İktisadi ve İdari Bilimler Fakültesi

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Citation - WoS: 8
Citation - Scopus: 8
A nonparametric panel data model for examining the contribution of tourism to economic growth
(Elsevier, 2023) Dogan, Ergun; Doğan, Ergun; Zhang, Xibin; 43080; İktisat
We apply a nonparametric panel data model with cross-sectional and time-varying coefficients to examine the relationship between tourist arrivals and economic growth in the Schengen area from 1995 to 2019. In contrast to the parametric models employed in other studies, our nonparametric model makes no assumption about functional form and, hence, allows us to model the relationship nonlinearly. We find that the tourism-economic growth relationship in the Schengen area is nonlinear and time-varying. While the relationship between tourism and economic growth was positive and significant during 1995-2003, it was negative and significant during the Global Financial Crisis (2007-2008) and the European recession of 2012-2013. One additional contribution of the study is the finding that total factor productivity (TFP) has been growing at 1.45% per year. The results also show that country-level TFP growth was disrupted during the aforementioned negative economic shocks.
Citation - WoS: 15
Citation - Scopus: 16
An Empirical Examination Of The Generalized Fisher Effect Using Cross-Sectional Correlation Robust Tests For Panel Cointegration
(Elsevier Science Bv, 2015) Omay, Tolga; Omay, Tolga; Yuksel, Asli; Yüksel, Aslı; Yuksel, Aydin; 19320; Çankaya Meslek Yüksekokulu; İşletme
This study examines the generalized Fisher hypothesis as applied to common stocks by using the recently proposed second generation panel cointegration tests. Unlike their predecessors, these new tests assume the existence of cross-section dependence in the data. For the sample analyzed, we report that these new tests, but not their predecessors, provide strong support for the existence of cointegration between stock and goods prices. Moreover, further analysis cannot reject the hypothesis that the cointegration relation is linear. Finally, our Fisher coefficient estimates are in the range between 0.68 and 1.27 and give support to the generalized Fisher hypothesis. (C) 2014 Elsevier B.V. All rights reserved.
Citation - WoS: 21
Citation - Scopus: 21
Business groups and internal capital markets
(Routledge Journals, Taylor & Francis Ltd, 2007) Gonenc, Halit; Kan, Ozgur B.; Karadagli, Ece C.
We compare the performance of firms affiliated with diversified business groups with the performance of unaffiliated firms in Turkey, all emerging market. We address the question of whether group-affiliated firms create internal capital markets or control large cash flows. Our findings indicate that group affiliation improves a firm accounting performance, but not stock market performance. Deviation of cash-flow rights front voting rights has a negative but insignificant effect on accounting performance, but a significant effect on market performance. We also find that a firm's accounting, but not stock market, performance increases with the level of group diversification. Our results show that internal capital markets play an important role for the existence of business groups in all emerging market context.
Citation - Scopus: 8
Competitiveness of Major Exporting Countries and Turkey in the World Fishery Market: A Constant Market Share Analysis
(2005) Klasra, M.A.; Klasra, Mushtaq Ahmad; Fidan, H.; Uluslararası Ticaret ve Finansman
The purpose of this study is to examine whether and to what extent the shares of selected countries' fishery exports in the world markets reflect their international competitiveness. The Constant Market Share (CMS) model, which decomposes export growth into some broad components (i.e., structural effects, market effects, commodity effects and competitive effects), is applied to examine this issue. The results of decomposition analysis revealed that structural factors have been more significant in explaining the growth of exports. The growth effects, though, appeared positive for each country, the exports of open economies like Canada, the United States, Iceland and Turkey benefited more from the growth of world exports. The analysis of commodity composition and market effects suggests that countries like Canada, the United States, Iceland and Turkey were pursuing the product differentiation policy and were penetrating in those markets, which have been growing relatively faster. These countries remained committed throughout the sample period (i.e., 1980-2000) to export their diversified products in fast-growing markets. The analysis of competitiveness effects, which are derived as a residual, show that Norway, Spain, the United States, Indonesia, Thailand, sChile and China were strong fishery exporters and increased their competitiveness during the sample period. Copyright © 2005 IAAEM.
