RISKY PORTFOLIO SELECTION THROUGH 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.

RAEI R.

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

Journal Paper

Paper Information

Journal: THE IRANIAN ACCOUNTING AND AUDITING REVIEW Year:2007 | Volume:13 | Issue:46 Page(s): 71-85

Download Full-Text

Persian Verion

View:

1,250

Download:

257

Cites:

Information Journal Paper

Title

RISKY PORTFOLIO SELECTION THROUGH NEURAL NETWORKS

Author(s)

RAEI R. | Issue Writer Certificate

Keywords

PORTFOLIOQ3

INVESTMENTQ2

MARKOWITZ MODELQ4

NEURAL NETWORKSQ2

Abstract

The major aim of this research is to achieve a more appropriate INVESTMENT model in PORTFOLIO selection for risk taker investors. In this research MARKOWITZ MODEL is the base for comparison in the PORTFOLIO theory, which can construct an optimum PORTFOLIO on the basis of the special assumptions. In the present study, along with MARKOWITZ MODEL, the created models through the Artificial NEURAL NETWORKS (ANN) are applied. Then they have been compared with MARKOWITZ MODEL in several cases of INVESTMENT. The learning pattern for NEURAL NETWORKS is “back propagation”. The PORTFOLIOs consist of twenty shares in the Tehran Stock Exchange (www.tse.ir), which has been studied for a period of thirteen months. In both Markowitz and ANN PORTFOLIOs, there is a significant difference between the daily return and the outcome of the INVESTMENT at the end of the INVESTMENT period in the test set. Both of these two models are statically and dynamically applied, and in both cases the NEURAL NETWORKS’ PORTFOLIOs outweighed the Markowitz’s PORTFOLIOs. In this research, the suggested PORTFOLIOs are for the risk takers and the major goal is to maximize the PORTFOLIOs’ return. The risk of the PORTFOLIOs constructed by the NEURAL NETWORKS is less than those of the MARKOWITZ MODEL. Furthermore, the transaction costs have not been computed in the Markowitz and NEURAL NETWORKS.This study shows that employment of NEURAL NETWORKS in PORTFOLIOs selection can be effective.

Cites

COMPARISON OF OPTIMIZATION METHODS AND ESTIMATION OF THE EXPECTED RETURN ON THE OPTIMAL PORTFOLIO SHARES

References

No record.

Cite

APA: Copy

RAEI, R.. (2007). RISKY PORTFOLIO SELECTION THROUGH NEURAL NETWORKS. THE IRANIAN ACCOUNTING AND AUDITING REVIEW, 13(46), 71-85. SID. https://sid.ir/paper/8123/en

Vancouver: Copy

RAEI R.. RISKY PORTFOLIO SELECTION THROUGH NEURAL NETWORKS. THE IRANIAN ACCOUNTING AND AUDITING REVIEW[Internet]. 2007;13(46):71-85. Available from: https://sid.ir/paper/8123/en

IEEE: Copy

R. RAEI, “RISKY PORTFOLIO SELECTION THROUGH NEURAL NETWORKS,” THE IRANIAN ACCOUNTING AND AUDITING REVIEW, vol. 13, no. 46, pp. 71–85, 2007, [Online]. Available: https://sid.ir/paper/8123/en

Related Journal Papers