Hansun, Seng and Suryadibrata, Alethea and Sandi, Donn Rithalna (2022) Deep Learning Approach in Predicting Property and Real Estate Indices. International Journal Advance Soft Computing, 14 (1). pp. 60-71. ISSN 2074-8523
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Abstract
The real estate market is one of the most impacted sectors from the Corona Virus Disease 2019 (COVID-19) pandemic that happened in early 2020 globally. Here, we tried to apply an extension of the Long Short-Term Memory (LSTM) deep learning method, known as the Bidirectional LSTM (Bi-LSTM) networks for stock price prediction. Our focus is on six stocks that were included in the LiQuid45 (LQ45) property and real estate sectors. A simple three-layers Bi-LSTM network is proposed for predicting the stocks’ closing prices. We found that the prediction results fall in the reasonable prediction category, except for Pembangunan Perumahan Tbk (PTPP). Bumi Serpong Damai Tbk (BSDE) got the highest accuracy result with more than 90% score, while PTPP got the lowest score with less than 8% score. The proposed Bi-LSTM network could provide a baseline result for developing a good trading strategy.
| Item Type: | Article |
|---|---|
| Keywords: | Bi-LSTM networks, deep learning, LQ45, property and real estate, stock price prediction. |
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 003 Systems (Computer Modeling and Simulation) 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 005 Computer Programming 600 Technology (Applied Sciences) > 640 Home and Family Management > 643 Housing > 643.1 Rental, Housing Security, Property |
| Divisions: | Faculty of Engineering & Informatics > Informatics |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 03 Nov 2023 07:15 |
| Last Modified: | 03 Nov 2023 07:15 |
| URI: | https://kc.umn.ac.id/id/eprint/27022 |
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