Stock price prediction using historical data and news articles: a survey

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

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Stock price prediction using historical data and news articles: a survey

International Journal of Computational Systems Engineering ◽

10.1504/ijcsyse.2021.10044070 ◽

2021 ◽

Vol 6 (4) ◽

pp. 182

Author(s):

Manoj Madhava Gore ◽

Vijay Kumar Dwivedi

Keyword(s):

Stock Price ◽

Historical Data ◽

Stock Price Prediction ◽

Price Prediction

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An Improved Elman Network for Stock Price Prediction Service

Security and Communication Networks ◽

10.1155/2020/8824430 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Bo Liu ◽

Qilin Wu ◽

Qian Cao

Keyword(s):

Neural Network ◽

Stock Price ◽

Historical Data ◽

Rapid Development ◽

Composite Index ◽

Model Parameters ◽

Short Term ◽

Elman Neural Network ◽

Stock Price Prediction ◽

Price Prediction

The rapid development of edge computing drives the rapid development of stock market prediction service in terminal equipment. However, the traditional prediction service algorithm is not applicable in terms of stability and efficiency. In view of this challenge, an improved Elman neural network is proposed in this paper. Elman neural network is a typical dynamic recurrent neural network that can be used to provide the stock price prediction service. First, the prediction model parameters and build process are analysed in detail. Then, the historical data of the closing price of Shanghai composite index and the opening price of Shenzhen composite index are collected for training and testing, so as to predict the prices of the next trading day. Finally, the experiment results validate that it is effective to predict the short-term future stock price by using the improved Elman neural network model.

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