The success of AdaBoost and its application in portfolio management

Yijian Chuan, Chaoyi Zhao, Zhenrui He and Lan Wu
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Yijian Chuan: School of Mathematical Sciences, Peking University, Beijing, P. R. China
Chaoyi Zhao: School of Mathematical Sciences, Peking University, Beijing, P. R. China
Zhenrui He: School of Mathematical Sciences, Peking University, Beijing, P. R. China
Lan Wu: #x2020;LMEQF, School of Mathematical Sciences, Peking University, Beijing, P. R. China

International Journal of Financial Engineering (IJFE), 2021, vol. 08, issue 02, 1-31

Abstract: We develop a novel approach to explain why AdaBoost is a successful classifier. By introducing a measure of the influence of the noise points (ION) in the training data for the binary classification problem, we prove that there is a strong connection between the ION and the test error. We further identify that the ION of AdaBoost decreases as the iteration number or the complexity of the base learners increases. We confirm that it is impossible to obtain a consistent classifier without deep trees as the base learners of AdaBoost in some complicated situations. We apply AdaBoost in portfolio management via empirical studies in the Chinese market, which corroborates our theoretical propositions.

Keywords: AdaBoost; interpolation; noise point; base learner; equal-weighted portfolio (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S2424786321420019

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