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On the relation between the fair value option and bid-ask spreads: descriptive evidence on the recognition of credit risk changes under IFRS

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Abstract

This paper investigates the relationship between the use of the fair value option for liabilities (FVO) under IAS 39 and information asymmetry across investors as reflected in bid-ask spreads. Using a sample of European banks for 2006–2010, we find descriptive evidence that banks using FVO exhibit lower bid-ask spreads, relative to non-adopters. We also document that lower bid-ask spreads are still present when the control group of non-adopters is held constant while the treatment group is reduced to adopters that recognise fair value changes attributable to own credit risk changes. Moreover, the comparison of adopters with own credit risk fair value changes to other adopters shows that the recognition of these fair values is not associated with higher bid-ask spreads. Stressing the limitations of our research and highlighting the existence of alternative plausible explanations, our findings appear not to support claims that recognising fair value changes attributable to changes in the own credit risk is detrimental to the transparency of financial statements.

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Notes

  1. When the entity’s credit quality improves this mechanism works vice versa.

  2. We concentrate on the liabilities side because the main interest of our study is to analyse differences in information asymmetry induced by the counterintuitive recognition of own credit risk changes. These changes only occur on the liabilities side of the balance sheet. Notwithstanding the foregoing, we cannot rule out associations from the joint application of the FVO for assets and liabilities.

  3. Nevertheless, Bischof et al. (2014b) also note that there is no standard way how analysts process fair value-related information which demonstrates how context specific the processing of fair values is.

  4. See IFRS 9.4.2.2.

  5. See IFRS 9.5.7.7 ff.

  6. See IFRS 9.5.7.8. The rationale behind this approach lies in the IASB’s (2010) view that the recognition of effects that stem from the credit risk of a fair value option liability does not provide useful information unless it prevents or decreases an accounting mismatch.

  7. Song (2008) examines income statement effects of FVO assets and liabilities as one netted position, resulting in either a gain or a loss.

  8. The entity here also signalises that the risk of the portfolio is managed by macro hedging techniques that comprise hedges of net positions in a portfolio of assets and liabilities (Whittington 2005).

  9. In his literature review, Landsman (2007) concludes that the existing overall research findings suggest that fair values are more informative to investors.

  10. In our analysis we do not regard banks that made use of the FVO under the first, the second or both criteria separately due to rather scarce disclosures regarding this matter. However, this does not affect the appropriateness of our research design since we expect both criteria to have the same association with information asymmetry.

  11. Moreover, Ball et al. (2012) expect the adoption of the FVO to be detrimental to the quality and quantity of management forecasts since gains and losses from mark-to-market accounting are difficult to forecast which increases the informational disadvantage of uninformed traders. We argue that a possible increase in reporting quality attained through the adoption according to the eligibility criteria under IFRS, which are nonexistent under U.S. GAAP, may be stronger associated with forecasting.

  12. Alternative dependent variables (e.g. zero-trading days and share turnover) are not employed due to their weaker theoretical background on capturing information asymmetry.

  13. Consistently, the spearman correlation between LogNUMEST and banks’ market value is around 70 % in our sample. Using alternatively the banks’ market value (4 months after a bank’s fiscal year end) as our size variable (instead of LogNUMEST) leaves the results qualitatively unchanged (untabulated).

  14. The bank sample used in the calculation of LogBA_CNTRY consists of 671 observations.

  15. LogBA_CNTRY allows for a more continuous differentiation between countries when compared to country-fixed effects. This variable also possesses a similar effect like country-fix effects since it only varies across countries and not within one single country. Consequently, our results are completely insensitive when country-fixed effects are used instead of LogBA_CNTRY.

  16. Unfortunately, an analysis based on banks that exclusively select the option for liabilities is not feasible due to an insufficient number of observations. Only 22 observations (out of 453) are available for these banks.

  17. The concern using bank fixed effects is that our variables of interest are time-invariant for the vast majority of the banks included in the sample. Almost 90 % of the banks do not change from being a non-adopter to being an adopter or vice versa. The fixed effects models are thus estimated for only around 10 % of the total sample. To this end, we also run OLS regressions using heteroskedasticity-robust standard errors clustered by bank (Rogers 1993). All inferences drawn from the main analysis are not affected qualitatively (untabulated).

