It is important to remember when assessing the financial soundness of a company that:
AbstractPurposeDespite the sophisticated regulatory regime established in Solvency II, analysts should be able to consider other less complex indicators of the soundness of insurers. The Zscore 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 Zscore 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. Show
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 Zscore best fits the data. In the second stage, the authors estimate and compare the explanatory power of the six Zscore 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 systemGMM 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 Zscore 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 this institutional 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 insurerlevel Zscores for macroprudential monitoring. Practical implicationsThe Zscore 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 the current regulation, the information provided by this accountingbased 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 Zscores in the insurance sector. The few available results on the predictive power of the Zscore are mixed and focus on the banking sector. 研究目的雖然在償付能力標準II 內已建立了精密的監管制度，但分析人員應可以考慮以不太複雑的指標，來分析保險公司的穩健程度。Z分數的估量在銀行業一向作為是個體風險的代理而使用，而Z分數如應用於保險業，或許會成為有用的工具。唯在文獻裏，學者和研究人員提出了不同的方法來計算這個指標。本文比較六個不同的Z分數估量方法，以研究出最適合保險公司的方法。我們使用一個最終數據集，包括在2010年至2017年期間在西班牙保險業界營運的183間公司（1382 個觀察）。 研究設計/方法/理念在首個階段，我們選擇使用一個方均根誤差(RMSE) 標準來衡量用來計算Z分數的各個平均值和標準差估量中哪個最適合使用於有關的數據。在第二個階段， 我們以普通最小平方 (OLS) 迴歸模型，去估計並比較被考慮的六個Z分數估量的解釋力。最後，我們以Arellano與Bover (1995), 以及Blundell與Bond (1998) 為動態追蹤資料模型而發展出來的系統廣義動差估計推定量，來發表我們基線方程式的結果。 研究結果我們發現，計算保險公司Z分數的最佳公式是把資產收益率及資本總額的現值，和在整個樣本期間計算出來的囘報的標準差結合起來的公式。 研究的局限/含意我們研究主要的局限為：研究只涉及西班牙的保險業；因此，研究結果的含意，必須在這個體制的背景框架下來闡釋。唯我們相信研究結果或許可外推至其它國家。未來的研究，應考慮納入不同國家作為研究對象，並分析保險公司層面的集成Z分數的功用，以求達到宏觀審慎監控的目的。 實際意義Z分數或許就微觀審慎監管而言是一個有用的早期警告器。這些以會計為基礎的估量而提供的資訊，除了較現時規例内已建立顯示保險公司穩健程度的各個指標更簡單外，還會幫助分析人員和投資者更了解保險公司的風險因素。 研究的原創性/價值據我們所知，本研究為首個研究，去探討並比較保險業內的Z分數的計算方法。以前關於Z分數預測能力的，為數不多並可供取閱的研究結果均不統一；而且，這些研究都聚焦探討銀行業。 Keywords
CitationMoreno, I., ParradoMartínez, P. and TrujilloPonce, A. (2022), "Using the Zscore to analyze the financial soundness of insurance firms", European Journal of Management and Business Economics, Vol. 31 No. 1, pp. 2239. https://doi.org/10.1108/EJMBE0920200261 Publisher:Emerald Publishing Limited Copyright © 2021, Ignacio Moreno, Purificación ParradoMartínez and Antonio TrujilloPonce LicensePublished in European Journal of Management and Business Economics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and noncommercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode 1. IntroductionThe insurance industry plays a crucial role in the economy by allowing individuals and companies to transfer risk through insurance and reinsurance activities and thus enhances financial stability (Das et al., 2003). This industry, which contributes significantly to economic growth and notably impacts investors and stakeholders, has become an important pillar of the financial sector (Haiss and Sümegi, 2008). Although insurance companies have traditionally been considered less risky than banks because they are less exposed to liquidity risk (Caporale et al., 2017), the increasing interactions among the insurance sector, financial markets and other financial intermediaries, as well as financial innovations, globalization and the deregulation of the financial system, have made the operations of financial intermediaries over the last decades more complex and potentially riskier (Sharpe and Stadnik, 2007). While the contagion effects from the failure of firms in the insurance sector may not be as consequential as those in the banking industry, they have relevant potential to disrupt the financial system and negatively impact the economy (Das et al., 2003). Therefore, the soundness of insurance firms is of major importance not only for the welfare of the financial sector and various stakeholders (Pasiouras and Gaganis, 2013) but also for the stability of the economy as a whole. Consequently, policy makers are working to upgrade regulatory and supervisory frameworks to reduce insolvency risk and promote confidence in the financial stability of the insurance sector. In this vein, European insurers have recently implemented Solvency II, a riskbased economic approach aimed at adopting solvency requirements that better reflect the risk of companies (Cummins et al., 2017). This new supervisory regime in the EU includes a risksensitivity requirement that is based on a prospective calculation to ensure accurate and timely interventions by the supervisor (the solvency capital requirement) [1]. Despite the sophisticated regulatory regime established in Solvency II, analysts should be able to consider other less complex indicators of the soundness of insurers. The Zscore