This paper proposes that computational complexity generates noise. The same asset is often held for completely different reasons by many funds following a wide variety of threshold-based trading rules. Under these conditions, we show it can be computationally infeasible to predict how these various trading rules will interact with one another, turning the net demand from these funds into unpredictable noise. This noise-generating mechanism can operate in a wide range of markets and also predicts how noise volatility will vary across assets. We confirm this prediction empirically using data on exchange-traded funds. (JEL G00, G02, G14). Received May 28 2019; editorial decision December 16 2020 by Editor Thierry Foucault.

The number of short campaigns by hedge funds has dramatically increased over the last two decades. Nearly 80% of campaigns are undertaken by activist hedge funds, particularly those that employ hostile tactics in their long campaigns. Short campaigns are associated with negative abnormal returns of –7%, with aggregate valuation effects similar in magnitude to the gains from long activism campaigns. In contrast to long campaigns, public communication is a critical component of short campaigns. We do not find evidence that such communication is manipulative. Overall, our analysis highlights the importance of short campaigns for understanding the economic impact of activist hedge funds.

Using administrative data on credit profiles matched with unemployment insurance (UI) for individuals in the U.S., we show that laid-off workers with access to Uber rely less on household debt, experience fewer delinquencies, and are less likely to apply for UI benefits. Our empirical strategy exploits both the staggered market entry of Uber across cities and the differential benefit of its entry across car owners based on car age, a key eligibility requirement of the platform. We conclude that the introduction of Uber reduced reliance on these alternative means of smoothing extreme income shocks.

This paper shows that proxy contests have a significant adverse effect on careers of incumbent directors. Following a proxy contest, directors experience a significant decline in the number of directorships not only in the targeted company, but also in other nontargeted companies. The results are established using the universe of all proxy contests during 1996–2010. To isolate the effect of the proxy contest, our empirical strategy uses within-firm variation in directors׳ exposure to the possibility of being voted out and exploits the predetermined schedule of staggered boards that allows only a fraction of directors to be nominated for election every year. We find that nominated directors relative to non-nominated ones lose 58% more seats on other boards. The evidence suggests the proxy-contest mechanism imposes a significant career cost on incumbent directors.

When a proxy contest is looming, the rate at which CEOs exercise options to sell (hold) the resulting shares slows down by 80% (accelerates by 60%), consistent with their desire to maintain or strengthen voting rights when facing challenges. Such deviations are closely aligned with features unique to proxy contests, such as the record dates and nomination status, and are more pronounced when the private benefits are higher or when the voting rights are more crucial. The distortions suggest that incumbents value their stocks higher than the market price when voting rights are valuable for defending control.

Using high-frequency disagreement data from the investor social network StockTwits, we find that greater unsophisticated disagreement facilitates informed buying and selling. During periods of overvaluation, the facilitating effect of disagreement on trading is dampened for informed buyers but is amplified for informed sellers. These findings are unexplained by sentiment, news, and retail order flow, and they remain when we measure disagreement overnight and disagreement of technical investors, which alleviates the concern that disagreement and informed trading respond to a common shock. These findings suggest that informed traders respond meaningfully but differently to valuation changes induced by unsophisticated disagreement.

We employ a regression discontinuity design to identify the real effects of share repurchases on other firm outcomes. The probability of share repurchases that increase earnings per share (EPS) is sharply higher for firms that would have just missed the EPS forecast in the absence of the repurchase, when compared with firms that “just beat” the EPS forecast. We use this discontinuity to show that EPS-motivated repurchases are associated with reductions in employment and investment, and a decrease in cash holdings. Our evidence suggests that managers are willing to trade off investments and employment for stock repurchases that allow them to meet analyst EPS forecasts.

Using a hand-collected sample of election nominations for more than 30, 000 directors over the period 2001–2010, we construct a novel measure of director proximity to elections called Years-to-election. We find that the closer directors of a board are to their next elections, the higher CEO turnover-performance sensitivity is. A series of tests, including one that exploits variation in Years-to-election that comes from other boards, supports a causal interpretation. Further analyses show that other governance mechanisms do not drive the relation between board Years-to-election and CEO turnover-performance sensitivity. We conclude that director elections have important implications for corporate governance. Received March 10, 2016; editorial decision May 19, 2017 by Editor Itay Goldstein.

This paper investigates the effects of college tuition on student debt and human capital accumulation. We exploit data from a random sample of undergraduate students in the United States and implement a research design that instruments for realized tuition with relatively large changes to the advertised tuition of students who enrolled at the same school in different cohorts. We find that $5,000 in higher tuition causally reduces the probability of graduating with a graduate degree by 3.1 percentage points and increases student debt by $1,480. Higher tuition also leads to a decline in mortgage balances and an increase in credit card delinquencies.

We train a machine learning method on a class of informed trades to develop a new measure of informed trading, informed trading intensity (ITI). ITI increases before earnings, mergers and acquisitions, and news announcements, and has implications for return reversal and asset pricing. ITI is effective because it captures nonlinearities and interactions between informed trading, volume, and volatility. This data‐driven approach can shed light on the economics of informed trading, including impatient informed trading, commonality in informed trading, and models of informed trading. Overall, learning from informed trading data can generate an effective informed trading measure.