On-line portfolio selection has been attracting increasing interests from artificial intelligence community in recent decades. Mean reversion, as one most frequent pattern in financial markets, plays an important role in some state-of-the-art strategies. Though successful in certain datasets, existing mean reversion strategies do not fully consider noises and outliers in the data, leading to estimation error and thus non-optimal portfolios, which results in poor performance in practice. To overcome the limitation, we propose to exploit the reversion phenomenon by robust L1-median estimator, and design a novel on-line portfolio selection strategy named "Robust Median Reversion" (RMR), which makes optimal portfolios based on the improved reversion estimation. Empirical results on various real markets 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 trading applications.
Portfolios, Robustness, Investment, Mathematical model, Estimation, Algorithm design and analysis, Data mining, L1-median, Portfolio selection, online learning, mean reversion, robust median reversion
Computer Sciences | Databases and Information Systems | Finance and Financial Management
Data Management and Analytics
IEEE Transactions on Knowledge and Data Engineering
Institute of Electrical and Electronics Engineers (IEEE)
HUANG, Dingjiang; ZHOU, Junlong; LI, Bin; HOI, Steven C. H.; and ZHOU, Shuigeng.
Robust median reversion strategy for online portfolio selection. (2016). IEEE Transactions on Knowledge and Data Engineering. 28, (9), 2480-2493. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3408
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