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    Determining the Necessary Criteria for the EU Membership by Using the Machine Learning Method: 2005-2010 Period Analysis
    (Elsevier Science Bv, 2011) Diskaya, Furkan; Emir, Senol; Orhan, Nazife
    In recent years, providing the required criteria put forward for the full membership by the union is one of the prior targets of Turkey in the European Union full membership process. The objective of this study is to be able to evaluate situations of the membership process among European countries, candidate countries and Turkey. In the data set used in the period of 2005-2010, it is seen that the global economic crisis becoming efficient towards the end of 2007 has affected both EU countries and candidate countries economically. This period which included the effect of global crisis was taken as a data set to the study in order to add the effect of global crisis into the evaluation. The study was tried to be analyzed by using artificial neural networks and decision trees algorithms by means of a variety of economic signs. For that purpose, the data between years of 2005-2010 were used in this study. Primarily, the relative importance of independent variables was found by means of multilayered artificial neural networks and then dimension reduction operation was done. The criteria thought as affecting the membership was tried to be found out by using the decision trees method. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility 7th International Strategic Management Conference
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    Measuring the Technical Efficiency of Telecommunication Sector within Global Crisis: Comparison of G8 Countries and Turkey
    (Elsevier Science Bv, 2011) Diskaya, Furkan; Emir, Senol; Orhan, Nazife
    In recent years, there has been a fierce competition in the telecommunication sector. Technologic competition has made the competition in the sector a kind of strategic war. The sector of which role increased in terms of economic developments has entered a reconstruction process at an equal rate in all countries. In fact, countries which apprehend the future of the sector will also determine the future of economy. In this respect the aim of this study is to make performance benchmarking by using Data Envelopment Analysis and Malmquist Total Factor Productivity Index on telecommunication sector which is thought as one of the most important signs of national economies in the global economic crisis environment. This benchmarking, which includes the period of global crisis between the years of 2007-2010, targets to measure to what extent countries have been affected from the global crisis environment by means of performance evaluation among the strongest telecom managements of countries such as Turkey and Group of Eight (G8) countries? In the study, annual activity reports of the managements and a variety of input and output variables acquired from various research institutions were used as a data set. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of 7th International Strategic Management Conference
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    Performance comparison of artificial neural network (ANN) and support vector machines (SVM) models for the stock selection problem: An application on the Istanbul Stock Exchange (ISE) - 30 index in Turkey
    (Academic Journals, 2012) Timor, Mehpare; Dincer, Hasan; Emir, Senol
    Support vector machines (SVM) and artificial neural networks (ANN) are machine learning methods that find a wide range of applications both in the field of engineering and social sciences. Recently, studies especially in the field of finance for the classification and estimation make it necessary to use these methods often in this area. In this study, different SVM and ANN models for the problem of stocks selection which provide maximum returns have been applied on different combinations of data sets which obtained from the balance sheets, stocks prices and the results of a comparative analysis has been presented. The findings show that SVM and ANN models including financial ratios give meaningful performance results for the stock selection.
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    A Stock Selection Model Based on Fundamental and Technical Analysis Variables by Using Artificial Neural Networks and Support Vector Machines
    (Academic Research Centre of Canada, 2012) Emir, Senol; Dincer, Hasan; Timor, Mehpare
    The basic aim of this article is to provide a model to explain stock performance utmost level. To reach this purpose, at the initial step, the model results composed of fundamental and technical analysis variables considered separately; in the second step, building the model composed of fundamental and technical analysis parameters which has best explaining ability was the focal point of this study. Artificial Neural Network (ANN) is an approach that has been widely used for financial classification problems for a long time. In addition, promising results of a novel machine learning method known as the Support Vector Machines (SVM) have been presented in several studies compared to the ANN. The stock performance results relying on fundamental analysis have shown more successful classification rates than the models based on technical analysis. Moreover, it was also experienced that the models constructed by using SVM method in the both type of analyses have shown more prominent results.