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
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.
| 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 |
| 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 |
| 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 |
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