Daily Asset Pricing Based on Deep Learning: Integrating No-Arbitrage Constraints and Market Dynamics - Chen, Yinlei (Journal of Computer Technology and Applied Mathematics, 2025)
We propose a novel deep learning approach to asset pricing that predicts individual stock returns using daily data while integrating no-arbitrage constraints and capturing market dynamics. Our model combines Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and Generative Adversarial Networks (GAN) to model the complex relationships between stock returns and various conditioning variables, including macroeconomic indicators, technical indicators, and market sentiment data. By incorporating the no-arbitrage condition into the deep learning framework, we enhance the accuracy and stability of asset pricing. We estimate a stochastic discount factor that explains asset returns from the conditional moment constraints implied by no-arbitrage. Our method outperforms traditional multi-factor models, such as the Fama-French model, in terms of Sharpe ratio, explained variation, and pricing errors. The GAN enforces the no-arbitrage constraint by identifying portfolio strategies that contain the most pricing information. The LSTM network uncovers hidden economic states, while the feedforward network captures the non-linear effects of conditioning variables. This research provides a new direction in asset pricing by applying deep learning to integrate market dynamics and enforce no-arbitrage constraints, offering more accurate pricing and valuable insights for generating profitable investment strategies.