rate exposures by hedging with derivatives and dynamically rebalancing their long and short portfolios; but on average, how much do they hedge and what is the resulting exposure? Similarly, we know that firms can reduce the sensitivity of current tax expense to accounting income by deliberately altering the timing and character of income for tax purposes; but on average, how successful is their tax planning, and what is the resulting effective tax rate? Ideally, the results of such descriptive studies provide preliminary answers to the questions and set the stage for further analysis of firms’ hedging and tax-planning activities. The estimated coefficients of G1 are generally much less than Dr. In Tables 2 and 3, the estimates are often less than 1% of Dr. These estimates strike me as being implausibly low, as would s estimates equal to about 1% of the statutory tax rate. In Tables 5 and 6, which allow for fixed firm and time effects, the estimates of Dr are generally about a third of their theoretical values. These estimates are much more reasonable. They are consistent with the notion that banks hedge a very substantial but plausible portion of their net interest-income exposure. I think the results also imply that future studies must either control for individual bank characteristics or divide bank samples into homogeneous subsamples before performing cross-sectional regression analyses like those underlying the results in Tables 2 and 3. To explore why the estimates of Dr are so tiny in Tables 2 and 3, one can extend the base case by adding a term, DG, that represents the change in gap over the period ending at Time 2. One can then decompose the total change in net interest income, r2G2 r1G1, into a rate variance G1 Dr and a volume variance r2 DG, that is: DNI1;2 ¼ r2 G2 r1 G1 ¼ G1 Dr þ r2 DG Most U.S. banks provide such a ‘‘postmortem’’ explanation for changes in all of the categories of net interest income in their annual reports. For instance, the Bank of America in its 2002 annual report states ‘‘[t]he changes for each category of interest income and expense are divided between the portion of change attributable to the variance in volume or rate for that category. . .’’ (p. 60). Thus, it seems that a regression of DNI1,2 on both G1 and DG would be necessary to obtain reliable estimates of the extent to which banks’
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maturity gap disclosures ‘‘indicate their net income that is exposed to interest-rate risk.’’ However, the main regression in the paper takes the following form: DNI1;2 ¼ b0 þ b1 G1 þ b2 ðAsset growthÞ þ other control variables þ noise At the conference, I remarked that DG would be a better regressor than asset growth; both are observable ex post. In response, the authors tried using ‘‘net growth in rate sensitive assets’’ and report that the substitution ‘‘did not make a material change in test results’’ (p. 14, emphasis added). This result is intriguing because, generally, increases in bank assets (e.g., loans) are associated with increases in liabilities (e.g., deposits). If assets and liabilities increase equally (dollar for dollar), then A L is constant and the change in gap is zero whatever the asset growth. In that case, asset growth is not a valid proxy for DG because assets could be growing rapidly while G was constant. More generally, under base case assumptions, the theoretical value of b2 using DG as the regressor would be BDNI1,2/BDG = r2; the theoretical value of b2 using DA as the regressor is BDNI1,2/BDA, which can be broken down as follows: BDNI1;2 =BDA ¼ BDNI1;2 =BDG BDG=BDA ¼ r2 ð1 BL=BAÞ Thus, if A L is constant, BL/BA = 1 and the estimated value of b2 should be zero. This is not what the authors find in Table 2. Indeed, b2 is generally very large compared with other estimated coefficients in the regression model and highly significant. Thus, the conclusion that assets do not increase dollar for dollar with liabilities seems warranted. Another naı¨ve but more plausible scenario is that liabilities are a constant percentage of assets: Lt = kAt. In Table 1, the authors report that generally k>1 (i.e., liabilities exceed assets) for fixed gap and k < 1 (assets exceed liabilities) for variable gap. I conjecture that while BL/BA is not equal to one, it is far from being zero across firms. Although a correlation matrix including A and L would be the best way to confirm this, one can crudely estimate BL/BA as follows: Let AL DGu A represent the deflated gap values reported in Table 1. Then: L ¼ ð1 DGÞ A One can then use the quantity (1 DG) as a rough estimate of BL/BA. Because the average values of DG range from 0.16 to + 0.34 (across both derivative users and nonusers), a plausible range for BL/BA across the years is 1.16 to 0.66—not one, but not different enough from one to allay my concern that growth in assets is probably not a good proxy for growth in gap. The facts that b2 is statistically significant, and that substituting DG for DA does not materially change the estimate of b2, deserve further examination in my view. The results in Tables 5 and 6, based on regression models incorporating fixed firm and time effects, are much more plausible. One expects regression coefficients equal to one under base case assumptions. The actual coefficients are around one third, less for
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derivative users. I think this regression setup provides a much better basis for further exploring associations between the magnitude of coefficients and bank-specific factors.