  18. In a similar spirit it can be hypothesised that the trigger for the change in credit risk has an influence on information asymmetry: differences might exist between banks that experienced changes in their credit risk as a direct result of a down- or upgrading by a rating agency and others that solely determine the change in credit risk as the residual amount of change in fair value that is not attributable to changes in the market risk. Again, the information disclosed by the banks in our sample regarding this matter is too scarce to analyse possible differences that arise from these factors.

  19. The sample includes only European banks for two reasons: first, we want to keep the data collection process manageable. Second, we aim at analysing an institutional framework being as homogenous as possible to mitigate confounding effects from institutional differences.

  20. Due to a relatively small sample size, our analyses use an unbalanced panel data set to maximize statistical power. We examine a potential survivorship bias in our sample twofold: we rerun our entire set of regression models (1) with a balanced data set and alternatively (2) by including an indicator variable equal to 1 if a bank is present in the sample for the entire five years and 0 otherwise. The inferences drawn from the ones reported are not affected qualitatively.

  21. Thereby, it is noteworthy that 240 out of 262 adopting banks exercise the FVO for both, assets and liabilities revealing an overlap to a high degree.

  22. In some cases the fair values of liabilities are not disclosed as numerical values but as statements like “the fair values of liabilities do not materially differ from their book values”. We treated these cases as if fair values would be exactly equal to book values.

  23. However, it has to be noted that this rationale does not apply to liabilities that are eligible because they are part of a contract that contains one or more substantive embedded derivatives.

  24. We also run a two stage treatment effects model with determinants in the first stage capturing regulatory quality index, Big 4 audit firm, total assets, and leverage. All inferences drawn from the main analyses remain qualitatively the same. Given the difficulties to meet the requirements inherent in the choice of the first-stage variables, a treatment effects model delivers, at the very best, only a very weak indication.

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Acknowledgments

We would like to thank Ulf Brüggemann, Kevin CW Chen, Peter Fiechter, Joachim Gassen, Urooj Khan, Paul Klumpes, Christoph Kuhner, Edgar Löw, and seminar participants at University of Cologne, Hong Kong University of Science and Technology, 2012 American Accounting Association Annual Meeting, and 2012 European Accounting Association Annual Congress for their valuable comments and suggestions. Finally, this paper benefited from the comments of two anonymous reviewers of Journal of Business Economics.

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Authors and Affiliations

  1. Department of Financial Accounting and Auditing (Treuhandseminar), University of Cologne, Albertus-Magnus-Platz, 50931, Cologne, Germany

    Felix Schneider & Duc Hung Tran

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  1. Felix Schneider
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  2. Duc Hung Tran
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Correspondence to Duc Hung Tran.

Appendix: Definition of variables

Appendix: Definition of variables

Variables

Definitions

LogBA

Log of the mean daily bid-ask spread averaged over the fourth month following fiscal year end. The daily spread is calculated as the difference between the ask and the bid price divided by the midpoint price

LogP

Log of the closing share price measured on the beginning of the fourth month following fiscal year end

LogTO

Log of the mean daily volume traded on per share basis averaged over the fourth month following fiscal year end

LogRET_SD

Log of the standard deviation of the stock returns measured over the fourth month following fiscal year end

LogFF

Log of the percentage of free float s hares measured on the beginning of the fourth month following fiscal year end

LogNUMEST

Log of the number of different analyst estimations available during the fourth month following fiscal year

LogBA_CNTY

Log of the average bid-ask spread for the bank’s domicile country. The daily percentage bid-ask spread is calculated by bank and year and then average across all sample banks within a country and a year

FVO

Indicator variable that equals one if the bank adopted the fair value option for liabilities and zero otherwise

CRE

Indicator variable that equals one if the bank adopted the fair value option for liabilities and recognized fair value changes attributable to changes in the credit risk, zero otherwise

FVOA

Indicator variable that equals one if the bank adopted fair value option for assets

FVOA_only

Indicator variable that equals one if the bank adopted fair value option only for assets and zero otherwise

FVOL_only

Indicator variable that equals one if the bank adopted the fair value option only for liabilities and zero otherwise

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Schneider, F., Tran, D.H. On the relation between the fair value option and bid-ask spreads: descriptive evidence on the recognition of credit risk changes under IFRS. J Bus Econ 85, 1049–1081 (2015). https://doi.org/10.1007/s11573-015-0776-2

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