Portfolio optimisation: bridging the gap between theory and practice

TL;DR

This paper addresses practical challenges in portfolio optimisation by proposing a two-stage framework that improves real-world applicability, incorporating features like transaction costs, asset classes, and borrowing costs, validated through extensive experiments.

Contribution

It introduces a novel two-stage approach that separates portfolio weight optimisation from trade generation, enabling the inclusion of complex features without excessive computational complexity.

Findings

Mitigates computational and numerical issues in practical portfolio optimisation

Successfully integrates futures, equities, and borrowing costs into a unified framework

Demonstrates effectiveness of the approach in realistic trading scenarios

Abstract

Portfolio optimisation is essential in quantitative investing, but its implementation faces several practical difficulties. One particular challenge is converting optimal portfolio weights into real-life trades in the presence of realistic features, such as transaction costs and integral lots. This is especially important in automated trading, where the entire process happens without human intervention. Several works in literature have extended portfolio optimisation models to account for these features. In this paper, we highlight and illustrate difficulties faced when employing the existing literature in a practical setting, such as computational intractability, numerical imprecision and modelling trade-offs. We then propose a two-stage framework as an alternative approach to address this issue. Its goal is to optimise portfolio weights in the first stage and to generate realistic trades in the second. Through extensive computational experiments, we show that our approach not only mitigates the difficulties discussed above but also can be successfully employed in a realistic scenario. By splitting the problem in two, we are able to incorporate new features without adding too much complexity to any single model. With this in mind we model two novel features that are critical to many investment strategies: first, we integrate two classes of assets, futures contracts and equities, into a single framework, with an example illustrating how this can help portfolio managers in enhancing investment strategies. Second, we account for borrowing costs in short positions, which have so far been neglected in literature but which significantly impact profits in long/short strategies. Even with these new features, our two-stage approach still effectively converts optimal portfolios into actionable trades.