Dodd–Frank 's impact on community‐bank investment models: A Bayesian structural time series analysis
Lee, Yen Teik
Caton, Gary L.
Gamble, Edward N.
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We use Bayesian structural time series (BSTS) methodology to test whether the Wall Street Reform and Consumer Protection Act of 2010 (DF) caused changes in community bank business models. The BSTS methodology uses the pre-DF period to create synthetic counterfactuals for community-bank dependent variables of interest. In the post-DF period, the counterfactuals become predictions of the dependent variables had DF not been enacted. Comparing post-DF predicted versus actual dependent variables allows us to estimate the causal impact of DF on these variables of interest. We find that relative to assets, community banks significantly reduce their lending activities and significantly increase investment in securities and excess reserves.
This is the peer reviewed version of the following article: [Dodd–Frank 's impact on community‐bank investment models: A Bayesian structural time series analysis. Accounting & Finance (2022)], which has been published in final form at https://doi.org/10.1111/acfi.13016. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions: https://authorservices.wiley.com/author-resources/Journal-Authors/licensing/self-archiving.html#3.
Lee, Y.T., Caton, G.L., Gamble, E.N. & Kerins, F. (2022) Dodd–Frank's impact on community-bank investment models: A Bayesian structural time series analysis. Accounting & Finance, 00, 1–18. Available from: https://doi. org/10.1111/acfi.13016