LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study
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.
Cite
@article{arxiv.2308.01915,
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},
journal= {arXiv preprint arXiv:2308.01915},
year = {2023}
}