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.
Investor harus memprediksi saham dengan tepat agar keuntungan maksimal sekaligus terhindar kebangkrutan. Namun bursa saham sulit dideteksi situasinya. Perilakunya berubah-ubah dipengaruhi berbagai faktor seperti situasi politik, ekonomi perusahaan dan global, maupun ekspektasi investor yang tersedia melalui berita. Penelitian ini bertujuan mengembangkan model yang dapat memprediksi saham lebih akurat mengkombinasikan indikator teknikal saham dan sentimen berita. Genetic algorithm (GA) mengoptimalisasi beberapa ensemble decision tree-based yang ditumpuk menggunakan metode stacked-generalization dengan konsep meta-learner digunakan dalam penelitian ini. Terdapat lima tahapan utama metodologi, dimulai pengumpulan data saham dan berita, praproses data, ekstraksi fitur indikator teknikal dan sentimen serta analisis data, selanjutnya pengembangan model. Serangkaian uji coba parameter crossover dan mutasi GA memberi hasil optimum pencarian kombinatorik hyper-parameter model dengan accuracy 81.63% dan f1-score 82.21%. Evaluasi model terhadap kombinasi jenis dataset mampu meningkatkan accuracy prediksi dari 75.91% menajdi 81.63%, dan f1-score dari 77.56% menjadi 82.21%. Terhadap evaluasi trading, metode yang diusulkan terbukti memberi return yang fantastis sebesar 121.27% dalam setahun, dengan nilai maximum drawdown yang paling kecil juga nilai sharpe ratio yang tinggi. Evaluasi tersebut melampaui hasil penelitian serupa terdahulu, bahkan jauh diatas performa pergerakan saham itu sendiri terindikasi melalui strategi buy & hold
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