The rise of quantitative investment strategies has been driven by increased data availability and advancements in financial modeling. This thesis introduces Causal Factor Investing (CFI), a novel approach that integrates causality and machine learning to enhance the performance and explainability of factor investing strategies. Traditional factor investing often suffers from specification errors due to its reliance on correlations rather than causal relationships, and machine learning methods are frequently criticized for their `black box' nature. CFI addresses these issues by using causal discovery methods, which are based on the mathematical properties of graph theory, to identify factors that have a cause-effect relationship with asset returns. These causal factors are then utilized as features in machine learning models to predict future returns, serving as investment signals for portfolio construction.

Our empirical analysis in the European corporate bond market utilized causal discovery algorithms including Fast Causal Inference (FCI) and Greedy Equivalence Search (GES). The use of GES in CFI improves portfolio performance compared to traditional factor investing, while FCI led to insufficient causal graphs. For the portfolios constructed with neural networks in CFI, the use of causal factors resulted in the best-performing portfolio.

Altogether, CFI contributes to the field of quantitative finance by offering an explainable and profitable approach to factor investing. For further research, we suggest exploring alternative causal discovery algorithms, including time-series causal discovery methods and other algorithms that account for hidden confounders to increase the accuracy of the causal graphs. Additionally, a practical improvement would be including transaction costs and adjust the model for risk constraints through optimization approaches.