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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Prata, M., Masi, G., Berti, L., Arrigoni, V., Coletta, A., Cannistraci, I., … Bartolini, N. (2024). Lob-based deep learning models for stock price trend prediction: a benchmark study. Artificial Intelligence Review, 57(5). https://doi.org/10.1007/s10462-024-10715-4