Day-ahead stock price forecasting using hybrid model

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Vafaei Ghaeini Vahid; KIMIAGARI ALIMOHAMMAD

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: INVESTMENT KNOWLEDGE Year:2019 | Volume:8 | Issue:30 Page(s): 313-328

Download Full-Text

Persian Verion

View:

487

Download:

Cites:

Information Journal Paper

Title

Day-ahead stock price forecasting using hybrid model

Author(s)

Vafaei Ghaeini Vahid | KIMIAGARI ALIMOHAMMAD | Issue Writer Certificate

Keywords

ANNQ2

ARMA-EGARCHQ2

ForecastingQ1

Wavelet TransformQ1

Abstract

Forecasting financial markets is an important issue in finance area and research studies. Importance of Forecasting on one hand and its complexity, on the other hand, researchers have done much work in this area and proposed many methods. In this research, we propose a hybrid model include Wavelet Transform, ARMA-EGARCH and NN for day-ahead Forecasting of stock market price in different markets. At first WT is used to decompose and reconstruct time series into detailed and approximated parts. And then we used ARMA-EGARCH and NN models respectively for Forecasting details and approximate series. In this model we used technical index by approximate part to the improvement of our NN model. Finally, we combine prediction of each model together. For validation, proposed model compare with ANN, ARIMA-GARCH and ARIMA-ANN models for Forecasting stocks price in UA and Iran markets. Our results indicate that proposed model has better performance than others model in both markets.

Cites

No record.

References

No record.

Cite

APA: Copy

Vafaei Ghaeini, Vahid, & KIMIAGARI, ALIMOHAMMAD. (2019). Day-ahead stock price forecasting using hybrid model. INVESTMENT KNOWLEDGE, 8(30 ), 313-328. SID. https://sid.ir/paper/402471/en

Vancouver: Copy

Vafaei Ghaeini Vahid, KIMIAGARI ALIMOHAMMAD. Day-ahead stock price forecasting using hybrid model. INVESTMENT KNOWLEDGE[Internet]. 2019;8(30 ):313-328. Available from: https://sid.ir/paper/402471/en

IEEE: Copy

Vahid Vafaei Ghaeini, and ALIMOHAMMAD KIMIAGARI, “Day-ahead stock price forecasting using hybrid model,” INVESTMENT KNOWLEDGE, vol. 8, no. 30 , pp. 313–328, 2019, [Online]. Available: https://sid.ir/paper/402471/en

Related Journal Papers