Abstract
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.
Irene Cannistraci
Ph.D. Student in Computer Science, Sapienza University of Rome
GLADIA Research Group
Visiting Researcher Student,
Helmholtz Munich
AIDOS Lab
I am a Ph.D. student in Computer Science passionate about Deep Learning.