2010/05/17 by Theodoros Tsagaris, Tsagaris, Theodoros, Ajay Jasra +4
Decision Sciences · Economics, Econometrics and Finance · Engineering · Mathematics · #Advanced Adaptive Filtering Techniques #Advanced Bandit Algorithms Research #Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #Financial Markets and Investment Strategies #Methodology (stat.ME) #Portfolio Management (q-fin.PM) #q-fin.CP #q-fin.PM #stat.ME
paper · pdf · doi:10.48550/arxiv.1005.2979
16 pages, 5 figures, submitted to journal
arxiv created 2010/05/17 · openalex publication_date 2010/05/17 · arxiv updated 2010/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.