measure, which has traditionally been used as a proxy of individual risk for the banking sector (Boyd et al., 2006; Laeven and Levine, 2009; Lepetit and Strobel, 2013; BaselgaPascual et al., 2015; Chiaramonte et al., 2015; Khan et al., 2017), may be a useful tool when applied in the insurance sector. The Zscore relates a firm's capital level to the variability in its return on assets (ROA), revealing how much variability in returns can be absorbed by capital without the firm becoming insolvent (Li et al., 2017). The popularity of the Zscore derives from its relative simplicity and the fact that it can be computed using accounting information alone. In contrast to marketbased risk measures, this indicator is applicable when dealing with an extensive number of unlisted as well as listed companies (Chiaramonte et al., 2016). Our research contributes to the body of knowledge by examining and comparing, for first time, different approaches to calculating the Zscore on a sample of insurance firms. The few available results on the predictive power of this indicator are mixed and focus on the banking sector (Lepetit and Strobel, 2013; Chiaramonte et al., 2016; Bongini et al., 2018). Our paper also adds to the literature on the factors that determine the risk of insurance companies. The Zscore, as a simple accountingbased measure, may help analysts and investors obtain a better understanding of risk factors in the insurance sector. Finally, we focus on the Spanish insurance sector, which is one of the largest in Europe (IMF, 2017). Specifically, Spain is among the top ten European countries in terms of gross premiums written and asset volume (EIOPA, 2017), and this country continues to lead in growth among the major Eurozone economies (MAPFRE, 2018). Although the implications of our findings must be framed in this institutional context, we think that our results could be extrapolated to other countries. The rest of the paper is organized as follows. The next section presents the basics of the Zscore calculation. The third section describes the data and methodology. The fourth section analyzes the main results. The final section concludes the paper. 2. Theoretical background and literature review2.1 Measuring the financial soundness of insurance companies: the ZscoreA broad strand of the literature has focused on the analysis of diverse measures of capitalization (i.e. the actual solvency margin, the required solvency margin, or the solvency ratio) to draw conclusions about the financial soundness of firms (e.g. Cummins and Nini, 2002; De Haan and Kakes, 2010; RubioMisas and FernándezMoreno, 2017; Moreno et al., 2020; among others). Nevertheless, limiting the analysis to insurers' capitalization could be too restrictive, and a wider approach is necessary to examine the different factors that influence the financial soundness of an insurer (see, e.g. Hu and Yu, 2014; Mankaï and Belgacem, 2016; Altuntas and Rauch, 2017; Cummins et al., 2017; Shim, 2017). The Zscore can be considered an alternative measure of risk and thus a good indicator of the financial soundness of insurers that takes into account factors beyond capitalization or the particular event of bankruptcy [2]. Although the Zscore is traditionally used as an indicator of individual risk in the banking literature (Boyd et al., 2006; Laeven and Levine, 2009; Maechler et al., 2010; Čihák and Hesse, 2010; Lepetit and Strobel, 2013; BaselgaPascual et al., 2015; Chiaramonte et al., 2015, 2016; Khan et al., 2017; among others), some recent studies have also used this measure to examine the financial soundness of insurance firms (see, e.g. Shim, 2011; Pasiouras and Gaganis, 2013; Altuntas and Rauch, 2017; Cummins et al., 2017; Shim, 2017; Alhassan and Biekpe, 2018; Gaganis et al., 2019, Pavić et al., 2019; RubioMisas, 2020). The Zscore may be a simple and effective predictor of insurer failure given the simplicity and transparency of its calculation (Plantin and Rochet, 2007) and because it can be computed for both unlisted and listed firms (Bongini et al., 2018). This measure can be applied to insurers with different risk strategies (Čihák and Hesse, 2010; Pasiouras and Gaganis, 2013): an institution may have the same or higher Zscore than other insurers with lower capitalization if it has higher riskadjusted returns. Nevertheless, the Zscore has some disadvantages to consider. First, as an accountingbased measure, its reliability depends on the quality of the underlying accounting and auditing framework, which is a serious concern in lessdeveloped countries. Additionally, as firms may smooth their accounting data over time, the Zscore may offer an excessively positive assessment of insolvency risk (Laeven and Majnoni, 2003). Second, as pointed out by Čihák (2007), the Zscore evaluates each firm separately, potentially overlooking the risk that distress in one financial institution may cause losses to other financial institutions in the system. In the same vein, Bongini et al. (2018) conclude that the Zscore has limitations in the macroprudential monitoring framework for detecting banking crises, at least in emerging economies, because accountingbased measures do not capture all of the dimensions of risk, such as contagion and interconnectedness (i.e. systemic risk) [3], [4]. 