Efficient Online Portfolio Selection with Heuristic AI Algorithm
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.References
Amit Agarwal, Elad Hazan, Satyen Kale, and Robert E Schapire. Algorithms for portfolio management based on the newton method. In Proceedings of the 23rd international conference on Machine learning, pages 9–16. ACM, 2006.
Allan Borodin, Ran El-Yaniv, and Vincent Gogan. Can we learn to beat the best stock. Journal of Artificial Intelligence Research,pages 579–594, 2004.
Thomas M. Cover. Universal portfolios. Mathematical Finance, 1(1):1–29, 1991.
Gyorfi,Lugosi,andFredericUdina. Nonparametric kernel-based sequential investment strategies. Mathematical Finance,16(2):337-357, 2006.
Gy˝ orfi, Lugosi, Frederic Udina, and Harro Walk. Nonparametric nearest neighbor based empirical portfolio selection strategies. Statistics and Decisions, 26(2):145–157, 2008.
David P. Helmbold, Robert E. Schapire, Yoram Singer, and Manfred K.Warmuth. On-line portfolio selection using multiplicative updates. Mathematical Finance, 8(4):325–347, 1998.
Bin Li and Steven C. H. Hoi. On-line portfolio selection with moving average reversion. In Proceedings of the International Conference on Machine Learning, 2012.
Bin Li, Steven CH Hoi, and Vivekanand Gopalkrishnan. Corn: Correlation-driven nonparametric learning approach for portfolio selection. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3):21, 2011.
Bin Li, Steven CH Hoi, Doyen Sahoo, and Zhi-Yong Liu. Moving average reversion strategy for on-line portfolio selection. Artificial
Intelligence, 222:104–123, 2015.
Bin Li, Peilin Zhao, Steven CH Hoi, and Vivekanand Gopalkrishnan. Pamr: Passive aggressive mean reversion strategy for portfolio selection. Machine learning, 87(2):221–258, 2012.
Raphael Nkomo, Alain Kabundi, et al. Kalman filtering and online learning algorithms for portfolio selection. Technical report,2013.
Paul Perry. Comparing olps algorithms on a diversified set of etfs. 2015.
Alfonso Ventura. Online Computational Algorithms for Financial Markets. PhD thesis, Università degli studi di Milano-Bicocca,2006.
Halbert White. A reality check for data snooping. Econometrica, 68(5):1097–1126, 2000.
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