This research aims to solve the problem of selection using clustering algorithm, in this research optimal portfolio is formation using the single index model, and the real data are consisting from the stocks Iraqi Stock Exchange in the period 1/1/2007 to 31/12/2019. because the data series have missing values ,we used the two-stage missing value compensation method, the knowledge gap was inability the portfolio models to reduce The estimation error , inaccuracy of the cut-off rate and the Treynor ratio combine stocks into the portfolio that caused to decline in their performance, all these problems required employing clustering technic to data mining and regrouping it within clusters with similar characteristics to outperform the portfolio building models to diagnose those stock that Causing a decrease in portfolio performance ,our conclusion is Clustering technic outperforms the diagnosis of those stocks that meet the condition of opting into the optimal portfolio according to common considerations, but after excluding them, the performance of optimal portfolio is rises
Exclusion optimal portfolio from outlier by using fuzzy c-means clustering - analytical research at the Iraqi Stock Exchange
optimal portfolio
genetic algorithm
objective programming
Publication Date
Thu Mar 30 2023
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of Some Methods for Estimating Nonparametric Binary Logistic Regression
Binary logistic regression model
Nadaraya–Watson method
Local Scoring algorithm
Cross validation method
generalized Cross validation
Plug in method.
انموذج الانحدار اللوجستي الثنائي
طريقه ناداريا –واتسون
خوارزمية التهديف الموضعي
طريقة العبور الشرعي
العبور الشرعي المعمم
طريقة الملئ المباشر .
In this research, the methods of Kernel estimator (nonparametric density estimator) were relied upon in estimating the two-response logistic regression, where the comparison was used between the method of Nadaraya-Watson and the method of Local Scoring algorithm, and optimal Smoothing parameter λ was estimated by the methods of Cross-validation and generalized Cross-validation, bandwidth optimal λ has a clear effect in the estimation process. It also has a key role in smoothing the curve as it approaches the real curve, and the goal of using the Kernel estimator is to modify the observations so that we can obtain estimators with characteristics close to the properties of real parameters, and based on medical data for patients with chro
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