Stock price prediction based on LM-BP neural network and over-point estimation by counting time intervals: Evidence from the Stock Exchange

Subject Areas : Financial engineering

Mohammadreza  Vatanparast ¹ (Department of Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran)
masoud  asadi ² (Ph.D. student of financial engineering, Rasht Branch, Islamic Azad University, Rasht, Iran)
Shaban  Mohammadi ³ (MSC. Accounting, Shahid Rajaee Faculty, Vocational University of Khorasan, Iran)
abbas  babaei ⁴ (Ph.D. student of financial engineering, Rasht Branch, Islamic Azad University, Rasht, Iran)

Received: 2018-11-18 Accepted : 2018-12-26 Published : 2019-06-22

Keywords: Stock Price Forecasting, LM-BP Neural Network, RDCI,

Abstract :

In this study, to determine the stock price forecasting method, a LM-BP neural network was presented based on time series with respect to open price, highest price, lowest price, package price and volume of transactions. In the present study 315 days of stock prices were chosen to create 10 samples and the test set includes stock prices from day 316 to day 320 and used the LM-BP neural network. In this research, the determination of the critical point of excess, asymmetry and counting of intervals were investigated. The curve MRE2-MRE1 was plotted and the precision related to the best prediction of the BP neural network was estimated based on several independent replicas. The post-test was performed using a Kupiec Test and a Christopherson test. The results showed that stock price prediction based on the LM-BP neural network and over-point estimation by counting the intervals resulted in better results than the existing methods.