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   https://doi.org/10.3390/e26100848  

   David, Jerry Jones; Sabhahit, Narayan G; Stramaglia, Sebastiano; Matteo, T Di; Boccaletti, Stefano; Jalan, Sarika (October 2024, Entropy)

   In stock markets, nonlinear interdependencies between various companies result in nontrivial time-varying patterns in stock prices. A network representation of these interdependencies has been successful in identifying and understanding hidden signals before major events like stock market crashes. However, these studies have revolved around the assumption that correlations are mediated in a pairwise manner, whereas, in a system as intricate as this, the interactions need not be limited to pairwise only. Here, we introduce a general methodology using information-theoretic tools to construct a higher-order representation of the stock market data, which we call functional hypergraphs. This framework enables us to examine stock market events by analyzing the following functional hypergraph quantities: Forman–Ricci curvature, von Neumann entropy, and eigenvector centrality. We compare the corresponding quantities of networks and hypergraphs to analyze the evolution of both structures and observe features like robustness towards events like crashes during the course of a time period. 

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2. Generating Realistic Stock Market Order Streams

   https://doi.org/10.1609/aaai.v34i01.5415  

   Li, Junyi; Wang, Xintong; Lin, Yaoyang; Sinha, Arunesh; Wellman, Michael (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks (GANs). Our Stock-GAN model employs a conditional Wasserstein GAN to capture history dependence of orders. The generator design includes specially crafted aspects including components that approximate the market's auction mechanism, augmenting the order history with order-book constructions to improve the generation task. We perform an ablation study to verify the usefulness of aspects of our network structure. We provide a mathematical characterization of distribution learned by the generator. We also propose statistics to measure the quality of generated orders. We test our approach with synthetic and actual market data, compare to many baseline generative models, and find the generated data to be close to real data. 

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3. Generating Realistic Stock Market Order Streams

   Li, Junyi; Wang, Xintong; Lin, Yaoyang; Sinha, Arunesh; Wellman, Michael P. (June 2019, ICML-19 Workshop on AI in Finance)
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