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Enterprise Risk Management, Risk-Taking, and Macroeconomic Implications: Evidence from Bank Mortgage Loan Management

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Abstract

We investigate how enterprise risk management (ERM) reshapes firm risk-taking behaviors. Using loan-level data, we find that ERM does not affect bank mortgage loan origination but increases loan sales. To strengthen identification, we employ a staggered difference-in-differences approach in matched samples and instrumental variables. The channel analysis reveals a stronger ERM impact on mortgage sales for high default-risk loans and when macroeconomic risk is greater, suggesting a risk-transfer effect. ERM influences firms by identifying risky business and external risks, thereby altering risk transfer/retention policies. Additionally, banks with ERM incorporate macroeconomic conditions when setting loan loss provisions, reducing credit market procyclicality.

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Data Availability

The datasets in this study are not publicly available but are available from the corresponding author on reasonable request.

Notes

  1. Speech of Governor Susan Bies at the National Credit Union Administration 2007 Risk Mitigation Summit, entitled “Enterprise Risk Management and Mortgage Lending,” available at https://www.federalreserve.gov/newsevents/speech/bies20070111a.htm.

  2. Consistent with the view, in the same speech that we cite in the epigram at the beginning of this article, Governor Susan Bies also stated: “Effectively managing the risk associated with mortgage lending involves much more than prudent underwriting. Experienced risk managers understand the need to carefully consider the risks should the housing market slow, interest rates change, or unemployment rise.”.

  3. “Investors deserve – and we will be looking for – a commitment by boards and executives to make enterprise risk management part of a firm or corporation’s culture… [ERM helps] us to inspire confidence by examining and eliminating, wherever feasible, existing threats to market stability and to the investors who keep those markets strong.” (Mary Schapiro, Chairman of SEC, March 23, 2011).

  4. Regulations require BHCs to be a source of strength for the banks and also require other banks under the same BHC to cross-guarantee the other affiliates. Houston et al. (1997) find that bank loan growth depends on the BHC.

  5. We repeated the manual search process used to identify the year of ERM adoption for all adopters and all years after adoption to verify whether these firms terminated their ERM program or not in any of the years. We could not find any instance of a firm terminating an ERM program. Thus, we conclude that all sample firms have maintained their ERM programs since adoption.

  6. Table OA1 in Online Appendix presents the sample description for each year. We report the numbers of metropolitan statistical areas, BHCs, ERM adopters vs. non-adopters, loan applications, loan approved, and loan sold.

  7. Our results still hold when we use concurrent independent variables.

  8. In Table OA2 in Online Appendix, we also two-way clustered standard errors at the BHC-year level. Our results hold.

  9. The results hold if we do not require the purchasers to be non-affiliated institutions.

  10. Larger and more complicated BHCs, such as Systemically Important Banks, would presumably adopt ERM programs earlier than their smaller counterparts driven by a higher degree of regulatory scrutiny and reputational consequences.

  11. Before the launch of the Dodd-Frank Act, mortgage loan distribution through securitization and through a loan sale would have achieved a similar objective of removing the loans from banks’ balance sheets (Acharya et al. 2013). However, the Dodd-Frank Act mandates originating banks to retain a certain threshold of risk (5 percent) for securitizations through the risk retention rule (Flynn et al. 2020). Therefore, during our sample period from 2010 to 2017, a loan sale would be more efficient in transferring risk away from banks’ balance sheets than securitization. Accordingly, banks would be incentivized to sell more loans rather than securitizing them. This shift from securitization to loan sales would increase loan sales, which is, however, independent of banks’ ERM adoption. In other words, our results between ERM and loan sales could be driven by confounding factors such as regulatory changes. Even if certain contemporaneous events, including the regulatory change in loan risk retention rules, may coincide with the surge of ERM adoption in timing, the additional dimension in the difference-in-differences approach—cross sectional differences between the treatment and control firms—would allow us to compare firms with ERM programs against appropriate counterfactuals to reveal the impact on loan sales attributed to ERM. In such a manner, our staggered difference-in-differences model effectively alleviates the concerns raised by confounding factors.

