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Hayat Dışı Sigorta Sektöründe Kârı Etkileyen Firma İçi Faktörlerin İncelenmesi: Bulanık Hedef Programlama Örneği

Yıl 2021, Cilt 6, Sayı 2, 332 - 355, 27.08.2021
https://doi.org/10.30784/epfad.871997

Öz

İnsanlar ve firmalar hayatları boyunca belirsizlikler ve risklerle karşılaşmaktadır. Faaliyetlerinden dolayı gerçekleşen her riskin sonucunda da bir maliyet ortaya çıkar. Risk kavramının büyüklüğünün bu durumda iyi hesaplanması ve yönetilmesi gerekmektedir. Bundan dolayı, sigorta, riskleri etkin ve verimli bir şekilde yönetmek için devreye girer. Bu çalışmada hayat dışı sigorta şirketlerinde firmaya ait değişkenlerin dönem kârı/zararı üzerindeki etkileri incelenmiştir. Sigorta firmalarının faaliyetlerinin devamını sağlamada önemli bir unsur olan maksimum kârı elde etmek için, kâr üzerinde etkisi olan firma içi değişkenlerin etkileri bulanık hedef programlama modeli kullanılarak araştırılmıştır. Bu çalışmada seçilen sigorta şirketlerinin modelde kullanılan değişkenleri şirketlerin mali değişkenleri, prim toplamı değişkenleri ve faaliyet gider değişkenleridir. Bu değişkenler ayrıntılı bir şekilde çalışmanın ilerleyen kısmında verilmiştir. Çalışmada 2014-2020 arası yıllık veriler kullanılmıştır. Çalışmadan elde edilen sonuçlara göre, sigorta şirketlerinin kârı sırasıyla mali değişkenler, faaliyet giderleri ve prim toplamı değişkeni kullanılarak tahmin edilmiştir. Ayrıca bulanık hedef programlama yönteminin şirketlerin kârını etkileyen değişkenlerin miktarını hesaplamada etkin sonuçlar verdiği görülmüştür.

