Daily Asset Pricing Based on Deep Learning: Integrating No-Arbitrage Constraints and Market Dynamics

Yinlei Chen

doi:10.70393/6a6374616d.333235

OPEN ACCESS |Research Article ||4 November 2025

Daily Asset Pricing Based on Deep Learning: Integrating No-Arbitrage Constraints and Market Dynamics

Yinlei Chen

Kyungil University

chenyinlei123@gmail.com

Kyungil University, 38428, Republic of Korea.

* Corresponding Author¹: Yinlei Chen, E-Mail: chenyinlei123@gmail.com

Publication

Accepted 2025 October 14 ; Published 2025 November 4

Journal of Computer Technology and Applied Mathematics, 2025, 2(6), 3007-4126.

Abstract

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.

Keywords

No-arbitrage , Asset Pricing , Stock Returns , Deep Learning , LSTM , CNN , GAN , Market Dynamics .

Metadata

DOI:

10.70393/6a6374616d.333235

ARK:

ark:/40704/JCTAM.v2n6a01

Pages: 1-10

References: 20

Disciplines: Artificial Intelligence and Intelligence

Subjects: Deep Learning

Cite This Article

APA Style

Chen, Y. (2025). Daily asset pricing based on deep learning: integrating no-arbitrage constraints and market dynamics. Journal of Computer Technology and Applied Mathematics, 2(6), 1-10. https://doi.org/10.70393/6a6374616d.333235

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
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