Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

1-2015

Abstract

Online portfolio selection (PS) has been extensively studied in artificial intelligence and machine learning communities in recent years. An important practical issue of online PS is transaction cost, which is unavoidable and nontrivial in real financial trading markets. Most existing strategies, such as universal portfolio (UP) based strategies, often rebalance their target portfolio vectors at every investment period, and thus the total transaction cost increases rapidly and the final cumulative wealth degrades severely. To overcome the limitation, in this paper we investigate new investment strategies that rebalances its portfolio only at some selected instants. Specifically, we design a novel on-line PS strategy named semi-universal portfolio (SUP) strategy under transaction cost, which attempts to avoid rebalancing when the transaction cost outweighs the benefit of trading. We show that the proposed SUP strategy is universal and has an upper bound on the regret. We present an efficient implementation of the strategy based on nonuniform random walks and online factor graph algorithms. Empirical simulation on real historical markets show that SUP can overcome the drawback of existing UP based transaction cost aware algorithms and achieve significantly better performance. Furthermore, SUP has a polynomial complexity in the number of stocks and thus is efficient and scalable in practice.

Keywords

Online portfolios, Artificial intelligence, Financial markets, Learning systems

Discipline

Computer Sciences | Databases and Information Systems | Finance and Financial Management | Portfolio and Security Analysis

Research Areas

Data Science and Engineering

Publication

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015): Buenos Aires, Argentina, July 25-31, 2015

First Page

178

Last Page

184

ISBN

9781577357384

Publisher

AAAI Press

City or Country

Menlo Park, CA

Copyright Owner and License

Authors

Additional URL

http://ijcai.org/papers15/Papers/IJCAI15-032.pdf

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