README.md

Deep Learning in Asset Pricing

Table of Contents

This repository contains empirical results in paper to estimate a general non-linear asset pricing model with a deep neural network applied to all U.S. equity data combined with all relevant macroeconomic and firm-specific information.

• Empirical Results
• Related Resources
• Author

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Empirical Results

We compare our GAN model, with a simple forecasting feedforward network model labeled as FFN, the linear special case of GAN labeled as LS and a regularized linear model labeled as EN.

• Sharpe Ratio

Model	SR (Train)	SR (Valid)	SR (Test)
LS	1.80	0.58	0.42
EN	1.37	1.15	0.50
FFN	0.45	0.42	0.44
GAN	2.68	1.43	0.75

• Explained Variation

Model	EV (Train)	EV (Valid)	EV (Test)
LS	0.09	0.03	0.03
EN	0.12	0.05	0.04
FFN	0.11	0.04	0.04
GAN	0.20	0.09	0.08

• Cross-Sectional R2

Model	XS-R2 (Train)	XS-R2 (Valid)	XS-R2 (Test)
LS	0.15	0.00	0.14
EN	0.17	0.02	0.19
FFN	0.14	-0.00	0.15
GAN	0.12	0.01	0.23

Related Resources

• Deep Learning in Asset Pricing (Chen, Pelger and Zhu 2019)
• Pre-trained Models
• Data
• [GitHub Repository]

Author

• Luyang Chen