  We predict asset returns and measure risk premia using a prominent technique
from artificial intelligence -- deep sequence modeling. Because asset returns
often exhibit sequential dependence that may not be effectively captured by
conventional time series models, sequence modeling offers a promising path with
its data-driven approach and superior performance. In this paper, we first
overview the development of deep sequence models, introduce their applications
in asset pricing, and discuss their advantages and limitations. We then perform
a comparative analysis of these methods using data on U.S. equities. We
demonstrate how sequence modeling benefits investors in general through
incorporating complex historical path dependence, and that Long- and Short-term
Memory (LSTM) based models tend to have the best out-of-sample performance.