Kaynakça

  • Aouni, B., Colapinto, C. and La Torre, D. (2014). A fuzzy goal programming model for venture capital investment decision making. INFOR: Information Systems and Operational Research, 52(3), 138-146. https://doi.org/10.3138/infor.52.3.138
  • Berry-Stölzle, T. R., Koissi, M. C. and Shapiro, A. F. (2010). Detecting fuzzy relationships in regression models: The case of insurer solvency surveillance in Germany. Insurance: Mathematics and Economics, 46(3), 554-567. https://doi.org/10.1016/j.insmatheco.2010.02.003
  • Chen, C. H., Lin, M. and Chen, G. C. (2011). Does financial and business performance affect market share? A case of non-life insurance industry in Taiwan. Journal of Statistics and Management Systems, 14(2), 453-465. https://doi.org/10.1080/09720510.2011.10701566
  • Chen, S. Y. and Lu, C. C. (2015). Assessing the competitiveness of insurance corporations using fuzzy correlation analysis and improved fuzzy modified TOPSIS. Expert Systems, 32(3), 392-404. https://doi.org/10.1111/exsy.12099
  • Chou, P. L. and Chang, Y. M. (2011). The effect of the insurance company act on the capital benefit of investment in Taiwan’s life insurance industry. Journal of Statistics and Management Systems, 14(6), 1041-1055. https://doi.org/10.1080/09720510.2011.10701600
  • Doumpos, M., Galariotis, E., Nocera, G. and Zopounidis, C. (2018). Multiattribute assessment of the financial performance of non-life insurance companies: Empirical evidence from Europe. In H. Masri, B. Perez-Gladish and C. Zopounidis (Eds.), Financial decision aid using multiple criteria (pp. 1-17). Springer, Cham. https://doi.org/10.1007/978-3-319-68876-3_1
  • Felício, J. A. and Rodrigues, R. (2015). Organizational factors and customers' motivation effect on insurance companies' performance. Journal of Business Research, 68(7), 1622-1629. https://doi.org/10.1016/j.jbusres.2015.02.006
  • Hasan, M. B., Islam, S. N. and Wahid, A. N. (2018). The effect of macroeconomic variables on the performance of non-life insurance companies in Bangladesh. Indian Economic Review, 53(1), 369-383. https://doi.org/10.1007/s41775-019-00037-6
  • Hatemi-J, A., Lee, C. C., Lee, C. C. and Gupta, R. (2019). Insurance activity and economic performance: Fresh evidence from asymmetric panel causality tests. International Finance, 22(2), 221-240. https://doi.org/10.1111/infi.12333
  • Jana, D. K., Sahoo, P. and Koczy, L. T. (2017). Comparative study on credibility measures of type-2 and type-1 fuzzy variables and their application to a multi-objective profit transportation problem via goal programming. International Journal of Transportation Science and Technology, 6(2), 110-126. https://doi.org/10.1016/j.ijtst.2017.06.002
  • Jayaraman, R., Liuzzi, D., Colapinto, C. and Malik, T. (2017). A fuzzy goal programming model to analyze energy, environmental and sustainability goals of the United Arab Emirates. Annals of Operations Research, 251(1-2), 255-270. https://doi.org/10.1007/s10479-015-1825-5
  • Jurčević, B. and Žaja, M. M. (2013). Banks and insurance companies efficiency indicators in the period of financial crisis: The case of the Republic of Croatia. Economic Research-Ekonomska Istraživanja, 26(1), 203-224. https://doi.org/10.1080/1331677X.2013.11517598
  • Kugler, M. and Ofoghi, R. (2005, September). Does insurance promote economic growth? Evidence from the UK. Paper presented at the Money Macro and Finance (MMF) Research Group Conference. Retrieved from https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.461.5253&rep=rep1& type=pdf
  • Lee, C. C. and Lin, C. W. (2016). Globalization, political institutions, financial liberalization, and performance of the insurance industry. The North American Journal of Economics and Finance, 36, 244-266. https://doi.org/10.1016/j.najef.2016.01.007
  • Marjanović, I. and Popović, Ž. (2020). Profitability determinants of insurance companies in the Republic of Serbia. In M. Janovicz-Lomott, K. Łyskawa and P. Polychronidou (Eds.), Economic and financial challenges for Balkan and Eastern European countries (pp. 133-159). Springer, Cham. https://doi.org/10.1007/978-3-030-39927-6_9
  • Nourani, M., Chandran, V. G. R., Kweh, Q. L. and Lu, W. M. (2018). Measuring human, physical and structural capital efficiency performance of insurance companies. Social Indicators Research, 137(1), 281-315. https://doi.org/10.1007/s11205-017-1584-6
  • Özkan, M. M. (2003). Bulanık hedef programlama. Bursa: Ekin Yayınevi.
  • Tanaka, H., Uejima, S. and Asai, K. (1982). Linear regression analysis with fuzzy model. IEEE Trans. Systems Man Cybern, 12, 903-907. Retrieved from https://pascal-francis.inist.fr/
  • Wong, J. T. (2020). Dynamic procurement risk management with supplier portfolio selection and order allocation under green market segmentation. Journal of Cleaner Production, 253, 119835. https://doi.org/10.1016/j.jclepro.2019.119835
  • Yang, Z. (2006). A two-stage DEA model to evaluate the overall performance of Canadian life and health insurance companies. Mathematical and Computer Modelling, 43(7-8), 910-919. https://doi.org/10.1016/j.mcm.2005.12.011

An Investigation of In-Company Factors Affecting Profits in Non-Life Insurance Sector: Fuzzy Goal Programming Example

Yıl 2021, Cilt 6, Sayı 2, 332 - 355, 27.08.2021
https://doi.org/10.30784/epfad.871997

