هانیه محمدعلی

هانیه محمدعلی

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فیلتر های جستجو: فیلتری انتخاب نشده است.
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۱.

Profit Rate Stickiness and Bank Specific Characteristics: Empirical Study of Panel Hidden Cointegration(مقاله علمی وزارت علوم)

کلید واژه ها: Banking profit rate Asymmetric behavior market concentration

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تعداد بازدید : 909 تعداد دانلود : 279
Profit rate stickiness means the asymmetric behavior of banking profit rate with respect to positive and negative shocks. Scrutinizing this behavior would suggest a new perspective on policy tools and banking supervision. In this regard, this paper applies hidden panel cointegration, proposed by Hatemi-J (2018), to study profit rate and bank specific characteristics nexus for all banks listed on Tehran Stock Exchange [TSE] during 2008-2017. This approach, in addition to analyzing the long-term relationship between variables, has another important capability for modeling asymmetry between variables. It has been shown that there is a long-run non-linear relationship between cumulative positive and negative components of variables. Then, asymmetric relationships are measured by using Panel DOLS. The results indicate that the main causes of profit rate asymmetry are liquidity and credit risks, and there is a downward direction of profit rate stickiness. Finally, the SCP paradigm is well-supported in Iran’s banking system. It seems Central Bank of Iran [CBI] needs to be mindful of anticompetitive effects of bank mergers firstly; and secondly, require banks to meet more stringent liquidity requirements and force them to stop roll over defaulted loans into new loans to increase the quality of banks’ assets.
۲.

Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange(مقاله علمی وزارت علوم)

کلید واژه ها: Forecasting Stock Market dynamic Neural Network Static Neural Network

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تعداد بازدید : 998 تعداد دانلود : 223
During the recent decades, neural network models have been focused upon by researchers due to their more real performance and on this basis, different types of these models have been used in forecasting. Now, there is a question that which kind of these models has more explanatory power in forecasting the future processes of the stock. In line with this, the present paper made a comparison between static and dynamic neural network models in forecasting (uninvariable) the return of Tehran Stock Exchange (TSE) index in order to find the best model to be used for forecasting this series. The data were collected daily from 26/11/2009 to 17/10/2014. The models examined in this study included two static models (Adaptive Neuro-Fuzzy Inference Systems ""ANFIS"" and Multi-layer Feed-forward Neural Network ""MFNN"") and a dynamic model (nonlinear neural network autoregressive model ""NNAR""). The findings showed that based on the Mean Square Error and Root Mean Square Error criteria, ANFIS model had a much higher forecasting ability compared to other models.

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