Citation - WoS: 17
Citation - Scopus: 21
Computing non-stationary (s, S) policies using mixed integer linear programming
(Elsevier Science Bv, 2018) Xiang, Mengyuan; Rossi, Roberto; Martin-Barragan, Belen; Tarim, S. Armagan; 6641
This paper addresses the single-item single-stocking location non-stationary stochastic lot sizing problem under the (s, S) control policy. We first present a mixed integer non-linear programming (MINLP) formulation for determining near-optimal (s, S) policy parameters. To tackle larger instances, we then combine the previously introduced MINLP model and a binary search approach. These models can be reformulated as mixed integer linear programming (MILP) models which can be easily implemented and solved by using off-the-shelf optimization software. Computational experiments demonstrate that optimality gaps of these models are less than 0.3% of the optimal policy cost and computational times are reasonable. (C) 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 7
Citation - Scopus: 10
Confidence-based reasoning in stochastic constraint programming
(Elsevier, 2015) Rossi, Roberto; Hnich, Brahim; Tarim, S. Armagan; Prestvvich, Steven; 6641
In this work we introduce a novel approach, based on sampling, for finding assignments that are likely to be solutions to stochastic constraint satisfaction problems and constraint optimisation problems. Our approach reduces the size of the original problem being analysed; by solving this reduced problem, with a given confidence probability, we obtain assignments that satisfy the chance constraints in the original model within prescribed error tolerance thresholds. To achieve this, we blend concepts from stochastic constraint programming and statistics. We discuss both exact and approximate variants of our method. The framework we introduce can be immediately employed in concert with existing approaches for solving stochastic constraint programs. A thorough computational study on a number of stochastic combinatorial optimisation problems demonstrates the effectiveness of our approach. (C) 2015 Elsevier B.V. All rights reserved.
Citation - WoS: 10
Citation - Scopus: 9
Deep learning method for compressive strength prediction for lightweight concrete
(Techno-press, 2023) Nanehkaran, Yaser A.; Azarafza, Mohammad; Pusatli, Tolga; Bonab, Masoud Hajialilue; Irani, Arash Esmatkhah; Kouhdarag, Mehdi; Derakhshani, Reza; 51704
Concrete is the most widely used building material, with various types including high-and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.
Citation - WoS: 68
Citation - Scopus: 81
Detecting stock-price manipulation in an emerging market: The case of Turkey
(Pergamon-elsevier Science Ltd, 2009) Ogut, Hulisi; Doganay, M. Mete; Aktas, Ramazan; 112010; 1109
This paper aims to develop methods that are capable of detecting manipulation in the Istanbul Stock Exchange. We take the difference between manipulated stock's and index's average daily return, average daily change in trading volume and average daily volatility and used these statistics as explanatory variables. The data in post-manipulation and pre-manipulation periods are used as non-manipulated instances while the data in the manipulation period are used as manipulated instances. Test performance of classification accuracy, sensitivity and specificity statistics for Artificial Neural Networks (ANN) and Support Vector Machine (SVM) are compared with the results of discriminant analysis and logistics regression (logit). We found that the data mining techniques (ANN and SVM) are better suited to detect stock-price manipulation than multivariate statistical techniques (discriminant analysis, logistics regression) as the performances of the data mining techniques in terms of total classification accuracy and sensitivity statistics are better than those of multivariate techniques. We also found that unit change in difference between average daily return of manipulated stock and the index has the largest effect while unit change in difference between average daily change in trading volume of manipulated stock and index has the least effect on multivariate classifiers' decision functions. (C) 2009 Elsevier Ltd. All rights reserved.
Citation - WoS: 5
Citation - Scopus: 6
Elliott wave principle and the corresponding fractional Brownian motion in stock markets: Evidence from Nikkei 225 index
(Pergamon-elsevier Science Ltd, 2016) Ilalan, Deniz; İlalan, Deniz; Bankacılık ve Finans
This paper examines one of the vital technical analysis indicators known as the Elliott wave principle. Since these waves have a fractal nature with patterns that are not exact, we first determine the dimension of them. Our second aim is to find a linkage between Elliott wave principle and fractional Brownian motion via comparing their Hausdorff dimensions. Thirdly, we consider the Nikkei 225 index during Japan asset price bubble, which is a perfect example of an Elliott wave. (C) 2016 Elsevier Ltd. All rights reserved.
Citation - WoS: 4
Citation - Scopus: 4
Estimation in multivariate nonnormal distributions with stochastic variance function
(Elsevier Science Bv, 2014) Islam, M. Qamarul
In this paper the problem of estimation of location and scatter of multivariate nonnormal distributions is considered. Estimators are derived under a maximum likelihood setup by expressing the non-linear likelihood equations in the linear form. The resulting estimators are analytical expressions in terms of sample values and, hence, are easily computable and can also be manipulated analytically. These estimators are found to be remarkably more efficient and robust as compared to the least square estimators. They also provide more powerful tests in testing various relevant statistical hypotheses. (C) 2013 Elsevier B.V. All rights reserved.
