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


The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen stateof-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.

arXiv preprint arXiv:2206.06182
Irene Cannistraci
Irene Cannistraci
Ph.D. Student in Computer Science, Sapienza University of Rome

I am a Ph.D. student passionate about Artificial Intelligence.