3. Suggestions for future research Banks’ omission of derivatives prior to FRR No. 48 introduces measurement error in gap, contributing to the tiny regression estimate of Dr in Tables 2 and 3 and probably also reducing the reliability of estimated coefficients in Tables 5 and 6. Aware of this, the authors report that estimated gap coefficients are less for derivative users than for nonusers as expected. In my view, however, repeating the exercise with derivatives included in gap is a priority now that sufficient FRR No. 48 data are available. Prior to performing that exercise, one can further exploit the study’s data as follows. The authors recognize that banks’ exposure to interest rates differs from simple proportionality with gap not only because banks use derivatives, but also because borrowers often hold prepayment options (banks are short put options) and depositors generally hold early withdrawal options (banks are short call options). An equally important factor, not explicitly mentioned, is that banks often offer credit facilities allowing customers to borrow additional funds at a prespecified rate of interest should the market rate increase (banks are short call options). Because banks are short of these customer-related options, favorable changes in interest rates are unlikely to benefit banks as much as unfavorable changes hurt them. This suggests a straightforward extension: Compare regression coefficients for favorable and unfavorable values of Dr. A favorable value of Dr occurs if (a) gap is positive and interest rates increase or (b) gap is negative and rates decrease. Although the interest-rate changes per se are favorable to banks in both (a) and (b), it is likely that some customers will exercise their options, so banks will not enjoy the full benefit of the rate changes. An unfavorable value of Dr occurs if (c) gap is positive and interest rates decrease or (d) gap is negative and rates increase. In competitive financial markets, banks need to pass on these rate changes to customers. Therefore, all else being equal, I expect the coefficient of G1 to be higher for unfavorable than for favorable interest-rate changes.1 It is costly for banks to hedge their short positions in the options that customers hold. However, there is a well-developed market for credit derivatives and an emerging market for the option-like securities that mimic credit facilities. Therefore, it would also be interesting to repeat the analysis for derivative users and nonusers, to see if derivative users are more symmetrically exposed to favorable and unfavorable interest-rate changes than nonusers. Another potentially fruitful project would involve reestimating all coefficients using FRR No. 48 data. This approach, similar to that of Thornton and Welker (2004), emphasizes the incremental information content of FRR No. 48 disclosures.2 The new 1
Notice that the authors’ examination of positive and negative interest-rate changes does not facilitate such a comparison because long and short positions differ across banks and for fixed and variable instruments. 2 Curiously, the authors measure ‘‘on-balance-sheet maturity gap’’ (p. 3, emphasis theirs) but then suggest that ‘‘gap disclosures provide additional information beyond what is provided in the balance sheet’’ (p. 4).
