/Deep_Learning_in_Asset_Pricing

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3350138

Primary LanguageJupyter Notebook

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.

  • 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

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