This article examines whether and to what extent private firms learn from the stock market. Using a large panel data set for the UK, I find that private firms’ investment responds positively to the valuation of public firms in the same industry. The sensitivity increases with price informativeness. To further pin down the information channel, I construct a price noise measure based on public firms’ unrelated minor segments and show that it positively affects the investment of private firms in the major-segment industry. The results are consistent with models featuring learning from noisy signals and are not driven by alternative channels in the absence of learning. My findings suggest that the stock market can have real effects on private firms through an information-spillover channel, even when these firms do not list their shares on the stock exchanges.

How do banks contribute to the green economy? Using a unique dataset detailing firms' revenue exposure to green business activities, we present new evidence that firms generating revenue from green products and services are associated with lower syndicated loan spreads. We find that the green revenue effects on loan spreads are attributable to firms' prospects tied to climate change-related opportunities and banks' environmental orientation. Foreign banks subject to mandatory environmental, social, and governance (ESG) disclosure regulations reduce the loan spreads to green revenue firms. We also find suggestive evidence that firms with green revenue tend to file more green patents following loan origination. While banks typically perceive green innovation as riskier and demand higher loan spreads, this effect is offset if a firm also generates green revenue. Collectively, our results highlight the pivotal role that banks play in channeling financial resources toward green business practices.

We examine the impact of the entry of new mutual funds on incumbents using the overlap in their portfolio holdings as a measure of competitive intensity. This simple metric delivers powerful economic results. Incumbents that have a high overlap with entrants subsequently engage in price competition by reducing management fees. Distribution fees, however, rise so that investors do not benefit as much from price competition. Funds with high overlap also experience quantity competition through lower investor flows, have lower alphas, and higher attrition rates. These effects only appear after the late 1990s, at which point there appears to be an endogenous structural shift in the competitive environment. We conclude that the mutual fund market has evolved into one that displays the hallmark features of a competitive market.

While Kosowski et al. (2006, Journal of Finance 61, 2551–2595) and Fama and French (2010, Journal of Finance 65, 1915–1947) both evaluate whether mutual funds outperform, their conclusions are very different. We reconcile their findings. We show that the Fama‐French method suffers from an undersampling problem that leads to a failure to reject the null hypothesis of zero alpha, even when some funds generate economically large risk‐adjusted returns. In contrast, Kosowski et al. substantially overreject the null hypothesis, even when all funds have a zero alpha. We present a novel bootstrapping approach that should be useful to future researchers choosing between the two approaches.

About 37% of Chinese listed firms have medical expertise, as measured by the existence of senior executives with a medical degree or medical-industry experience. Using the COVID-19 outbreak in China as a natural experiment, we find that the stock returns of firms with medical expertise, excluding those within the healthcare and pharmaceutical industry, are significantly higher than those without. The positive impact is more pronounced if a CEO or Chairman has medical expertise and if the firm is not state-owned. Overall, this study underlines the importance of diversified executive human capital on firm performance through disentangling macro shocks.

We study an information sale problem in which a monopolist proxy advisor sells recommendations to a firm’s shareholders for corporate voting. We find that even an unconflicted proxy advisor skews its recommendations based on its clients’ beliefs or preferences. A novel bias-quantity relationship affects firm value. Under some parameters, shareholders’ biased beliefs or preferences can lead shareholders to make more information purchases, which enhances their collective decision-making. Thus, firm value may increase despite the negative effects of biased proxy voting recommendations. JEL D82, G34, L15 Received: April 16, 2019; editorial decision March 25, 2020 by Editor Uday Rajan.

We study competitive effects of foreign listings on U.S. stock exchanges over a 50-year period and show that U.S. rival firms respond strongly negatively (weakly positively) to foreign listings (delistings). The performance decline of U.S. firms is related to the competitive advantages that foreign firms receive from their cross-listings, such as stronger financial benefits, higher growth prospects, and better visibility, rather than market or industry valuation timing or existing market competition. This decline is especially pronounced when cross-listings come from proximate or developed markets. Our findings highlight an important role of international markets in influencing the performance of U.S. firms. Received February 19, 2019; editorial decision September 18, 2019 by Editor Isil Erel.

In portfolio risk minimization, the inverse covariance matrix prescribes the hedge trades in which a stock is hedged by all the other stocks in the portfolio. In practice with finite samples, however, multicollinearity makes the hedge trades too unstable and unreliable. By shrinking trade sizes and reducing the number of stocks in each hedge trade, we propose a “sparse” estimator of the inverse covariance matrix. Comparing favorably with other methods (equal weighting, shrunk covariance matrix, industry factor model, nonnegativity constraints), a portfolio formed on the proposed estimator achieves significant out-of-sample risk reduction and improves certainty equivalent returns after transaction costs.