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disclosures are potentially incrementally informative in at least four ways: (1) They include derivatives. (2) They group financial instruments in time buckets based on management’s best estimates of expected (not contractual) maturity. (3) They offer a finer breakdown of maturity timing. (4) They enjoin management to discuss the disclosures in their 10-K Management Discussion and Analyses (MD&A).3 An MD&A provides investors with a view of the bank through the eyes of management. Banks are likely to trade off variables strategically. For example, depending on their geographic location and clientele base, some banks offer free checking but pay little or no deposit interest, while others charge for checking but pay competitive rates of interest. Some banks find it cost effective to hedge with derivatives while others do not. By outlining and discussing the rationale for such tradeoffs, the MD&A should facilitate researchers parsing bank samples into subsamples that are likely to yield consistent cross-sectional regression coefficients. Yet another potentially fruitful extension of the research would analyze banks’ fairvalue exposures to interest-rate changes.4 Banks with fixed-rate exposure are vulnerable to fair-value volatility when interest rates change because of liquidity and capital-adequacy concerns. Following SFAS No. 133, banks carry derivatives at fair value, so one can see if banks’ derivative exposures implied by FRR No. 48 disclosures eventuate when interest rates change. The results would be interesting because banks can terminate derivative exposures by instantaneously settling the contracts or by taking offsetting positions in an impersonal marketplace. Altering net interest income exposures stemming from loan and deposit positions is comparatively costly because it can jeopardize fee income and other benefits flowing from customer-specific capital.5 Thus, inferences concerning fair-value exposures from static, end-of-quarter positions depicted by FRR No. 48 disclosures may be less reliable than those concerning net interest-income exposures. Moreover, since banks successfully resisted marking loans and deposits to market, it is likely that confirmation of their full fair-value sensitivity will continue to elude both researchers and financial statement users.
4. Conclusions Although the authors could have enhanced the description of banks’ sensitivity to interest-rate changes using their existing data, the study is a useful addition to the literature. The results are consistent with the intuition that banks are more than passive conduits from savers to borrowers. Derivative users generally reduce their net interest exposures to market-rate changes. Even nonderivative users’ net-income sensitivity is generally much less than the gap times interest-rate changes. This suggests that bank
3 FRR No. 48 disclosures also have the potential to enhance investor consensus as to banks’ exposure to interest-rate changes (Linsmeier, Thornton, Venkatachalam, & Welker, 2002). 4 Bodurtha and Thornton (2002) describe the distinction between fair-value exposures and cash flow/net income exposures. 5 The authors’ finding that banks’ 1-year gap helps predict 3-year-ahead net interest income is consistent with both the existence of customer relationship capital and persistence in banks’ lending and borrowing policies.
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activities possess operating as well as financial characteristics (Feltham & Ohlson, 1995), probably stemming from customer-relationship capital. The authors’ derivation and measurement of gap give future researchers a good starting point in modeling banks’ exposures to interest rates, whether they use call report data or FRR No. 48 data. I do not think that the results of the study justify the SEC’s issuance of FRR No. 48. Justification would more logically come from evidence of the incremental information content of the actual FRR No. 48 disclosures, and regression coefficients that are closer to theoretical values, given gap measures based on banks’ derivative and nonderivative positions.
Acknowledgements The author gratefully acknowledges financial support from the Social Sciences and Humanities Research Council of Canada (Grant No. 410-03-1046) and the Canadian Accounting Standards Board. He also thanks Michael Welker for helpful discussions concerning this commentary.
References Ahmed, A., Beatty, A., & Bettinghaus, B. (2004). Evidence on the efficacy of interest rate risk disclosures by commercial banks. International Journal of Accounting. Begley, J., Chamberlain, S., & Li, Y. (2003). Modeling goodwill for banks: A residual income approach with empirical tests. University of British Columbia working paper presented at the October 2003 Contemporary Accounting Research Conference, Toronto, Canada. Bodurtha, J., & Thornton, D. (2002, Fall). FAS 133 option fair value hedges: Financial-engineering and financialaccounting perspectives. Journal of Derivatives, 62 – 79. Feltham, G., & Ohlson, J. (1995). Valuation and clean surplus accounting for operating and financial activities. Contemporary Accounting Research, 11, 689 – 731. Linsmeier, T., Thornton, D., Venkatachalam, M., & Welker, M. (2002, April). The effect of mandated market risk disclosures on trading volume sensitivity to interest rate, exchange rate and commodity price movements. The Accounting Review, 343 – 77. Securities, and Exchange Commission (SEC). (1997). Disclosure of Accounting Policies for Derivative Financial Instruments, and Derivative Commodity Instruments, and Disclosure of Quantitative, and Qualitative Information about Market Risk Inherent in Derivative Financial Instruments, Other Financial Instruments, and Derivative Commodity Instruments; Final Rule (Amendment to Section 210.4-08(n) of Regulation S-X, and to Item 310 of Regulation S-B). Release Nos. 33-7386; 34-38223; IC-22487; FR-48; International Series No. 1047; File No. S7-35-95 (January 31, 1997). Washington, DC: SEC. 62 FR No .27, February 10, 1997. Thornton, D., & Welker, M. (2004, Winter). The effect of oil and gas producers’ FRR No. 48 disclosures on investors’ risk assessments. Journal of Accounting, Auditing & Finance, 85 – 114.
