How can we predict transportation stock prices using artificial intelligence? Findings from experiments with Long Short-Term Memory based algorithms
Inflation growth in Indonesia and other countries impacts the currency value and investors' purchasing power, particularly in the transportation sector. This research explores the impact of inflation growth in Indonesia and comparable nations on currency valuation and the purchasing power of in...
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Elsevier
2024-11-01
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| Series: | International Journal of Information Management Data Insights |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266709682400082X |
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| author | Dinar Ajeng Kristiyanti Willibrordus Bayu Nova Pramudya Samuel Ady Sanjaya |
| author_facet | Dinar Ajeng Kristiyanti Willibrordus Bayu Nova Pramudya Samuel Ady Sanjaya |
| author_sort | Dinar Ajeng Kristiyanti |
| collection | DOAJ |
| description | Inflation growth in Indonesia and other countries impacts the currency value and investors' purchasing power, particularly in the transportation sector. This research explores the impact of inflation growth in Indonesia and comparable nations on currency valuation and the purchasing power of investors, with a focus on the transportation sector. Data collection was carried out from April to October 2023 by scraping stock data from several transportation stocks such as: AKSI.JK, CMPP.JK, SAFE.JK, SMDR.JK, TMAS.JK, and WEHA. The research primarily aims to forecast stock prices in Indonesia's transportation sector, utilizing data mining techniques within the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, which includes stages such as business understanding, data preparation, modeling, evaluation, and deployment. It employs the Long Short-Term Memory (LSTM) algorithm, assessing different hyperparameter activation functions (linear, ReLU, sigmoid, tanh) and optimizers (ADAM, ADAGRAD, NADAM, RMSPROP, ADADELTA, SGD, ADAMAX) to refine prediction accuracy. Findings demonstrate the ReLU activation function and ADAM optimizer's effectiveness, highlighted by evaluation metrics such as Mean Absolute Error (MAE) of 0.0092918, Mean Absolute Percentage Error (MAPE) of 0.06422, and R-Squared of 96 %. The study notably identifies significant growth in Temas (TMAS.JK) stock from April to October 2023, surpassing other sector stocks. Additionally, a web-based application for predicting transportation stock prices has been developed, facilitating user inputs like ticker, activation-optimizer choice, and date range. |
| format | Article |
| id | doaj-art-c8a8a4b97a2a493cb4e597677e4b8b92 |
| institution | OA Journals |
| issn | 2667-0968 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj-art-c8a8a4b97a2a493cb4e597677e4b8b922025-08-20T02:38:10ZengElsevierInternational Journal of Information Management Data Insights2667-09682024-11-014210029310.1016/j.jjimei.2024.100293How can we predict transportation stock prices using artificial intelligence? Findings from experiments with Long Short-Term Memory based algorithmsDinar Ajeng Kristiyanti0Willibrordus Bayu Nova Pramudya1Samuel Ady Sanjaya2Corresponding author.; Departement Information System, Faculty of Engineering and Informatics, Universitas Multimedia Nusantara, Jl. Scientia Boulevard, Tangerang 15111, IndonesiaDepartement Information System, Faculty of Engineering and Informatics, Universitas Multimedia Nusantara, Jl. Scientia Boulevard, Tangerang 15111, IndonesiaDepartement Information System, Faculty of Engineering and Informatics, Universitas Multimedia Nusantara, Jl. Scientia Boulevard, Tangerang 15111, IndonesiaInflation growth in Indonesia and other countries impacts the currency value and investors' purchasing power, particularly in the transportation sector. This research explores the impact of inflation growth in Indonesia and comparable nations on currency valuation and the purchasing power of investors, with a focus on the transportation sector. Data collection was carried out from April to October 2023 by scraping stock data from several transportation stocks such as: AKSI.JK, CMPP.JK, SAFE.JK, SMDR.JK, TMAS.JK, and WEHA. The research primarily aims to forecast stock prices in Indonesia's transportation sector, utilizing data mining techniques within the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, which includes stages such as business understanding, data preparation, modeling, evaluation, and deployment. It employs the Long Short-Term Memory (LSTM) algorithm, assessing different hyperparameter activation functions (linear, ReLU, sigmoid, tanh) and optimizers (ADAM, ADAGRAD, NADAM, RMSPROP, ADADELTA, SGD, ADAMAX) to refine prediction accuracy. Findings demonstrate the ReLU activation function and ADAM optimizer's effectiveness, highlighted by evaluation metrics such as Mean Absolute Error (MAE) of 0.0092918, Mean Absolute Percentage Error (MAPE) of 0.06422, and R-Squared of 96 %. The study notably identifies significant growth in Temas (TMAS.JK) stock from April to October 2023, surpassing other sector stocks. Additionally, a web-based application for predicting transportation stock prices has been developed, facilitating user inputs like ticker, activation-optimizer choice, and date range.http://www.sciencedirect.com/science/article/pii/S266709682400082XActivationLong short-term memoryOptimizerPredictionTransportation stock |
| spellingShingle | Dinar Ajeng Kristiyanti Willibrordus Bayu Nova Pramudya Samuel Ady Sanjaya How can we predict transportation stock prices using artificial intelligence? Findings from experiments with Long Short-Term Memory based algorithms International Journal of Information Management Data Insights Activation Long short-term memory Optimizer Prediction Transportation stock |
| title | How can we predict transportation stock prices using artificial intelligence? Findings from experiments with Long Short-Term Memory based algorithms |
| title_full | How can we predict transportation stock prices using artificial intelligence? Findings from experiments with Long Short-Term Memory based algorithms |
| title_fullStr | How can we predict transportation stock prices using artificial intelligence? Findings from experiments with Long Short-Term Memory based algorithms |
| title_full_unstemmed | How can we predict transportation stock prices using artificial intelligence? Findings from experiments with Long Short-Term Memory based algorithms |
| title_short | How can we predict transportation stock prices using artificial intelligence? Findings from experiments with Long Short-Term Memory based algorithms |
| title_sort | how can we predict transportation stock prices using artificial intelligence findings from experiments with long short term memory based algorithms |
| topic | Activation Long short-term memory Optimizer Prediction Transportation stock |
| url | http://www.sciencedirect.com/science/article/pii/S266709682400082X |
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