Optimizing Stock Price Forecasting using Elman RNN with Distributed Training and Hyperparameter Tuning

DOI

10.2991/978-94-6239-715-6_23How to use a DOI?

Keywords

Distributed training; financial forecasting; hyperparameter tuning; RNN; stock prediction

Abstract

The fluctuation of stock prices is influenced by various internal factors, such as tax policies and earnings per share, as well as external factors including economic conditions and political situations. These nonlinear and volatile characteristics present significant challenges for accurate prediction. This study applies a Recurrent Neural Network (RNN), specifically the Elman architecture, to forecast the daily closing prices of Bank Rakyat Indonesia (BBRI) stock using historical data from 2003 to 2024.To enhance computational efficiency, a distributed training strategy using data parallelism was employed, allowing faster model training on large-scale datasets. Additionally, hyperparameter tuning was carried out to optimize model performance. The best-performing model, optimized through extensive tuning, uses 9 time steps, 16 hidden neurons, a learning rate of 0.00011, ReLU activation, RMSProp optimizer, and Xavier Normal initialization. Evaluation results show that the model achieved a Mean Absolute Error (MAE) of 79.18 IDR, a Root Mean Square Error (RMSE) of 107.20 IDR, and a Mean Absolute Percentage Error (MAPE) of 1.58%. Furthermore, distributed training significantly accelerated the training process up to 33 times faster com- pared to conventional single-machine setups. These findings demonstrate the importance of distributed computing and thorough hyperparameter optimization in enhancing the performance of deep learning models for financial time series forecasting.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Anwar Rifai
AU  - Ahmad Zubaid Muzzakki
AU  - Mohammad Syafrullah
AU  - Riskiana Wulan
PY  - 2026
DA  - 2026/06/20
TI  - Optimizing Stock Price Forecasting using Elman RNN with Distributed Training and Hyperparameter Tuning
BT  - Proceedings of the International Conference on Cross- Disciplinary Academic Research 2025 - Track 2 Advances in Business & Economics, Social Science, Communications & Media (ICAR-T2 2025)
PB  - Atlantis Press
SP  - 309
EP  - 320
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-715-6_23
DO  - 10.2991/978-94-6239-715-6_23
ID  - Rifai2026
ER  -