Öz

People and companies face uncertainties and risks throughout their lives. A cost arises because of every risk arising from their activities. The size of the risk concept should be well calculated and managed in this case. Therefore, insurance steps in to manage risks effectively and efficiently. In this study, the effects of firm variables on period profit / loss in non-life insurance companies were examined. To obtain maximum profit, which is an important element in ensuring the continuity of the activities of insurance companies, the effects of intra-firm variables that have an impact on profit have been investigated using fuzzy goal programming model. The variables of the insurance companies selected in this study used in the model are financial variables, premium total variables and operating expense variables. These variables are given in detail later in study. Annual data between 2014-2020 were used in the study. According to the results obtained from the study, the profit of insurance companies was estimated using the financial variables, operating expenses, and total premiums, respectively. In addition, it has been observed that the fuzzy goal programming method gives effective results in calculating the amount of variables that affect the profit of companies.

Kaynakça

  • Aouni, B., Colapinto, C. and La Torre, D. (2014). A fuzzy goal programming model for venture capital investment decision making. INFOR: Information Systems and Operational Research, 52(3), 138-146. https://doi.org/10.3138/infor.52.3.138
  • Berry-Stölzle, T. R., Koissi, M. C. and Shapiro, A. F. (2010). Detecting fuzzy relationships in regression models: The case of insurer solvency surveillance in Germany. Insurance: Mathematics and Economics, 46(3), 554-567. https://doi.org/10.1016/j.insmatheco.2010.02.003
  • Chen, C. H., Lin, M. and Chen, G. C. (2011). Does financial and business performance affect market share? A case of non-life insurance industry in Taiwan. Journal of Statistics and Management Systems, 14(2), 453-465. https://doi.org/10.1080/09720510.2011.10701566
  • Chen, S. Y. and Lu, C. C. (2015). Assessing the competitiveness of insurance corporations using fuzzy correlation analysis and improved fuzzy modified TOPSIS. Expert Systems, 32(3), 392-404. https://doi.org/10.1111/exsy.12099
  • Chou, P. L. and Chang, Y. M. (2011). The effect of the insurance company act on the capital benefit of investment in Taiwan’s life insurance industry. Journal of Statistics and Management Systems, 14(6), 1041-1055. https://doi.org/10.1080/09720510.2011.10701600
  • Doumpos, M., Galariotis, E., Nocera, G. and Zopounidis, C. (2018). Multiattribute assessment of the financial performance of non-life insurance companies: Empirical evidence from Europe. In H. Masri, B. Perez-Gladish and C. Zopounidis (Eds.), Financial decision aid using multiple criteria (pp. 1-17). Springer, Cham. https://doi.org/10.1007/978-3-319-68876-3_1
  • Felício, J. A. and Rodrigues, R. (2015). Organizational factors and customers' motivation effect on insurance companies' performance. Journal of Business Research, 68(7), 1622-1629. https://doi.org/10.1016/j.jbusres.2015.02.006
  • Hasan, M. B., Islam, S. N. and Wahid, A. N. (2018). The effect of macroeconomic variables on the performance of non-life insurance companies in Bangladesh. Indian Economic Review, 53(1), 369-383. https://doi.org/10.1007/s41775-019-00037-6
  • Hatemi-J, A., Lee, C. C., Lee, C. C. and Gupta, R. (2019). Insurance activity and economic performance: Fresh evidence from asymmetric panel causality tests. International Finance, 22(2), 221-240. https://doi.org/10.1111/infi.12333
  • Jana, D. K., Sahoo, P. and Koczy, L. T. (2017). Comparative study on credibility measures of type-2 and type-1 fuzzy variables and their application to a multi-objective profit transportation problem via goal programming. International Journal of Transportation Science and Technology, 6(2), 110-126. https://doi.org/10.1016/j.ijtst.2017.06.002
  • Jayaraman, R., Liuzzi, D., Colapinto, C. and Malik, T. (2017). A fuzzy goal programming model to analyze energy, environmental and sustainability goals of the United Arab Emirates. Annals of Operations Research, 251(1-2), 255-270. https://doi.org/10.1007/s10479-015-1825-5
  • Jurčević, B. and Žaja, M. M. (2013). Banks and insurance companies efficiency indicators in the period of financial crisis: The case of the Republic of Croatia. Economic Research-Ekonomska Istraživanja, 26(1), 203-224. https://doi.org/10.1080/1331677X.2013.11517598
  • Kugler, M. and Ofoghi, R. (2005, September). Does insurance promote economic growth? Evidence from the UK. Paper presented at the Money Macro and Finance (MMF) Research Group Conference. Retrieved from https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.461.5253&rep=rep1& type=pdf
  • Lee, C. C. and Lin, C. W. (2016). Globalization, political institutions, financial liberalization, and performance of the insurance industry. The North American Journal of Economics and Finance, 36, 244-266. https://doi.org/10.1016/j.najef.2016.01.007
  • Marjanović, I. and Popović, Ž. (2020). Profitability determinants of insurance companies in the Republic of Serbia. In M. Janovicz-Lomott, K. Łyskawa and P. Polychronidou (Eds.), Economic and financial challenges for Balkan and Eastern European countries (pp. 133-159). Springer, Cham. https://doi.org/10.1007/978-3-030-39927-6_9
  • Nourani, M., Chandran, V. G. R., Kweh, Q. L. and Lu, W. M. (2018). Measuring human, physical and structural capital efficiency performance of insurance companies. Social Indicators Research, 137(1), 281-315. https://doi.org/10.1007/s11205-017-1584-6
  • Özkan, M. M. (2003). Bulanık hedef programlama. Bursa: Ekin Yayınevi.
  • Tanaka, H., Uejima, S. and Asai, K. (1982). Linear regression analysis with fuzzy model. IEEE Trans. Systems Man Cybern, 12, 903-907. Retrieved from https://pascal-francis.inist.fr/
  • Wong, J. T. (2020). Dynamic procurement risk management with supplier portfolio selection and order allocation under green market segmentation. Journal of Cleaner Production, 253, 119835. https://doi.org/10.1016/j.jclepro.2019.119835
  • Yang, Z. (2006). A two-stage DEA model to evaluate the overall performance of Canadian life and health insurance companies. Mathematical and Computer Modelling, 43(7-8), 910-919. https://doi.org/10.1016/j.mcm.2005.12.011

