Data-Driven Portfolio Construction: Machine Learning Applications for Investment Optimization

Music City Center 

Optimizing investment portfolios is a long-standing challenge in finance, requiring a balance between maximizing returns and minimizing risk. The integration of traditional portfolio theory with advancements in machine learning has made it increasingly feasible to construct optimal portfolios. This study leverages machine learning techniques to enhance portfolio optimization across all sectors of the S&P 500. We incorporate historical returns, risk factors, dividends, price-to-earnings (PE) ratios, debt-to-equity ratios, and recommendation scores from S&P 500 constituents to identify the best stocks from each sector. Deep learning models are then trained to predict future returns for individual stocks. These predictions are used for portfolio construction, employing modern portfolio theory principles and advanced optimization techniques such as mean-variance optimization and the three-factor asset pricing model. The performance of machine learning-driven portfolios is evaluated against traditional benchmarks using metrics such as the Sharpe ratio, Sortino ratio, and maximum drawdown.

Investment Portfolio

Machine Learning

Optimization 

Section on Statistical Learning and Data Science