2.2 Different approaches to calculating the ZscoreAs stated previously, the basic principle behind the Zscore is to relate the capital ratio to the variability in the ROA so that one can know how much variability in returns can be absorbed by capital without the firm becoming insolvent (Li et al., 2017): where Eq/TA denotes the equitytototal assets ratio and σROA represents the standard deviation (SD) of the ROA.Default is expected to occur when losses consume capital (i.e. when ROA + Eq/TA ≤ 0 or, equivalently, when ROA ≤ − Eq/TA). Then, if we assume that ROA is a random variable, the Zscore represents the number of standard deviations between the expected value of the ROA, E(ROA) and the negative values of ROA, ROA = − Eq/TA, that would result in insolvency (Hannan and Hanweck, 1988). In other words, it indicates the number of standard deviations that the ROA would have to fall to deplete equity and force a failure. As in the banking sector, equity serves as a buffer against unforeseen losses and is critical for an insurer's ability to meet its obligations (Cummins et al., 2017). Hannan and Hanweck (1988) show that the Chebyshev inequality for any symmetric distribution allows us to assume the following upper bound on the probability of default (PD): Therefore, a higher Zscore is associated with a higher distance to default (a lower probability of insolvency). It does not require strong assumptions about the distribution of ROA (see, e.g. Strobel, 2011), which represents an especially attractive advantage from a practitioner's point of view. The literature reports different approaches to calculating this measure. The most basic formulation defines the Zscore as the sum of the values in the current period t of the firm's ROA (ROAt) and equitytototal assets ratio (Eq/TAt) divided by the SD of ROA calculated with data from the current year (t) and the two previous years, i.e. t−1 and t−2 (σROA3) (Pasiouras and Gaganis, 2013; Chiaramonte et al., 2016; Cummins et al., 2017) [5]: Delis and Staikouras (2011) and BaselgaPascual et al. (2015) derive a Zscore measure (Zs2) that uses data from the two previous years to calculate σROA at time t (σROA2): Maechler et al. (2010) and Chiaramonte et al. (2016) compute the Zscore as the sum of the threeyear moving average of ROA (ROAμ3) and the threeyear moving average of the equitytototal assets ratio (Eq/TAμ3) divided by σROA3 [6]: Yeyati and Micco (2007) compute the Zscores for each firm and year combining ROAμ3 with Eq/TAt and σROA3: Boyd et al. (2006) and Chiaramonte et al. (2016) calculate the Zscore as the sum of ROAt and Eq/TAμ3 divided by σROA3: Finally, Beck and Laeven (2006) and Hesse and Čihák (2007) estimate a Zscore measure that combines ROAt and Eq/TAt with the SD of ROA calculated over the full period (σROAT): 3. Data and methodology3.1 SampleOur sample includes most of the insurance companies operating in Spain from 2008–2017. Data were obtained from the database maintained by the Spanish regulatory authority, the Directorate General of Insurance and Pension Funds (Dirección General de Seguros y Fondos de Pensiones) (DGSFP), an administrative body within the Ministry of Economy and Business [7]. However, as some of the components of the Zscore employ data from up to two years prior to the calculation date (i.e. t−2, t−1), this reduces our time span to the period 2010–2017 [8]. In addition, we do not consider social benefit institutions and reinsurance specialists because they have singular characteristics that may distort our analysis. We use unconsolidated financial statements, thereby reducing the possibility of introducing aggregation bias into the results. Merged insurers are considered to be separate firms before the merger and a single company afterward. Finally, we remove observations with abnormal ratios or extreme values from the sample, ensuring that the analysis is not affected by potential measurement error or misreporting. After applying these filters, we obtain a final dataset consisting of an unbalanced panel with 183 insurers and 1,382 observations (see Table 1). We have a minimum of five consecutive observations for each company, with 77.60% of the insurers being observed over the entire period. 