This paper empirically examines the role of soft information in the competitive interaction between relationship and transaction banks. Soft information can be interpreted as a valuable signal about the quality of a firm that is observable to a relationship bank, but not to a transaction bank. We show that borrowers self-select to relationship banks depending on whether their observed soft information is positive or negative. Competition affects the investment in learning the soft information from firms by relationship banks and transaction banks asymmetrically. Relationship banks invest more; transaction banks invest less in soft information, exacerbating the selection effect.

We use the financial crisis of 2007–2009 as a laboratory to examine the costs and benefits of teams versus single managers in asset management. We find that when a fund uses complex trading strategies involving the use of CDS team-managed funds outperform solo-managed funds. This may be due to the greater diversity of expertise, experience and skill of teams relative to single managers. During the financial crisis, however, the performance premium of teams becomes negative, which may be because of the slower decision times of teams, which are especially costly during times of rapidly changing market conditions.

We investigate whether pre-publication revisions of bank financial statements contain forward-looking information about bank risk. Using 7.4 million observations of monthly financial reports from all banks in Brazil during 2007–2019, we show that 78 % of all revisions occur before the publication of these statements. The frequency, missing of reporting deadlines, and severity of revisions are positively related to future bank risk. Using machine learning techniques, we provide evidence on mechanisms through which revisions affect bank risk. Our findings suggest that private information about pre-publication revisions is useful for supervisors to monitor banks.

We reassess the recent finding that no established portfolio strategy outperforms the naively diversified portfolio, 1/N, by developing a constrained minimum-variance portfolio strategy on a shrinkage theory based framework. Our results show that our constrained minimum-variance portfolio yields significantly lower out-of-sample variances than many established minimum-variance portfolio strategies. Further, we observe that our portfolio strategy achieves higher Sharpe ratios than 1/N, amounting to an average Sharpe ratio increase of 32.5% across our six empirical datasets. We find that our constrained minimum-variance strategy is the only strategy that achieves the goal of improving the Sharpe ratio of 1/N consistently and significantly. At the same time, our developed portfolio strategy achieves a comparatively low turnover and exhibits no excessive short interest.

We investigate the implications of providing loan officers with a nonlinear compensation structure that rewards loan volume and penalizes poor performance. Using a unique data set provided by a large international commercial bank, we examine the main activities that loan officers perform: loan prospecting, screening, and monitoring. We find that when loan officers are at risk of losing their bonuses, they increase prospecting and monitoring. We further show that loan officers adjust their behavior more toward the end of the month when bonus payments are approaching. These effects are more pronounced for loan officers with longer tenures at the bank.