The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

Rico Bayu Wiranata, Rico Bayu Wiranata and Arif Djunaidy, Arif Djunaidy (2021) The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review. Jurnal Ilmu Komputer dan Informasi, 14 (2). pp. 91-112. ISSN e-ISSN 2502-9274

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Systematic literature review; Stock market prediction methods; Indicator technical dataset; Machine Learning; Algorithm, Big Data Analysis
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Mrs Ni Made Yunia Dwi Savitri
Date Deposited:	18 Nov 2022 07:26
Last Modified:	18 Nov 2022 07:26
URI:	http://eprints.triatmamulya.ac.id/id/eprint/1850

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