Discussion
Discussion of ‘‘Evidence on the efficacy of interest rate risk disclosures by commercial banks’’ Daniel B. Thornton * Queen’s University, School of Business, 99 University Avenue, Kingston, ON, Canada K7L 3N6
1. Introduction Canadian researchers and policymakers, who can count the number of domestic banks on their fingers, surely envy the research opportunity afforded by the availability of data for more than 10,000 U.S. banks. Ahmed, Beatty, and Bettinghaus (2004) exploit this opportunity in a novel way. Rather than testing a hypothesis, they describe the extent to which banks’ maturity-gap disclosures ‘‘indicate their net income that is exposed to interest-rate risk.’’ This is an interesting exercise because although it is well known that customer-relationship issues complicate banks’ exposures (Begley, Chamberlain, and Li, 2003), there is little descriptive evidence relating banks’ maturity-gap disclosures to their actual net income sensitivity to interest-rate changes. This paper provides such evidence. The first section of my discussion addresses its interpretation. The authors see associations between gap and future net interest income as evidence justifying the disclosure requirements of SEC Financial Reporting Release No. 48 (FRR No. 48, SEC, 1997). A better basis for such a conclusion, beyond the present study’s scope, would be to reestimate the regression coefficients using actual FRR No. 48 data and see if the magnitude of the new estimates were more consistent with expectations than the ones obtained using pre-FRR No. 48 data. The second section of the discussion offers suggestions along these lines for future studies and some proposals for further exploiting pre-FRR No. 48 data.
2. Interpretation of results To facilitate the discussion, I simplify the authors’ model as follows. At time t, let a bank’s maturity gap be Gt = At Lt, where At represents the bank’s interest-earning assets
* Tel.: +1-613-533-6194. E-mail address: [email protected] (D.B. Thornton). 0020-7063/$30.00 D 2004 University of Illinois. All rights reserved. doi:10.1016/j.intacc.2004.06.004
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and Lt its interest-bearing liabilities. In a base case, the difference between the bank’s net interest income for the year ending at Time 2 and its net interest income for the year ending at Time 1 can be represented as DNI1,2 = G1 Dr. In this expression, Dr is the beginning-of-year change in an interest rate applicable to both assets and liabilities. The gap, G1, is the same as it was during Year 1 and is constant during Year 2 because either the bank is not growing or any increases in financial assets (loans) are coincidental with increases in liabilities (deposits). Although it is unrealistic, I find this base case helpful in organizing my thinking about the authors’ analyses. The authors perform cross-sectional regressions of DNI1,2 on G1 and other variables meant to capture some complexities that the base case omits, finding that the coefficient of G1 is less than Dr. In a sense, this is like regressing current tax expense on net income across a sample of firms and finding that the slope coefficient is less than the statutory tax rate (s). Such results are not surprising. Yet, they are interesting because they raise questions about why the estimated coefficients are not equal to Dr and s, respectively. We know that banks can dampen their interest-rate exposures by hedging with derivatives and dynamically rebalancing their long and short portfolios; but on average, how much do they hedge and what is the resulting exposure? Similarly, we know that firms can reduce the sensitivity of current tax expense to accounting income by deliberately altering the timing and character of income for tax purposes; but on average, how successful is their tax planning, and what is the resulting effective tax rate? Ideally, the results of such descriptive studies provide preliminary answers to the questions and set the stage for further analysis of firms’ hedging and tax-planning activities. The estimated coefficients of G1 are generally much less than Dr. In Tables 2 and 3, the estimates are often less than 1% of Dr. These estimates strike me as being implausibly low, as would s estimates equal to about 1% of the statutory tax rate. In Tables 5 and 6, which allow for fixed firm and time effects, the estimates of Dr are generally about a third of their theoretical values. These estimates are much more reasonable. They