CNNpred: CNN-based stock market prediction using a diverse set of variables

By Ehsan Hoseinzade, Saman Haratizadeh. Expert systems with applications, 2019

551 citations recorded by the source corpus.

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.

Subfields: Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b6b3cbd4d71264e1d533a9933419b05f045233a8

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In Management Science and Operations Research

Citations in the corpus

Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model (2020, background): “Moreover, this layer reduces the computational cost of the learning process and handles any overﬁtting issues (Hoseinzade and Haratizadeh 2019).”
Applications of deep learning in stock market prediction: recent progress (2020, background): “…Learning Hoseinzade et al. (2019) Transfer Learn- ing+CNN PCA+ANN (Zhong & Enke, 2017), ANN (Kara et al., 2011), CNN (Gunduz et al., 2017), CNN (Hoseinzade & Haratizadeh, 2019) Nguyen & Yoon (2019) Transfer Learn- ing+LSTM SVM, RF, KNN, LSTM (Fischer & Krauss, 2018) We show the change…”
Deep Learning for Stock Market Prediction (2020, background): “Hoseinzade and Haratizadeh [24] proposed a Convolutional Neural Networks (CNNs) framework, which can be applied to various data collections (involving different markets) to explore features for predicting the future movement of the markets.”
A Survey of Forex and Stock Price Prediction Using Deep Learning (2021, methodology): “We found that paper [22] had the highest accuracy compared to any other papers, paper [16] had the highest return rate followed by papers [14,18,21].”