A local adaptive learning system for online portfolio selection

Abstract

Online portfolio selection is an important problem in financial trading which has attracted increasing interests from the machine learning and data mining community. Most existing state-of-the-art systems, however, rely on the defensive strategies, which lack adaptivity to some extent and may lose investment opportunities in the real financial market. In this paper, we propose a novel local adaptive learning model which seamlessly integrates both defensive and aggressive strategies to enhance the adaptivity and profitability of the whole portfolio system. Different from some popular portfolio selection systems that assume a predefined price tendency, we set up an evaluation function to predict the tendency and activate the corresponding selection strategies. The total capital is allocated according to the value expectations of different assets. Through taking the bests of the complementary strategies, it can make a good balance between wealth returns and risks. In addition, our system is capable of dealing with financial data in linear time, which is suitable for real-time trading applications. Experimental results on several benchmark datasets show that our model outperforms some state-of-the-art ones both in effectiveness and efficiency.