are consistent with the notion that banks hedge a very substantial but plausible portion of their net interest-income exposure. I think the results also imply that future studies must either control for individual bank characteristics or divide bank samples into homogeneous subsamples before performing cross-sectional regression analyses like those underlying the results in Tables 2 and 3. To explore why the estimates of Dr are so tiny in Tables 2 and 3, one can extend the base case by adding a term, DG, that represents the change in gap over the period ending at Time 2. One can then decompose the total change in net interest income, r2G2 r1G1, into a rate variance G1 Dr and a volume variance r2 DG, that is: DNI1;2 ¼ r2 G2 r1 G1 ¼ G1 Dr þ r2 DG Most U.S. banks provide such a ‘‘postmortem’’ explanation for changes in all of the categories of net interest income in their annual reports. For instance, the Bank of America in its 2002 annual report states ‘‘[t]he changes for each category of interest income and expense are divided between the portion of change attributable to the variance in volume or rate for that category. . .’’ (p. 60). Thus, it seems that a regression of DNI1,2 on both G1 and DG would be necessary to obtain reliable estimates of the extent to which banks’
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maturity gap disclosures ‘‘indicate their net income that is exposed to interest-rate risk.’’ However, the main regression in the paper takes the following form: DNI1;2 ¼ b0 þ b1 G1 þ b2 ðAsset growthÞ þ other control variables þ noise At the conference, I remarked that DG would be a better regressor than asset growth; both are observable ex post. In response, the authors tried using ‘‘net growth in rate sensitive assets’’ and report that the substitution ‘‘did not make a material change in test results’’ (p. 14, emphasis added). This result is intriguing because, generally, increases in bank assets (e.g., loans) are associated with increases in liabilities (e.g., deposits). If assets and liabilities increase equally (dollar for dollar), then A L is constant and the change in gap is zero whatever the asset growth. In that case, asset growth is not a valid proxy for DG because assets could be growing rapidly while G was constant. More generally, under base case assumptions, the theoretical value of b2 using DG as the regressor would be BDNI1,2/BDG = r2; the theoretical value of b2 using DA as the regressor is BDNI1,2/BDA, which can be broken down as follows: BDNI1;2 =BDA ¼ BDNI1;2 =BDG BDG=BDA ¼ r2 ð1 BL=BAÞ Thus, if A L is constant, BL/BA = 1 and the estimated value of b2 should be zero. This is not what the authors find in Table 2. Indeed, b2 is generally very large compared with other estimated coefficients in the regression model and highly significant. Thus, the conclusion that assets do not increase dollar for dollar with liabilities seems warranted. Another naı¨ve but more plausible scenario is that liabilities are a constant percentage of assets: Lt = kAt. In Table 1, the authors report that generally k>1 (i.e., liabilities exceed assets) for fixed gap and k < 1 (assets exceed liabilities) for variable gap. I conjecture that while BL/BA is not equal to one, it is far from being zero across firms. Although a correlation matrix including A and L would be the best way to confirm this, one can crudely estimate BL/BA as follows: Let AL DGu A represent the deflated gap values reported in Table 1. Then: L ¼ ð1 DGÞ A One can then use the quantity (1 DG) as a rough estimate of BL/BA. Because the average values of DG range from 0.16 to + 0.34 (across both derivative users and nonusers), a plausible range for BL/BA across the years is 1.16 to 0.66—not one, but not different enough from one to allay my concern that growth in assets is probably not a good proxy for growth in gap. The facts that b2 is statistically significant, and that substituting DG for DA does not materially change the estimate of b2, deserve further examination in my view. The results in Tables 5 and 6, based on regression models incorporating fixed firm and time effects, are much more plausible. One expects regression coefficients equal to one under base case assumptions. The actual coefficients are around one third, less for
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derivative users. I think this regression setup provides a much better basis for further exploring associations between the magnitude of coefficients and bank-specific factors.
