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

Matteo Prata, Giuseppe Masi, Leonardo Berti, Viviana Arrigoni, Andrea Coletta +4 more
7/5/2023
q-fin.TRcs.LG

Official code: matteoprata/LOBCAST (123 stars, last commit 2024-05-09).

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.

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Code Implementations(1)

matteoprata/LOBCASTOfficial58%

LOBCAST is a Python-based open-source framework for stock market trend forecasting using Limit Order Book (LOB) data. 🤖📈

12333PythonOct 13, 20222 years ago
deep-learningfi-2010financelimit-order-booklob+3 more

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Cite this paper

@article{prata2023lobbased,
  title  = {LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study},
  author = {Matteo Prata and Giuseppe Masi and Leonardo Berti and Viviana Arrigoni and Andrea Coletta and Irene Cannistraci and Svitlana Vyetrenko and Paola Velardi and Novella Bartolini},
  year   = {2023},
  eprint = {2308.01915},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.01915v2}
}

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