Stock price analysis using machine learning method(Non-sensory-parametric backup regression algorithm in linear and nonlinear mode)
The most common starting point for investors when buying a stock is to look at the trend of price changes. In recent years, different models have been used to predict stock prices by researchers, and since artificial intelligence techniques, including neural networks, genetic algorithms and fuzzy lo...
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Islamic Azad University of Arak
2021-04-01
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| Series: | Advances in Mathematical Finance and Applications |
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| Online Access: | http://amfa.iau-arak.ac.ir/article_666737_a014223f88ee72075c1ae3295084173c.pdf |
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doaj-b4dcaae2b2394627b6f81026709df2d62021-05-23T05:01:31ZengIslamic Azad University of ArakAdvances in Mathematical Finance and Applications2538-55692645-46102021-04-016228530110.22034/amfa.2019.1869838.1232666737Stock price analysis using machine learning method(Non-sensory-parametric backup regression algorithm in linear and nonlinear mode)Aliasgar Davoodi Kasbi0Iman Dadashi1Kaveh Azinfar2Department of Accounting, Babol Branch, Islamic Azad University, Babol, IranDepartment of Accounting, Babol Branch, Islamic Azad University, Babol, IranDepartment of Accounting, Babol Branch, Islamic Azad University, Babol, IranThe most common starting point for investors when buying a stock is to look at the trend of price changes. In recent years, different models have been used to predict stock prices by researchers, and since artificial intelligence techniques, including neural networks, genetic algorithms and fuzzy logic, have achieved successful re-sults in solving complex problems; in this regard, more exploitation Are. In this research, the prediction of stock prices of companies accepted in the Tehran Stock Exchange using artificial intelligence algorithm (non-sensory-parametric support vector regression algorithm in linear and nonlinear mode) has been investigated. The results of the research show that the PINSVR algorithm in nonlinear mode has been able to predict the stock price over the years, rather than linear mode.http://amfa.iau-arak.ac.ir/article_666737_a014223f88ee72075c1ae3295084173c.pdfstock priceaccounting variablesartificial intelligence algorithmbackup vector regression |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Aliasgar Davoodi Kasbi Iman Dadashi Kaveh Azinfar |
| spellingShingle |
Aliasgar Davoodi Kasbi Iman Dadashi Kaveh Azinfar Stock price analysis using machine learning method(Non-sensory-parametric backup regression algorithm in linear and nonlinear mode) Advances in Mathematical Finance and Applications stock price accounting variables artificial intelligence algorithm backup vector regression |
| author_facet |
Aliasgar Davoodi Kasbi Iman Dadashi Kaveh Azinfar |
| author_sort |
Aliasgar Davoodi Kasbi |
| title |
Stock price analysis using machine learning method(Non-sensory-parametric backup regression algorithm in linear and nonlinear mode) |
| title_short |
Stock price analysis using machine learning method(Non-sensory-parametric backup regression algorithm in linear and nonlinear mode) |
| title_full |
Stock price analysis using machine learning method(Non-sensory-parametric backup regression algorithm in linear and nonlinear mode) |
| title_fullStr |
Stock price analysis using machine learning method(Non-sensory-parametric backup regression algorithm in linear and nonlinear mode) |
| title_full_unstemmed |
Stock price analysis using machine learning method(Non-sensory-parametric backup regression algorithm in linear and nonlinear mode) |
| title_sort |
stock price analysis using machine learning method(non-sensory-parametric backup regression algorithm in linear and nonlinear mode) |
| publisher |
Islamic Azad University of Arak |
| series |
Advances in Mathematical Finance and Applications |
| issn |
2538-5569 2645-4610 |
| publishDate |
2021-04-01 |
| description |
The most common starting point for investors when buying a stock is to look at the trend of price changes. In recent years, different models have been used to predict stock prices by researchers, and since artificial intelligence techniques, including neural networks, genetic algorithms and fuzzy logic, have achieved successful re-sults in solving complex problems; in this regard, more exploitation Are. In this research, the prediction of stock prices of companies accepted in the Tehran Stock Exchange using artificial intelligence algorithm (non-sensory-parametric support vector regression algorithm in linear and nonlinear mode) has been investigated. The results of the research show that the PINSVR algorithm in nonlinear mode has been able to predict the stock price over the years, rather than linear mode. |
| topic |
stock price accounting variables artificial intelligence algorithm backup vector regression |
| url |
http://amfa.iau-arak.ac.ir/article_666737_a014223f88ee72075c1ae3295084173c.pdf |
| work_keys_str_mv |
AT aliasgardavoodikasbi stockpriceanalysisusingmachinelearningmethodnonsensoryparametricbackupregressionalgorithminlinearandnonlinearmode AT imandadashi stockpriceanalysisusingmachinelearningmethodnonsensoryparametricbackupregressionalgorithminlinearandnonlinearmode AT kavehazinfar stockpriceanalysisusingmachinelearningmethodnonsensoryparametricbackupregressionalgorithminlinearandnonlinearmode |
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