Browsing by Author "Yosef, Fekadu"

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    Determinants of Non- performing Loans: Evidence from Commercial Banks in Ethiopia
    (Addis Ababa University, 2018-02) Yosef, Fekadu; Abebaw, Kassie (PhD)
    The study adopted a quantitative research approach and used data collected from the National Bank of Ethiopia, Central Statistical Agency and financial statement of nine commercial banks from 2006-2017. Descriptive and random effect multiple regression analysis are employed to analyze the unbalanced panel data. Findings of the study show that return on equity and capital adequacy have negative and significant impact on NPLs. Whereas, loan loss provision and loan to deposit have positive significant relationship with NPLs. The study also showed that GDP, NIM and UM are statistically insignificant factors of NPLs. The finding of this study is important since once identifying the determinants of NPLs might enable management body to make appropriate lending policies that prevent the occurrence of NPLs. The study recommended bank managers to better emphasize on the management of current assets specially loans. Furthermore, it is preferable for commercial banks to concentrate or diversify their credit portfolio by calculating risk relative to its return in order to increase return on equity and to reduce the level of nonperforming loans.
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    Predicting Fertility Rate in Ethiopia Using Data Mining Techniques
    (A.A.U, 2016-10) Yosef, Fekadu; Wondwossen, Mulugeta(Dr.)
    Introduction: Fertility rates are at a very high levels in Africa and some Arabic countries, followed next by the countries of Central and South America. Some of the social factors that can influence fertility rates are: race, level of education, religion, use of contraceptive methods, abortion, impact of immigration, etc. Data mining is a collection of techniques for efficient automated discovery of previously unknown, valid, novel, useful and understandable patterns in large databases. Objective: The main objective of this study is to apply data mining to predict fertility rate in Ethiopia, particularly for four research centers named as Arbaminch DSS, Dabat DSS, Gilgel Gibe DSS and Kilite Awelaelo DSS. This can greatly support for policy makers, planners, and healthcare providers working on the control of fertility rate in Ethiopia. Methods and Material: The methodology used for this research was a hybrid six-step Cios Knowledge Discovery Process. The required data was collected from the data warehouse built for this purpose that stores data from four different research centers for the period of 2007 - 2015. The researcher used two popular data mining algorithms (C4 J48 Decision Trees and Naïve Bayes Classifier) to develop the predictive model using a larger dataset (68,033 cases). The researcher also used a 10-fold cross validation and 90% split test mode for data mining methods of the two predictive models for performance comparison purposes. Results: The results indicated that the decision tree (J48 algorithm) is the best predictor with pruned parameter of the tree of 10-fold cross-validation mode; it has 76.4% accuracy on the holdout dataset (this predictive accuracy is better than any reported in the literature), Naïve Bayes Classifier came out to be the second with supervised discretization has 69% accuracy. Conclusion: The results from this study confirmed the application of data mining for predicting fertility rate in Ethiopia. In the future, more classification studies by using a possible large amount of HDSS dataset with epidemiological information and employing other classification algorithms, tools and techniques could yield better results.