Title: Machine Learning for Financial Asset Pricing
Description: Asset pricing is concerned with understanding the drivers of asset prices, helping investors to better understand risks underpinning asset allocation. This research will employ machine learning (ML) techniques to uncover new links between economic fundamentals and asset prices, allowing the identification of mis-priced securities. ML-based techniques, such as dimensionality reduction, deep learning, regression trees and cluster analysis have helped uncover complex non-linear associations across multiple fields but remain relatively unexplored in the field of financial asset pricing. In this research, improved asset pricing precision will result from discerning between long-run fundamentals and short-run fluctuations. Economic intuition will be developed through the use of interpretable ML. The research has direct FinTech related applications, including to the fields of asset management, trading strategies and risk management.