Deep Sequence Modeling: Development and Applications in Asset Pricing
This work addresses the problem of improving asset pricing predictions for investors by leveraging sequential dependencies, but it is incremental as it applies existing deep learning methods to financial data.
The paper tackles predicting asset returns and measuring risk premia by applying deep sequence modeling, such as LSTM, to U.S. equities data, showing that LSTM-based models achieve the best out-of-sample performance.
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.