3.2 Choosing the best Zscore approachWe explore which of the different methods for computing the Zscore reported in section 2.2 is best when using actual data. Following a procedure similar to that employed by Lepetit and Strobel (2013) for the banking sector, we opt for a root mean squared error (RMSE) criterion to evaluate which estimator best fits the data by minimizing the weighted average RMSE of the N insurers j given by where x and μxest are, respectively, the realized and predicted values of the different variables that are used to compute the timevarying Zscore measures Zs1 to Zs6 (i.e. ROAt, ROAμ3, Eq/TAt, Eq/TAμ3, σROA2, σROA3 and σROAT). All these equations are calculated for the full sample for each period t∈{1…T}.The RMSE is the square root of the mean of the squares of all of the errors, and it is considered to be an excellent error metric for numerical predictions. Naturally, a lower parameter outcome is preferable. Moreover, we estimate and compare the explanatory power of the following multivariate empirical model for the Spanish insurance sector: where Y denotes the different approaches to estimating the Zscore (in logarithmic form) for insurer i (i.e. Zs1 to Zs6) in year t; [9] FSi, t denotes a set of firmspecific accounting variables that the literature has recognized as good predictors of insurer risk, Di,t represents two dummy variables that control for the specialization (life versus nonlife) and the organizational form (mutual versus stock company) of the insurer, It represents a variable that accounts for the possible effect of industry concentrations on insurer risk, and Mt denotes a set of year dummy variables that account for macroeconomic conditions and timespecific effects. In the regression above, α is the intercept term, and β, γ, θ and δ are vectors of coefficient estimates. Last, εi, t is the disturbance term.Table 2 summarizes the explanatory variables that are included in the present study and their expected Zscore signs—remember that the Zscore (i.e. the distance to default) operates in the opposite direction of insurer risk: the higher the Zscore is, the lower the risk. We use the natural logarithm of total assets to account for the effect of size on risk and expect a positive relationship between size and the Zscore, as financially distressed insurers are typically small in size (Sharpe and Stadnik, 2007). To examine the influence of profitability on insurer risk, we divide profits after tax by total assets (i.e. ROA). Sharpe and Stadnik (2007) conclude that insurers with low ROA are at higher risk of failure. In the same vein, Caporale et al. (2017) report that highly profitable insurers are less likely to become insolvent because they manage their expenses effectively and can set competitive premium rates. We include the equitytototal assets ratio to control for the effect of capitalization on insurer risk. When measuring default risk, the capital used to cover the insurance business is a key factor. Insurance firms should hold enough capital to cover the policies they underwrite (Caporale et al., 2017). Altuntas and Rauch (2017) find that higher levels of capitalization are associated with higher levels of financial stability in the insurance sector. To account for the effect of reinsurance on the Zscore models, we use the ratio of reinsurance premiums paid to total premiums earned. Reinsurance allows insurers to transfer part of their risk to third parties and results in more predictable future losses, thereby reducing the probability of default (Shiu, 2011; Caporale et al., 2017). Consequently, we expect a positive relationship between the use of reinsurance by the insurer and the Zscore. We choose the share of equity securities in total assets to measure the effect of investment risk on insurer risk and expect a negative relationship between portfolio risk and our distancetodefault measures. Similar to Ho et al. (2013) and Altuntas and Rauch (2017), we proxy underwriting risk with the SD of the loss ratio, defined as incurred losses divided by premiums earned net of reinsurance, over the sample period. As stated by Cummins and Sommer (1996), underwriting risk refers to the risk that loss payments will be greater than the expected losses allowed for in the premiums charged to policyholders. We thus anticipate a negative association between underwriting risk and the Zscore. To account for the effect of the time lag between the issuance and payment of claims on insurer risk, we use the ratio of technical provisions (i.e. loss reserves) over incurred losses. Longtail lines of insurance (i.e. insurance with a longer time lag between policy issuance and the payment of claims) may have a negative effect on insurer solvency because longtail lines tend to generate less income from underwriting than shortertail lines (De Haan and Kakes, 2010). We therefore expect this variable to have a negative effect on the Zscore. We account for the ownership structure of the firm and insurer specialization using two dummy variables [10]. Altuntas and Rauch (2017) report that