Browsing by Author "Ataman, Gorkem"

Now showing 1 - 3 of 3

Citation - WoS: 8
Citation - Scopus: 8
How service quality in hospitals varies based on hospital ownership and demographics: a study on Turkish patients living urban areas
(ROUTLEDGE JOURNALS TAYLOR & FRANCIS LTD, 2022) Emel Yarimoglu; Gorkem Ataman; Yarimoglu, Emel; Ataman, Gorkem
Recently measuring service quality in hospitals has become crucial topic since the increasing importance of healthcare sector. The aim of the study was to show the differences in patients' service quality perceptions based on hospital ownership and demographics in Turkey. Service quality was measured by SERVPERF scale and the survey was conducted with 715 patients chosen by convenience sampling in Izmir City Turkey using face-to-face survey technique. Between-subject factorial ANOVA designs (2*2 and 2*3) were used in data analysis. In findings main effects for hospital ownership were found to be significant in all models. It showed that service quality in private hospitals was perceived higher than public hospitals. However main effects for demographics were not significant. The interaction effects between hospital ownership and only three demographics (age marital status and income) were found to be significant. These showed that younger patients perceived service quality higher in private hospitals while older patients perceived it lower. Single patients perceived service quality higher in private hospitals than married patients while the opposite was observed for public hospitals. The medium or high-income level patients perceived service quality higher in private hospitals but the opposite was obtained for public hospitals.
USING MARKOV CHAINS IN PREDICTION OF STOCK PRICE MOVEMENTS: A STUDY ON AUTOMOTIVE INDUSTRY
(INT JOURNAL CONTEMPORARY ECONOMICS & ADMINISTRATIVE SCIENCES, 2018) Mustafa Gurol Durak; Ece Acar; Gorkem Ataman; Acar, Ece; Ataman, Gorkem; Durak, Mustafa Gurol
Stock price prediction is on the agenda of most researchers based on the uncertainty in its nature. In past two decades the literature on the development of prediction models for stock prices has extended dramatically. These studies mostly focused on specific industries such as banking and finance petroleum manufacturing and automotive. In line with prior studies the aim of this study is also to investigate the efficiency of Markov Chains Model which is one of the most commonly applied models in predicting the stock price movements for the firms operating in automotive industry and to reveal the possible contribution it can make to the decision making process of investors. Automotive industry is not only a major and industrial force worldwide but also is a locomotive power that serves to many other industries. Thus this study considers the firms operating in automotive industry and daily closing stock prices of all 13 automotive companies listed in Turkish Stock Market are collected for the calendar year of 2015. By defining three possible states (decrease increase and no change) individual state transition probability matrixes are formed for each company. Then using the probabilities provided with these matrixes different investment strategies are evaluated for the first five working days of 2016. According to the results of analysis it is concluded that applying Markov Chains generates a positive income or at least minimizes the loss.
USING MARKOV CHAINS IN PREDICTION OF STOCK PRICE MOVEMENTS: A STUDY ON AUTOMOTIVE INDUSTRY
(VARAZDIN DEVELOPMENT & ENTREPRENEURSHIP AGENCY, 2017) Gorkem Ataman; Ece Acar; Mustafa Gurol Durak; Ataman, Gorkem; Acar, Ece; Durak, Mustafa Gurol; M Cingula; M Przygoda; K Detelj
Stock price prediction is on the agenda of most researchers based on the uncertainty of its nature. In past two decades the literature on the development of prediction models for stock prices has extended dramatically. These studies mostly focused on specific industries such as banking and finance petroleum manufacturing and automotive. In line with prior studies the aim of this study is also to provide a means for investors helping them predict price movements of stocks from automotive industry by using Markov Chains as it is one of the most commonly applied models. Automotive industry is not only a major and industrial force worldwide but also is a locomotive power that serves to many other industries. Daily closing stock price data of all 13 automotive companies listed in Borsa Istanbul (BIST) are collected for the calendar year of 2015. By defining three possible states (decrease increase and no change) individual state transition probability matrixes are formed for each company. Then using the probabilities provided with these matrixes different investment strategies are evaluated in the first five working days of 2016. According to the results of analysis it is concluded that applying Markov Chains generates a positive income or at least minimizes the loss.