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Intelligent Models for Stock Price Prediction: A Comprehensive Review

Intelligent Models for Stock Price Prediction: A Comprehensive Review

Kwabena Ansah, Ismail Wafaa Denwar, Justice Kwame Appati
Copyright: © 2022 |Volume: 15 |Issue: 1 |Pages: 17
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781683180340|DOI: 10.4018/JITR.298616
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MLA

Ansah, Kwabena, et al. "Intelligent Models for Stock Price Prediction: A Comprehensive Review." JITR vol.15, no.1 2022: pp.1-17. http://doi.org/10.4018/JITR.298616

APA

Ansah, K., Denwar, I. W., & Appati, J. K. (2022). Intelligent Models for Stock Price Prediction: A Comprehensive Review. Journal of Information Technology Research (JITR), 15(1), 1-17. http://doi.org/10.4018/JITR.298616

Chicago

Ansah, Kwabena, Ismail Wafaa Denwar, and Justice Kwame Appati. "Intelligent Models for Stock Price Prediction: A Comprehensive Review," Journal of Information Technology Research (JITR) 15, no.1: 1-17. http://doi.org/10.4018/JITR.298616

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Abstract

Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes challenging among the investors and shareholders to invest the money to make profits. This paper is a review of stock price prediction, focusing on metrics, models, and datasets. It presents a detailed review of 30 research papers suggesting the methodologies, such as Support Vector Machine Random Forest, Linear Regression, Recursive Neural Network, and Long Short-Term Movement based on the stock price prediction. Aside from predictions, the limitations, and future works are discussed in the papers reviewed. The commonly used technique for achieving effective stock price prediction is the RF, LSTM, and SVM techniques. Despite the research efforts, the current stock price prediction technique has many limits. From this survey, it is observed that the stock market prediction is a complicated task, and other factors should be considered to accurately and efficiently predict the future.