Deep Learning for Predicting Asset Returns

By Guanhao Feng, Jingyu He, Nicholas G. Polson. 2018

61 citations recorded by the source corpus.

Deep learning searches for nonlinear factors for predicting asset returns. Predictability is achieved via multiple layers of composite factors as opposed to additive ones. Viewed in this way, asset pricing studies can be revisited using multi-layer deep learners, such as rectified linear units (ReLU) or long-short-term-memory (LSTM) for time-series effects. State-of-the-art algorithms including stochastic gradient descent (SGD), TensorFlow and dropout design provide imple- mentation and efficient factor exploration. To illustrate our methodology, we revisit the equity market risk premium dataset of Welch and Goyal (2008). We find the existence of nonlinear factors which explain predictability of returns, in particular at the extremes of the characteristic space. Finally, we conclude with directions for future research.

Subfields: Electrical and Electronic Engineering, Finance, Management Science and Operations Research, Economics & Operations Research, Time-Series & Forecasting

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “[97] Return of S&P500 1926-2016 Fundamental Features: 1s DNN MSPE Tensorflow”
Bond Risk Premia with Machine Learning∗ (2018, unknown): “An uncontroversial result of the term structure literature is that the first three principal convention is followed by, e.g., Feng et al. (2018) who count only the hidden layers. components – dubbed level, slope and curvature – summarize almost 99% of the information in the cross-section of yields.”
Deep Learning in Asset Pricing (2019, methodology): “Our approach outperforms out-of-sample all other bench(2)These models include Gu et al. (2019b), Messmer (2017) or Kelly et al.”