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

• URL: http://arxiv.org/abs/2308.01915v2
• Date: Tue, 19 Sep 2023 20:52:54 GMT
• Title: LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study
• Authors: Matteo Prata, Giuseppe Masi, Leonardo Berti, Viviana Arrigoni, Andrea Coletta, Irene Cannistraci, Svitlana Vyetrenko, Paola Velardi, Novella Bartolini
• Abstract summary: We develop an open-source framework that incorporates data preprocessing, DL model training, evaluation and profit analysis. Our experiments reveal that all models exhibit a significant performance drop when exposed to new data, thereby raising questions about their real-world market applicability.
• Score: 4.714825039388054
• License: http://creativecommons.org/licenses/by/4.0/
• 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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