Volatility Degree Forecasting of Stock Market by Stochastic Time Strength Neural Network
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-10-30
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
In view of the applications of artificial neural networks in economic and financial forecasting, a stochastic time strength function is introduced in the backpropagation neural network model to predict the fluctuations of stock price changes.
In this model, stochastic time strength function gives a weight for each historical datum and makes the model have the effect of random movement, and then we investigate and forecast the behavior of volatility degrees of returns for the Chinese stock market indexes and some global market indexes.
The empirical research is performed in testing the prediction effect of SSE, SZSE, HSI, DJIA, IXIC, and S&P 500 with different selected volatility degrees in the established model.
American Psychological Association (APA)
Mo, Haiyan& Wang, Jun. 2013. Volatility Degree Forecasting of Stock Market by Stochastic Time Strength Neural Network. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-1009395
Modern Language Association (MLA)
Mo, Haiyan& Wang, Jun. Volatility Degree Forecasting of Stock Market by Stochastic Time Strength Neural Network. Mathematical Problems in Engineering No. 2013 (2013), pp.1-11.
https://search.emarefa.net/detail/BIM-1009395
American Medical Association (AMA)
Mo, Haiyan& Wang, Jun. Volatility Degree Forecasting of Stock Market by Stochastic Time Strength Neural Network. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-1009395
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1009395