Using deep learning to detect price change indications in financial markets

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Abstract

Forecasting financial time-series has long been among the most challenging problems in financial market analysis. In order to recognize the correct circumstances to enter or exit the markets investors usually employ statistical models (or even simple qualitative methods). However, the inherently noisy and stochastic nature of markets severely limits the forecasting accuracy of the used models. The introduction of electronic trading and the availability of large amounts of data allow for developing novel machine learning techniques that address some of the difficulties faced by the aforementioned methods. In this work we propose a deep learning methodology, based on recurrent neural networks, that can be used for predicting future price movements from large-scale high-frequency time-series data on Limit Order Books. The proposed method is evaluated using a large-scale dataset of limit order book events.

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