CNN with Limit Order Book Data for Stock Price Prediction

Abstract

This work presents a remarkable and innovative short-term forecasting method for Financial Time Series (FTS). Most of the approaches for FTS modeling work directly with prices, given the fact that transaction data is more reachable and more widely available. For this particular work, we will be using the Limit Order Book (LOB) data, which registers all trade intentions from market participants. As a result, there is more enriched data to make better predictions. We will be using Deep Convolutional Neural Networks (CNN), which are good at pattern recognition on images. In order to accomplish the proposed task we will make an image-like representation of LOB and transaction data, which will feed up into the CNN, therefore it can recognize hidden patterns to classify FTS in short-term periods. We will present step by step methodology to encode financial time series into an image-like representation. Results present an impressive performance, ranging between 63% and 66% in Directional Accuracy (DA), having advantages in reducing model parameters as well as to make inputs time invariant.