Mean-Reverting Portfolio Design with Budget Constraint
Abstract
This paper considers the mean-reverting portfolio design problem arising from statistical arbitrage in the financial markets. We first propose a general problem formulation aimed at finding a portfolio of underlying component assets by optimizing a mean-reversion criterion characterizing the mean-reversion strength, taking into consideration the variance of the portfolio and an investment budget constraint. Then several specific problems are considered based on the general formulation, and efficient algorithms are proposed. Numerical results on both synthetic and market data show that our proposed mean-reverting portfolio design methods can generate consistent profits and outperform the traditional design methods and the benchmark methods in the literature.
Keywords
Cite
@article{arxiv.1701.05016,
title = {Mean-Reverting Portfolio Design with Budget Constraint},
author = {Ziping Zhao and Daniel P. Palomar},
journal= {arXiv preprint arXiv:1701.05016},
year = {2018}
}
Comments
Paper submitted to IEEE Transactions on Signal Processing. Part of this work will appear in the Proceedings of the 50th annual Asilomar conference on signals, systems, and computers, Nov. 6-9, 2016, CA, USA