On-Line Portfolio Selection

Robust Median Reversion Strategy for On-Line Portfolio Selection

Portfolio Selection (PS) problem is concerned with determining a portfolio for allocating the wealth among a set of assets to achieve some financial objectives in the long run. There are two main mathematical models for this problem: the meanvariance model [Markowitz, 1952] and the Kelly investment [Kelly, 1956]. In general, mean-variance theory, which trades off between the expected return (mean) and risk (variance) of a portfolio, is suitable for single-period (batch) Portfolio Selection. So far as Robust Median Reversion is the first algorithm Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence 2006 that exploits the reversion phenomenon by robust L1-median estimator. Though simple in nature, Robust Median Reversion can release better estimation than existing algorithms and has been empirically validated via extensive experiments on real markets.

Robust Median Reversion Strategy

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, the reversion phenomenon by robust L1-median estimator is proposed to be exploited, and  a novel on-line portfolio selection strategy named “Robust Median Reversion” (RMR) has been designed, which makes optimal portfolios based on the improved reversion estimation. Empirical results on various real markets show that Robust Median Reversion can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results. Robust Median Reversion runs in linear time, and thus is suitable for large-scale trading applications.