Paul C. Tetlock

Retail Short Selling and Stock Prices

Using proprietary data on millions of trades by retail investors, we provide the first large-scale evidence that retail short selling predicts negative stock returns. A portfolio that mimics weekly retail shorting earns an annualized risk-adjusted return of 9%. The predictive ability of retail short selling lasts for one year and is not subsumed by institutional short selling. In contrast to institutional shorting, retail shorting best predicts returns in small stocks and those that are heavily bought by other retail investors. Our findings are consistent with retail short sellers having unique insights into the retail investor community and small firms’ fundamentals.

Optimal Liquidity Provision for Decision Makers

Although prices in financial markets play an important role in improving allocative efficiency in the real economy, few models of securities markets explicitly incorporate resource allocation decisions. In this paper, we study the equilibrium in a securities market when the market price provides valuable information that can improve allocative efficiency. We show that a decision maker will subsidize liquidity in an illiquid securities market to gather valuable information about her decision payoffs. We also show that a decision makers liquidity subsidy improves expected social welfare by enhancing allocative efficiency, but does not induce the socially optimal level of information acquisition. Finally, we demonstrate that the mere act of linking the allocation decision to the market price will typically enhance liquidity in the securities market. Overall, our results highlight the potential of using securities markets for information to improve public and private decisions.

Designing Information Markets for Decision Making

People often make important decisions based on information elicited from experts with uncertain preferences. We provide a theoretical rationale for the use of information markets in decision making tasks. Specifically, we show that markets for claims on decision-relevant variables can be efficient incentive schemes for eliciting information. Our model shows decision makers will subsidize liquidity in illiquid decision markets to gather valuable information. Our model also shows that the mere act of linking the decision to the market price will typically enhance liquidity in the market. Overall, our results highlight the potential for using information markets in diverse decision making tasks.

Why Do Investors Trade

We estimate a structural model of strategic trader behavior that sheds light on the determinants of trading volume and stock returns. Our novel identification approach exploits enormous empirical variation in trading and volatility associated with the time of day and public news arrival. Over 95% of trading occurs during regular market hours (9:30am to 4pm), even though prices exhibit considerable volatility during extended hours, especially when news arrives. For the model to explain the data, discretionary liquidity trading must constitute the bulk of trading volume and must increase significantly after news arrives. However, from 2001 to 2010, informed trading increasingly contributes to volume and stock price discovery because our estimate of the cost of acquiring private information falls by a factor of 12 in this decade.We propose and estimate a structural model of daily stock market activity to test competing theories of trading volume. The model features informed rational speculators and uninformed agents who trade either to hedge endowment shocks or to speculate on perceived information. To identify the model parameters, we exploit enormous empirical variation in trading volume, market liquidity, and return volatility associated with regular and extended-hours markets as well as news arrival. We find that the model matches market activity well when we allow for overconfidence. At plausible values of overconfidence and risk aversion, overconfidence--not hedging--explains nearly all uninformed trading, while rational informed speculation accounts for most overall trading. Without overconfident investors, over 99% of trading volume disappears even when informed rational traders disagree maximally. These findings illustrate that modest overconfidence can help explain stark patterns in stock market activity.

Harnessing the Power of Information: A New Approach to Economic Development

This paper offers a new approach to economic development, which we call performance-based policy. The basic idea is to get better information to implement better development decisions. The approach combines the use of information markets with payments for performance. An information market is a market for a contract that yields a payment based on the outcome of an uncertain future event, such as the number of people infected by HIV in Africa in 2010. We show how these markets can provide real-time information on the likely benefits and costs of different development projects. We argue that information markets combined with pay-for-performance contracts have the potential to revolutionize the way aid agencies, foundations, non-governmental organizations, and the private sector promote economic development. In addition to providing economic benefits, performance-based policy could lead to greater accountability and transparency in economic development. Despite its great potential, the approach has some limitations, particularly in information markets with little trading activity.

Chapter 18 - The Role of Media in Finance

Abstract This chapter reviews and synthesizes a rapidly growing subfield that analyzes the relation between media and financial markets. Research in this area identifies novel data sources, such as newspaper articles, Internet search queries, and posts on social networks, and employs inventive empirical methods, notably textual analysis, to quantify the rich information environment in modern financial markets. Such data and methods enable powerful tests of theories and have the potential to address longstanding puzzles in finance, such as why trading volume and stock price volatility are so high.