  Traditional machine learning methods have been widely studied in financial
innovation. My study focuses on the application of deep learning methods on
asset pricing. I investigate various deep learning methods for asset pricing,
especially for risk premia measurement. All models take the same set of
predictive signals (firm characteristics, systematic risks and macroeconomics).
I demonstrate high performance of all kinds of state-of-the-art (SOTA) deep
learning methods, and figure out that RNNs with memory mechanism and attention
have the best performance in terms of predictivity. Furthermore, I demonstrate
large economic gains to investors using deep learning forecasts. The results of
my comparative experiments highlight the importance of domain knowledge and
financial theory when designing deep learning models. I also show return
prediction tasks bring new challenges to deep learning. The time varying
distribution causes distribution shift problem, which is essential for
financial time series prediction. I demonstrate that deep learning methods can
improve asset risk premium measurement. Due to the booming deep learning
studies, they can constantly promote the study of underlying financial
mechanisms behind asset pricing. I also propose a promising research method
that learning from data and figuring out the underlying economic mechanisms
through explainable artificial intelligence (AI) methods. My findings not only
justify the value of deep learning in blooming fintech development, but also
highlight their prospects and advantages over traditional machine learning
methods.
