Keeping the Faith (and the Returns): An AI Approach to Values-based Investing 

Abstract

We demonstrate a new approach for SRI investing that uses techniques from Artificial Intelligence (AI) to enhance investor returns. Focusing on faith-based investing, our paper draws on Large Language Models (LLMs) and Deep Reinforcement Learning (DRL) to address the challenges posed by moral constraints on portfolio selection. Using the Global X S&P500 CATH ETF (the largest Catholic values fund) as an example, we use textual analysis to identify companies consistent with the values mandate, allowing us to create "synthetic CATH" portfolios of different sizes and with longer time horizons. We further optimize each portfolio using DRL to arrive at an optimal set of portfolio weights that maximize out-of-sample Sharpe ratios. Using the tools of AI, we demonstrate dramatic improvements in risk-adjusted returns closing the performance gap in values-based investing.

Keywords: Faith-based investing, artificial intelligence, large language models, machine learning, reinforcement learning

JEL Classification: G11, G12, G24