The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review
Dublin Core
Title
The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review
Subject
systematic literature review, stock market prediction method, stock prediction dataset, prediction method improvement, prediction framework, machine learning
Description
This literature review identifies and analyzes research topic trends, types of data sets, learning
algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81
studies were investigated, which were published regarding stock predictions in the period January
2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results.Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization.
algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81
studies were investigated, which were published regarding stock predictions in the period January
2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results.Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization.
Creator
Rico Bayu Wiranata, Arif Djunaidy
Source
http://dx.doi.org/10.21609/jiki.v14i2.935
Publisher
Faculty of Computer Science Universitas Indonesia
Date
2021-07-04
Contributor
Sri Wahyuni
Rights
e-ISSN : 2502-9274 printed ISSN : 2088-7051
Format
PDF
Language
English
Type
Text
Coverage
Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Files
Collection
Citation
Rico Bayu Wiranata, Arif Djunaidy, “The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review,” Repository Horizon University Indonesia, accessed May 22, 2025, https://repository.horizon.ac.id/items/show/8825.