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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, Macroeconomics
Comovement and return predictability in asset markets: An experiment with two Lucas trees (2021)
With: Charles Noussair (University of Arizona)
Abstract: Using a laboratory experiment, we investigate whether comovement can emerge between two risky assets, despite their fundamentals not being correlated. The ‘Two trees’ asset pricing model developed by Cochrane et al. (2007) guides our experimental design and its predictions serve as our source of hypotheses. The model makes time-series and cross-section return predictions following a shock to one of the two assets’ dividend distributions. As the model predicts, we observe (1) positive contemporaneous correlation between the two assets, (2) positive autocorrelation in the shocked asset, and (3) time-series and cross-sectional return predictability from the dividend-price ratio. In line with the rational foundations of the model, the model's predictions have stronger support in markets with relatively sophisticated agents.
Keywords: Contagion, Asset Pricing, Experimental Finance, Time series Momentum, Return Predictability
Published in Journal of Economic Behaviour & Organization, 185, 671-687
Contests with money and time: Experimental evidence on overbidding in all-pay auctions (2020)
With: Charles Noussair (The University of Arizona) and Adriana Breaban (Erasmus University Rotterdam)
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.
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