CNNpred: CNN-based stock market prediction using a diverse set of variables. (1st September 2019)

• Record Type: Journal Article
• Authors: Hoseinzade, Ehsan; Haratizadeh, Saman
• Is Part Of: Expert systems with applications. Volume 129(2019)
• Page Start: 273
• Page End: 285
• Publication Date: 2019-09-01
• Subjects: Stock markets prediction -- Deep learning -- Convolutional neural networks -- CNN -- Feature extraction; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.03.029
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: 3D-CNNpred is the first 3-dimensional CNN model designed for stock market prediction. CNNpred successfully combines various sources of information for prediction. CNNs filters are designed to better handle financial data. Deep CNN-based framework significantly outperforms shallow ANNs. CNNpred is profitable in 4 out of 5 tested indices in presence of transaction costs. Abstract: Feature extraction from financial data is one of the most important problems in market prediction domain for which many approaches have been suggested. Among other modern tools, convolutional neural networks (CNN) have recently been applied for automatic feature selection and market prediction. However, in experiments reported so far, less attention has been paid to the correlation among different markets as a possible source of information for extracting features. In this paper, we suggest a CNN-based framework, that can be applied on a collection of data from a variety of sources, including different markets, in order to extract features for predicting the future of those markets. The suggested framework has been applied for predicting the next day's direction of movement for the indices of S&P 500, NASDAQ, DJI, NYSE, and RUSSELL based on various sets of initial variables. The evaluations show a significant improvement in prediction's performance compared to the state of the art baseline algorithms.