mutual insurance firms have higher Zscore levels because their future cash flows are less risky. In addition, these authors state that the incentive to increase risk after issuing policies should be much lower for mutual insurers than for stock insurers due to their organizational structure. We also expect a positive coefficient for the dummy that identifies life insurance companies, as nonlife insurers are considered riskier than life insurance companies because they operate as “risk takers” (Chen and Wong, 2004). In the same vein, Gründl et al. (2016) consider nonlife insurers to be riskier because of the uncertainty of claim payments and the difficulty in predicting threats. Finally, we measure industry concentration using the HerfindahlHirschman index. There is no consensus regarding the expected relationship between industry concentration and insurer risk. The “concentrationstability” view states that because large firms are likely to earn more profits due to their market power, a concentrated industry is more stable. Therefore, this view favors greater values for the Zscore. However, the “concentrationfragility” view affirms that the toobigtofail protective mechanism may lead to excessive risktaking by managers (Moreno et al., 2020), resulting in lower values of the Zscore. In this vein, Shim (2017) shows that a higher market concentration is associated with decreased financial stability in the US propertyliability insurance industry. 4. Results4.1 Analysis of the different Zscore measuresTable 3 reports some descriptive statistics for the six different timevarying Zscore measures. The results for Zs1, Zs3, Zs4 and Zs5 are very similar, with means (as calculated per insurer) in the interval of 3.693–3.787. Zs2 presents a higher mean and SD. Zs6, on the other hand, has results that are very different from those of the other measures, with average means and standard deviations in a lower range as well as a smaller average coefficient of variation (0.320). We also observe that mutual insurance companies present higher mean values than stock companies for each of the six Zscore measures considered. Similarly, we find differences in these measures between life and nonlife specialized insurers, although in this case, they are not as large as in the former. Finally, the lowest average Zscore is reported in 2010, whereas the highest Zscore mean values are found in 2016 and 2017, which is in line with the improvements in the Spanish economy. Table 4 presents the average correlation coefficients of our six different Zscore measures, confirming the existence of three clusters: Zs1, Zs3, Zs4 and Zs5 have correlation coefficients close to 1, whereas the coefficients for Zs2 and Zs6 are much lower. Table 5 shows the results of the weighted average RMSE for each of the components of the Zscores considered in the current study (i.e. Zs1–Zs6), indicating that Zs6 is the Zscore measure that best fits the data. Therefore, according to this criterion, the best way to calculate the Zscore is using the values of ROA and Eq/TA in current period t together with the SD of ROA calculated over the full sample, as proposed by Beck and Laeven (2006) and Hesse and Čihák (2007). In Table 6, we estimate and compare the explanatory power of the six Zscore measures considered. First, however, we perform a multicollinearity analysis for the previously selected independent variables (see Table 7). We confirm that collinearity is not a problem by calculating the variance inflation factor (VIF); the calculated value for this factor is less than 4 (and close to 1) for most of the variables [11]. The regression model that uses Zs6 presents the highest explanatory power, with values for the adjusted R2 slightly higher than 30%. The rest of the models exhibit values close to 20%, except for Zs2, for which the adjusted R2 falls to 12%. Therefore, we confirm that the Zs6 model is the best option for explaining insurer risk, in accordance with the results reported by the RMSE criterion [12]. 4.2 Analysis of the determinants of insurer riskBecause some of the firmspecific factors that influence insurer risk may be endogenous (e.g. insurers might need to increase their capital ratio if they become riskier) and others are difficult to measure or identify in an equation (e.g. managerial ability), in Table 8, we report the results of our baseline equation using the systemGMM estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998) for dynamic panel data models. The persistence of risk has been well documented in the banking literature (e.g. BaselgaPascual et al., 2015). We are able to use the systemGMM method because we have information on all of the analyzed variables for at least five consecutive years for each insurer [13]. As proposed by Windmeijer (2005), we employ a twostep estimation procedure with finitesample corrected standard