Browsing by Author "Karacabey, A. Argun"

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    İhracat performansı ile işletme stratejileri ilişkisi
    (Sosyal Bilimler Enstitüsü, 2010) Kahveci, Eyüp; Karacabey, A. Argun; İşletme
    Export performance is mainly defined as the output obtained from the company's international sales. In spite of the researches about explaining determinants of export performance, there is no consensus on the determinants of export performance.In the study, detailed information about the studies on export performance has been given. Then previous researches on export performance has grouped into two categories taking into account RBV which focus on firms internal activities and industrial organization theory which focus on firm?s external environment.In addition, balance sheet and income statement data were collected from firms which exports continously from 2002 to 2008 and exports over sector average in DB?17 manufacture of textile and DB - 18 manufacture of wearing apparel, dressing and dyeing of fur which placed Central Bank of Turkey? (CBRT ) company accounts databese?s manufacturing sectors. The collected data analysed by DEA by taking into account two different strategy, RBV and industrial organization theory. Although DEA has been used as many times for evaluating performance of firms, it has not been used mainly for evaluating export performance firm. Thus, this study is very important in terms of using DEA as an analytical performance measurement tool in export performance.Using resource-based view (RBV) of the firm and industrial organization theory as a theoretical backround; and DEA as an analytical tool, export performance of the firms has been evaluated. Inputs are defined seperately for RBV and industrial organization theory. Two stage DEA was used for RBV and one stage DEA was used for industrial organization theory. Accordingly, inputs for RBV were used as total assets, number of employees and paid-up capital, however, inputs for industrial organization theory was used as share of employees and share of export in Turkish textile sector. Outputs were used in this study are net profit and export profitability. Since export oriented firms have been chosen in this study, it is assumed that net profit is export performance indicator.
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    Zaman serileri analizi ve yapay sinir ağları ile tahmin: Yabancı portföy yatırımları üzerine uygulama
    (Sosyal Bilimler Enstitüsü, 2009) Yıldız, Doğan; Karacabey, A. Argun; İşletme
    To forecast the amount and direction of movements of money is critical importance for fund managers in global financial market which was affected by information technology every day. Therefore, the model used to predict future value of a variable is playing significant role for managers while making a decision. When fund managers made decision for investing international portfolio, they have to evaluate push and pull factors of the country?s to make the decision about time to enter the country and leave the country and also size of this movements. In this framework, work in foreign portfolio investment are thought to affect a variety of factors, January 1997 - December 2008 covers the period by using monthly data, different evaluation approaches and the models used in forecasting models are compared success. The first method of these is ARIMA models, time series analysis method, based on the lagged values of the variable. It is found in the study that the forecasting performance with ARMA is only 17 percent. This performance, of course, is not enough to make some inferences, so the other method is used, as well as ARMA, which is called VAR. VAR model uses affecting factors on the dependent variable with their lagged values with the lagged values of the variable. The analysis of VAR result gives 81 percent forecasting performance, which is relatively acceptable result. This result is high, but the difference of variables to get the name of ensuring stability of the original variables to bring the values and limitations of interpretation of variables in nonlinear analysis such as in the linear direction of operation is to make predictions with Artificial Neural Networks. Artificial Neural Networks do not have any limitations which were seen in ARMA and VAR models. Variables which are used in VAR model with their lagged values are used in Artificial Neural Networks with their original values. Artificial Neural Networks gave 95 percent of model performance result. These results showed that Artificial Neural Networks have more successful results than Time Series Analysis models.