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Activity Number:
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472
- Winners: Business and Economic Statistics Student Paper Awards
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #321005
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Title:
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Cross-Sectional Analysis of Conditional Stock Returns: Quantile Regression with Machine Learning
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Author(s):
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Guoliang Ma* and Cindy Yu and Haitao Li
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Companies:
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Iowa State University and Iowa State University and Cheung Kong Graduate School of Business
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Keywords:
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quantile regression;
machine learning;
asset pricing;
density forecast;
long-short investment
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Abstract:
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We develop machine learning methods to forecast conditional quantiles of stock returns in the cross-section through quantile regression. Machine learning makes it possible to capture highly nonlinear relations between conditional quantiles and a large number of return predictors. We adopt Bayesian optimization with a Gaussian process that significantly improves the efficiency of hyperparameter tuning in machine learning. Simulation studies show that our methods accurately predict the conditional quantiles and consequently the whole conditional distributions of complicated data-generating processes. Empirical results show that our methods can identify stocks with extreme positive or negative returns and achieve superior performance in long-short investing.
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Authors who are presenting talks have a * after their name.
