Research

Published Papers

Distinguishing the Effect of Overconfidence from Rational Best-Response on Information Aggregation

Review of Financial Studies, 2009

In this paper I study -- theoretically and in a laboratory setting -- a new bilateral game in which subjects estimate the value of an asset and have incentives to learn from each other. By inducing subjects to overconfidence is some treatments, I find that overconfidence has a causal effect on the dispersion and convergence of these estimates. Interestingly, I show that even when subjects are not overconfident, qualitatively results may be observed due to subjects’ strategic response to each other’s errors. 

Predicting Risk from Financial Reports with Regression

Proceedings of the North American Association for Computational Linguistics Human Language Technologies Conference, Boulder, CO, May/June 2009

with Dimitry Levin, Bryan Routledge, Jacob Sagi, and Noah Smith

In this paper, we take a novel approach to predicting long-horizon firm volatility. Instead of relying on statistical properties of returns, we extract the information relevant for firm volatility from the Management Discussion and Analysis (“MD&A”) section in annual reports by applying machine learning to the text. We find that a model that uses both past volatility and text data significantly outermost out-of-sample standard benchmarks.  

Securities Auctions under Moral Hazard: An Experimental Study

Review of Finance, 2009

with John Morgan

We study, both theoretically and in the lab, the performance of debt and equity auctions in the presence of both private information and hidden effort. We show that the revenues to sellers in debt and equity auctions differ systematically depending on the returns to entrepreneurial effort. Using a controlled laboratory experiments we test the model’s predictions and find strong support for the theory. 

Coordination in the Presence of Asset Markets

American Economic Review, 2011

with Anthony Kwasnica and Roberto Weber

In this paper, we explore experimentally the influence of two-sided futures asset markets on behavior and outcomes in an economic activity characterized by multiplicity of equilibria (a coordination game with Pareto-ranked equilibria). Across several experiments, we find that the presence of an asset market has a negative effect on output (efficiency) and that this cannot be explained entirely by the distorted payoffs of a portfolio incentive effect. 

Investor Inattention and the Market Impact of Summary Statistics

Management Science, Special Issue on Behavioral Economics and Finance, 2012

with Thomas Gilbert, Lars Lochstoer, and Ataman Ozyildirim

We show that U.S. stock and Treasury futures prices respond sharply to recurring stale information releases. In particular, we identify a unique macroeconomic series — the U.S. Leading Economic Index (LEI) — which is released monthly and constructed as a summary statistic of previously released inputs. We show that a front-running strategy that trades S&P 500 futures in the direction of the announcement a day before its release and then trades in the opposite direction of the announcement following its release generates an average annual return of close to 8%. 

Trading Complex Assets

Journal of Finance, 2013

with Bruce Ian Carlin and Richard Lowery

We perform an experimental study to assess the effect of complexity on asset trading. We find that higher complexity leads to increased price volatility, lower liquidity, and decreased trade efficiency especially when repeated bargaining takes place. This is caused by complexity altering the bidding strategies used by traders, making them less inclined to trade, even when we control for estimation error across treatments. As such, it appears that adverse selection plays an important role in explaining the trading abnormalities caused by complexity. 

Business Microloans for U.S. Subprime Borrowers

Journal of Financial and Quantitative Analysis, 2016

with Cesare Fracassi, Mark J. Garmaise, and Gabriel Natividad

We show that business microloans to U.S. subprime borrowers have a very large impact on subsequent firm success. Using data on startup loan applicants from a lender that employed an automated algorithm in its application review, we implement a regression discontinuity design assessing the causal impact of receiving a loan on firms. Startups receiving funding are dramatically more likely to survive, enjoy higher revenues, and create more jobs

Is Investor Rationality Time Varying? Evidence from the Mutual Fund Industry

Behavioral Finance: Where do Investors Biases Come From?, Itzhak Venezia [ed.], World Scientific Publishing Co., 2016

with Vincent Glode, Burton Hollifield, and Marcin Kacperczyk

We provide novel evidence that mutual fund returns are predictable after periods of high market returns but not after periods of low market returns. Performance predictability is more pronounced for funds catering to retail investors than for funds catering to institutional investors, suggesting that unsophisticated investors make systematic mistakes in their capital allocation decisions. 

