Combination of Historical Stock Data and External Factors In Improving Stock Price Prediction Performance
Stock price prediction continues to be a major focus for investors today, some previous studies often focus on technical analysis using historical stock price data and ignore external factors that can affect stock prices. The purpose of this research is to overcome the shortcomings of previous resea...
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| Format: | Article |
| Language: | English |
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Departement of Electrical Engineering, Faculty of Engineering, Universitas Brawijaya
2024-08-01
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| Series: | Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) |
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| Online Access: | https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/1707 |
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| _version_ | 1850110595686203392 |
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| author | Anita Sjahrunnisa Nanik Suciati Shintami Chusnul Hidayati |
| author_facet | Anita Sjahrunnisa Nanik Suciati Shintami Chusnul Hidayati |
| author_sort | Anita Sjahrunnisa |
| collection | DOAJ |
| description | Stock price prediction continues to be a major focus for investors today, some previous studies often focus on technical analysis using historical stock price data and ignore external factors that can affect stock prices. The purpose of this research is to overcome the shortcomings of previous research by creating a stock price prediction model that combines historical stock data consisting of date, high, low, open, close, adj close, volume and external factors such as days, interest rates, inflation, and dividends. The data used came from 33 companies from 11 industrial sectors in Indonesia for 2267 trading days and evaluated the prediction performance using MSE, MAPE and R-squared. The results show a significant improvement in the evaluation metrics when external factors are added. This shows the importance of such factors in improving the prediction analysis and increasing the reliability of the prediction model. This approach is expected to not only overcome the limitations of traditional methods but also utilize a combination of deep learning and machine learning to improve prediction accuracy. Thus, this research not only provides new insights in the field of financial analysis but also provides new insights and solutions for investors to make more informed and less risky decisions. |
| format | Article |
| id | doaj-art-a2bb9e8cd9b546ab9a63b2889004455c |
| institution | OA Journals |
| issn | 2460-8122 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Departement of Electrical Engineering, Faculty of Engineering, Universitas Brawijaya |
| record_format | Article |
| series | Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) |
| spelling | doaj-art-a2bb9e8cd9b546ab9a63b2889004455c2025-08-20T02:37:48ZengDepartement of Electrical Engineering, Faculty of Engineering, Universitas BrawijayaJurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems)2460-81222024-08-01182303610.21776/jeeccis.v18i2.17072140Combination of Historical Stock Data and External Factors In Improving Stock Price Prediction PerformanceAnita Sjahrunnisa0Nanik Suciati1Shintami Chusnul Hidayati2Institut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh NopemberStock price prediction continues to be a major focus for investors today, some previous studies often focus on technical analysis using historical stock price data and ignore external factors that can affect stock prices. The purpose of this research is to overcome the shortcomings of previous research by creating a stock price prediction model that combines historical stock data consisting of date, high, low, open, close, adj close, volume and external factors such as days, interest rates, inflation, and dividends. The data used came from 33 companies from 11 industrial sectors in Indonesia for 2267 trading days and evaluated the prediction performance using MSE, MAPE and R-squared. The results show a significant improvement in the evaluation metrics when external factors are added. This shows the importance of such factors in improving the prediction analysis and increasing the reliability of the prediction model. This approach is expected to not only overcome the limitations of traditional methods but also utilize a combination of deep learning and machine learning to improve prediction accuracy. Thus, this research not only provides new insights in the field of financial analysis but also provides new insights and solutions for investors to make more informed and less risky decisions.https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/1707gruattentionrandom forest regressorstock |
| spellingShingle | Anita Sjahrunnisa Nanik Suciati Shintami Chusnul Hidayati Combination of Historical Stock Data and External Factors In Improving Stock Price Prediction Performance Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) gru attention random forest regressor stock |
| title | Combination of Historical Stock Data and External Factors In Improving Stock Price Prediction Performance |
| title_full | Combination of Historical Stock Data and External Factors In Improving Stock Price Prediction Performance |
| title_fullStr | Combination of Historical Stock Data and External Factors In Improving Stock Price Prediction Performance |
| title_full_unstemmed | Combination of Historical Stock Data and External Factors In Improving Stock Price Prediction Performance |
| title_short | Combination of Historical Stock Data and External Factors In Improving Stock Price Prediction Performance |
| title_sort | combination of historical stock data and external factors in improving stock price prediction performance |
| topic | gru attention random forest regressor stock |
| url | https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/1707 |
| work_keys_str_mv | AT anitasjahrunnisa combinationofhistoricalstockdataandexternalfactorsinimprovingstockpricepredictionperformance AT naniksuciati combinationofhistoricalstockdataandexternalfactorsinimprovingstockpricepredictionperformance AT shintamichusnulhidayati combinationofhistoricalstockdataandexternalfactorsinimprovingstockpricepredictionperformance |