  12. A model with BHC fixed effects generates within-firm estimators. Because the ERM never-switchers’ ERM status stays constant throughout the sample period (ERM always equals either 0 or 1), a concern is that the coefficient of interest, ERM, may be subsumed by the BHC fixed effects. Hence, we test whether our findings hold after removing the BHC fixed effects. However, even if ERM never-switchers’ ERM status does not change throughout the sample, their other firm characteristics and the characteristics of mortgage loan applicants of these BHCs vary over time. Therefore, in a BHC fixed effects model, including ERM never-switchers would still contribute to the estimation of the control variables as well as year fixed effects and MSA fixed effects, which in turn would affect our dependent variable estimation. For example, without never-switchers the fixed effect estimation in year t would be only based on switchers, which could be biased because the year t effect for switchers and never-switches may differ. Because controlling for BHC fixed effects helps rule out the confounding factors due to time-invariant firm-level unobservable variables, we still prefer a model with BHC fixed effects.

  13. For the model specification in Table 4, we use all the control variables in column (4) of Table 3 because it includes the most comprehensive set of control variables.

  14. Consistent with the model without BHC fixed effects, untabulated results show ERM does not affect mortgage loan origination in the staggered difference-in-differences model with BHC fixed effects.

  15. To alleviate the concern regarding a spurious relation between ERM implementation and mortgage loan sale, we perform placebo tests by falsifying ERM implementation (Bernile et al. 2017; Neel and Xu 2023). Specifically, we first obtain the empirical distribution of ERM implementation, and then randomly assign ERM (ERM adoption and its timing) to BHCs in the sample of approved mortgage loans according to the distribution. We then re-estimate the staggered difference-in-differences model in the propensity score matched sample with the randomly assigned ERM. We repeat the procedure 1,000 times for the specification in column (3) of Table 4 and present the distribution of the coefficient estimates and t-statistics in Model (1) of Table OA7 in Online Appendix. In addition, we perform similar placebo tests for all specifications in Table 3 and present the distribution of the coefficient estimates and t-statistics in Models (2)-(5) of Table OA7 in Online Appendix. In all models, the mean and the median of the ERM coefficients are indistinguishable from zero. Also, the actual coefficient estimates and t-statistics of all models are larger than (or equal to) the 95th percentiles of those from the placebo tests. Overall, the placebo test results suggest that our regression results are not driven by chance or any unobserved exogenous shocks. Also, it suggests that our manually identified ERM adoption years of BHCs seem to be accurate and reflect the economic benefits of actual ERM implementation.

  16. Duchin and Sosyura (2014) also point out that the loan-to-income ratio is a widely accepted measure of loan risk in the mortgage industry. For instance, regulators use the loan-to-income ratio to determine whether a mortgage loan is eligible for the Federal Home Affordable Modification Program.

  17. In a booming economy, banks tend to relax their underwriting standards and be more aggressive in taking risks, potentially writing loans with bad quality. Thus, this practice amplifies the economic expansions. At the same time, the aggressive risk-taking leads to lower loan loss provisions. However, when economic recessions hit, banks start to set higher loan loss provisions when bank funds become strained, which aggravates the credit crunch and magnifies the recessions (Berger and Udell 2003; Balla and McKenna 2009).

  18. We use the loan-level data in the previous regressions and BHC-level data in the loan loss provisions regressions. Hence, the observation numbers in this table are much smaller than those in the other tables.

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Authors and Affiliations

  1. College of Business Administration, University of Wisconsin – La Crosse, La Crosse, WI, USA

    Shiang Liu

  2. G. Brint Ryan College of Business, University of North Texas, Denton, TX, USA

    Jianren Xu

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  1. Shiang Liu
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Table 10

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Liu, S., Xu, J. Enterprise Risk Management, Risk-Taking, and Macroeconomic Implications: Evidence from Bank Mortgage Loan Management. J Financ Serv Res (2024). https://doi.org/10.1007/s10693-024-00422-0

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