Efficient Online Portfolio Selection with Heuristic AI Algorithm

Amril Nazir

Abstract


Online portfolio selection is one of the most important problems in several research communities, including finance, engineering, statistics, artificial intelligence, and machine learning, etc. The primary aim of online portfolio selection is to determine portfolio weights in every investment period (i.e., daily, weekly, monthly etc.) to maximize the investor’s final wealth after the end of investment period (e.g., 1 year or longer). In this paper, we present an efficient online portfolio selection strategy that employs a heuristic artificial intelligence (AI) algorithm to maximise the total wealth based on historical stock prices. Based on empirical studies conducted on recent historical datasets for the period 2000 to 2017 on four different stock markets (i.e., NYSE, S&P500, DJIA, and TSX), the proposed algorithm has been shown to outperform both Anticor and OLMAR —the two most prominent portfolio selection strategies in contemporary literature. The algorithm achieved 34.22 of the total wealth while Anticor and OLMAR returned 1.08, and 4.52 respectively, on the NYSE market. On the S&P market, the algorithm returned 15.39 of the total wealth while the Anticor and OLMAR returned 6.20 and 2.2,respectively. On the TSX market, the algorithm returned 2.43 of the total wealth while Anticor and Olmar returned 0.96 and 0.41, respectively.


Keywords


artificial intelligence, applications and expert systems, heuristic AI algorithm, online portfolio selection,portfolio optimization, simulation and modelling.

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DOI: 10.19139/soic.v7i2.484

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