Knowledge that Transforms

We extend the Fama–MacBeth regression framework for cross-sectional return prediction to incorporate big data and machine learning. Our extension involves a three-step procedure for generating return forecasts based on Fama–MacBeth regressions with regularization and predictor selection as well as forecast combination and encompassing. As a by-product, it provides estimates of characteristic payoffs. We also develop three performance measures for assessing cross-sectional return forecasts, including a generalization of the popular time-series out-of-sample R2 statistic to the cross section. Applying our extension to over 200 firm characteristics, our cross-sectional return forecasts significantly improve out-of-sample predictive accuracy and provide substantial economic value to investors. Overall, our results suggest that a relatively large number of characteristics matter for determining cross-sectional expected returns. Our new method is straightforward to implement and interpret, and it performs well in our application.

I test the hypothesis that the use of leverage by market speculators can increase the likelihood and magnitude of crashes in asset prices. Using a direct leverage measure derived from US public filings, I find that (1) stocks held by highly leveraged hedge funds subsequently have more negatively skewed returns than stocks held by less leveraged funds and (2) upon extremely negative earnings surprises or funding liquidity shocks, stocks owned by high-leverage funds exhibit abnormal price declines and subsequent reversal. Consistent with the fire sale and contagion hypothesis, high-leverage funds tend to reduce the position following negative news about a stock, and such selling can extend to other stocks in the portfolio, increasing the crash proneness of the stocks they hold.

While it is established that idiosyncratic volatility is negatively priced in the cross-section of stock returns, the relation between idiosyncratic volatility and hedge fund returns is largely unexplored. We document that hedge funds with high idiosyncratic volatility earn higher future risk-adjusted returns of 6 percent p.a. than hedge funds with low idiosyncratic volatility. The outperformance arises because hedge funds trade high idiosyncratic volatility stocks wisely. They pick high volatility stocks when they are underpriced and short-sell high volatility stocks when they are overpriced. Our results support the notion that hedge funds’ idiosyncratic volatility is a measure of managerial skill.

We analyze the impact of $40 billion of corporate subsidies given by US local governments on their borrowing costs. We find that winning counties experience a 15.2 basis points (bps) increase in bond yield spread as compared to the losing counties. The increase in yields is higher (18–26 bps) when the subsidy deal is associated with a lower jobs multiplier or when the winning county has a lower debt capacity. However, a high jobs multiplier does not seem to alleviate the debt capacity constraints of local governments. Our results highlight the potential costs of corporate subsidies for local governments.

We investigate the propagation of environmental performance among competitors and in customer–supplier relationships. We find a significant causal effect among competitors, while the propagation from customers to suppliers and vice versa appears insignificant or does not survive identification tests. The effect is stronger among firms in highly concentrated competitor networks and toward firms with less market and bargaining power than their competitors. We also find significantly stronger propagation of environmental performance among competitors engaged in joint research and development activity. These results show that the propagation stems from both competitive pressure and technological spillover. Importantly, we find that propagation is strong when the competitor improves its environmental performance and when the firm’s own environmental performance is poor initially, alleviating concerns that improvements in performance are concentrated among firms, which are already green. Overall, network effects among competing firms are a significant force shaping environmental performance, and a force mostly for good.

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.

A blockchain replaces central counterparties with time-consuming consensus protocols to record the transfer of ownership. This settlement latency slows cross-exchange trading, exposing arbitrageurs to price risk. Off-chain settlement, instead, exposes arbitrageurs to costly default risk. We show with Bitcoin network and order book data that cross-exchange price differences coincide with periods of high settlement latency, asset flows chase arbitrage opportunities, and price differences across exchanges with low default risk are smaller. Blockchain-based trading thus faces a dilemma: Reliable consensus protocols require time-consuming settlement latency, leading to arbitrage limits. Circumventing such arbitrage costs is possible only by reinstalling trusted intermediation, which mitigates default risk.

This article introduces a new measure of a firm’s negative impact on biodiversity, the corporate biodiversity footprint (CBF), and studies whether it is priced in an international sample of stocks. On average, the CBF does not explain the cross-section of returns between 2019 and 2022. However, a biodiversity footprint premium (higher returns for firms with larger footprints) began emerging in October 2021 after the Kunming Declaration, which capped the first part of the UN Biodiversity Conference (COP15). Consistent with this finding, stocks with large footprints lost value in the days after the Kunming Declaration. The launch of the Taskforce on Nature-related Financial Disclosures (TNFD) in June 2021 had a similar effect. These results indicate that investors have started to require a risk premium upon the prospect of, and uncertainty about, future regulation or litigation to preserve biodiversity.

We examine whether time-varying investor demand affects hedge fund companies’ decision to start new funds. We find significantly more fund inceptions in hot markets than in cold markets. Funds opened in hot markets exhibit weaker long-term performance, shorter survival time, and greater fraud risk. Investor clientele also varies with market conditions. Investors in hot markets appear to be less sophisticated, which may provide opportunities for more low-quality funds to enter the industry. Overall, inceptions due to high investor demand are not in the best interest of investors.

Climate change poses new challenges for portfolio management. In our not-yet-low carbon world, investors face a trade-off between minimizing their exposure to climate risks and maximizing the benefits of portfolio diversification. This article investigates how investors and financial intermediaries navigate this trade-off. After the release of Morningstar’s novel carbon risk metrics in April 2018, mutual funds labeled as “low carbon” experienced a significant increase in investor demand, especially those with high risk-adjusted returns. Fund managers actively reduced their exposure to firms with high carbon risk scores, especially stocks with returns that correlated more with the funds’ portfolios and were thus less useful for diversification. These findings shed light on whether and how climate-related information can re-orient capital flows in a low carbon direction.