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On the Directional Predictability of Equity Premium Using Machine Learning Techniques

Iworiso, Jonathan and Vrontos, Spyridon (2020) 'On the Directional Predictability of Equity Premium Using Machine Learning Techniques.' Journal of Forecasting, 39 (3). 449 - 469. ISSN 0277-6693

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Abstract

This paper applies a plethora of machine learning techniques to forecast the direction of the U.S. equity premium. Our techniques include benchmark binary probit models, classification and regression trees (CART), along with penalized binary probit models. Our empirical analysis reveals that the sophisticated machine learning techniques significantly outperformed the benchmark binary probit forecasting models, both statistically and economically. Overall, the discriminant analysis classifiers are ranked first among all the models tested. Specifically, the high dimensional discriminant analysis (HDDA) classifier ranks first in terms of statistical performance, while the quadratic discriminant analysis (QDA) classifier ranks first in economic performance. The penalized likelihood binary probit models (Least Absolute Shrinkage and Selection Operator, Ridge, Elastic Net) also outperformed the benchmark binary probit models, providing significant alternatives to portfolio managers.

Item Type: Article
Uncontrolled Keywords: Directional Predictability, Recursive Window, Forecasting, Binary Probit, CART, Penalized Binary Probit
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Elements
Date Deposited: 19 Nov 2019 10:04
Last Modified: 21 Jul 2020 11:15
URI: http://repository.essex.ac.uk/id/eprint/25771

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