MODELLING OF STOCK PRICE FORECASTING USING FUZZY NEURAL NETWORKS

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.

AZAR A.; AFSAR A.

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

Journal Paper

Paper Information

Journal: Iranian Journal of Trade Studies Year:2006 | Volume:10 | Issue:40 Page(s): 33-52

Download Full-Text

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

Persian Verion

View:

4,178

Download:

Cites:

Information Journal Paper

Title

MODELLING OF STOCK PRICE FORECASTING USING FUZZY NEURAL NETWORKS

Author(s)

AZAR A. | AFSAR A. | Issue Writer Certificate

Keywords

FUZZY NEURAL NETWORKSQ3

FUZZY LOGICQ2

NEURAL NETWORKSQ3

STOCK PRICEQ2

FORECASTQ2

Abstract

Stock price FORECASTing is very important in stock exchange, because it indicates desirable or undesirable investing condition for investors and stockholders. Among FORECASTing methods, NEURAL NETWORKS and FUZZY LOGIC were used in many areas and have some advantages and disadvantages. So, this paper intends to combine fuzzy reasoning theory with NEURAL NETWORKS in order to improve the precision and convergence speed of FORECASTing model. Thus, a neural network-driven fuzzy reasoning system is proposed on the basis of improved Takagi-Sugeno reasoning model.In this paper, a neuro-fuzzy approach was employed to FORECAST STOCK PRICE and it was compared with ARIMA model based on six statistical parameters. The experimental results showed that the NEURAL NETWORKS/fa?page=1&sort=1&ftyp=all&fgrp=all&fyrs=all" target="_blank">FUZZY NEURAL NETWORKS based on six indices measurement was better than ARIMA model and has such properties as fast convergence, high precision and strong function approximation ability and it is suitable for real STOCK PRICE FORECASTing.

Cites

References

ارزیابی روشهای پیش بینی ترکیبی: با رویکردهای شبکه های عصبی - کلاسیک در حوزه اقتصاد

Cite

APA: Copy

AZAR, A., & AFSAR, A.. (2006). MODELLING OF STOCK PRICE FORECASTING USING FUZZY NEURAL NETWORKS. IRANIAN JOURNAL OF TRADE STUDIES (IJTS), 10(40), 33-52. SID. https://sid.ir/paper/7409/en

Vancouver: Copy

AZAR A., AFSAR A.. MODELLING OF STOCK PRICE FORECASTING USING FUZZY NEURAL NETWORKS. IRANIAN JOURNAL OF TRADE STUDIES (IJTS)[Internet]. 2006;10(40):33-52. Available from: https://sid.ir/paper/7409/en

IEEE: Copy

A. AZAR, and A. AFSAR, “MODELLING OF STOCK PRICE FORECASTING USING FUZZY NEURAL NETWORKS,” IRANIAN JOURNAL OF TRADE STUDIES (IJTS), vol. 10, no. 40, pp. 33–52, 2006, [Online]. Available: https://sid.ir/paper/7409/en

Related Journal Papers