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|Title:||Robust and adaptive algorithms for online portfolio selection||Authors:||Tsagaris, T.
Quantitative trading strategies
|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|
|Appears in Collections:||Staff Publications|
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