A Quantitative Framework for Portfolio Governance Using Machine Learning Techniques

Abstract

This research explores how machine learning, a data-driven technology, can transform the management of investment portfolios. The objective is to assess whether machine learning can surpass the performance of traditional approaches, such as Modern Portfolio Theory, which have been established for decades. We explored various machine learning techniques, including those that predict stock prices, group investments based on patterns, and dynamically reallocate assets. Our comprehensive analysis leveraged a robust dataset spanning stock prices, economic indicators, as well as news and social media sentiment. Rigorous data processing and rigorous testing revealed that machine learning techniques substantially outclassed traditional approaches, generating higher returns while incurring lower risk, as reflected by a Sharpe ratio of 1.9 versus 1.3 for Modern Portfolio Theory. This technique also proved more adept at navigating volatile market conditions. Although this research faces challenges such as addressing noisy data or excessively complex models, the findings indicate that machine learning could be a transformative innovation in enhancing investment management practices. While the findings show promising results, there remains scope for further improvements, particularly in devising real-time adaptation mechanisms and ensuring equitable outcomes for all investors.

The integration of machine learning into financial modeling presents a paradigm shift from traditional linear parametric methods, offering a more versatile framework for addressing complex challenges in portfolio governance (Dixon, and Halperin (2019)).