文献类型：期刊文献

英文题名：Robust Median Reversion Strategy for Online Portfolio Selection

作者：Huang, Dingjiang[1];Zhou, Junlong[2];Li, Bin[3];Hoi, Steven C. H.[4];Zhou, Shuigeng[5,6]

机构：[1]East China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R China;[2]Shanghai Futures Exchange, Shanghai 200122, Peoples R China;[3]Wuhan Univ, Econ & Management Sch, Luojia Hill, Wuhan 430072, Peoples R China;[4]Singapore Management Univ, Sch Informat Syst, 80 Stamford Rd, Singapore 178902, Singapore;[5]Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China;[6]Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China

年份：2016

卷号：28

期号：9

起止页码：2480

外文期刊名：IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

收录：;EI(收录号：20163402724547);WOS:【SSCI(收录号:WOS:000384234700016)，SCI-EXPANDED(收录号:WOS:000384234700016)】；

基金：This work was partially done when the first author was visiting the Computer Science Department, University of California Santa Cruz, he would like to thank Professor Manfred Warmuth for his warm invitation and hospitality. The work was partially supported by the National Natural Science Foundation of China (11501204, 71401128), the Natural Science Foundation of Shanghai (15ZR1408300), the Key Projects of Fundamental Research Program of Shanghai Municipal Commission of Science and Technology (14JC1400300), Shanghai Key Laboratory of Intelligent Information Processing (IIPL-2014-001), the special Postdoctoral Science Foundation of China (201104247), the project of SRF for ROCS, SEM and Singapore Ministry of Education Academic Research Fund Tier 1 Grant (14-C220-SMU-016).

语种：英文

外文关键词：Portfolio selection; online learning; mean reversion; robust median reversion; L-1-median

摘要：Online portfolio selection has attracted increasing attention from data mining and machine learning communities in recent years. An important theory in financial markets is mean reversion, which plays a critical role in some state-of-the-art portfolio selection strategies. Although existing mean reversion strategies have been shown to achieve good empirical performance on certain datasets, they seldom carefully deal with noise and outliers in the data, leading to suboptimal portfolios, and consequently yielding poor performance in practice. In this paper, we propose to exploit the reversion phenomenon by using robust L-1-median estimators, and design a novel online portfolio selection strategy named "Robust Median Reversion" (RMR), which constructs optimal portfolios based on the improved reversion estimator. We examine the performance of the proposed algorithms on various real markets with extensive experiments. Empirical results show that RMR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results. Finally, RMR runs in linear time, and thus is suitable for large-scale real-time algorithmic trading applications.