Discretionary loan loss provision behavior in the US banking industry

Abstract

Earnings management can be opportunistic and add noise to earnings, or informative about a firm’s underlying economic performance and add information to financial reports. Our study examines earnings management in banks with different levels of information asymmetry. We compare earnings management between public and private banks by using discretionary loan loss provisions (DLLPs) as a proxy. We use a dataset of US public and private banks from 1986:Q1 to 2013:Q4 and provide evidence of greater earnings management in public banks than private banks. We also examine the conditions that motivate managers to engage in earnings management. DLLPs are used to send private information to investors, consistent with our signaling hypothesis. We also find evidence that capital requirements alter DLLPs, consistent with our capital management hypothesis. Banks with relatively low (high) earnings tend to decrease (increase) their earnings through manipulation of DLLPs, inconsistent with our income-smoothing hypothesis. This study extends the current literature on earnings management between public and private banks. Our discussion provides a better understanding of the determinants of bank earnings management.

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Notes

  1. 1.

    Levitt (1998) The numbers game. Speech delivered at New York University Center for Law and Business, September 28.

  2. 2.

    Ahmed et al. (1999) do not find evidence of the signaling hypothesis, in contrast with Wahlen’s (1994) study, which suggests that the difference in their results is due to the difference in time period of study.

  3. 3.

    We also test with alternative proxies for income smoothing incentives such as banks with high deviation from (earnings) average of industry or banks with high earnings volatility and high or low earnings. The findings remain unchanged.

  4. 4.

    Hope et al. (2013) also find that, in some circumstances, public firms are more likely to act opportunistically by managing earnings. Public firms also face reduced demand for information, which leads to lower accrual quality.

  5. 5.

    For example, as noted by Chiang et al. (2007), most bank deposits in the United States are at least partially insured.

  6. 6.

    Dechow et al. (2010) recognize the complexity of earnings quality measurement depends both on firms’ financial performance and on their accounting systems.

  7. 7.

    Ahmed et al. (1999) do not find evidence of the signaling hypothesis, in contrast with Wahlen’s (1994) study, which suggests that the difference in their results is due to the difference in time period of study.

  8. 8.

    This is often referred to as the collective action problem.

  9. 9.

    Additional work by Ali and Zhang (2015) indicates CEOs engage in more earnings management during their early tenure, likely to improve their perceived performance. This evidence, coupled with that of Hazarika et al. (2012) suggests that banks with greater turnover are more likely to see greater earnings management.

  10. 10.

    Hazarika et al. (2012).

  11. 11.

    Dichev et al. (2013) note that in order to avoid violating debt covenants or meet performance expectations, there is greater emphasis on contractual considerations. The lack of stock liquidity also mitigates the short-termism in private banks (Ferreira et al. 2014).

  12. 12.

    Call Report data in the Federal Reserve database begins in 1976:Q1.

  13. 13.

    The non-bank literature usually uses the Jones-type model. The two most popular models are the cross-sectional Jones model (Jones 1991) and the modified Jones model (Dechow et al. 1995).

  14. 14.

    There are four models in Beatty and Liao (2014). We use model (d) in the majority of our tests. The difference between this model and the other three models is the simultaneous inclusion of the (lagged) allowance for loan losses (\(alw_{it - 1}\)) and the net charge-off (\(cho_{it}\)), which allows researchers to better capture the underlying behavior of LLPs. The loan loss allowance reflects the value of loans the bank estimates to be uncollectable. The inclusion of initial loan loss allowance aims to control any over- (under-) accrual present at the beginning of the current quarter which would require downward (upward) adjustment of LLPs during the current period (Kanagaretnam et al. 2010; Beck and Narayanamoorthy 2013). Net charge-offs, contrary to LLPs, represent defaults on loans held during the period. Since current net charge-offs reflect information of future net charge-offs, they can in turn influence expectations of the collectability of current loans and current LLPs (Beaver and Engel 1996).

  15. 15.

    As noted by Lee et al. (2013), market value is only fractional cointegrated with residual earning. This could be partially explained by manipulation of loan loss provisions by bank management.

  16. 16.

    Prior literature in the accrual-based approach usually tests for a particular sign (see, e.g., DeFond and Jiambalvo 1994; Dechow et al. 1995; Teoh et al. 1998), whereas recent research uses unsigned measures to “detect” which firms are more likely to use discretion in accounting in the absence of a precise direction (see, e.g., Dechow and Dichev 2002; Frankel et al. 2002; Leuz et al. 2003). The use of signed DLLPs allows us to observe the directional DLLPs within a specific reporting context and incentive structure (Francis et al. 2006).

