Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Portfolio Blending via Thompson Sampling / 1983
Weiwei Shen, Jun Wang

As a definitive investment guideline for institutions and individuals, Markowitz's modern portfolio theory is ubiquitous in financial industry. However, its noticeably poor out-of-sample performance due to the inaccurate estimation of parameters evokes unremitting efforts of investigating effective remedies. One common retrofit that blends portfolios from disparate investment perspectives has received growing attention. While even a naive portfolio blending strategy can be empirically successful, how to effectually and robustly blend portfolios to generate stable performance improvement remains less explored. In this paper, we present a novel online algorithm that leverages Thompson sampling into the sequential decision-making process for portfolio blending. By modeling blending coefficients as probabilities of choosing basis portfolios and utilizing Bayes decision rules to update the corresponding distribution functions, our algorithm sequentially determines the optimal coefficients to blend multiple portfolios that embody different criteria of investment and market views. Compared with competitive trading strategies across various benchmarks, our method shows superiority through standard evaluation metrics.