Articlediamond2023

Stock price forecasting in Indonesia stock exchange using deep learning: a comparative study

Agus Tri Haryono · Riyanarto Sarno · Kelly Rossa Sungkono

35 citations0/0 files storedInternational Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering

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Stock Market Forecasting Methods Financial Analysis and Corporate Governance Hydrological Forecasting Using AI

Abstract

In 2022, the Indonesia stock exchange (IDX) listed 825 companies, making it challenging to identify low-risk companies. Stock price forecasting and price movement prediction are vital issues in financial works. Deep learning has previously been implemented for stock market analysis, with promising results. Because of the differences in architecture and stock issuers in each study report, a consensus on the best stock price forecasting model has yet to be reached. We present a methodology for comparing the performance of convolutional neural networks (CNN), gated recurrent units (GRU), long short-term memory (LSTM), and graph convolutional networks (GCN) layers. The four layers types combination yields 11 architectures with two layers stacked maximum, and the architectures are performance compared in stock price predicting. The dataset consists of open, highest, lowest, closed price, and volume transactions and has 2,588,451 rows from 727 companies in IDX. The best performance architecture was chosen by a vote based on the coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute percent error (MAPE), and f1-score. TFGRU is the best architecture, producing the finest results on 315 companies with an average score of RMSE is 553.327, MAPE is 0.858, and f1-score is 0.456.

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2020A CNN-BiLSTM-AM method for stock price prediction 2020Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis 2019Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data 2020Prediction model for stock price trend based on recurrent neural network 2021Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM)2020Predicting the Trend of Stock Market Index Using the Hybrid Neural Network Based on Multiple Time Scale Feature Learning 2019Evaluation of forecasting methods from selected stock market returns 2021Predicting LQ45 financial sector indices using RNN-LSTM

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