Fast recursive portfolio optimization

Laurence Irlicht ()
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Laurence Irlicht: IFM Investors, Postal: IFM Investors, Level 29, 2 Lonsdale Street, Melbourne 3000, Australia.

Algorithmic Finance, 2014, vol. 3, issue 3-4, 173-188

Abstract: Institutional equity portfolios are typically constructed via taking expected stock returns and then applying the computationally expensive processes of covariance matrix estimation and mean-variance optimization. Unfortunately, these computational costs make it prohibitive to comprehensively backtest and tune higher frequency strategies over long histories. In this paper, we introduce a recursive algorithm which significantly lowers the computational cost of calculating the covariance matrix and its inverse as well as an iterative heuristic which provides a very fast approximation to mean-variance optimization. Together, these techniques cut backtesting time to a fraction of that of standard techniques. Where possible, the additional step of caching pre-calculated covariance matrices, can result in overall backtesting speeds up to orders of magnitude faster than the standard methods. We demonstrate the efficacy of our approach by selecting a prediction strategy in a fraction of the time taken by standard methods.

Keywords: Portfolio optimization; algorithmic finance; covariance estimation; quadratic optimization; computational finance; mathematical programming; Backtesting (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:ris:iosalg:0030

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