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Predicting LQ45 Financial Sector Indices using RNN-LSTM

Hansun, Seng and Young, Julio Cristian (2021) Predicting LQ45 Financial Sector Indices using RNN-LSTM. Journal of Big Data, 8 (104).

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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 (3D Graphics, Digital Video, Data Mining, Augmented Reality)
300 Social Sciences > 330 Economics
500 Science and Mathematic > 510 Mathematics > 519 Probabilities and Applied Mathematics
Divisions: Fakultas Teknik Informatika > Program Studi Informatika
Depositing User: mr admin umn
Date Deposited: 06 Oct 2021 02:59
Last Modified: 06 Oct 2021 02:59

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