Information, Trading, and Volatility: Evidence from Firm-Specific News

Review of Financial Studies, Volume 32, Issue 3, 1 March 2019

with Jacob Boudoukh, Ronen Feldman, and Matthew Richardson

What moves stock prices? Prior literature concludes that the revelation of private information through trading, and not public news, is the primary driver. We revisit the question by using textual analysis to identify fundamental information in news. We find that this information accounts for 49.6% of overnight idiosyncratic volatility (vs. 12.4% during trading hours), with a considerable fraction due to days with multiple news types. As applications, we use our measure of public information arrival to reinvestigate two important contributions in the literature related to individual R2s of stock returns on aggregate factors, namely Roll (1988) and Morck, Yeung and Yu (2000).

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Working Papers

Fake News: Evidence from Financial Markets

with Tobias Moskowitz and Marina Niessner

Using a unique dataset of fake stock promotion articles prosecuted by the Securities and Exchange Commission, we examine the impact of fake news. In addition, we use a linguistic algorithm to detect deception in expression for a much larger set of news content using the fake articles as a training sample. We find increased trading activity and temporary price impact from fake news about small firms, but no impact for large firms. Using the SEC investigation as a shock to investor awareness of fake news, we find a marked decrease in reaction to news, particularly content deemed less authentic, but also legitimate news. These findings, including the indirect spillover effects on other news, are most pronounced for small firms with high retail ownership and for the most circulated articles. Understanding the motivation behind the fake articles, we find that small firms engage in corporate actions and insider trading designed to profit from the fake articles, consistent with concerns of coordinated stock price manipulation. No such patterns are observed for large firms. The setting offers a unique opportunity to quantify the direct and indirect impact of fake news.

Aggregate Sentiment and Investment: An Experimental Study

with Donja Darai, Anthony Kwasnica, and Roberto Weber

Sentiment indices, such as measures of consumer confidence, are often discussed as potential indicators of future investment, consumption and growth. However, documenting a causal relationship between consumer confidence and output using field data has been challenging. We rely on the high degree of control afforded by laboratory environments to experimentally test a simple model of investment with complementarities and time-varying fundamentals. Our experiment manipulates the presence of aggregate confidence measures to test both how they reflect available information and how they influence future output. We find that an aggregate sentiment measure can be as effective as a highly precise exogenous public signal in coordinating behavior on more efficient equilibria. Furthermore, our analysis indicates that the confidence measure also impacts expectations by influencing beliefs about aggregate investment.

Information Content of Public Firm Disclosures and the Sarbanes-Oxley Act

with Bryan Routledge, Jacob Sagi, and Noah Smith

The paper develops a novel measure of annual reports’ informativeness that is based on a text regression. We used the text regression to predict, out of sample, firm return volatility. Using the relative performance of the text model as a proxy for the informativeness of reports, we show that the MD&A sections are significantly more informative after the passage of SOX. We further show that this additional information is associated with a reduction in share illiquidity, suggesting that the information divulged was new to investors. 

Information Environment and the Geography of Firms and Investors

with Gennaro Bernile and Johan Sulaeman

We develop a model linking stock ownership and returns to the distribution of private information and quality of public information. Supporting the model, we find that the firm’s information environment affects investors’ propensity to hold and trade its stocks, but its effects hinge on investors’ access to private information. Nearby investors with potential access decrease their holdings when private information becomes more dispersed and public information quality improves, whereas distant investors display opposite patterns. 

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Work in Progress

Collective Self Deception

with Florian Schneider and Roberto Weber

We run a set of experiments that involve group tasks to understand the difference between self-deception of individuals relative to that of groups.

Text Betas

With Jacob Sagi

We apply machine learning to the text contained in annual reports to develop an-ante measure of firm-level CAPM betas.

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© 2017 by Dr. Shimon Kogan

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