Angie Andrikogiannopoulou

Research

We use novel data on individual activity in a sports betting market to study the effect of past performance sequences on individual behavior in a real market. The revelation of fundamental values in this market enables us to disentangle whether behavior is caused by sentiment or by superior information about market mispricings, hence to cleanly test in a real setting two sentiment-based theories of momentum and reversals — the regime-shifting model of Barberis, Shleifer, and Vishny (1998) and the gambler’s/hot-hand fallacy model of Rabin (2002). Furthermore, our long panel allows us to calculate the proportions of individuals who exhibit each type of behavior. We find that i) three quarters of individuals exhibit trend-chasing behavior; ii) seven times as many individuals exhibit behavior consistent with Barberis, Shleifer, and Vishny (1998) as exhibit behavior consistent with Rabin ( 2002); and iii) no individuals earn superior returns from momentum trading.

Barras, Scaillet, Wermers (2010) propose the False Discovery Rate to separate skill (alpha) from luck in fund performance. Using simulations with parameters informed by the data, we find that this methodology is overly conservative and underestimates the proportion of nonzero-alpha funds. E.g., 65% of funds with economically large alphas of ±2% are misclassified as zero-alpha. This bias arises from the low signal-to-noise ratio in fund returns and the consequent low statistical power. Our results raise concerns regarding the FDR’s applicability in performance evaluation and other domains with low power, and can materially change its conclusion that most funds have zero alpha.

Online Appendix

Using trading data from a sports-wagering market, we estimate individual dynamic risk preferences within the prospect-theory paradigm. This market’s experimental-like features greatly facilitate preference estimation, while our long panel enables us to study whether preferences vary across individuals and depend on earlier outcomes. Our estimates extend support for existing experimental findings — mild utility curvature, moderate loss aversion, probability overweighting of extreme outcomes — to a real financial market, but also reveal that risk attitude is heterogeneous and history-dependent. Applying our estimates to a portfolio-choice problem, we show prospect theory can better explain the prevalence of the disposition effect than previously thought.

Online Appendix

Estimating Mutual Fund Skill: A New Approach

(with Filippos Papakonstantinou)

Revise & Resubmit at the Review of Finance

We propose a novel methodology that jointly estimates the proportions of skilled/unskilled funds and the cross-sectional distribution of skill in the mutual fund industry. We model this distribution as a three-component mixture of a point mass at zero and two components — one negative, one positive — that we estimate semi-parametrically. This generalizes previous approaches and enables information-sharing across funds in a data-driven manner. We find that the skill distribution is non-normal (asymmetric and fat-tailed). Furthermore, while the majority of funds have negative alpha, a substantial 13% generate positive alpha. Our approach improves out-of-sample portfolio performance and significantly alters asset allocation decisions.

Online Appendix

We develop a structural model of behavior that accounts for rich heterogeneity in individuals' risk preferences, and we estimate it using a panel dataset of individual activity in a sports wagering market. The number and variety of lottery choices we observe enables us to distinguish the key features of prospect theory — value-function curvature, loss aversion, and probability weighting. Our Bayesian hierarchical mixture model enables us to estimate the proportion of distinct utility "types" characterized by the different prospect theory features, and thus to evaluate their relative prevalence in the population. We find that utility curvature alone does not explain the choices we observe and that, while loss aversion is important, probability weighting is the most prevalent behavioral feature of risk attitudes: Two-thirds of individuals exhibit loss aversion, but all exhibit probability weighting.

Online Appendix