Using the <i>Z</i>-score to analyze the financial soundness of insurance firms

Purpose Despite the sophisticated regulatory regime established in Solvency II, analysts should be able to consider other less complex indicators of soundness insurers. The Z -score measure, which has traditionally been used as a proxy individual risk banking sector, may useful tool when applied insurance sector. However, different methods for calculating this indicator have proposed literature. This paper compares six approaches examine one best fits companies. authors use final dataset 183 firms (1,382 observations) operating Spanish sector during period 2010–2017. Design/methodology/approach In first stage, opt root mean squared error (RMSE) criterion evaluate various and SD estimates that are compute data. second estimate compare explanatory power measures considered by using an ordinary least squares (OLS) regression model. Finally, report results baseline equation system-GMM estimator developed Arellano Bover (1995) Blundell Bond (1998) dynamic panel data models. Findings find formula is combines current value return on assets (ROA) capitalization with returns calculated over full sample period. Research limitations/implications main limitation research it addresses only consequently, implications findings must framed institutional context. think could extrapolated countries. Future including countries analyzing usefulness aggregated insurer-level -scores macroprudential monitoring. Practical early warning microprudential supervision. addition being insurers simpler than those regulation, information provided accounting-based measure help investors obtain better understanding firms' factors. Originality/value To authors’ knowledge, study few available predictive mixed focus