Advancing Portfolio Construction and Optimization: AI's Role in Boosting Returns, Lowering Risks, and Streamlining Efficiency

12 Pages Posted: 4 Mar 2024

Date Written: February 5, 2024

Abstract

This paper is a practical guide on how Artificial Intelligence (AI) and Machine Learning (ML) can support professional investors in portfolio construction and optimisation and identifies three methods for seamlessly integrating ML-based portfolio construction into an existing investment process. It provides a compelling comparative analysis of traditional techniques and modern ML-based approaches to portfolio construction and optimisation. The paper illustrates how, unlike traditional tools such as mean-variance optimisation and the capital asset pricing model, ML methods adapt dynamically to market changes, acting like a navigation system or GPS in the ever-evolving financial terrain. This adaptability enables ML based portfolios to outperform traditional methods through better predictive analytics, automated rebalancing and risk management, leading to more efficient, scalable and customised portfolio solutions. The paper argues that integrating ML into portfolio construction is not just an upgrade, but a significant innovation in asset management. This offers precision and efficiency beyond the capabilities of traditional methods, thereby increasing portfolio returns, reducing risk, and improving efficiency.

Keywords: Portfolio Construction, Portfolio Optimization, Investment, Asset Management, Portfolio Management, Artificial Intelligence, Finance

Suggested Citation

Schopf, Michael, Advancing Portfolio Construction and Optimization: AI's Role in Boosting Returns, Lowering Risks, and Streamlining Efficiency (February 5, 2024). Available at SSRN: https://ssrn.com/abstract=4717163 or http://dx.doi.org/10.2139/ssrn.4717163

Michael Schopf (Contact Author)

Schopf Meta Consult (SMC) ( email )

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