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Seng Hansun
Universitas Multimedia Nusantara
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6619-9751
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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.
Keywords
Deep Learning, LQ45 financial sector indices, LSTM networks, Prediction, Stock market
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View the published article at Journal of Big Data.
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Received 12 Jun, 2021
Reviewer #8 agreed
On 04 Jun, 2021
Reviewer #6 agreed
On 02 Jun, 2021
Reviewer #7 agreed
On 02 Jun, 2021
Reviewer #5 agreed
On 02 Jun, 2021
Review #1 received
Received 02 Jun, 2021
Review #2 received
Received 02 Jun, 2021
Reviewer #4 agreed
On 01 Jun, 2021
Reviewer #3 agreed
On 01 Jun, 2021
Reviews received
Received 01 Jun, 2021
Reviewers invited
Invitations sent on 01 Jun, 2021
Reviewer #2 agreed
On 31 May, 2021
Reviewer #1 agreed
On 31 May, 2021
Editor assigned
On 03 May, 2021
Submission checks complete
On 03 May, 2021
Editor invited
On 03 May, 2021
First submitted
On 27 Apr, 2021
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Subject Areas
Biotechnology and Bioengineering
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at Journal of Big Data.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Seng Hansun
Universitas Multimedia Nusantara
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6619-9751
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Journal of Big Data.
Version 1
Posted 12 May, 2021
No community comments so far
Editorial decision: Major Revision
On 02 Jul, 2021
Review #5 received
Received 17 Jun, 2021
Review #3 received
Received 12 Jun, 2021
Review #4 received
Received 12 Jun, 2021
Reviewer #8 agreed
On 04 Jun, 2021
Reviewer #6 agreed
On 02 Jun, 2021
Reviewer #7 agreed
On 02 Jun, 2021
Reviewer #5 agreed
On 02 Jun, 2021
Review #1 received
Received 02 Jun, 2021
Review #2 received
Received 02 Jun, 2021
Reviewer #4 agreed
On 01 Jun, 2021
Reviewer #3 agreed
On 01 Jun, 2021
Reviews received
Received 01 Jun, 2021
Reviewers invited
Invitations sent on 01 Jun, 2021
Reviewer #2 agreed
On 31 May, 2021
Reviewer #1 agreed
On 31 May, 2021
Editor assigned
On 03 May, 2021
Submission checks complete
On 03 May, 2021
Editor invited
On 03 May, 2021
First submitted
On 27 Apr, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Biotechnology and Bioengineering
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Journal of Big Data.
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.
Figure 1
Figure 2
Figure 3
Figure 4
The full text of this article is available to read as a PDF.
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