Application of Ensemble Transformer-RNNs on Stock Price Prediction of Bank Central Asia

Authors

  • Muhammad Rizki Nur Majiid Computer Science Department, BINUS Graduate Program – Master of Computer Science, Bina Nusantara University, Jl. Raya Kebon Jeruk No. 27, Jakarta Barat, 11530, INDONESIA https://orcid.org/0000-0002-0198-510X
  • Renaldy Fredyan Computer Science Department, BINUS Graduate Program – Master of Computer Science, Bina Nusantara University, Jl. Raya Kebon Jeruk No. 27, Jakarta Barat, 11530, INDONESIA https://orcid.org/0000-0002-4722-6408
  • Gede Putra Kusuma Computer Science Department, BINUS Graduate Program – Master of Computer Science, Bina Nusantara University, Jl. Raya Kebon Jeruk No. 27, Jakarta Barat, 11530, INDONESIA https://orcid.org/0000-0003-4241-997X

Keywords:

Stock Prediction, Deep Learning, Transformer Model, Multi-Head Attention Mechanism, Ensemble Model

Abstract

Breaking news information about the stock market is gathered from numerous finance websites. Internet portals offer free financial information about businesses. The impact of significant financial reforms, the environment, natural disasters, and news events on the stock market is minimal. The online financial platform generates many time series data. Additionally, we include data from the USD, CNY, Gold, and Oil unrelated to the stock share but relevant. With the use of various machine learning algorithms, market reforms are projected. Those datasets are collected from yahoo finance and investing.com, along with other stock market aspects. These models are trained to utilize an extended dataset, including open price, close price, low price, high price, and volume, from Bank Central Asia’s stock price. The Ensemble Transformer LSTM (ET-LSTM) and Ensemble Transformer GRU (ET-GRU) architecture forecast the stock price for the following day. The data set is improved using a variety of deep learning approaches to get more accurate findings. Both suggested approaches use ensemble architecture to deliver 9% of MAPE. Market movements are perfectly aligned in terms of high and low stock prices. Algorithms for high-frequency trading can further enhance the outcomes.

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The architecture of ET-RNN combined with CNN using ensemble architecture.

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Published

17.02.2023

How to Cite

Rizki Nur Majiid , M. ., Fredyan , R. ., & Putra Kusuma, G. . (2023). Application of Ensemble Transformer-RNNs on Stock Price Prediction of Bank Central Asia. International Journal of Intelligent Systems and Applications in Engineering, 11(2), 471–477. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2656

Issue

Vol. 11 No. 2 (2023)

Section

Research Article

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