The Success of AdaBoost and Its Application in Portfolio Management.pdf

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.

The Success of AdaBoost and Its Application in Portfolio Management Yijian Chuan‡ , Chaoyi Zhao‡ , Zhenrui He‡ and Lan Wu†,∗ ‡ School of Mathematical Sciences, Peking University, Beijing, China arXiv:2103.12345v1 [stat.ML] 23 Mar 2021 † LMEQF, School of Mathematical Sciences, Peking University, Beijing, China…

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