Short-term stock market price trend prediction using a comprehensive deep learning system

• Main Authors: Jingyi Shen, M. Omair Shafiq
• Format: Article
• Language: English
• Published: SpringerOpen 2020-08-01
• Series: Journal of Big Data
• Subjects: Prediction; Deep learning; Stock market trend; Feature engineering
• Online Access: http://link.springer.com/article/10.1186/s40537-020-00333-6

Abstract In the era of big data, deep learning for predicting stock market prices and trends has become even more popular than before. We collected 2 years of data from Chinese stock market and proposed a comprehensive customization of feature engineering and deep learning-based model for predicting price trend of stock markets. The proposed solution is comprehensive as it includes pre-processing of the stock market dataset, utilization of multiple feature engineering techniques, combined with a customized deep learning based system for stock market price trend prediction. We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built. The system achieves overall high accuracy for stock market trend prediction. With the detailed design and evaluation of prediction term lengths, feature engineering, and data pre-processing methods, this work contributes to the stock analysis research community both in the financial and technical domains.