A novel adjusted learning algorithm for online portfolio selection using peak price tracking approach

Abstract

Online Portfolio Selection (OLPS) has attracted extensive interest in recent years. Accurate prediction of future prices and determining the optimal portfolio selection strategy based on the estimated return is a challenging topic in machine learning. We propose a novel adjusted learning algorithm based on peak price tracking for OLPS to tackle this challenge. The algorithm is based on an aggressive strategy with residual information and transaction costs. We first propose an adjusted online portfolio selection algorithm using Peak Price Tracking Approach (PPTA) to improve the accuracy of return prediction by introducing λ to adjust the impact of peak price and residual term on the predicted price. We then build the Net Profit Maximization (NPM) model with transaction costs. Finally, we integrate the PPTA and the NPM algorithms into a new algorithm called the PPTA-NPM to maximize the cumulative return. Extensive benchmark data experiment results and statistical analysis show that the PPTA algorithm significantly improves the accuracy in predicting the future price, and the integrated PPTA-NPM algorithm is superior to multiple classic OLPS algorithms.