Citation - WoS: 59
Citation - Scopus: 74
FDI inflow as an international business operation by MNCs and economic growth: an empirical study on Turkey
(Elsevier Science Bv, 2014) Temiz, Dilek; Temiz, Dilek; Gokmen, Aytac; Gökmen, Aytaç; 52039; 17660; Uluslararası Ticaret ve Finansman
The issue of foreign direct investment (FDI) has been affecting the world economy for years and is a considerable subject for both developed and developing countries. FDI is the fixed form of international business operation made across the national borders made mostly by the multi national corporations (MNCs). The positive impact of FDI inflow in a host country is expected to emerge as capital accumulation, technology transfer, know-how acquisition, innovative capacity and economic growth eventually. In this study, it is aimed to address the FDI literature depending on comprehensive international publications and then to analyze the FDI inflow and GDP growth in Turkey with econometric methods. The relation between FDI inflow and GDP growth is analyzed by using the Johansen cointegration test and Granger causality analysis. Afterwards, a regression equation is estimated by using the ordinary least squares method (OLS). Prior to applying the Cointegration test, the stationarity and integration degrees of the series are determined by the augmented Dickey-Fuller test (ADF). Consequently, resting on the results of entire analysis, it is possible to mention that no significant relation is determined between the FDI inflow and GDP growth in Turkey both in the short and long run. (C) 2013 Elsevier Ltd. All rights reserved.
Citation - WoS: 21
Citation - Scopus: 24
Heuristic policies for the stochastic economic lot sizing problem with remanufacturing under service level constraints
(Elsevier Science Bv, 2018) Kilic, Onur A.; Tunc, Huseyin; Tarim, S. Armagan; 6641
In this paper, we address the stochastic economic lot sizing problem with remanufacturing under service level constraints. The problem emerges in hybrid production systems where demand can be met via two alternative sources: manufacturing new products and remanufacturing returned products. The deterministic counterpart of this problem has been considered in the literature and it is shown to be NP-Hard. We focus on the case where period demands and returns are stochastic. The optimal solution to this problem is not a deterministic production schedule but a control policy, yet its structure has not yet been characterized. We propose two heuristic policies for the problem that make use of simple decision rules to control manufacturing and remanufacturing operations and present mathematical models thereof. (C) 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 3
High persistence and nonlinear behavior in financial variables: A more powerful unit root testing in the estar framework
(Mdpi, 2021) Omay, Tolga; Omay, Tolga; Corakci, Aysegul; Hasdemir, Esra; 103299; Çankaya Meslek Yüksekokulu
In this study, we consider the hybrid nonlinear features of the Exponential Smooth Transition Autoregressive-Fractional Fourier Function (ESTAR-FFF) form unit root test. As is well known, when developing a unit root test for the ESTAR model, linearization is performed by the Taylor approximation, and thereby the nuisance parameter problem is eliminated. Although this linearization process leads to a certain amount of information loss in the unit root testing equation, it also causes the resulting test to be more accessible and consistent. The method that we propose here contributes to the literature in three important ways. First, it reduces the information loss that arises due to the Taylor expansion. Second, the research to date has tended to misinterpret the Fourier function used with the Kapetanios, Shin and Snell (2003) (KSS) unit root test and considers it to capture multiple smooth transition structural breaks. The simulation studies that we carry out in this study clearly show that the Fourier function only restores the Taylor residuals of the ESTAR type function rather than accounting forthe smooth structural break. Third, the new nonlinear unit root test developed in this paper has very strong power in the highly persistent near unit root environment that the financial data exhibit. The application of the Kapetanios Shin Snell- Fractional Fourier (KSS-FF) test to ex-post real interest rates data of 11 OECD countries for country-specific sample periods shows that the new test catches nonlinear stationarity in many more countries than the KSS test itself.
