Portfolio Optimization for Cointelated Pairs: Financial Mathematics or Machine Learning?

20 Pages Posted: 21 Sep 2017 Last revised: 30 Jul 2019

See all articles by Babak Mahdavi-Damghani

Babak Mahdavi-Damghani

University of Oxford - Oxford-Man Institute of Quantitative Finance

Konul Mustafayeva

King's College London

Cristin Buescu

King's College London, Department of Mathematics

Stephen Roberts

University of Oxford - Oxford-Man Institute of Quantitative Finance

Date Written: May 9, 2019

Abstract

We investigate the problem of dynamic portfolio optimization in continuous-time, finite-horizon setting for a portfolio of two stocks. The stocks follow the Cointelation model recently introduced [16], [19]. The proposed optimization methods are twofold. First, in what we call a Dynamics Switching approach, we compute the optimal weights using the mean- variance criterion and the power utility maximization. We show that dynamically switching between these two optimal strategies by introducing a triggering function can further improve the portfolio returns. We contrast this with the Machine Learning clustering methodology inspired by the band-wise Gaussian mixture model [21], [19]. Though both of these methods have their benefits, we show that Machine Learning approach yields better result most of the time.

Keywords: Cointelation, Portfolio Optimization, Mean- Variance Criterion, Power Utility Maximization, Band-wise Gaussian Mixture, LSTM, Partial Differential Equation, Deep Learning, Cryptocurrency, Bitcoin, Altoin, Pairs Trading

Suggested Citation

Mahdavi-Damghani, Babak and Mustafayeva, Konul and Buescu, Cristin and Roberts, Stephen, Portfolio Optimization for Cointelated Pairs: Financial Mathematics or Machine Learning? (May 9, 2019). Available at SSRN: https://ssrn.com/abstract=3039171 or http://dx.doi.org/10.2139/ssrn.3039171

Babak Mahdavi-Damghani (Contact Author)

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

United Kingdom

Konul Mustafayeva

King's College London ( email )

Strand
London, England WC2R 2LS
United Kingdom

Cristin Buescu

King's College London, Department of Mathematics ( email )

Strand
London, WC2R 2LS
United Kingdom

Stephen Roberts

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

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