We introduce a test to assess mutual funds’ “conditional” performance that is based on updated information and corrects data snooping bias. Our method, named the functional false discovery rate “plus” ($ {\mathrm{fFDR}}^{+} $), incorporates fund characteristics in estimating fund performance free of data snooping bias. Simulations suggest that the $ {\mathrm{fFDR}}^{+} $ controls well the ratio of false discoveries and gains considerable power over prior methods that do not account for extra information. Portfolios of funds selected by the $ {\mathrm{fFDR}}^{+} $ outperform other tests not accounting for information updating, highlighting the importance of evaluating mutual funds from a conditional perspective.

I develop a dynamic model to investigate how labor mobility impacts firms’ decisions. In the model, firms make investment and financing decisions, hire labor with different skill and mobility levels, and set wages through bargaining. The model predicts that, in response to an increase in labor mobility, high-skill firms operate with lower financial leverage, become less responsive to investment opportunities, and invest at lower rates, while low-skill firms remain unaffected. I confirm these predictions in the data using shocks to workers’ mobility across firms. The results are useful in understanding the effects of labor mobility changes driven by government policies or technological shocks, such as the rise of remote work.

We investigate whether chief financial officers (CFOs) serving on U.S. corporate boards benefit shareholders in M&A transactions. We find that acquisitions made by firms with CFOs on boards have significantly better acquirer announcement returns. This is due to the CFO director’s ability to select targets with better strategic and financial fit. CFO board membership can create shareholder value if there are effective governance regimes restraining managerial entrenchment and CFOs’ interests are closely aligned with those of shareholders through equity ownership. Furthermore, sitting on boards enables CFOs to secure more and cheaper financing for their acquisitions.

Corporate bonds’ book-to-market ratios predict returns computed from transaction prices. Senior bonds (even investment grade) with the 20% highest ratios outperform the 20% lowest by 3%–4% annually after non-parametrically controlling for numerous liquidity, default, microstructure, and priced-risk attributes: yield-to-maturity, bid–ask spread, duration/maturity, credit spread/rating, past returns, coupon, size, age, industry, and structural model equity hedges. Spreads for all-bond samples are larger. An efficient bond market would not exhibit the observed decay in the ratio’s predictive efficacy with implementation delays, small yield-to-maturity spreads, or similar-sized spreads across bonds with differing risks. A methodological innovation avoids liquidity filters and censorship that bias returns.

We examine the effects of a severe climate event on local firms. Our data include 8,218 business credit reports and a detailed survey of 273 businesses in the area affected by Hurricane Harvey. Delinquent credit balances doubled in areas with the worst flooding, although nonflooded areas also had significant credit impairments. Only independent businesses showed signs of distress; subsidiaries of larger firms did not. Firms were largely uninsured and often were denied credit postdisaster. Many funded recovery informally, such as through friends and family. Our findings suggest that several financial frictions compound the challenges posed by a severe climate event.

Machine learning-based stock market beta estimators outperform established benchmark models both statistically and economically. Analyzing the predictability of time-varying market betas of U.S. stocks, we document that machine learning-based estimators produce the lowest forecast and hedging errors. They also help to create better market-neutral anomaly strategies and minimum variance portfolios. Among the various techniques, random forests perform the best overall. Model complexity is highly time-varying. Historical stock market betas, turnover, and size are the most important predictors. Compared to linear regressions, allowing for nonlinearity and interactions significantly improves predictive performance.

We develop methods for testing factor models when the weights in portfolios of factors and test assets can vary with lagged information. We derive and evaluate consistent standard errors and finite sample bias adjustments for unconditional maximum squared Sharpe ratios and their differences. Bias adjustment using a second-order approximation performs well. We derive optimal zero-beta rates for models with dynamically trading portfolios. Factor models’ Sharpe ratios are larger but standard test asset portfolios’ maximum Sharpe ratios are larger still when there is dynamic trading. As a result, most of the popular factor models are rejected.

A novel arbitrage-free model of nominal U.S. Treasuries that decomposes yields into frictionless expected rates, frictionless term premiums, and liquidity premiums produces four key results from Jan. 1987 to Aug. 2023. First, liquidity loadings are larger than for the slope and higher-order principal components. Second, the countercyclicality of required nominal Treasury returns owes to liquidity, if anything, not frictionless term premiums. Third, Federal Reserve large-scale asset purchases generally work through expected rates and frictionless term premiums, not liquidity premiums. Fourth, given similar estimates using TIPS, inflation expectations are less moored around the Federal Reserve’s price objectives than other models say.