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LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study

Author

Listed:
  • Matteo Prata
  • Giuseppe Masi
  • Leonardo Berti
  • Viviana Arrigoni
  • Andrea Coletta
  • Irene Cannistraci
  • Svitlana Vyetrenko
  • Paola Velardi
  • Novella Bartolini

Abstract

The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL models focusing on Stock Price Trend Prediction (SPTP) based on Limit Order Book (LOB) data. To carry out this study, we developed LOBCAST, an open-source framework that incorporates data preprocessing, DL model training, evaluation and profit analysis. Our extensive experiments reveal that all models exhibit a significant performance drop when exposed to new data, thereby raising questions about their real-world market applicability. Our work serves as a benchmark, illuminating the potential and the limitations of current approaches and providing insight for innovative solutions.

Suggested Citation

  • Matteo Prata & Giuseppe Masi & Leonardo Berti & Viviana Arrigoni & Andrea Coletta & Irene Cannistraci & Svitlana Vyetrenko & Paola Velardi & Novella Bartolini, 2023. "LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study," Papers 2308.01915, arXiv.org, revised Sep 2023.
    • Handle: RePEc:arx:papers:2308.01915
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    References listed on IDEAS

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