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.
Original language | English (US) |
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Pages (from-to) | 1239-1261 |
Number of pages | 23 |
Journal | Quantitative Finance |
Volume | 20 |
Issue number | 8 |
DOIs | |
State | Published - Aug 2 2020 |
All Science Journal Classification (ASJC) codes
- General Economics, Econometrics and Finance
- Finance
Keywords
- Asset allocation
- Portfolio allocation
- Portfolio optimization
- Statistical learning theory
- Stochastic programming