Asset Pricing with Attention Guided Deep Learning
57 Pages Posted: 2 Feb 2022 Last revised: 19 Jul 2022
Date Written: November 25, 2021
Deep learning methods, which can accommodate wide ranges of various stock characteristics to identify optimal investment portfolio or stochastic discount factor (SDF), have been criticised for extracting their superior performances from difficult to arbitrage stocks, high limits-to-arbitrage market conditions or extreme turnovers. We introduce \attention-guided deep learning, which, in a data driven way, allows identifying the most influential time-varying firm characteristics contributing to SDF. Attention dramatically improves SDF performance and attention to multiple firm characteristics reduces portfolio rebalancing costs. The attention guided SDF outperforms existing models after excluding small and micro-cap stocks, avoids extreme portfolio weights, and unlike other models, exhibits the best performance during market regimes with the highest price efficiency.
Keywords: No-arbitrage, optimal portfolio, conditional asset pricing, deep learning, attention, big data
JEL Classification: G10, G12, G13, G14
Suggested Citation: Suggested Citation