Citation - WoS: 4
Citation - Scopus: 5
Hysteresis and stochastic convergence in Eurozone unemployment rates: evidence from panel unit roots with smooth breaks and asymmetric dynamics
(inst Badan Gospodarczych, 2022) Corakci, Aysegul; Omay, Tolga; Omay, Tolga; Hasanov, Mubariz; 103299; Çankaya Meslek Yüksekokulu
Research background: Studying the dynamic characteristics of unemployment rate is crucial for both economic theory and macroeconomic policies. Despite numerous research, the empirical evidence about stochastic behaviour of the unemployment rate remains disputable. It has been widely agreed that most economic variables, including unemployment rates, are characterized by both structural breaks and nonlinearities. However, a little work is done to examine both features simultaneously. Purpose of the article: In this paper, we analyse the stationarity properties of unemployment rates of Euro area member countries. Also, we aim to test stochastic convergence of unemployment rates among member countries. Our empirical procedures explicitly allow for simultaneous gradual breaks and nonlinearities in the series. Methods: This paper develops a new unit root test procedure for panel data, allowing for both gradual structural breaks and asymmetric adjustment towards equilibrium. We carry out Monte Carlo simulations to examine small sample performance of the proposed test procedure and compare it to the existing test procedures. We apply the newly proposed test to examine the stochastic properties of the unemployment rates of Euro-member countries as well as relative unemployment rates vis-a-vis the Eurozone unemployment rate. Findings & value added: We find that the newly developed test procedure outperforms existing tests in highly nonlinear settings. Also, these tests reject the null hypothesis of unit root in more cases when compared to the existing tests. We find stationarity in the series only after allowing for structural breaks in the data generating process. Allowing for nonlinear and asymmetric adjustment in addition to gradual breaks provides evidence of stationarity in more cases. Furthermore, our results suggest that relative unemployment rate series are stationary, providing evidence in favour of stochastic convergence in unemployment rates. Overall, our results imply a limited room for coordinated economic policy to fight unemployment in the Eurozone.
Citation - WoS: 19
Citation - Scopus: 24
Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions
(Pergamon-elsevier Science Ltd, 2009) Celikyilmaz, Asli; Doğanay, Mehmet Mete; Tuerksen, I. Burhan; Aktas, Ramazan; Doganay, M. Mete; Ceylan, N. Basak; 122648; 1109; 112010; 108611; İşletme
In building an approximate fuzzy classifier system, significant effort is laid oil estimation and fine tuning of fuzzy sets. However, in such systems little thought is given to the way in which membership functions are combined within fuzzy rules. In this paper, a robust method, improved fuzzy classifier functions (IFCF) design is proposed for two-class pattern recognition problems. A supervised hybrid improved fuzzy Clustering for classification (IFC-C) algorithm is implemented for structure identification. IFC-C algorithm is based oil it dual optimization method, which yields simultaneous estimates of the parameters of (c-classification functions together with fuzzy c partitioning of dataset based oil a distance measure. The merit of novel IFCF is that the information oil natural grouping of data samples i.e., the membership values, are utilized as additional predictors of each fuzzy classifier function to improve accuracy of system model. Improved fuzzy classifier functions are approximated using statistical and soft computing approaches. A new semi-non-parametric inference mechanism is implemented for reasoning. The experimental results Of the new modeling approach indicate that the new IFCF is it promising method for two-class pattern recognition problems. (c) 2007 Elsevier Ltd. All rights reserved.
Citation - WoS: 8
Citation - Scopus: 9
Is there convergence in renewable energy deployment? Evidence from a new panel unit root test with smooth and sharp structural breaks
(Pergamon-elsevier Science Ltd, 2023) Corakci, Aysegul; Omay, Tolga; Omay, Tolga; 103299; Çankaya Meslek Yüksekokulu
This study examines whether the contribution of renewable energy to the total primary energy supply converges in a panel of 24 OECD countries over the period 1960-2020. To this end, a new panel unit root test that allows for both sharp and smooth breaks is proposed to test for the stochastic convergence hypothesis. Although renewable energy convergence is not rejected when the newly proposed test is applied to the full panel of OECD countries, it found only moderate support within the members of the panel using a sequential panel selection methodology. In fact, in two high-income OECD countries, the contribution of renewable energy to the primary energy supply shows no sign of convergence: Poland and Iceland. Therefore, the renewable energy shares seem to be converging to a common steady state in only a group of OECD countries over the long run. This uneven pattern of convergence, in turn, suggests that the OECD countries are still far away from developing a common sustainable renewable energy target, calling for urgent international policy cooperation to encourage the divergent econo-mies to seek out the menu of policies that ensure the worldwide success of renewable energy transformation.
