Dr. Shimon Kogan

Recent developments in machine learning and artificial intelligence have enabled the inference of Big 5 personality traits from a single photograph of an individual. This study evaluates the ability of the novel Photo Big 5 personality measure to forecast labor market outcomes. Using the universe of LinkedIn data for MBA graduates, as well as detailed admissions and matriculation records from a top-tier MBA program, we show that the Photo Big 5 is highly predictive of initial compensation. Compared to older survey-based personality measures, the Photo Big 5 is readily available to all employers and less prone to manipulation, and thus has the potential to be widely adopted in firm hiring and promotions processes.

Momentum is one of the most widespread, persistent, and puzzling phenomenon in asset pricing. The prevailing explanation for momentum is that investors under-react to new information, and thus asset prices tend to drift over time. We use a unique feature of cryptocurrency markets: the fact that they are open 24/7, and report returns over the last 24 hours. Thus, the one-day return is subject to predictable fluctuations based on the removal of lagged information. We show that investors respond positively to changes in reported returns that are unrelated to any new release of information, or change in the asset fundamentals. We call this behavioral anomaly "Pure Momentum".

We show retail investors are highly responsive to changes in trading commission fees. Using a triple-difference research design around the removal of fees for retail investors on the international retail broker platform, eToro, we show investors responded by trading approximately 30% more frequently, in smaller order sizes, and increasing portfolio turnover. Removing fees also spurred retail investors to reallocate their portfolios and diversify. Retail investors’ gross return performance did not significantly change around the fee removal despite trading more often, but retail investors earned significantly higher returns on a net basis after accounting for fees incurred in the pre-period. Finally, using demographic information, we show removing fees disproportionately affected inexperienced investors with lower deposit amounts and lesser technological sophistication both by expanding the extensive margin of investors and changing trading activity for the intensive margin of investors. Together, our results suggest commission fees play an influential role as a speed bump for retail investor participation, trading activity, and diversification.

Despite positive and significant earnings announcement premia, we find that institutional investors reduce their exposure to stocks before earnings announcements. A novel result on the sensitivity of flows to individual stock returns provides a potential explanation. We show that extreme announcement returns for an individual holding lead to substantial outflows, controlling for overall performance, and they increase the probability of managers leaving the fund. Reducing the exposure to these stocks before the announcement mitigates the outflows. We build a model to describe and quantify this tradeoff. Overall, the paper identifies a new dimension of limits to arbitrage for institutions.

Responsible, or ESG-based, investment often exhibits strong biases against "sin" stocks rather than favoring "angel" stocks. We design an experiment to study whether these biases can be micro-founded via individual preferences. By controlling for the magnitude of negative and positive social externalities, we find that negative externalities have thrice the impact on investment choices. The asymmetry is pervasive and heterogeneous. Beyond rationalizing important stylized empirical facts, our findings help direct the growing theoretical literature seeking to model the implications of non-pecuniary investor behavior.

A large body of literature documents the link between textual communication (e.g., news articles, earnings calls) and firm fundamentals, either through pre-defined “sentiment” dictionaries or through machine learning approaches. Surprisingly, little is known about why textual communication matters. In this paper, we take a step in that direction by developing a new methodology to automatically classify statements into objective (“facts”) and subjective (“opinions”) and apply it to transcripts of earnings calls. The large scale estimation suggests several novel results: (1) Facts and opinions are both prominent parts of corporate disclosure, taking up roughly equal parts, (2) higher prevalence of opinions is associated with investor disagreement, (3) anomaly returns are realized around the disclosure of opinions rather than facts, and (4) facts have a much stronger correlation with contemporaneous financial performance but facts and opinions have an equally strong association with financial results for the next quarter.

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.

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. 

We use novel data from a global retail brokerage to study how shocks to uncer- tainty affect personal investment around the world. We consider three empirical uncertainty shocks that have been proposed by the literature — terrorism, natu- ral disasters, and large stock price jumps. We then consider how within-country uncertainty shocks affect investment and delegation in global assets on the broker- age. Importantly, the within-country uncertainty shocks (e.g., a natural disaster in Spain) are unlikely to affect world asset fundamentals (e.g., the SPX500). This allows us to isolate the effects of uncertainty shocks on personal risk aversion from their effects on asset fundamentals. We find that uncertainty shocks have scant effects on personal investing. They primarily affect delegation to asset managers on the brokerage, but that the direction of the effects depends on the type of uncertainty shock. Investors increase their delegation by 5% following terrorist activity and reduce delegation by 8% following positive and negative stock market jumps. These findings suggest that the exogenous uncertainty shocks proposed by the literature have heterogeneous effects on individual investment.