Intelligent model for forecasting fluctuations in the gold price
Purpose: The present study aims to identify the most important variables affecting the fluctuations of gold prices. It is the first Iranian research in which the fluctuations in this market are modeled using non-linear Bayesian Model Averaging (BMA) and deep neural network approaches.Methodology: It...
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| Format: | Article |
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Ayandegan Institute of Higher Education, Tonekabon,
2024-09-01
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| Series: | تصمیم گیری و تحقیق در عملیات |
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| Online Access: | https://www.journal-dmor.ir/article_211080_de928baa73a6a2349d52f83432818f61.pdf |
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| author | Mahdieh Tavassoli Mahnaz Rabeei Kiamars Fathi Hafshejani |
| author_facet | Mahdieh Tavassoli Mahnaz Rabeei Kiamars Fathi Hafshejani |
| author_sort | Mahdieh Tavassoli |
| collection | DOAJ |
| description | Purpose: The present study aims to identify the most important variables affecting the fluctuations of gold prices. It is the first Iranian research in which the fluctuations in this market are modeled using non-linear Bayesian Model Averaging (BMA) and deep neural network approaches.Methodology: It is applied research where monthly data collected from 2010 to 2022 were used. It evaluates 35 factors playing a role in gold price fluctuations. GARCH and random fluctuation models are used to extract gold price fluctuations. TVPDMA, TVPDMS, and BMA models are used to identify the most important variables causing gold price fluctuations. Furthermore, the deep learning approach is used to investigate how effective the selected variables are in gold price fluctuations.Findings: The results indicated that Support Vector (SV) models were more accurate than GARCH models in capturing fluctuations and that BMA outperformed TVPDMA and TVPDMS. Additionally, 12 variables were identified as influential in gold price fluctuations, with in-market factors playing a more significant role than out-of-market factors. The study also employed Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP) neural network models in deep learning mode to predict gold price fluctuations. It was concluded that global interest rates had the most significant impact on fluctuations in the gold price, with the Pivot Point DeMark's Index making the greatest contribution.Originality/Value: The gold market is known for its volatility, and accurate predictions about its future can significantly impact decision-making. Understanding the gold price and making correct forecasts can help inform decisions about buying and selling gold in global markets, and determine the most favorable times for transactions and investments. Therefore, it is crucial to accurately predict the gold price from various perspectives. This research attempted to develop an intelligent model for forecasting fluctuations in the gold price. |
| format | Article |
| id | doaj-art-77d121a6b702406caeb0355d38bd1007 |
| institution | Kabale University |
| issn | 2538-5097 2676-6159 |
| language | fas |
| publishDate | 2024-09-01 |
| publisher | Ayandegan Institute of Higher Education, Tonekabon, |
| record_format | Article |
| series | تصمیم گیری و تحقیق در عملیات |
| spelling | doaj-art-77d121a6b702406caeb0355d38bd10072025-01-30T15:04:05ZfasAyandegan Institute of Higher Education, Tonekabon,تصمیم گیری و تحقیق در عملیات2538-50972676-61592024-09-019363164810.22105/dmor.2024.483590.1881211080Intelligent model for forecasting fluctuations in the gold priceMahdieh Tavassoli0Mahnaz Rabeei1Kiamars Fathi Hafshejani2Department of Information Technology Management, South Tehran Branch, Islamic Azad University, Tehran, Iran.Department of Economics, Modeling and Optimization Research Center in Engineering Sciences, South Tehran Branch, Islamic Azad University, Tehran, Iran.Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran.Purpose: The present study aims to identify the most important variables affecting the fluctuations of gold prices. It is the first Iranian research in which the fluctuations in this market are modeled using non-linear Bayesian Model Averaging (BMA) and deep neural network approaches.Methodology: It is applied research where monthly data collected from 2010 to 2022 were used. It evaluates 35 factors playing a role in gold price fluctuations. GARCH and random fluctuation models are used to extract gold price fluctuations. TVPDMA, TVPDMS, and BMA models are used to identify the most important variables causing gold price fluctuations. Furthermore, the deep learning approach is used to investigate how effective the selected variables are in gold price fluctuations.Findings: The results indicated that Support Vector (SV) models were more accurate than GARCH models in capturing fluctuations and that BMA outperformed TVPDMA and TVPDMS. Additionally, 12 variables were identified as influential in gold price fluctuations, with in-market factors playing a more significant role than out-of-market factors. The study also employed Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP) neural network models in deep learning mode to predict gold price fluctuations. It was concluded that global interest rates had the most significant impact on fluctuations in the gold price, with the Pivot Point DeMark's Index making the greatest contribution.Originality/Value: The gold market is known for its volatility, and accurate predictions about its future can significantly impact decision-making. Understanding the gold price and making correct forecasts can help inform decisions about buying and selling gold in global markets, and determine the most favorable times for transactions and investments. Therefore, it is crucial to accurately predict the gold price from various perspectives. This research attempted to develop an intelligent model for forecasting fluctuations in the gold price.https://www.journal-dmor.ir/article_211080_de928baa73a6a2349d52f83432818f61.pdfgold priceoil priceforecastinggarchbayesian averaging |
| spellingShingle | Mahdieh Tavassoli Mahnaz Rabeei Kiamars Fathi Hafshejani Intelligent model for forecasting fluctuations in the gold price تصمیم گیری و تحقیق در عملیات gold price oil price forecasting garch bayesian averaging |
| title | Intelligent model for forecasting fluctuations in the gold price |
| title_full | Intelligent model for forecasting fluctuations in the gold price |
| title_fullStr | Intelligent model for forecasting fluctuations in the gold price |
| title_full_unstemmed | Intelligent model for forecasting fluctuations in the gold price |
| title_short | Intelligent model for forecasting fluctuations in the gold price |
| title_sort | intelligent model for forecasting fluctuations in the gold price |
| topic | gold price oil price forecasting garch bayesian averaging |
| url | https://www.journal-dmor.ir/article_211080_de928baa73a6a2349d52f83432818f61.pdf |
| work_keys_str_mv | AT mahdiehtavassoli intelligentmodelforforecastingfluctuationsinthegoldprice AT mahnazrabeei intelligentmodelforforecastingfluctuationsinthegoldprice AT kiamarsfathihafshejani intelligentmodelforforecastingfluctuationsinthegoldprice |