Please use this identifier to cite or link to this item: https://doi.org/10.1080/14697688.2012.691175
Title: Robust and adaptive algorithms for online portfolio selection
Authors: Tsagaris, T.
Jasra, A. 
Adams, N.
Keywords: Adaptive systems
Portfolio allocation
Quantitative trading strategies
Statistics
Issue Date: Nov-2012
Citation: Tsagaris, T., Jasra, A., Adams, N. (2012-11). Robust and adaptive algorithms for online portfolio selection. Quantitative Finance 12 (11) : 1651-1662. ScholarBank@NUS Repository. https://doi.org/10.1080/14697688.2012.691175
Abstract: 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 four real data sets. Our methods outperform the benchmark allocation techniques in these data sets in terms of both computational demand and financial performance. © 2012 Copyright Taylor and Francis Group, LLC.
Source Title: Quantitative Finance
URI: http://scholarbank.nus.edu.sg/handle/10635/125062
ISSN: 14697688
DOI: 10.1080/14697688.2012.691175
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