İşletme Bölümü

Permanent URI for this communityhttps://hdl.handle.net/20.500.12416/24

Browse

Now showing 1 - 13 of 13

Citation - WoS: 18
Citation - Scopus: 23
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: 68
Citation - Scopus: 82
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: 13
Citation - Scopus: 19
Firm leverage and investment decisions in an emerging market
(Springer, 2010) Umutlu, Mehmet; Umutlu, Mehmet; 38254; İşletme
In this study, the effect of leverage on investment is analyzed by employing panel data methods for the Turkish non-financial firms that are quoted on Istanbul Stock Exchange. For one-way error component models, it is shown that there is a negative impact of leverage on investment for only firms with low Tobin's Q. These results are in conformity with the previous literature and agency theories of corporate finance stating that leverage has a disciplining role for firms with low growth opportunities. However, when the model is extended to include the time effects in a two-way error component model, the relation between leverage and investment disappears.
Citation - Scopus: 21
Forecasting stock market volatility: Further international evidence
(2006) Balaban, E.; Bayar, A.; Faff, R.W.
This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility in fifteen stock markets. Volatility is defined as within-month standard deviation of continuously compounded daily returns on the stock market index of each country for the ten-year period 1988 to 1997. The first half of the sample is retained for the estimation of parameters while the second half is for the forecast period. The following models are employed: a random walk model, a historical mean model, moving average models, weighted moving average models, exponentially weighted moving average models, an exponential smoothing model, a regression model, an ARCH model, a GARCH model, a GJR-GARCH model, and an EGARCH model. First, standard (symmetric) loss functions are used to evaluate the performance of the competing models: mean absolute error, root mean squared error, and mean absolute percentage error. According to all of these standard loss functions, the exponential smoothing model provides superior forecasts of volatility. On the other hand, ARCH-based models generally prove to be the worst forecasting models. Asymmetric loss functions are employed to penalize under-/over-prediction. When under-predictions are penalized more heavily, ARCH-type models provide the best forecasts while the random walk is worst. However, when over-predictions of volatility are penalized more heavily, the exponential smoothing model performs best while the ARCH-type models are now universally found to be inferior forecasters.
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: 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: 1
Citation - Scopus: 0
Local actors' actions in Turkish cinema during the 1990s: a political economy perspective
(Routledge Journals, Taylor & Francis Ltd, 2019) Kalemci, R. Arzu; 42537
This study analyzes changes in Turkish cinema in the 1990s. During this time, Turkish cinema was exposed to changes resulting from globalization and the foreign domination of cinema that came along with it. More recently, Turkish cinema has seen noticeable growth. By adopting a political economy perspective, this study investigates how the local actors of Turkish cinema, which were on the defensive, were able to overcome significant challenges.
Citation - WoS: 2
Citation - Scopus: 9
Predicting Financial Failure Of The Turkish Banks
(World Scientific Publ Co Pte Ltd, 2006) Doganay, M. Mete; Ceylan, Nildag Basak; Aktas, Ramazan; 112010
Banks are the most important financial institutions in Turkey because other financial institutions are not developed efficiently yet. Turkish banks experienced financial difficulties and a substantial amount of banks failed in the past. This event urged the government to initiate measures to prevent banks from getting into financial difficulties. As a result of these measures, Turkish banking system currently seems to be very attractive for the foreign investors willing to invest in this sector. One of the main concerns of the foreign investors is a possibility of a new banking crisis although it is very remote at this time. The purpose of this study is to develop early warning systems predicting the financial failure at least three years ahead of financial date. A number of multivariate statistical models such as multiple regression, discriminant analysis, logit, probit are used. We found that the most appropriate model is logit. The significant variables obtained from the models explain very well the causes of the bank failures. Our models can be used to assist interested parties to predict the probability of financial failure of Turkish banks.
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.
Citation - WoS: 91
Citation - Scopus: 113
The degree of financial liberalization and aggregated stock-return volatility in emerging markets
(Elsevier Science Bv, 2010) Umutlu, Mehmet; Umutlu, Mehmet; Akdeniz, Levent; Altay-Salih, Aslihan; 38254; 178057; İşletme
In this study, we address whether the degree of financial liberalization affects the aggregated total volatility of stock returns by considering the time-varying nature of financial liberalization. We also explore channels through which the degree of financial liberalization impacts aggregated total volatility. We document a negative relation to the degree of financial liberalization after controlling for size, liquidity, country. and crisis effects, especially for small and medium-sized markets. Moreover, the degree of financial liberalization transmits its negative impact on aggregated total volatility through aggregated idiosyncratic and local volatilities. Overall, our results provide evidence in favor of the view that the broadening of the investor base due to the increasing degree of financial liberalization causes a reduction in the total volatility of stock returns. (C) 2009 Elsevier B.V. All rights reserved.
Citation - WoS: 30
Citation - Scopus: 33
Understanding Protestant and Islamic Work Ethic Studies: A Content Analysis of Articles
(Springer, 2019) Kalemci, R. Arzu; Tuzun, Ipek Kalemci; 42537
This study focuses on two main arguments about the secularization of Protestant work ethic (PWE) and the uniqueness of Islamic work ethic (IWE). By adopting a linguistic point of view, this study aims to grasp a common understanding of PWE and IWE in the field of work ethic research. For this purpose, 109 articles using the keywords PWE and IWE in their titles were analyzed using content analysis. The findings support the argument that emphasizes universally shared values of PWE. In addition, the findings reveal that IWE provides a unique perspective on how to improve organizational performance, but at the same time differs in work orientation and commitment across cultures.

Browse

Browsing İşletme Bölümü by Scopus Q "Q1"