Publications

Uncovering Sparsity and Heterogeneity in Firm-Level Return Predictability Using Machine Learning
with Theodoros Evgeniou and Rodolfo Prieto   Article Internet Appendix SSRN Version
Journal of Financial and Quantitative Analysis, 2023, 58(8), 3384-3419
Abstract

We develop an approach that combines the estimation of monthly firm-level expected returns with an assignment of firms to (possibly) latent groups, both based on observable characteristics, using machine learning principles with linear models. The best-performing methods are flexible two-stage sparse models that capture group-membership predictive relationships. Portfolios formed to exploit such group-varying predictions based on a parsimonious set of characteristics deliver economically meaningful returns with low turnover. We propose statistical tests based on nonparametric bootstrapping for our results, and detail how different characteristics may matter for different groups of firms, making comparisons to the existing literature.

Working Papers

Soothing Investors: The Impact of Manager Communication on Mutual Fund Flows
Job Market Paper, solo-authored
Presented at NBER Behavioral Finance, FIRS PhD Sessions (awarded Travel Grant)
Abstract

I show that communication by fund managers to their investor clients fosters trust and encourages these investors to bear risk. Using an institutional setting that enables causal identification, I find that more detailed communication about risk encourages investors to increase their holdings in the market portfolio, driving flows into the stock market. I rule out learning about risk, returns or manager skill, and other potential explanations. Instead, my analysis shows this communication soothes investors' anxiety and alleviates their effective risk aversion, consistent with the money doctors framework of Gennaioli, Shleifer, and Vishny (2015).

 

Machine Traders, Human Behavior, and Model (Mis)Specification
solo-authored   SSRN Slides
Presented at SFS Cavalcade North America, Chicago Booth ML in Economics Summer Institute, Cambridge Conf. on Alternative Finance
Abstract

I examine how investors utilize data, exploiting a setting in which investors design machine-driven trading strategies under controlled yet realistic conditions. Investors disagree considerably in how they interpret identical information, leading to widely dispersed trading strategies and performance outcomes. Inexperienced investors underweight variables with predictive power for returns, and instead exhibit a bias towards variables with which they are more familiar. With experience, investors learn to overcome their bias, and benefit substantially from additional data availability. Investors' familiarity bias leads them to mis-specify their models of the world, and is encoded by the machine traders they design.

 

Mutual Fund Market Structure and Company Fee Competition: Theory and Evidence
with Richard Grice   SSRN Slides
Presented at FIRS, NSF Network Science & Economics Conference (poster, coauthor)
Abstract

We investigate whether competition between the fund companies that offer mutual funds constrains individual fund fees. We document that a substantial fraction of individual fund fee variation is explained by company-wide components. Moreover, we show using SEC prospectus download data that company-level attributes influence investors' consideration of companies. We connect these facts with a model of fee competition between co-considered fund companies, characterising the competitive landscape and associated equilibrium fees. Calibrating the model, we derive a testable prediction for competitively constrained fees. The prediction successfully explains cross-sectional variation in company-level average fees, identifying the influence of company competition on fees.

 

Expectations and Attention to Experience
with Heiner Beckmeyer   SSRN Slides
Presented at CICF, Applied Young Economists Webinar

Abstract

We measure which past experiences determine investors' expectations about the market's future Sharpe Ratio. We first introduce a simple method to recover individuals' subjective Sharpe Ratios from a rich source of survey microdata. These subjective expectations are procyclical, extrapolative, cross-sectionally correlated with individual demographic characteristics, and well explained by a low-dimensional latent factor structure. We then use a customized machine learning estimation technology to estimate an economic model of experience effects that generalizes the lifetime weighting scheme of Malmendier and Nagel (2011). The model includes the influence of demographic characteristics in how past experiences determine individuals' future expectations, and succeeds in explaining a larger fraction of survey belief heterogeneity. We find that households' aggregate wealth share held in equities is strongly correlated with the share of investors who have experienced positive Sharpe Ratios (as measured by our model), thus confirming that experience effects drive investor flows. We also contribute a new set of facts on the role of demographic characteristics and the outsized influence of past recessions on how individuals learn from their experiences.

Other Research

Kernels for Time Series With Irregularly-Spaced Multivariate Observations
with Franz J. Király

Brief write-up of some machine learning methodology results from my UCL MSc dissertation.

Discussions

  • "Sources of Return Predictability" by Beata Gafka, Pavel Savor and Mungo Wilson, at MFA 2024  
  • "It’s Not What You Say, but How You Say It — Charismatic Rhetoric in Earnings Conference Calls" by Wolfgang Breuer, Andreas Knetsch and Sami Uddin, at BFGA 2023   Slides
  • "The Quality of Financial Advice: What Influences Client Recommendations?" by Philippe d'Astous, Irina Gemmo and Pierre-Carl Michaud, at SAFE Household Finance Workshop 2023   Slides
  • "Institutional Investor Attention" by Alan Kwan, Yukun Liu and Ben Matthies, at BFWG 2023   Slides
  • "The Subjective Risk and Risk Premia of Institutional Investors" by Spencer J. Couts, Andrei S. Gonçalves and Johnathan A. Loudis, at INSEAD Finance Symposium 2023   Slides
  • "Friend or Foe? Bilateral Political Relations and the Portfolio Allocation of Foreign Institutional Investors" by Stefano Lugo and Maurizio Montone, at FMCG 2023   Slides
  • "Active Mutual Fund Common Owners' Returns and Proxy Voting Behavior" by Ben Charoenwong, Zhenghui Ni and Qiaozhi Ye, at AFBC 2022   Slides
  • "This Time is Different: Investing Preferences in the Age of Robinhood" by Valeria Fedyk, at Dauphine Finance PhD Workshop 2022   Slides
  • "Information Acquisition and Usage of Retail Investors: Evidence from Web Views and Watchlists" by Yuecheng Jia, Shu Yan and Hongyu Zhang, at FMA 2021   Slides
  • "Does FinTech Compete with or Complement Bank Finance?" by Rebel A. Cole, Douglas J. Cumming and Jon Taylor, at EFMA 2021   Slides
  • "Machine Learning Classification Methods and Portfolio Allocation: An Examination of Market Efficiency" by Yang Bai and Kuntara Pukthuanthong, at Miami Herbert Winter Conference on ML & Business 2021   Slides