State of the Art Review for Applying Computational Intelligence and Machine Learning Techniques to Portfolio Optimisation

TL;DR

This paper reviews advanced computational intelligence and machine learning methods for portfolio optimization, evaluating current state-of-the-art techniques and suggesting directions for future research in financial management applications.

Contribution

It provides a comprehensive overview of the latest computational and machine learning approaches applied to portfolio optimization, highlighting gaps and future opportunities.

Findings

Traditional and machine learning techniques show promise in portfolio management.

Current methods improve optimization efficiency and accuracy.

Recommendations for future research directions are provided.

Abstract

Computational techniques have shown much promise in the field of Finance, owing to their ability to extract sense out of dauntingly complex systems. This paper reviews the most promising of these techniques, from traditional computational intelligence methods to their machine learning siblings, with particular view to their application in optimising the management of a portfolio of financial instruments. The current state of the art is assessed, and prospective further work is assessed and recommended