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We present an online approach to portfolio selection. The motivation iswithin the context of algorithmic trading, which demands fast and recursiveupdates of portfolio allocations, as new data arrives. In particular, we lookat two online algorithms: Robust-Exponentially Weighted Least Squares (R-EWRLS)and a regularized Online minimum Variance algorithm (O-VAR). Our methods usesimple ideas from signal processing and statistics, which are sometimesoverlooked in the empirical financial literature. The two approaches areevaluated against benchmark allocation techniques using 4 real datasets. Ourmethods outperform the benchmark allocation techniques in these datasets, interms of both computational demand and financial performance.

Robust and Adaptive Algorithms for Online Portfolio Selection