3. Suggestions for future research Banks’ omission of derivatives prior to FRR No. 48 introduces measurement error in gap, contributing to the tiny regression estimate of Dr in Tables 2 and 3 and probably also reducing the reliability of estimated coefficients in Tables 5 and 6. Aware of this, the authors report that estimated gap coefficients are less for derivative users than for nonusers as expected. In my view, however, repeating the exercise with derivatives included in gap is a priority now that sufficient FRR No. 48 data are available. Prior to performing that exercise, one can further exploit the study’s data as follows. The authors recognize that banks’ exposure to interest rates differs from simple proportionality with gap not only because banks use derivatives, but also because borrowers often hold prepayment options (banks are short put options) and depositors generally hold early withdrawal options (banks are short call options). An equally important factor, not explicitly mentioned, is that banks often offer credit facilities allowing customers to borrow additional funds at a prespecified rate of interest should the market rate increase (banks are short call options). Because banks are short of these customer-related options, favorable changes in interest rates are unlikely to benefit banks as much as unfavorable changes hurt them. This suggests a straightforward extension: Compare regression coefficients for favorable and unfavorable values of Dr. A favorable value of Dr occurs if (a) gap is positive and interest rates increase or (b) gap is negative and rates decrease. Although the interest-rate changes per se are favorable to banks in both (a) and (b), it is likely that some customers will exercise their options, so banks will not enjoy the full benefit of the rate changes. An unfavorable value of Dr occurs if (c) gap is positive and interest rates decrease or (d) gap is negative and rates increase. In competitive financial markets, banks need to pass on these rate changes to customers. Therefore, all else being equal, I expect the coefficient of G1 to be higher for unfavorable than for favorable interest-rate changes.1 It is costly for banks to hedge their short positions in the options that customers hold. However, there is a well-developed market for credit derivatives and an emerging market for the option-like securities that mimic credit facilities. Therefore, it would also be interesting to repeat the analysis for derivative users and nonusers, to see if derivative users are more symmetrically exposed to favorable and unfavorable interest-rate changes than nonusers. Another potentially fruitful project would involve reestimating all coefficients using FRR No. 48 data. This approach, similar to that of Thornton and Welker (2004), emphasizes the incremental information content of FRR No. 48 disclosures.2 The new 1
Notice that the authors’ examination of positive and negative interest-rate changes does not facilitate such a comparison because long and short positions differ across banks and for fixed and variable instruments. 2 Curiously, the authors measure ‘‘on-balance-sheet maturity gap’’ (p. 3, emphasis theirs) but then suggest that ‘‘gap disclosures provide additional information beyond what is provided in the balance sheet’’ (p. 4).
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disclosures are potentially incrementally informative in at least four ways: (1) They include derivatives. (2) They group financial instruments in time buckets based on management’s best estimates of expected (not contractual) maturity. (3) They offer a finer breakdown of maturity timing. (4) They enjoin management to discuss the disclosures in their 10-K Management Discussion and Analyses (MD&A).3 An MD&A provides investors with a view of the bank through the eyes of management. Banks are likely to trade off variables strategically. For example, depending on their geographic location and clientele base, some banks offer free checking but pay little or no deposit interest, while others charge for checking but pay competitive rates of interest. Some banks find it cost effective to hedge with derivatives while others do not. By outlining and discussing the rationale for such tradeoffs, the MD&A should facilitate researchers parsing bank samples into subsamples that are likely to yield consistent cross-sectional regression coefficients. Yet another potentially fruitful extension of the research would analyze banks’ fairvalue exposures to interest-rate changes.4 Banks with fixed-rate exposure are vulnerable to fair-value volatility when interest rates change because of liquidity and capital-adequacy concerns. Following SFAS No. 133, banks carry derivatives at fair value, so one can see if banks’ derivative exposures implied by FRR No. 48 disclosures eventuate when interest rates change. The results would be interesting because banks can terminate derivative exposures by instantaneously settling the contracts or by taking offsetting positions in an impersonal marketplace. Altering net interest income exposures stemming from loan and deposit positions is comparatively costly because it can jeopardize fee income and other benefits flowing from customer-specific capital.5 Thus, inferences concerning fair-value exposures from static, end-of-quarter positions depicted by FRR No. 48 disclosures may be less reliable than those concerning net interest-income exposures. Moreover, since banks successfully resisted marking loans and deposits to market, it is likely that confirmation of their full fair-value sensitivity will continue to elude both researchers and financial statement users.
