Stock price analysis using machine learning method(Non-sensory-parametric backup regression algorithm in linear and nonlinear mode)

Subject Areas : Financial Engineering

Aliasgar  Davoodi Kasbi ¹ (Department of Accounting, Babol Branch, Islamic Azad University, Babol, Iran)
Iman  Dadashi ² (Department of Accounting, Babol Branch, Islamic Azad University, Babol, Iran)
Kaveh  Azinfar ³ (Department of Accounting, Babol Branch, Islamic Azad University, Babol, Iran)

Received: 2019-05-27 Accepted : 2019-08-01 Published : 2021-04-01

Keywords: backup vector regression, stock price, artificial intelligence algorithm, accounting variables,

Abstract :

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.

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