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We develop a novel approach to explain why AdaBoost is a successfulclassifier. By introducing a measure of the influence of the noise points (ION)in the training data for the binary classification problem, we prove that thereis a strong connection between the ION and the test error. We further identifythat the ION of AdaBoost decreases as the iteration number or the complexity ofthe base learners increases. We confirm that it is impossible to obtain aconsistent classifier without deep trees as the base learners of AdaBoost insome complicated situations. We apply AdaBoost in portfolio management viaempirical studies in the Chinese market, which corroborates our theoreticalpropositions.

The Success of AdaBoost and Its Application in Portfolio Management