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DOI: 10.14569/IJACSA.2024.0150305
PDF

Generative Adversarial Neural Networks for Realistic Stock Market Simulations

Author 1: Badre Labiad
Author 2: Abdelaziz Berrado
Author 3: Loubna Benabbou

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 3, 2024.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Stock market simulations are widely used to create synthetic environments for testing trading strategies before deploying them to real-time markets. However, the weak realism often found in these simulations presents a significant challenge. Improving the quality of stock market simulations could be facilitated by the availability of rich and granular real Limit Order Books (LOB) data. Unfortunately, access to LOB data is typically very limited. To address this issue, a framework based on Generative Adversarial Networks (GAN) is proposed to generate synthetic realistic LOB data. This generated data can then be utilized for simulating downstream decision-making tasks, such as testing trading strategies, conducting stress tests, and performing prediction tasks. To effectively tackle challenges related to the temporal and local dependencies inherent in LOB structures and to generate highly realistic data, the framework relies on a specific data representation and preprocessing scheme, transformers, and conditional Wasserstein GAN with gradient penalty. The framework is trained using the FI-2010 benchmark dataset and an ablation study is conducted to demonstrate the importance of each component of the proposed framework. Moreover, qualitative and quantitative metrics are proposed to assess the quality of the generated data. Experimental results indicate that the framework outperforms existing benchmarks in simulating realistic market conditions, thus demonstrating its effectiveness in generating synthetic LOB data for diverse downstream tasks.

Keywords: Limit order book simulations; transformers; wasserstein GAN with gradient penalty; FI-2010 benchmark dataset

Badre Labiad, Abdelaziz Berrado and Loubna Benabbou, “Generative Adversarial Neural Networks for Realistic Stock Market Simulations” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150305

@article{Labiad2024,
title = {Generative Adversarial Neural Networks for Realistic Stock Market Simulations},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150305},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150305},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Badre Labiad and Abdelaziz Berrado and Loubna Benabbou}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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