Cost-Sensitive Prediction of Stock Price Direction: Selection of Technical Indicators
Stock market forecasting using technical indicators (TIs) is widely applied by investors and researchers. Using a minimal number of input features is crucial for successful prediction. However, there is no consensus about what constitutes a suitable collection of TIs. The choice of TIs suitable for...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8861031/ |
| _version_ | 1819174316860768256 |
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| author | Yazeed Alsubaie Khalil El Hindi Hussain Alsalman |
| author_facet | Yazeed Alsubaie Khalil El Hindi Hussain Alsalman |
| author_sort | Yazeed Alsubaie |
| collection | DOAJ |
| description | Stock market forecasting using technical indicators (TIs) is widely applied by investors and researchers. Using a minimal number of input features is crucial for successful prediction. However, there is no consensus about what constitutes a suitable collection of TIs. The choice of TIs suitable for a given forecasting model remains an area of active research. This study presents a detailed investigation of the selection of a minimal number of relevant TIs with the aim of increasing accuracy, reducing misclassification cost, and improving investment return. Fifty widely used TIs were ranked using five different feature selection methods. Experiments were conducted using nine classifiers, with several feature selection methods and various alternatives for the number of TIs. A proposed cost-sensitive fine-tuned naïve Bayes classifier managed to achieve better overall investment performance than other classifiers. Experiments were conducted on datasets consisting of daily time series of 99 stocks and the TASI market index. |
| first_indexed | 2024-12-22T20:37:03Z |
| format | Article |
| id | doaj.art-1c16e8af546a49ad8cd5d56ec8f91913 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:37:03Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1c16e8af546a49ad8cd5d56ec8f919132022-12-21T18:13:27ZengIEEEIEEE Access2169-35362019-01-01714687614689210.1109/ACCESS.2019.29459078861031Cost-Sensitive Prediction of Stock Price Direction: Selection of Technical IndicatorsYazeed Alsubaie0https://orcid.org/0000-0003-4001-8144Khalil El Hindi1https://orcid.org/0000-0003-2457-9961Hussain Alsalman2Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaStock market forecasting using technical indicators (TIs) is widely applied by investors and researchers. Using a minimal number of input features is crucial for successful prediction. However, there is no consensus about what constitutes a suitable collection of TIs. The choice of TIs suitable for a given forecasting model remains an area of active research. This study presents a detailed investigation of the selection of a minimal number of relevant TIs with the aim of increasing accuracy, reducing misclassification cost, and improving investment return. Fifty widely used TIs were ranked using five different feature selection methods. Experiments were conducted using nine classifiers, with several feature selection methods and various alternatives for the number of TIs. A proposed cost-sensitive fine-tuned naïve Bayes classifier managed to achieve better overall investment performance than other classifiers. Experiments were conducted on datasets consisting of daily time series of 99 stocks and the TASI market index.https://ieeexplore.ieee.org/document/8861031/Cost-sensitivefeature selectionmachine learningmarket trendpredictionstock market |
| spellingShingle | Yazeed Alsubaie Khalil El Hindi Hussain Alsalman Cost-Sensitive Prediction of Stock Price Direction: Selection of Technical Indicators IEEE Access Cost-sensitive feature selection machine learning market trend prediction stock market |
| title | Cost-Sensitive Prediction of Stock Price Direction: Selection of Technical Indicators |
| title_full | Cost-Sensitive Prediction of Stock Price Direction: Selection of Technical Indicators |
| title_fullStr | Cost-Sensitive Prediction of Stock Price Direction: Selection of Technical Indicators |
| title_full_unstemmed | Cost-Sensitive Prediction of Stock Price Direction: Selection of Technical Indicators |
| title_short | Cost-Sensitive Prediction of Stock Price Direction: Selection of Technical Indicators |
| title_sort | cost sensitive prediction of stock price direction selection of technical indicators |
| topic | Cost-sensitive feature selection machine learning market trend prediction stock market |
| url | https://ieeexplore.ieee.org/document/8861031/ |
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