Deep Limit Order Book Forecasting
This work addresses the challenge of applying deep learning to financial markets for academics and practitioners, highlighting limitations in traditional metrics and offering a more practical evaluation approach.
The study tackled the problem of forecasting high-frequency Limit Order Book mid-price changes using deep learning, finding that stock microstructural characteristics affect model efficacy and that high forecasting power does not guarantee actionable trading signals. It proposed an operational framework to evaluate prediction practicality based on transaction accuracy.
We exploit cutting-edge deep learning methodologies to explore the predictability of high-frequency Limit Order Book mid-price changes for a heterogeneous set of stocks traded on the NASDAQ exchange. In so doing, we release `LOBFrame', an open-source code base to efficiently process large-scale Limit Order Book data and quantitatively assess state-of-the-art deep learning models' forecasting capabilities. Our results are twofold. We demonstrate that the stocks' microstructural characteristics influence the efficacy of deep learning methods and that their high forecasting power does not necessarily correspond to actionable trading signals. We argue that traditional machine learning metrics fail to adequately assess the quality of forecasts in the Limit Order Book context. As an alternative, we propose an innovative operational framework that evaluates predictions' practicality by focusing on the probability of accurately forecasting complete transactions. This work offers academics and practitioners an avenue to make informed and robust decisions on the application of deep learning techniques, their scope and limitations, effectively exploiting emergent statistical properties of the Limit Order Book.