4. Conclusions Although the authors could have enhanced the description of banks’ sensitivity to interest-rate changes using their existing data, the study is a useful addition to the literature. The results are consistent with the intuition that banks are more than passive conduits from savers to borrowers. Derivative users generally reduce their net interest exposures to market-rate changes. Even nonderivative users’ net-income sensitivity is generally much less than the gap times interest-rate changes. This suggests that bank
3 FRR No. 48 disclosures also have the potential to enhance investor consensus as to banks’ exposure to interest-rate changes (Linsmeier, Thornton, Venkatachalam, & Welker, 2002). 4 Bodurtha and Thornton (2002) describe the distinction between fair-value exposures and cash flow/net income exposures. 5 The authors’ finding that banks’ 1-year gap helps predict 3-year-ahead net interest income is consistent with both the existence of customer relationship capital and persistence in banks’ lending and borrowing policies.
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activities possess operating as well as financial characteristics (Feltham & Ohlson, 1995), probably stemming from customer-relationship capital. The authors’ derivation and measurement of gap give future researchers a good starting point in modeling banks’ exposures to interest rates, whether they use call report data or FRR No. 48 data. I do not think that the results of the study justify the SEC’s issuance of FRR No. 48. Justification would more logically come from evidence of the incremental information content of the actual FRR No. 48 disclosures, and regression coefficients that are closer to theoretical values, given gap measures based on banks’ derivative and nonderivative positions.
Acknowledgements The author gratefully acknowledges financial support from the Social Sciences and Humanities Research Council of Canada (Grant No. 410-03-1046) and the Canadian Accounting Standards Board. He also thanks Michael Welker for helpful discussions concerning this commentary.
References Ahmed, A., Beatty, A., & Bettinghaus, B. (2004). Evidence on the efficacy of interest rate risk disclosures by commercial banks. International Journal of Accounting. Begley, J., Chamberlain, S., & Li, Y. (2003). Modeling goodwill for banks: A residual income approach with empirical tests. University of British Columbia working paper presented at the October 2003 Contemporary Accounting Research Conference, Toronto, Canada. Bodurtha, J., & Thornton, D. (2002, Fall). FAS 133 option fair value hedges: Financial-engineering and financialaccounting perspectives. Journal of Derivatives, 62 – 79. Feltham, G., & Ohlson, J. (1995). Valuation and clean surplus accounting for operating and financial activities. Contemporary Accounting Research, 11, 689 – 731. Linsmeier, T., Thornton, D., Venkatachalam, M., & Welker, M. (2002, April). The effect of mandated market risk disclosures on trading volume sensitivity to interest rate, exchange rate and commodity price movements. The Accounting Review, 343 – 77. Securities, and Exchange Commission (SEC). (1997). Disclosure of Accounting Policies for Derivative Financial Instruments, and Derivative Commodity Instruments, and Disclosure of Quantitative, and Qualitative Information about Market Risk Inherent in Derivative Financial Instruments, Other Financial Instruments, and Derivative Commodity Instruments; Final Rule (Amendment to Section 210.4-08(n) of Regulation S-X, and to Item 310 of Regulation S-B). Release Nos. 33-7386; 34-38223; IC-22487; FR-48; International Series No. 1047; File No. S7-35-95 (January 31, 1997). Washington, DC: SEC. 62 FR No .27, February 10, 1997. Thornton, D., & Welker, M. (2004, Winter). The effect of oil and gas producers’ FRR No. 48 disclosures on investors’ risk assessments. Journal of Accounting, Auditing & Finance, 85 – 114.