Asset Pricing

Asset pricing research aims to understand and predict the returns of financial assets, seeking to explain why some assets outperform others. Current research heavily utilizes machine learning, employing diverse architectures like deep learning (including recurrent neural networks and autoencoders), Gaussian processes, and ensemble methods to model complex, non-linear relationships between asset characteristics, macroeconomic factors, and returns. These advancements improve the accuracy of return predictions and risk assessments, leading to better portfolio optimization strategies and a deeper understanding of market dynamics. The resulting improvements in model accuracy and interpretability have significant implications for both investment management and financial theory.