On Data-Driven Log-Optimal Portfolio: A Sliding Window Approach

TL;DR

This paper introduces a data-driven sliding window method for log-optimal portfolio selection, resulting in a dynamic trading strategy that outperforms traditional fixed-weight approaches in empirical tests.

Contribution

It presents a novel sliding window approach that produces time-varying portfolio weights, improving trading performance over classical methods.

Findings

Higher cumulative returns compared to classical log-optimal portfolios

Demonstrates effectiveness through empirical studies

Produces dynamic, time-varying portfolio weights

Abstract

In this paper, we propose a data-driven sliding window approach to solve a log-optimal portfolio problem. In contrast to many of the existing papers, this approach leads to a trading strategy with time-varying portfolio weights rather than fixed constant weights. We show, by conducting various empirical studies, that the approach possesses a superior trading performance to the classical log-optimal portfolio in the sense of having a higher cumulative rate of returns.