LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study
This work provides a benchmark for researchers and practitioners in finance, highlighting limitations in current deep learning approaches for stock prediction, but it is incremental as it focuses on evaluation rather than new methods.
The study benchmarked fifteen deep learning models for stock price trend prediction using limit order book data, finding that all models experienced significant performance drops on new data, questioning their real-world applicability.
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.