We offer an explanation for the forward premium puzzle in foreign exchange markets based upon investor overconfidence. In the model, overconfident individuals overreact to their information about future inflation, which causes greater overshooting in the forward rate than in the spot rate. Thus, when agents observe a signal of higher future inflation, the consequent rise in the forward premium predicts a subsequent downward correction of the spot rate. The model can explain the magnitude of the forward premium bias and several other stylized facts related to the joint behaviour of forward and spot exchange rates. Our approach is also consistent with the availability of profitable carry trade strategies.

We empirically assess industry tournament incentives for CEOs, as measured by the compensation gap between a CEO at one firm and the highest-paid CEO among similar (industry, size) firms. We find that firm performance, firm risk, and the riskiness of firm investment and financial policies are positively associated with the external industry pay gap. The industry tournament effects are stronger when industry, firm, and executive characteristics indicate high CEO mobility and a higher probability of the aspirant executive winning. (

ABSTRACT This study provides the first large-scale study of the performance of expected-return proxies (ERPs) internationally. Analyst-forecast-based ICCs are sparsely populated and not robustly associated with future returns. Earnings-model-forecast-based ICCs are well-populated, but are unreliable outside the U.S. We adapt and extend the log-linear and present-value (LPV) framework—combining an accounting valuation anchor, its expected growth, and market prices—for estimating ERPs internationally, and implement a correction for the use of stale accounting data. An LPV ERP anchored on the book value of equity is positively associated with future returns in 26 of 29 equity markets, and largely subsumes the predictive ability of a broad set of firm characteristics previously shown to be associated with expected returns. JEL Classifications: D83; G12; G14; M41.

We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. These biases are likely to be relevant for a large portion of research settings in finance, accounting, and law that rely on staggered treatment timing, and can result in Type-I and Type-II errors. We summarize three alternative estimators developed in the econometrics and applied literature for addressing these biases, including their differences and tradeoffs. We apply these estimators to re-examine prior published results and show, in many cases, the alternative causal estimates or inferences differ substantially from prior papers.

Applying a “co-search” algorithm to Internet traffic at the SEC׳s EDGAR website, we develop a novel method for identifying economically related peer firms and for measuring their relative importance. Our results show that firms appearing in chronologically adjacent searches by the same individual (Search-Based Peers or SBPs) are fundamentally similar on multiple dimensions. In direct tests, SBPs dominate GICS6 industry peers in explaining cross-sectional variations in base firms׳ out-of-sample: (a) stock returns, (b) valuation multiples, (c) growth rates, (d) R&D expenditures, (e) leverage, and (f) profitability ratios. We show that SBPs are not constrained by standard industry classification, and are more dynamic, pliable, and concentrated. We also show that co-search intensity captures the degree of similarity between firms. Our results highlight the potential of the collective wisdom of investors — extracted from co-search patterns — in addressing long-standing benchmarking problems in finance.

Abstract We use New York City (NYC) taxi data to identify trips between mutual fund offices and local firm headquarters. NYC funds overweight the stocks of local firms they visit via taxi, and firm visits are associated with superior investment performance. Firm visits are elevated prior to earnings announcements, and mutual fund trades that are associated with firm taxi visits predict earnings surprises. The results are generally stronger when fund and firm executives share educational connections. Additional tests support the conclusion that funds’ local bias and investment performance are driven by portfolio managers’ efforts and ability to actively gather material information.