Please join the Goergen Institute for Data Science for (Re-)Imag(in)ing Price Trends, a research seminar with Dacheng Xiu, Professor of Econometrics and Statistics at the Booth School of Business, University of Chicago.
Abstract: We reconsider the idea of trend-based predictability using methods that flexibly learn price patterns that are most predictive of future returns, rather than testing hypothesized or pre-specified patterns (e.g., momentum and reversal). Our raw predictor data are images—stock-level price charts—from which we elicit the price patterns that best predict returns using machine learning image analysis methods. The predictive patterns we identify are largely distinct from trend signals commonly analyzed in the literature, give more accurate return predictions, translate into more profitable investment strategies, and are robust to a battery of specification variations. They also appear context-independent: Predictive patterns estimated at short time scales (e.g., daily data) give similarly strong predictions when applied at longer time scales (e.g., monthly), and patterns learned from US stocks predict equally well in international markets.
Bio: Dacheng Xiu is a Professor of Econometrics and Statistics at Booth School of Business, University of Chicago. His work has appeared in Econometrica, Journal of Political Economy, Journal of Finance, Review of Financial Studies, Journal of the American Statistical Association, and Annals of Statistics. He is a Co-Editor for the Journal of Financial Econometrics, an Associate Editor for the Journal of Econometrics, Journal of Business & Economic Statistics, Management Science, Journal of Applied Econometrics, and Journal of Empirical Finance. He has received several recognitions for his research, including the Fellow of the Society for Financial Econometrics, the Fellow of the Journal of Econometrics, the 2018 Swiss Finance Institute Outstanding Paper Award, the 2018 AQR Insight Award, and the Best Conference Paper Prize at the 2017 Annual Meeting of the European Finance Association.
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Meeting ID: 933 3400 4858
Friday, April 16 at 2:00pm to 3:00pmVirtual Event