Deep Learning Approaches for Predicting Intraday Price Movements: An Evaluation of RNN Variants on High-Frequency Stock Data

Mochamad Ridwan (Badan Pusat Statistik, Departemen Statistik IPB University)
Kusman Sadik (Department of Statistics, IPB University)
Farit Mochamad Afendi (Department of Statistics, IPB University)

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St

This study discusses the comparison of four recurrent neural networks (RNN) models: Simple RNN, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional RNN (BiRNN), in forecasting minute-level stock price time series data. The performance of these four models is evaluated using the Mean Absolute Percentage Error (MAPE) on a stock dataset from Bank Central Asia (BBCA.JK). The experimental results reveal that the GRU model exhibits the best performance with an average MAPE of 0.0255%, followed by the LSTM model with an average MAPE of 0.0377%. The BiRNN model also demonstrates good performance with an average MAPE of 0.0668%, while the Simple RNN has the highest average MAPE at 0.5118%. This suggests that more complex recurrent architectures like GRU and LSTM have better capabilities in capturing patterns in high-frequency time series data. This study can be expanded by exploring other models such as CNN, conducting tests on diverse datasets, and experimenting with a wider range of hyperparameter variations. Additional variables such as economic indicators, global market data, and social data can also offer a more comprehensive understanding of factors influencing stock prices.