Stock Price Prediction using Machine Learning and Swarm Intelligence
Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results....
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| Format: | Article |
| Language: | English |
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Shahid Rajaee Teacher Training University
2020-01-01
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| Series: | Journal of Electrical and Computer Engineering Innovations |
| Subjects: | |
| Online Access: | https://jecei.sru.ac.ir/article_1421_6d94cea044bc908eba67a7ad40fe184e.pdf |
| _version_ | 1817985306145587200 |
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| author | I. Behravan S. M. Razavi |
| author_facet | I. Behravan S. M. Razavi |
| author_sort | I. Behravan |
| collection | DOAJ |
| description | Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem.Methods: In this paper, a novel machine learning approach, which works in two phases, is introduced to predict the price of a stock in the next day based on the information extracted from the past 26 days. In the first phase of the method, an automatic clustering algorithm clusters the data points into different clusters, and in the second phase a hybrid regression model, which is a combination of particle swarm optimization and support vector regression, is trained for each cluster. In this hybrid method, particle swarm optimization algorithm is used for parameter tuning and feature selection. Results: The accuracy of the proposed method has been measured by 5 companies’ datasets, which are active in the Tehran Stock Exchange market, through 5 different metrics. On average, the proposed method has shown 82.6% accuracy in predicting stock price in 1-day ahead.Conclusion: The achieved results demonstrate the capability of the method in detecting the sudden jumps in the price of a stock. |
| first_indexed | 2024-04-13T23:56:12Z |
| format | Article |
| id | doaj.art-09926bb4a0324d9f98c50218652532db |
| institution | Directory Open Access Journal |
| issn | 2322-3952 2345-3044 |
| language | English |
| last_indexed | 2024-04-13T23:56:12Z |
| publishDate | 2020-01-01 |
| publisher | Shahid Rajaee Teacher Training University |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering Innovations |
| spelling | doaj.art-09926bb4a0324d9f98c50218652532db2022-12-22T02:23:53ZengShahid Rajaee Teacher Training UniversityJournal of Electrical and Computer Engineering Innovations2322-39522345-30442020-01-0181314010.22061/jecei.2020.6898.3461421Stock Price Prediction using Machine Learning and Swarm IntelligenceI. Behravan0S. M. Razavi1Department of Electrical and Computer Engineering, University of Birjand, Birjand, IranDepartment of Electrical and Computer Engineering, University of Birjand, Birjand, IranBackground and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem.Methods: In this paper, a novel machine learning approach, which works in two phases, is introduced to predict the price of a stock in the next day based on the information extracted from the past 26 days. In the first phase of the method, an automatic clustering algorithm clusters the data points into different clusters, and in the second phase a hybrid regression model, which is a combination of particle swarm optimization and support vector regression, is trained for each cluster. In this hybrid method, particle swarm optimization algorithm is used for parameter tuning and feature selection. Results: The accuracy of the proposed method has been measured by 5 companies’ datasets, which are active in the Tehran Stock Exchange market, through 5 different metrics. On average, the proposed method has shown 82.6% accuracy in predicting stock price in 1-day ahead.Conclusion: The achieved results demonstrate the capability of the method in detecting the sudden jumps in the price of a stock.https://jecei.sru.ac.ir/article_1421_6d94cea044bc908eba67a7ad40fe184e.pdftehran stock exchange marketautomatic clusteringfeature selectionparticle swarm optimizationsupport vector regression |
| spellingShingle | I. Behravan S. M. Razavi Stock Price Prediction using Machine Learning and Swarm Intelligence Journal of Electrical and Computer Engineering Innovations tehran stock exchange market automatic clustering feature selection particle swarm optimization support vector regression |
| title | Stock Price Prediction using Machine Learning and Swarm Intelligence |
| title_full | Stock Price Prediction using Machine Learning and Swarm Intelligence |
| title_fullStr | Stock Price Prediction using Machine Learning and Swarm Intelligence |
| title_full_unstemmed | Stock Price Prediction using Machine Learning and Swarm Intelligence |
| title_short | Stock Price Prediction using Machine Learning and Swarm Intelligence |
| title_sort | stock price prediction using machine learning and swarm intelligence |
| topic | tehran stock exchange market automatic clustering feature selection particle swarm optimization support vector regression |
| url | https://jecei.sru.ac.ir/article_1421_6d94cea044bc908eba67a7ad40fe184e.pdf |
| work_keys_str_mv | AT ibehravan stockpricepredictionusingmachinelearningandswarmintelligence AT smrazavi stockpricepredictionusingmachinelearningandswarmintelligence |