Transaction costs have a first-order effect on the performance of currency portfolios. Proportional costs based on quoted bid–ask spread are relatively small, but when a fund is large, costs due to the trading volume price impact are sizable and quickly erode returns, leaving many popular strategies unprofitable. A mean–variance-transaction-cost optimized approach (MVTC) that accounts for costs in the optimization efficiently tackles the problem with only relatively minor negative implications on before-cost profitability. MVTC is robust even when the price impact of trading is severe. Finally, we introduce an accurate extrapolation approach to expand the sample of the realized Amihud measure of Ranaldo and Santucci de Magistris (2022) from 12 to 26 currencies and from 2012 back in time to 1986.

We study the stock market performance of firms with Jewish board members during the “Dreyfus Affair” in 19th century France. In a context of widespread latent antisemitism, initial accusations made against the Jewish officer Alfred Dreyfus led to short-lived abnormal negative returns for Jewish-connected firms. However, investors betting on these firms earned higher returns during the period corresponding to Dreyfus' rehabilitation, starting with the publication of the famous op-ed J'Accuse! in 1898. Our conceptual framework illustrates how diminishing antisemitic biases among investors might plausibly explain these effects. Our paper provides novel insights on how antisemitism can increase and decrease over short periods of time at the highest socio-economic levels in response to certain events, which in turn can affect firm value in financial markets.

Theories based on incomplete contracting suggest that small organizations have a comparative advantage in activities that make extensive use of “soft” information. We provide evidence consistent with small banks being better able to collect and act on soft information than large banks. In particular, large banks are less willing to lend to informationally “difficult” credits, such as firms with no financial records. Moreover, after controlling for the endogeneity of bank-firm matching, we find that large banks lend at a greater distance, interact more impersonally with their borrowers, have shorter and less exclusive relationships, and do not alleviate credit constraints as effectively.

Machine-learning methods exploit fund characteristics to select tradable long-only portfolios of mutual funds that earn significant out-of-sample annual alphas of 2.4% net of all costs. The methods unveil interactions in the relation between fund characteristics and future performance. For instance, past performance is a particularly strong predictor of future performance for more active funds. Machine learning identifies managers whose skill is not sufficiently offset by diseconomies of scale, consistent with informational frictions preventing investors from identifying the outperforming funds. Our findings demonstrate that investors can benefit from active management, but only if they have access to sophisticated prediction methods.