ABSTRACT Using the relationship between institutional trades and sequential public information, this study provides a systematic way to identify institutional trades that are informative about future equity returns. By studying the US financial institutions from 1994 to 2016, I show that institutional trades initiated by managers responding proactively to upcoming informational signals strongly predict future stock returns. The predictability of informed institutions is more evident for stocks with higher information asymmetry and in periods of higher profit opportunities. The informed institutions outperform the uninformed ones by 2% on an annualized basis and their performance gap is persistent. Importantly, the return predictability of informed institutional trades is not subsumed by the return‐predictive signals documented in prior research, computed either from institutional holdings or from financial statements. Further analyses show that the informed institutional investors derive their superior ability to forecast future stock returns from processing corporate fundamentals and acquiring private information. This study derives a novel return predictor using the institutions' proactive trading behavior and identifies various informational sources of informed traders.

We analyze fund managers’ reputation concerns in an equilibrium model, in which we tie together a number of seemingly unrelated phenomena. The model shows that because of reputation concerns, hedge fund managers, especially those with an average reputation, prefer strategies with negatively skewed return distributions. One subtle consequence of this preference is that capital sometimes appears slow moving, leaving profitable investment opportunities unexploited, yet other times appears fast moving, causing large capital relocation and price fluctuations in the absence of fundamental news. More broadly, the analysis demonstrates a limitation of market discipline: fund managers may distort their investments precisely because of market discipline.

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 develop a novel term-structure model for pricing GDP-linked bonds, hypothetical securities with cash-flows indexed to the level of U.S. GDP. For this purpose, we rely on a term-structure model of equity yields estimated using the prices of dividend swaps, which we assume span GDP growth. Our approach provides a novel way of estimating the relative cost of conventional and GDP-linked bonds, as well as measuring more general market-based expectations of (and risks around) GDP growth. Our model predicts that U.S. GDP-linked bonds would typically have yields lower than those on conventional Treasury bonds with the same maturity in our sample from 2010 to 2017. Positive expected future GDP growth lowers the yield on GDP-linked bonds relative to conventional bonds, which typically more than offsets the estimated GDP risk premium demanded by investors for holding GDP risk.

Identifying the factors that drive the cross-section of expected returns is challenging for at least three reasons. First, the choice of testing approach (time series versus cross-sectional) will deliver different sets of factors. Second, varying test portfolio sorts changes the importance of candidate factors. Finally, given the hundreds of factors that have been proposed, test multiplicity must be dealt with. We propose a new method that makes measured progress in addressing these key challenges. We apply our method in a panel regression setting and shed some light on the puzzling empirical result that the market factor drives the bulk of the variance of stock returns, but is often knocked out in cross-sectional tests. In our setup, the market factor is not eliminated. Further, we bypass arbitrary portfolio sorts and instead execute our tests on individual stocks with no loss in power. Finally, our bootstrap implementation, which allows us to impose the null hypothesis of no cross-sectional explanatory power, naturally controls for the multiple testing problem.

The constant maturity zero-coupon yield curve for the US Treasuries is one of the most studied datasets. We construct a new yield curve using a non-parametric kernel-smoothing method with a novel adaptive bandwidth specifically designed to fit the Treasury yields. Our curve is globally smooth while still capturing important local variation. Economically, we show that applying our data leads to different conclusions from using the leading alternative data of Gürkaynak et al. (2007) (GSW) when we repeat two popular studies of Cochrane and Piazzesi (2005) and Giglio and Kelly (2018). Statistically, we show our dataset preserves information in the raw data and has much smaller pricing errors than GSW. Our new yield curve is maintained and updated online, complemented by bandwidths that summarize information content in the raw data.

We conjecture that high stock liquidity negatively affects firm valuation by inducing inefficient investment. Using takeovers of public targets to study the empire-building motive, we find that a liquid firm is more likely than an illiquid firm to acquire a public firm. Such a takeover by a bidder with higher stock liquidity destroys bidder value to a larger degree. These patterns occur in both stock and cash acquisitions and hold after we use decimalization of tick size as a quasi-exogenous shock to stock liquidity. Finally, we show that financial constraints and corporate governance play important roles in the effects of stock liquidity on empire building in mergers and acquisitions.

Recent studies find mixed evidence on the performance of volatility-managed portfolios. We show that strategies scaled by downside volatility exhibit significantly better performance than strategies scaled by total volatility. The improved performance is evident in spanning regressions, direct Sharpe-ratio comparisons, and real-time trading strategies. A decomposition analysis indicates that the enhanced performance of downside volatility-managed portfolios is primarily due to return timing, i.e., downside volatility negatively predicts future returns. We find that employing fixed-weight strategies significantly improves the performance of volatility-managed portfolios for real-time investors. Our results hold for nine equity factors and a broad sample of 94 anomaly portfolios.

We study whether hedge funds make charitable donations to further their business interests. We find that donations are driven by poor fund flows and performance. Post-donation, donor funds experience lower outflows compared to matched non-donors. One-off donations and donations to charities which hold fundraising events catering to the hedge fund community are more likely to mitigate outflows after poor performance. These findings are consistent with strategic motivations driving at least some donations. While the economics of donations initially appear quite favorable to the hedge funds, the benefits from donations are not scalable. Moreover, investors punish donors through greater redemptions if poor performance persists post-donation.

Using a unique database of Chinese firms bribing initial public offering (IPO) regulators, we examine the impact of bribing on IPO pricing. Our findings suggest that bribing firms are younger, smaller, more volatile in their operating activities, and more generous in compensating underwriters and management. Most important, bribing firms price their IPO shares more aggressively than non-bribing firms and exhibit a higher price-to-earnings ratio, lower first-day return, and poorer post-IPO stock performance. Additional analyses suggest that both bribing and non-bribing firms exhibit negative announcement returns after the arrest of corrupt officials. However, the effect is stronger for bribing firms. Overall, bribing firms are systematically more aggressive than their non-bribing counterparts. They concede less to IPO investors and reward underwriters and management for helping them access the capital market.