Failure threats of insurance companies: A case study of financial environments of Jordan
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DOIhttp://dx.doi.org/10.21511/imfi.18(3).2021.11
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Article InfoVolume 18 2021, Issue #3, pp. 113-126
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Insurance firms are known to have unique financial failure characteristics that affect the financial environment of the countries. Therefore, the purpose of this study is to assess the validity of the model used in predicting the financial failures of insurance companies. The model is believed to help in stabilizing the financial environment of the countries by predicting any collapses in the insurance sector. A discriminate regression technique was used to test 28 indicators chosen from 11 financial failure model parameters. 11 parameters of the model are the following: solvency, profitability, operational capabilities, structural soundness, capital expansion capacity, capital adequacy, reinsurance and actuarial issues, management soundness, capital expansion capacity, earnings and profitability, and liquidity. The results of the study proved that 22 variables from 11 parameters were significant; the study also validated the use of the financial failure model as a stable predictor of the financial failure of ASE insurance firms. The stability of the insurance industry is interpreted through the minimum deviation between the real and measured performances. The deviation was present in 3 out of 95 observations, and it affected only 3 firms out of 19, 1 firm out of 3 turned out to be affected by the risker deviation which is the type II error distorted observation. To conclude, the study by mentioning that insurance firms are not threatened by failure or distress and the financial failure model is a valid prediction model.
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JEL Classification (Paper profile tab)G22, G17, N25, C10
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References30
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Tables7
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Figures0
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- Table 1. Group statistics
- Table 2. Equality of group means
- Table 3. Correlation pooled within group matrices
- Table 4. Equality of covariance matrices
- Table 5. Summary of canonical discriminant functions
- Table 6. Classification statistics
- Table 7. Comparison of real performance vs. predicted performance
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- Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
- Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETATM analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking & Finance, 1(1), 29-54.
- Altman, E. I., Heine, M. L., Zhang, L., & Yen, J. (2007). Corporate financial distress diagnosis in China (Salomon Center Working Paper). New York University.
- Amendola, A., Restaino, M., & Sensini, L. (2015). An analysis of the determinants of financial distress in Italy: A competing risks approach. International Review of Economics & Finance, 37, 33-41.
- Bose, I., & Pal, R. (2006). Predicting the survival or failure of click-and-mortar corporations: A knowledge discovery approach. European Journal of Operational Research, 174(2), 959-982.
- Bae, J. (2012). Predicting financial distress of the South Korean manufacturing industries. Expert Systems with Applications, 39(10), 9159-9165.
- Carlos, S.-C. (1996). Self-organizing neural networks for financial diagnosis. Decision Support Systems, 17(3), 227-238.
- Chen, W. S., & Du, Y. K. (2009). Using neural networks and data mining techniques for the financial distress prediction model. Expert Systems with Applications, 36(2), 4075-4086.
- Christidis, A., & Gregory, A. (2010). Some New Models for Financial Distress Prediction in the UK. Xfi – Centre for Finance and Investment, 10.
- Das, U., Davis, N., & Podpiera, R. (2003). Insurance and Issues in Financial Soundness (Working Paper WP/03/138). International Monetary Fund.
- Dairui, L., & Jia, L. (2009). Determinants of financial distress of ST and PT companies: A panel analysis of Chinese listed companies. SSRN.
- Graham, B. (2003). Public-Utilities and Financial Enterprises. SerenityStocks.
- Gestel, T. V., Baesens, B., Suykens, J. A. K., Van den Poel, D., Baestaens, D.-E., & Willekens, M. (2006). Bayesian kernel based classification for financial distress detection. European Journal of Operational Research, 172(3), 979-1003.
- Geng, R., Bose, I., & Chen, X. (2015). Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European Journal of Operational Research, 241(1), 236-247.
- Gepp, A., & Kumar, K. (2015). Predicting Financial Distress: A Comparison of Survival Analysis and Decision Tree Techniques. Procedia Computer Science, 54, 396-404.
- Halpern, P., Kieschnickb, R., & Rotenberg, W. (2009). Determinants of financial distress and bankruptcy in highly levered transactions. The Quarterly Review of Economics and Finance, 49(3), 772-783.
- Jahur, M. S., & Quadir, S. M. N. (2012). Financial Distress in Small and Medium Enterprises (SMES) of Bangladesh: Determinants and Remedial Measures. Economia. Seria Management, 15(1), 46-61.
- Jardin, P. du, & Severin, E. (2012). Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time. European Journal of Operational Research, 221(2), 378-396.
- Jo, H., Han, I., & Lee, H. (1997). Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis. Expert Systems with Applications, 13(2), 97-108.
- Kumar, P., & Ghimire, R. (2013). Testing of Financial Performance of Nepalese Life Insurance Companies by CARMELS Parameters. Journal of Business and Management.
- Li, H., & Sun, J. (2009). Gaussian case based reasoning for business-failure prediction with empirical data in China. Information Sciences, 179(1-2), 89-108.
- Lin, F., Liang, D., Yeh, C.-C., & Huang, J.-C. (2014). Novel feature selection methods to financial distress prediction. Expert Systems with Applications, 41(5), 2472-2483.
- Manzaneque, M., Priego, A., & Merino, E. (2016). Corporate governance effect on financial distress likelihood: Evidence from Spain. Revista de Contabilidad, 19(1), 111-121.
- Ntoiti, J. (2013). Determinants of Financial Distress Facing Local Authorities in Service Delivery in Kenya (Thesis). Jomo Kenyatta University of Agriculture and Technology.
- Smajla, N. (2014). Measuring Financial Soundness of Insurance Companies by Using CARMELS Model – Case of Croatia. Interdisciplinary Management Research, 10, 600-609.
- Sevim, C., Oztekin, A., Bali, O., Gumus, S., & Guresen, E. (2014). Developing an early warning system to predict currency crises. European Journal of Operational Research, 237(3), 1095-1104.
- Simpson, S., & Damoah, O. (2008). An Evaluation of Financial Health of Non-Life Insurance Companies from Developing Countries: The Case of Ghana. 21st Australasian Finance and Banking Conference.
- Tinoco, M., & Wilson, N. (2013). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30, 394-419.
- Wilson, R. L., & Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision Support Systems, 11(5), 545-557.
- Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59-82.