A0630
Title: Adaptive online portfolio selection with transaction costs
Authors: Sini Guo - The University of Hong Kong (Hong Kong) [presenting]
Abstract: As an application of machine learning techniques in financial fields, online portfolio selection has attracted great attention from practitioners and researchers, making timely sequential decision-making available when market information is constantly updated. For online portfolio selection, transaction costs incurred by changes of investment proportions on risky assets significantly impact the investment strategy and the return in the long-term investment horizon. However, in many online portfolio selection studies, transaction costs are usually neglected in the decision-making process. We consider an adaptive online portfolio selection problem with transaction costs. The adaptive online moving average method (AOLMA) is proposed to predict the future returns of risky assets by incorporating an adaptive decaying factor into the moving average method, which improves the accuracy of return prediction. The net profit maximization model (NPM) is then constructed where transaction costs are considered in each decision-making process. The adaptive online net profit maximization algorithm (AOLNPM) is designed to maximize the cumulative return by integrating AOLMA and NPM. Numerical experiments show that AOLNPM dominates several state-of-the-art online portfolio selection algorithms in terms of various performance metrics, i.e., cumulative return, mean excess return, Sharpe ratio, Information ratio and Calmar ratio.
