The Use Of The Multivariate Statistical Methods In The Performance Analysis Of Non-Life Insurance Companies

Yıl 2018, Cilt: 10 Sayı: 18, 56 - 69, 01.01.2018

Övgücan Karadağ Erdemir Hüseyin Tatlıdil

https://doi.org/10.14784/marufacd.460659

Öz

Multivariate statistical methods have been used for performance analysis in recent years. Performance
of companies in insurance sector, which is one of the important service industry, can be evaluated using
multivariate statistical methods. In the present study, the performance of 30 non-life insurance companies
operating in Turkey between 2010 and 2014 are analyzed by multivariate statistical methods such as Principal
Component Analysis (PCA) and Multidimensional Scaling (MDS). Using the data in “Insurance and
Private Pensions Reports” which are published by Republic of Turkey Prime Ministry Undersecretariat of
Treasury Insurance Auditing Board at the end of each year, PCA and MDS analyzes are conducted. First
PCA is carried out for each year then using the results of PCA, score values are calculated and insurance
companies are ranked by these scores. Results of ranking are supported by rank correlation matrix. Performance
of non-life insurance companies is investigated on the plane using MDS. Results of PCA, rank correlation
and MDS are compared and interpreted.

Anahtar Kelimeler

Non-Life Insurance Companies, Financial Performance, Principal Component Analysis, Rank Correlation, Multidimensional Scaling

Kaynakça

• DENUIT, Michel, DHAENE, Jan, GOOVAERTS, Marc and KAAS, Rob, (2006) “Actuarial Theory for Dependent Risks: Measures, Orders and Models”, John Wiley & Sons. HAO, James, C.J. and CHOU, Lin-Yhi, (2005) “The Estimation of Efficiency for Life Insurance Industry: The Case of Taiwan”, Journal of Assian Economics 16, 847-860. HARTON, Kane, (2010) “Scale Advantage-Using Data Envelopment Analysis to Detect Economies of Scale in the Insurance Industry”, Proocedings of the 45th Annual Conference of the ORSNZ, November. HO, Chien-Ta Bruce and WU, Desheng Dash, (2009) “Online Banking Performance Evaluation Using Data Envelopment Analysis and Principal Componenrt Analysis, Computers & Operations Research 36, 6, 1835-1842. MASHHADIFARAHANI, Omid, REZANI, Narges and HATAMI-SHIRKOUHI, Loghman, (2013) “A combined Principal Component Analysis-Regression Analysis Model to Study the Effect on Technical Efficiency of Bad Loans in Bank Industry”, Tecnicki Vjesnik 20, 6, 1071-1075. RENCHER, Alvin, C., (2002) “Principal Component Analysis”, Methods of Multivariate Analysis, Second Edition, John Wiley and Sons, 380-407. SHANMUGAM, Ramalingam and JOHNSON, Charles, (2007) “At a Crossroad of Data Envelopment and Principal Component Analyses”, Omega 35, 351-364. TATLIDİL, Hüseyin, (2002) “Uygulamalı Çok Değişkenli İstatistiksel Analiz”, Akademi Matbaası, Ankara. TONE, Kaoru and SAHOO, Biresh, K., (2005) “Evaluating Cost Efficiency and Returns to Scale in the Life Insurance Corporation of India Using Data Envelopment Analysis”, Socio-Economic Planning Sciences 39, 261–285. WANG, Fu-Kwun and DU, Timon, C., (2000) “Using Principal Component Analysis in Process Performance for Multivariate Data”, Omega 28, 185-194. YANG, Zijiang, (2006) “A Two-Stage DEA Model to Evaluate The Overall Performance of Canadian Life and Health Insurance Companies”, Mathematical and Computer Modelling 43, 7, 910-919. ZHONGSHENG, Liu and DONG, Li, (2008) “Scale Expanding and Efficiency Improvement of Commercial Banks of China”, Processing of International Symposiums on Information, ISIP, 705-709. ZHU, Joe, (1998) “Data Envelopment Analysis vs. Principal Component Analysis: An Illustrative Study of Economic Performance of Chinese Cities”, European Journal of Operational Research, 111(1), 50-61. Internet References https://www.hazine.gov.tr/