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

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

Prediksi Saham Menggunakan Model Ensemble Berbasis Pohon Keputusan Dalam Kerangka Stacking Yang Mempertimbangkan Indikator Teknikal Dan Sentimen Berita

Investor harus memprediksi saham dengan tepat agar keuntungan maksimal sekaligus terhindar kebangkrutan. Namun bursa saham sulit dideteksi karena perilakunya tidak hanya dipengaruhi oleh faktor historis harga dan indikator teknikal, tetapi juga dipengaruhi oleh situasi politik, kinerja perusahaan, maupun perekonomian global yang tersedia melalui berita. Penelitian ini bertujuan mengembangkan model prediksi saham yang mengkombinasikan indikator teknikal saham dan sentimen berita menggunakan metode stacking ensemble berbasis pohon keputusan. Dalam metode ini, beberapa pengklasifikasi berbasis algoritma pohon keputusan digunakan sebagai base-learner yang kemudian ditumpuk menggunakan algoritma stacking sebagai meta-learner. Masing-masing base-learner dibangun melalui proses optimasi hyper-parameter pohon keputusan menggunakan algoritma genetika. Algoritma stacking kemudian digunakan untuk menumpuk hasil prediksi semua base-learner untuk membangun model prediksi akhir. Model prediksi yang dikembangkan dalam penelitian ini diuji coba menggunakan data sahan dari tiga bank nasional utama, yaitu Bank Rakyat Indonesia (BBRI), Bank Mandiri (BMRI), dan Bank Negara Indonesia (BBNI). Uji coba menggunakan data saham ketiga bank tersebut memberikan hasil akurasi berturut-turut sebesar 83,67%; 87,34%; 86,53% dan hasil f1-score berturut-turut sebesar 83,05%; 86,22%; 83,90%. Dampak dari hasil prediksi terhadap evaluasi perdagangan saham BBRI, BMRI, dan BBNI berturut-turut memberikan return sebesar 94,91%; 103,64%, 105,40% dan keuntungan bersih dalam setahun berturut-turut sebesar 102,30%; 122,52%; 119,94%. Hasil evaluasi perdagangan tersebut memberikan nilai maximum drawdown yang cenderung paling kecil dan nilai rasio sharpe yang tinggi. ========================================================================================== Investors must predict stocks correctly in order to maximize their profits while avoiding bankruptcy. However, the stock market is difficult to detect because its behavior is not only influenced by historical price factors and technical indicators, but is also influenced by the political situation, company performance, and the global economy that is available through the news. This study aims to develop a stock prediction model that combines stock technical indicators and related news sentiment using a decision tree-based stacking ensemble method. In this method, several classifiers based on decision tree algorithms are used as base-learners which are then stacked using a stacking algorithm as a meta-learner. Each base-learner is built through a decision tree hyper-parameter optimization process using a genetic algorithm. The stacking algorithm is then used to stack the predicted results of all base-learners to build the final prediction model. The prediction model developed in this study was tested using valid data from three main national banks, namely Bank Rakyat Indonesia (BBRI), Bank Mandiri (BMRI), and Bank Negara Indonesia (BBNI). The experimental results using the stock data of these three banks gave accuracy of 83.67%, 87.34%, 86.53%, respectively; and f1-score results of 83.05%, 86.22%, 83.90%, respectively. The impact of the prediction results on the evaluation of trading shares of BBRI, BMRI, and BBNI respectively gave a return of 94.91%, 103.64%, 105.40% and a year's net profit in a row of 102.30%, 122.52%, 119.94%. Moreover, the results of the trade evaluation provide a maximum drawdown value that tends to be the smallest with a high sharpe ratio value