文献类型：会议论文

英文题名：Semi-Universal Portfolios with Transaction Costs

作者：Huang, Dingjiang[1,2,3];Zhu, Yan[1];Li, Bin[4];Zhou, Shuigeng[2,3];Hoi, Steven C. H.[5]

机构：[1]East China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R China;[2]Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China;[3]Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China;[4]Wuhan Univ, Econ & Management Sch, Wuhan 430072, Hubei, Peoples R China;[5]Singapore Management Univ, Sch Informat Syst, Singapore 178902, Singapore

会议论文集：1st International Workshop on Social Influence Analysis / 24th International Joint Conference on Artificial Intelligence (IJCAI)

会议日期：JUL 25-31, 2015

会议地点：Buenos Aires, ARGENTINA

语种：英文

外文关键词：Financial markets - Learning systems - Costs - Artificial intelligence - Electronic trading - Investments

摘要：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 non-uniform 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.