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme
Yayınlanma Tarihi Ağustos 2021
Bölüm Makaleler
Yazarlar

Yusuf AKGÜL (Sorumlu Yazar)
CUMHURİYET ÜNİVERSİTESİ, ZARA UYGULAMALI BİLİMLER YÜKSEKOKULU
0000-0001-7327-3913
Türkiye


Fuat ÇAMLIBEL
SİVAS CUMHURİYET ÜNİVERSİTESİ, ZARA AHMET ÇUHADAROĞLU MESLEK YÜKSEKOKULU, BANKACILIK BÖLÜMÜ
0000-0002-0439-2502
Türkiye


Selma ÇAMLIBEL Bu kişi benim
TURİZM FAKÜLTESİ
0000-0002-6075-2285
Türkiye

Destekleyen Kurum Sivas Cumhuriyet Üniversitesi
Proje Numarası ZARAVDYO-002
Yayımlanma Tarihi 27 Ağustos 2021
Yayınlandığı Sayı Yıl 2021, Cilt 6, Sayı 2

Kaynak Göster

APA Akgül, Y. , Çamlıbel, F. & Çamlıbel, S. (2021). Hayat Dışı Sigorta Sektöründe Kârı Etkileyen Firma İçi Faktörlerin İncelenmesi: Bulanık Hedef Programlama Örneği . Ekonomi Politika ve Finans Araştırmaları Dergisi , 6 (2) , 332-355 . DOI: 10.30784/epfad.871997

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