Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data
This addresses the problem of stock price prediction for financial analysts, but it appears incremental as it applies existing ML methods to a known domain without breakthrough results.
The paper tackled forecasting stock price movements using limit order book data by combining handcrafted and ML-extracted features, evaluating three classifiers across different setups and scenarios, and found that machine learning is highly suitable for this task, though no concrete performance numbers were provided.
Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis. In this paper, we use Machine Learning (ML) algorithms for the prediction of future price movements using limit order book data. Two different sets of features are combined and evaluated: handcrafted features based on the raw order book data and features extracted by ML algorithms, resulting in feature vectors with highly variant dimensionalities. Three classifiers are evaluated using combinations of these sets of features on two different evaluation setups and three prediction scenarios. Even though the large scale and high frequency nature of the limit order book poses several challenges, the scope of the conducted experiments and the significance of the experimental results indicate that Machine Learning highly befits this task carving the path towards future research in this field.