Optimizing a portfolio of mean-reverting assets with transaction costs via a feedforward neural network

John M. Mulvey, Yifan Sun, Mengdi Wang and Jing Ye

Quantitative Finance, 2020, vol. 20, issue 8, 1239-1261

Abstract: Optimizing a portfolio of mean-reverting assets under transaction costs and a finite horizon is severely constrained by the curse of high dimensionality. To overcome the exponential barrier, we develop an efficient, scalable algorithm by employing a feedforward neural network. A novel concept is to apply HJB equations as an advanced start for the neural network. Empirical tests with several practical examples, including a portfolio of 48 correlated pair trades over 50 time steps, show the advantages of the approach in a high-dimensional setting. We conjecture that other financial optimization problems are amenable to similar approaches.

Date: 2020
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DOI: 10.1080/14697688.2020.1729994

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