1. The macroeconomy and the cross-section of international equity index returns: a machine learning approach (2020)
Abstract: The paper evaluates the out-of-sample predictive potential of machine learning methods in the cross-section of international equity index returns using firm fundamentals and macroeconomic predictors. The relatively small number of equity indices in the cross-section compared to the multitude of predictive signals makes this an ideal setting to examine return predictability using machine learning techniques. I find that macroeconomic signals seem to substantially improve out-of-sample performance, especially when non-linear features are incorporated via neural networks. The performance of a long-short country bet based on forecasted returns cannot be explained by standard definitions of risk.
Keywords: Asset Pricing, Equity Indices, Return Forecasting, Machine Learning, Neural Networks, Macroeconomic predictability
2. Contagion and return predictability in asset markets: Two Lucas trees in the lab (2019)
Abstract: Using a laboratory experiment, we investigate if contagion can emerge between two risky assets despite an absence of correlation among their fundamentals. To guide our experimental design, we use the ‘Two trees’ asset pricing model developed by Cochrane et al. (2007). The model makes time-series and cross-section return predictions following a shock to one of the assets’ dividend share. Consistent with the predictions of the model, we observe positive autocorrelation in the shocked asset, a positive contemporaneous correlation between the two risky assets, and time-series and cross-sectional return predictability using the dividend-price ratio. In line with the rational foundation of the model mechanics, the model’s predictions have higher support in markets with more sophisticated agents.
Keywords: Contagion, Asset Pricing, Experimental Finance, Time series Momentum, Return Predictability
Conferences and Seminar Presentations: ASSA American Economic Association Conference (USA, 2020, Poster); University of Oxford (UK, 2019); 14th Nordic Conference on Behavioral and Experimental Economics (Germany, 2019); IAREP/SABE Conference on Behavioral Economics (Ireland, 2019); SEF 10th Annual Experimental Finance Conference (Denmark, 2019); University College London (UK, 2019); TIBER Symposium on Psychology and Economics (The Netherlands, 2019, Poster); Conference on Theory and Experiments in Monetary Economics (USA, 2018, presented by co-author)
CentER Discussion Paper Series; Vol. 2020-014, Center for Economic Research
Contests with money and time: Experimental evidence on overbidding in all-pay auctions (2020)
Abstract: Competition for a prize frequently takes the form of dedicating time toward winning a contest. Those who spend more time become more likely to obtain the prize. We model this competition as an all-pay auction under incomplete information and report an experiment in which expenditures and rewards are in terms of time. We correlate behavior in this game with behavior in an all-pay auction played with money bids. We also consider how two measures of sophistication, the Cognitive Reflection Test (CRT) score, and performance on a probability calibration task, correlate with behavior. We find strong similarities in overall behavior between the auctions conducted with money and with time. Bidding greater than equilibrium levels is typical, and as a consequence, average earnings are negative in both auctions. Thus, the result that there is overdissipation of rent in all-pay auctions extends to competition in terms of time.
Published at Journal of Economic Behaviour & Organization, 171, 391-405