Robust and Adaptive Algorithms for Online Portfolio Selection

Details Robust and Adaptive Algorithms for Online Portfolio Selection Robust and Adaptive Algorithms for Online Portfolio Selection

2010-05-17

arxiv.org/abs/1005.2979v1

Economy

Quantitative Finance

Portfolio Management

16 pages, 5 figures, submitted to journal

Scientific paper

We present an online approach to portfolio selection. The motivation is within the context of algorithmic trading, which demands fast and recursive updates of portfolio allocations, as new data arrives. In particular, we look at two online algorithms: Robust-Exponentially Weighted Least Squares (R-EWRLS) and a regularized Online minimum Variance algorithm (O-VAR). Our methods use simple ideas from signal processing and statistics, which are sometimes overlooked in the empirical financial literature. The two approaches are evaluated against benchmark allocation techniques using 4 real datasets. Our methods outperform the benchmark allocation techniques in these datasets, in terms of both computational demand and financial performance.

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