A model for stock price forecasting based on ARMA systems

Author

Anaghi, M.F. ; Norouzi, Yaser

Author_Institution

Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran, Iran

fYear

2012

fDate

12-15 Dec. 2012

Firstpage

265

Lastpage

268

Abstract

The Prediction of the future values of a stock market signal on the basis of its past and present data series, is one of the most necessities of all financial applications. In this study, one special stock market signal is considered and analyzed using “ARMA” model with different number of poles and zeros, in order to estimate the values for the next days` prices. The estimated and the actual data for the next day is compared and the amount of error for each system is calculated, resulting into selection of most efficient model.

Keywords

autoregressive moving average processes; forecasting theory; pricing; stock markets; ARMA systems; autoregressive moving average model; data series; financial applications; next day price values estimation; pole and zero; stock market signal; stock price forecasting model; Autoregressive processes; Computational modeling; Forecasting; Hidden Markov models; Mathematical model; Predictive models; Stock markets; Autoregressive Moving Average(ARMA); Concepts of Price and Return; Error in Mean Square sense; White Gaussian Noise(WGN) signal;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Computational Tools for Engineering Applications (ACTEA), 2012 2nd International Conference on

Conference_Location

Beirut

Print_ISBN

978-1-4673-2488-5

Type

conf

DOI

10.1109/ICTEA.2012.6462880

Filename

6462880