Author:
Prata Matteo,Masi Giuseppe,Berti Leonardo,Arrigoni Viviana,Coletta Andrea,Cannistraci Irene,Vyetrenko Svitlana,Velardi Paola,Bartolini Novella
Abstract
AbstractThe 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.
Funder
JPMorgan Chase and Company
Università degli Studi di Roma La Sapienza
Publisher
Springer Science and Business Media LLC
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