Forecasting Stock Prices in Tehran Stock Exchange Using Recurrent Neural Network Optimized by Artificial Bee Colony Algorithm

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.

BABAJANI JAFAR; TAGHVA MOHAMMAD REZA; BULU GHASEM; ABDOLLAHI MOHSEN

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

Journal Paper

Paper Information

Journal: JOURNAL OF FINANCIAL MANAGEMENT STRATEGY Year:2019 | Volume:7 | Issue:25 Page(s): 195-238

Download Full-Text

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

Persian Verion

View:

434

Download:

Cites:

Information Journal Paper

Title

Forecasting Stock Prices in Tehran Stock Exchange Using Recurrent Neural Network Optimized by Artificial Bee Colony Algorithm

Author(s)

BABAJANI JAFAR | TAGHVA MOHAMMAD REZA | BULU GHASEM | ABDOLLAHI MOHSEN | Issue Writer Certificate

Keywords

Stock price predictionQ1

Stepwise regression-correlation (SRCS)Q1

Recursive Neural network (RNN)Q1

Artificial bee colony algorithm (ABC)Q1

Abstract

This research in a hybrid approach, using Recurrent Neural Networks (RNN) based on Artificial Bee Colony algorithm (ABC), is going to provide the optimal model to predict the stock prices in Tehran Stock Exchange. For this purpose, using the stock datafor companies listed in the first market of the Tehran Stock Exchange, traded between 􀋻 􀋹 􀋺 􀋺 and the end of 􀋻 􀋹 􀋺 􀋾 , after the definition of different technical and fundamental variables, using step by step regression-correlation, factors affecting stock prices in Tehran Stock Exchange were selected as model input is defined. Then the artificial bee colony algorithm in an atmosphere of parametric design is used to optimize the weights and biases of recurrent neural network. To evaluate the model, several criteria for a given stock of listed companies in Tehran Stock Exchange are used. The results show that the neural network optimized with artificial bee colony algorithm has considerable accuracy, compared to other forecasting methods.

Cites

No record.

References

No record.

Cite

APA: Copy

BABAJANI, JAFAR, TAGHVA, MOHAMMAD REZA, BULU, GHASEM, & ABDOLLAHI, MOHSEN. (2019). Forecasting Stock Prices in Tehran Stock Exchange Using Recurrent Neural Network Optimized by Artificial Bee Colony Algorithm. JOURNAL OF FINANCIAL MANAGEMENT STRATEGY, 7(25 ), 195-238. SID. https://sid.ir/paper/386299/en

Vancouver: Copy

BABAJANI JAFAR, TAGHVA MOHAMMAD REZA, BULU GHASEM, ABDOLLAHI MOHSEN. Forecasting Stock Prices in Tehran Stock Exchange Using Recurrent Neural Network Optimized by Artificial Bee Colony Algorithm. JOURNAL OF FINANCIAL MANAGEMENT STRATEGY[Internet]. 2019;7(25 ):195-238. Available from: https://sid.ir/paper/386299/en

IEEE: Copy

JAFAR BABAJANI, MOHAMMAD REZA TAGHVA, GHASEM BULU, and MOHSEN ABDOLLAHI, “Forecasting Stock Prices in Tehran Stock Exchange Using Recurrent Neural Network Optimized by Artificial Bee Colony Algorithm,” JOURNAL OF FINANCIAL MANAGEMENT STRATEGY, vol. 7, no. 25 , pp. 195–238, 2019, [Online]. Available: https://sid.ir/paper/386299/en

Related Journal Papers