RT Journal Article
T1 Using the Z-score to analyze the financial soundness of insurance firms
A1 Moreno, Ignacio
A1 Parrado-Martínez, Purificación
A1 Trujillo-Ponce, Antonio
K1 Insurance sector
K1 Z-score
K1 Economic crisis
K1 Financial soundness
K1 European financial system
AB PurposeDespite the sophisticated regulatory regime established in Solvency II, analysts should be able to consider other less complex indicators of the soundness of insurers. The Z-score measure, which has traditionally been used as a proxy of individual risk in the banking sector, may be a useful tool when applied in the insurance sector. However, different methods for calculating this indicator have been proposed in the literature. This paper compares six different Z-score approaches to examine which one best fits insurance companies. The authors use a final dataset of 183 firms (1,382 observations) operating in the Spanish insurance sector during the period 2010–2017.Design/methodology/approachIn the first stage, the authors opt for a root mean squared error (RMSE) criterion to evaluate which of the various mean and SD estimates that are used to compute the Z-score best fitsthe data. In the second stage, the authors estimate and compare the explanatory power of the six Z-score measures that are considered by using an ordinary least squares (OLS) regression model. Finally, the authors report the results of the baseline equation using the system-GMM estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998) for dynamic panel data models.FindingsThe authors find that the best formula for calculating the Z-score of insurance firms is the one that combines the current value of the return on assets (ROA) and capitalization with the SD of the returns calculated over the full sample period.Research limitations/implicationsThe main limitation of the research is that it addresses only the Spanish insurance sector, and consequently, the implications of the findings must be framed in thisinstitutional context. However, the authors think that the results could be extrapolated to other countries. Future research should consider including different countries and analyzing the usefulness of aggregated insurer-level Z-scores for macroprudential monitoring.Practical implicationsThe Z-score may be a useful early warning indicator for microprudential supervision. In addition to being an indicator of the soundness of insurers simpler than those established in thecurrent regulation, the information provided by this accounting-based measure may help analysts and investors obtain a better understanding of insurance firms’ risk factors.Originality/valueTo the best of the authors’ knowledge, this study is the first to examine and compare different approaches to calculating Z-scores in the insurance sector. The few available results on the predictive power of the Z-score are mixed and focus on the banking sector.
PB Emerald Publishing Limited
YR 2022
FD 2022
LK https://hdl.handle.net/10433/22258
UL https://hdl.handle.net/10433/22258
LA en
NO European Journal of Management and Business Economics Vol. 31 No. 1, 2022 pp. 22-39
NO The authors acknowledge the financial support of the Regional Government of Andalusia, Spain (Research Groups SEJ-289 and SEJ-555).
NO Departamento de Economía Financiera y Contabilidad. Universidad Pablo de Olavide.
DS RIO
RD May 9, 2026