Financial Markets Prediction with Deep Learning

TL;DR

This paper introduces a novel one-dimensional CNN model for predicting financial market movements, automatically extracting features from raw trading data, and demonstrating improved robustness and profitability over previous machine learning methods.

Contribution

The paper presents a new CNN architecture that automatically learns features from raw financial data, avoiding biases from traditional technical indicators, and shows superior predictive performance.

Findings

CNN outperforms traditional technical indicators in feature extraction.

Model achieves more robust and profitable trading strategies.

Backtesting on six futures datasets confirms effectiveness.

Abstract

Financial markets are difficult to predict due to its complex systems dynamics. Although there have been some recent studies that use machine learning techniques for financial markets prediction, they do not offer satisfactory performance on financial returns. We propose a novel one-dimensional convolutional neural networks (CNN) model to predict financial market movement. The customized one-dimensional convolutional layers scan financial trading data through time, while different types of data, such as prices and volume, share parameters (kernels) with each other. Our model automatically extracts features instead of using traditional technical indicators and thus can avoid biases caused by selection of technical indicators and pre-defined coefficients in technical indicators. We evaluate the performance of our prediction model with strictly backtesting on historical trading data of six futures from January 2010 to October 2017. The experiment results show that our CNN model can effectively extract more generalized and informative features than traditional technical indicators, and achieves more robust and profitable financial performance than previous machine learning approaches.