The Success of AdaBoost and Its Application in Portfolio Management
This work provides theoretical insights into AdaBoost's performance for binary classification and demonstrates its application in finance, but it is incremental as it builds on existing methods with new analysis.
The authors tackled the problem of explaining AdaBoost's success by introducing a noise influence measure (ION) and proving its connection to test error, showing that ION decreases with iteration or base learner complexity, and applied AdaBoost to portfolio management in the Chinese market with empirical validation.
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.