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

被引:9
|
作者
Mulvey, John M. [1 ]
Sun, Yifan [2 ]
Wang, Mengdi [1 ]
Ye, Jing [1 ]
机构
[1] Princeton Univ, Dept Operat Res & Financial Engn, Princeton, NJ 08544 USA
[2] Carnegie Mellon Univ, Dept Math Sci, Pittsburgh, PA 15213 USA
关键词
Asset allocation; Portfolio allocation; Portfolio optimization; Statistical learning theory; Stochastic programming; PARTIAL-DIFFERENTIAL-EQUATIONS; OPTIMAL INVESTMENT; SELECTION; CONSUMPTION; OPTIMIZATION; RETURNS; MODEL; APPROXIMATION; DECISIONS; RULES;
D O I
10.1080/14697688.2020.1729994
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
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.
引用
收藏
页码:1239 / 1261
页数:23
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