Hansun, Seng and Young, Julio Cristian (2021) Predicting LQ45 Financial Sector Indices using RNN-LSTM. Journal of Big Data, 8 (104).
Full text not available from this repository.Abstract
As one of the most popular financial market instruments, the stock has formed one of the most massive and complex financial markets in the world. It could handle millions of transactions within a short period and highly unpredictable. In this study, we aim to implement a famous Deep Learning method, namely the Long Short-Term Memory (LSTM) networks, for the stock price prediction. We limit the stocks to those that are included in the LQ45 financial sectors indices, i.e., BBCA, BBNI, BBRI, BBTN, BMRI, and BTPS. We propose to use a simple three layers LSTM network architecture in predicting the stocks’ closing prices and found that the prediction results fall in the reasonable forecasting category. It is worthy to note that two of the considered stocks, i.e., BBCA and BMRI, have the lowest MAPE values at 19.1020 and 18.6135 which fall in the good forecasting results. Hence, the proposed LSTM model is recommended to be used on those two stocks.
| Item Type: | Article |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods 300 Social Sciences > 330 Economics 500 Science and Mathematic > 510 Mathematics > 519 Probabilities and Applied Mathematics |
| Divisions: | Faculty of Engineering & Informatics > Informatics |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 06 Oct 2021 02:59 |
| Last Modified: | 06 Oct 2021 02:59 |
| URI: | https://kc.umn.ac.id/id/eprint/18549 |
Actions (login required)
 |
View Item |
