Examination of AI-based ESG-scores as a valid source of alpha in the Swedish investing landscape

Sammanfattning: Investing based on environmental, social, and governmental (ESG) criteria has grown rapidly in recent years. The trend has been driven by both an increased interest in sustainability, and the fact that ESG related corporate events have been shown to influence stock prices. In tandem with this development, a number of companies has pioneered methods to quantify a firm’s ESG performance. One of these companies is Sanctify Financial Technologies. In this thesis, numerous machine learning models are used to try and predict stock prices. ESG scores from Sanctify are then incorporated in some of the models, these models are then compared to identical models without access to these scores. The predicted stock prices are then inserted into a custom-made trading algorithm that creates daily investment portfolios that maximises the expected Sharpe ratio. The benchmarks used for evaluating the models are the Sharpe ratio, the Sortino ratio and gross returns. A random forest regressor using various moving averages of the ESG scores ends up performing the best. With a Sharpe ratio of 1.185, a Sortino ratio 1.658 and gross returns of 60.5%, it outperforms the OMXS30GI index on all three benchmarks during 2020-2021. In general, the performance of the models varies widely and indicates somewhat low predictive power. Excess returns are still achieved however, and the results indicate predictive performance for the Sancify scores on stock prices.