Machine Learning Classification Methods and Portfolio Allocation: An Alternative Probabilistic Perspective

99 Pages Posted: 10 Sep 2020 Last revised: 17 Nov 2021

See all articles by Yang Bai

Yang Bai

University of Missouri, Finance Department

Kuntara Pukthuanthong

University of Missouri, Columbia

Date Written: January 7, 2021

Abstract

We frame the asset pricing problem as a machine learning classification problem. The predictions on 3.34 million observations lead to zero-investment portfolios yielding significant out-of-sample economic gains. Through directly measured accuracies, binomial tests suggest that the classifiers can extract forward-looking contents from historical information. The classifiers exploit the differences in return state transition uncertainties. As reflected by the pre-realization variances of multi-class predicted probabilities, the classifiers are more confident subjectively when predicting high-trading-friction stocks. Consistently, only trading frictions contribute to out-of-sample predictability throughout 26,302 distinct stocks' lifetimes. The adjustment of the classifiers' obsession over certain return states increases the performance.

Keywords: Artificial neural network, big data, binomial test, classification, dropout additive regression tree, gradient boosting machine, information theory, machine learning, portfolio allocation, out-of-sample prediction, random forest, return state transition

JEL Classification: C14, C38, C55, G11, G14

Suggested Citation

Bai, Yang and Pukthuanthong, Kuntara, Machine Learning Classification Methods and Portfolio Allocation: An Alternative Probabilistic Perspective (January 7, 2021). Available at SSRN: https://ssrn.com/abstract=3665051 or http://dx.doi.org/10.2139/ssrn.3665051

Yang Bai (Contact Author)

University of Missouri, Finance Department ( email )

MO
United States

Kuntara Pukthuanthong

University of Missouri, Columbia ( email )

Robert J. Trulaske, Sr. College of Business
403 Cornell Hall
Columbia, MO 65211
United States
6198076124 (Phone)

HOME PAGE: https://www.kuntara.net/

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