Prediction the Return Fluctuations with Artificial Neural Networks’ Approach

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Taherinia Masoud; RASHIDI BAGHI MOHSEN

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Journal Paper

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Journal: Advances in Mathematical Finance and Applications Year:2019 | Volume:4 | Issue:2 Page(s): 103-114

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Title

Prediction the Return Fluctuations with Artificial Neural Networks’ Approach

Author(s)

Taherinia Masoud | RASHIDI BAGHI MOHSEN | Issue Writer Certificate

Keywords

Earning quality

Artificial neural networks

Prediction

Abstract

Time changes of return, inefficiency studies performed and presence of effective factors on share return rate are caused development modern and intelligent methods in estimation and evaluation of share return in stock companies. Aim of this research is Prediction of return using financial variables with artificial neural network approach. Therefore, the statistical population of this study includes 120 listed companies in Tehran stock securities during 2007 to 2017. Independent variables in this research are market variables (Earning quality, free cash flow) and dependent variable is share return. The obtained outputs from estimation of the Artificial neural networks and results obtained from estimation, using of this method with evaluation scales concerning random amount and comparing it with adjusted R, we found that there is meaningful relation between the associated variables and return. However, such network has the least error than other networks.

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APA: Copy

Taherinia, Masoud, & RASHIDI BAGHI, MOHSEN. (2019). Prediction the Return Fluctuations with Artificial Neural Networks’ Approach. ADVANCES IN MATHEMATICAL FINANCE AND APPLICATIONS, 4(2), 103-114. SID. https://sid.ir/paper/349562/en

Vancouver: Copy

Taherinia Masoud, RASHIDI BAGHI MOHSEN. Prediction the Return Fluctuations with Artificial Neural Networks’ Approach. ADVANCES IN MATHEMATICAL FINANCE AND APPLICATIONS[Internet]. 2019;4(2):103-114. Available from: https://sid.ir/paper/349562/en

IEEE: Copy

Masoud Taherinia, and MOHSEN RASHIDI BAGHI, “Prediction the Return Fluctuations with Artificial Neural Networks’ Approach,” ADVANCES IN MATHEMATICAL FINANCE AND APPLICATIONS, vol. 4, no. 2, pp. 103–114, 2019, [Online]. Available: https://sid.ir/paper/349562/en

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