Ensembling Portfolio Strategies for Long-Term Investments: A Distribution-Free Preference Framework for Decision-Making and Algorithms

TL;DR

This paper introduces a distribution-free framework for combining multiple long-term investment strategies, enabling robust decision-making and outperforming individual strategies without relying on statistical assumptions.

Contribution

It presents a novel, assumption-free decision-making framework for ensembling strategies, applicable to any number of strategies, including infinite, and demonstrates its effectiveness through numerical experiments.

Findings

Proposed strategies outperform individual strategies in cumulative wealth.

Accelerated variant significantly improves performance.

Strategies maintain robustness across market conditions.

Abstract

This paper investigates the problem of ensembling multiple strategies for sequential portfolios to outperform individual strategies in terms of long-term wealth. Due to the uncertainty of strategies' performances in the future market, which are often based on specific models and statistical assumptions, investors often mitigate risk and enhance robustness by combining multiple strategies, akin to common approaches in collective learning prediction. However, the absence of a distribution-free and consistent preference framework complicates decisions of combination due to the ambiguous objective. To address this gap, we introduce a novel framework for decision-making in combining strategies, irrespective of market conditions, by establishing the investor's preference between decisions and then forming a clear objective. Through this framework, we propose a combinatorial strategy construction, free from statistical assumptions, for any scale of component strategies, even infinite, such that it meets the determined criterion. Finally, we test the proposed strategy along with its accelerated variant and some other multi-strategies. The numerical experiments show results in favor of the proposed strategies, albeit with small tradeoffs in their Sharpe ratios, in which their cumulative wealths eventually exceed those of the best component strategies while the accelerated strategy significantly improves performance.