Citation - WoS: 2
Citation - Scopus: 2
Market reaction to grouping equities in stock markets: An empirical analysis on Borsa Istanbul
(Elsevier, 2017) Yildiz, Yilmaz; Pirgaip, Burak; Karan, Mehmet Baha; Pirgaip, Burak; 252136; Bankacılık ve Finans
The main aim of this study is to investigate the market reaction to stock grouping announcements in Borsa Istanbul which requires stocks to be classified into groups "A ", "B" and "C" according to their market capitalization and floating rates. By utilizing event study analysis, our results suggest that grouping announcements have significant effect on stock prices and trading volume. The event day positive (negative) relationship between abnormal return and volume for the upgraded (downgraded) stocks supports the downward sloping demand curve hypothesis. Moreover, findings also suggest that stocks which are upgraded to Group A are exposed to more attention which is in line with the attention hypothesis. The reverse is valid for the downgraded firms. We find no evidence of price reversals and long-term symmetrical liquidity effect which lead us to reject price pressure and liquidity hypotheses. Finally, we reach controversial evidence for the information hypothesis. Copyright (c) 2017, Borsa Istanbul Anonim Sirketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NCND license
Citation - WoS: 7
Citation - Scopus: 7
Mothers in Cases of Incest in Turkey: Views and Experiences of Professionals
(Springer/plenum Publishers, 2013) Kardam, Filiz; Kardam, Filiz; Bademci, Emine; Siyaset Bilimi ve Uluslararası İlişkiler
This paper aims to understand how professionals view non-offending mothers in cases of incest. Its data is based on a larger qualitative research project with 98 professionals in Turkey, including both frontline workers and those who join the process after the disclosure of abuse and are contacted professionally in incest cases. In spite of the differences in their views, the interviewed professionals have acknowledged the critical role of the mother in various phases of incest from disclosure of abuse to the treatment of the victim. However, they have also pointed out the insufficiencies and ambivalences of the mothers in terms of dealing properly with incest by underlining their economic and social vulnerability. The results reflected that the mothers need to be perceived in another light, understood better and empowered according to their needs to become vital partners within the support system combating incestuous abuse.
Citation - WoS: 39
Citation - Scopus: 37
Prediction of bank financial strength ratings: the case of Turkey
(Elsevier Science Bv, 2012) Ogut, Hulisi; Doganay, M. Mete; Ceylan, Nildag Basak; Aktas, Ramazan; 112010; 108611; 1109
Bank financial strength ratings have gained widespread popularity especially after the recent financial turmoil. Rating agencies were criticized because of their ratings and failure to predict the bankruptcy of the banks. Based on this observation, we investigate whether the forecast of the rating of bank's financial strength using publicly available data is consistent with those of the credit rating agency. We use the data of Turkish banks for this investigation. We take a country-specific approach because previous studies found that proxies used for environmental factors (political, economic, and financial risk of the country) did not have any explanatory power and it is hard to find international data for other important factors such as franchise value, concentration, and efficiency. We use two popular multivariate statistical techniques (multiple discriminant analysis and ordered logistic regression) to estimate a suitable model and we compare their performances with those of two mostly used data mining techniques (Support Vector Machine and Artificial Neural Network). Our results suggest that our predictions are consistent with those of Moody's financial strength rating in general.. The important factors in rating are found to be profitability (measured by return on equity), efficient use of resources, and funding the businesses and the households instead of the government that shows efficient placement of the funds. (C) 2012 Elsevier B.V. All rights reserved.
Citation - WoS: 18
Citation - Scopus: 26
Prediction of financial information manipulation by using support vector machine and probabilistic neural network
(Pergamon-elsevier Science Ltd, 2009) Ogut, Hulisi; Aktas, Ramazan; Alp, Ali; Doganay, M. Mete; 1109; 6974; 112010
Different methods have been used to predict financial information manipulation that can be defined as the distortion of the information in the financial statements. The purpose of this paper is to predict financial information manipulation by using support vector machine (SVM) and probabilistic neural network (PNN). A number of financial ratios are used as explanatory variables. Test performance of classification accuracy, sensitivity and specificity statistics for PNN and SVM are compared with the results of discriminant analysis, logistics regression (logit), and probit classifiers, which have been used in other studies. We have found that the performance of SVM and PNN are higher than that of the other classifiers analyzed before. Thus, both classifiers can be used as automated decision support system for the detection of financial information manipulation. (C) 2008 Elsevier Ltd. All rights reserved.

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