We investigate the effects of countercyclical prudential buffers on bank risk-taking. We exploit the introduction of dynamic loan loss provisioning in Spain, mandating that banks use historical average loss rates in their estimation of loan loss provisions. We find that dynamic loan loss provisioning is associated with reductions in timely loan loss provisioning. Banks that previously recognized loan losses in a timely fashion exhibit the greatest reductions in timeliness and consequently extend loans to riskier borrowers with lower accounting quality. Our results have policy implications for the debate on the use of financial reporting requirements in mitigating capital pro-cyclicality.

We provide the first comprehensive analysis of the properties of investment recommendations generated by “Robo-Analysts,” which are human analyst-assisted computer programs conducting automated research analysis. Our results indicate that Robo-Analyst recommendations differ from those produced by traditional “human” research analysts across several important dimensions. First, Robo-Analysts produce a more balanced distribution of buy, hold, and sell recommendations than do human analysts and are less likely to recommend “glamour” stocks and firms with prospective investment banking business. Second, automation allows Robo-Analysts to revise their recommendations more frequently than human analysts and incorporate information from complex periodic filings. Third, while Robo-Analysts' recommendations exhibit weak short-window return reactions, they have long-term investment value. Specifically, portfolios formed based on the buy recommendations of Robo-Analysts significantly outperform those of human analysts. Overall, our results suggest that automation in the sell-side research industry can benefit investors.