  17. 17.

    In an unreported test, we find that all these differences are significant at 1%.

  18. 18.

    One may argue that there is a change in bank financial reporting with SFAS 115 that became effective end of 1993 and early 1994. Those are transition years. After 1994, reports are not comparable to 1992 and before. We then re-run the analysis on data from 1994:Q1 to 2013:Q4, and find similar results.

  19. 19.

    One may argue that, with the adoption of SFAS 157 in 2007, banks have no more leeway to manage earnings through security sales. Thus, we re-estimate Eq. (2) with the sample until 2007. The results are still robust.

  20. 20.

    We adopt the criterion common in the literature: \(\frac{{{ \hbox{max} }\left( {assets_{public} ,assets_{private} } \right)}}{{{ \hbox{min} }\left( {assets_{public} ,assets_{private} } \right)}} < 2\). Following Kothari et al. (2005), we use performance-based matching and get quantitatively similar results.

  21. 21.

    We use the matching with replacement procedure, which produces a higher quality of match since it reduces the large difference in size between public and private banks. However, this procedure comes at the cost of reducing efficiency since fewer distinct observations are used. This reports how successful the matching techniques are in mitigating such observable difference between both types of banks.

  22. 22.

    We detail the first-stage in Sect. 5.3.

  23. 23.

    We retain only untreated observations whose propensity scores fall inside the interval defined for the treated group. We impose a tolerance level of 0.5% on the maximum propensity score distance allowed (caliper), to minimize the risk of bad matches.

  24. 24.

    Using this oversampling matching leads to a trade-off between bias and variance. Since more information is used to construct the counterfactual for each participant, leading to a decreased variance, it increases bias resulting from poorer matches.

  25. 25.

    We also test for several variations of the first-stage model following prior research. First, we use lagged value of explanatory variables since one may argue that a bank’s choice to go public is based on its underlying economics of the past year (Kim and Yi 2006). We also add the components of loan portfolio as in Nichols et al. (2009). Inclusion or exclusion of these variables has no qualitative effect on our results.

  26. 26.

    We do not use crisis dummies alone since they are subsumed by the time fixed-effects. However, replacing time fixed-effects with dummies of crises do not change our results.

  27. 27.

    We refer to the effective Federal Funds rate, which can be viewed here: https://fred.stlouisfed.org/series/FEDFUNDS.

  28. 28.

    https://www.fdic.gov/bank/analytical/quarterly/2016-vol10-4/article1.pdf.

  29. 29.

    Hong et al. (2019) find a positive relationship between DLLPs and lead analyst coverage. Increased use of DLLPs decreases the accuracy of analyst reports and thus incentivizes lead analysts to avoid analyzing banks whose earnings are more difficult to assess.

  30. 30.

    See, e.g., Beaver et al. (1989), Wahlen (1994), Beaver and Engel (1996), Liu and Ryan (1995), and Liu et al. (1997), who find positive stock price reactions when future cash flow prospects improve. In contrast, Ahmed et al. (1999) do not find support for the signaling incentives in banks, while Kanagaretnam et al. (2004) find inconsistent evidence.

  31. 31.

    Increasing LLPs would lower managers’ compensation due to earnings-related compensation. The literature also documents the use of dividends to convey private information.

  32. 32.

    We select a 75% cutoff to both ensure the firms in the “high incentive” sample have high incentives to signal and also to ensure we have enough observations in both samples to ensure there are no econometric issues. This method has been employed by many researchers in earnings management, including Cheng and Warfield (2005).

  33. 33.

    Liu et al. (1997) show that signaling findings hold for the 4th quarter. We rerun our analysis with only 4th quarter data, and find similar results.

  34. 34.

    https://www.fdic.gov/regulations/safety/manual/Sect.2-1.pdf.

  35. 35.

    We follow the methods of researchers such as Defond and Park (1997) who create binary variables based on earnings.

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Funding was provided by National Foundation for Science and Technology Development.

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Tran, D.V., Hassan, M.K. & Houston, R. Discretionary loan loss provision behavior in the US banking industry. Rev Quant Finan Acc 55, 605–645 (2020). https://doi.org/10.1007/s11156-019-00854-z

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Keywords

  • Bank listing status
  • Discretionary loan loss provisions
  • Earnings management
  • Information asymmetry

JEL Classification

  • G21
  • G28
  • G34
  • G38