errors, which provides less biased coefficient estimates and more accurate standard errors. We treat insurer characteristics (except their organizational form and their specialization) as endogenous variables by using suitable instruments for both the equation in levels and the equation in differences [14]. Industry concentration and macroeconomic control variables (i.e. year dummies) are considered strictly exogenous. We verify the validity of the instruments by using Hansen's Jtest of overidentifying restrictions. The higher values of the lagged dependent variables (except for the Zs2 regression) confirm the dynamic character of the model specification, indicating strong persistence; i.e. the adjustment of risk is very slow. As expected, the regression coefficients indicate a positive relationship between size and the Zscore; i.e. larger firms are less risky than smaller firms, supporting previous findings in the literature (Chen and Wong, 2004; Pasiouras and Gaganis, 2013; Shim, 2017). We demonstrate a strong positive relationship between ROA and the Zscore but only when Zs6 is used as the dependent variable. This result supports the hypothesis that highly profitable insurers are less likely to become insolvent because they manage expenses effectively and can set competitive premium rates (Caporale et al., 2017). The relationship with capitalization, as measured by the equitytototal assets ratio, is positive and statistically significant in all of the analyzed models. This finding corroborates the hypothesis that more capitalized insurers have higher Zscores, in line with Shim (2011) and Altuntas and Rauch (2017). We also report that a greater use of reinsurance may increase the financial soundness of firms; i.e. higher levels of reinsurance result in lower insurer risk by transferring part of this risk to third parties (as found by Alhassan and Biekpe, 2018). However, this result applies only to the Zs6 approach and has low statistical significance. The positive coefficient we find for the dummy that identifies mutual insurance companies is in accordance with the hypothesis that mutual companies are more financially stable than stock insurers because in mutual companies, the policyholders are also the owners of the firms. Therefore, managers' incentives to increase asset risk are lower in mutual companies than in stock firms (Shim, 2017). Similar to Pasiouras and Gaganis (2013), we also find a positive coefficient for the dummy that identifies life insurance companies, corroborating the hypothesis that life insurers are more financially stable than nonlife insurance companies, which may operate as ‘risk takers'. The relationship between industrial concentration and the Zscore is negative and significant only for the Zs6 approach. This result supports the concentrationfragility view, providing empirical evidence against the tendency toward increasing concentration that the Spanish insurance market is currently experiencing. Finally, we do not find statistical significance for the variables that measure portfolio risk, underwriting risk or whether a business is longtailed in any of the six models considered. Once again, we observe that the Zscore measure that incorporates the most statistically significant variables in the risk model is the one that combines the current values of ROA and capitalization with the SD of ROA calculated over the full period. 5. ConclusionsBearing in mind the increasing relevance of risk supervision in the insurance sector, this paper aims to explore insurers' financial soundness from a wider perspective, considering factors beyond capitalization or the particular event of bankruptcy. The Zscore, which has been widely used in the banking literature, can be considered an appropriate alternative measure of risk and thus a good indicator of the financial soundness of insurance firms. This measure relates the insurer's capital level to variability in its returns, revealing how much variability in returns can be absorbed by capital without the firm becoming insolvent. Higher Zscores are indicative of a higher distancetodefault ratio and thus greater financial soundness. By comparing six different approaches to calculating the Zscore with a final dataset of 183 insurers (1,382 observations) operating in the Spanish insurance sector during the period 2010–2017, we find that the best measure for calculating the Zscore is the one that combines current ROA and capitalization values with the SD of ROA calculated over the full period. This approach (i.e. Zs6) has the advantage of enable the construction of timevarying Zscores that do not require initial observations to be dropped (Lepetit and Strobel, 2013). Information provided by the Zscore, in addition to that given by more complex riskbased models, may be helpful for microprudential supervision. Moreover, because this indicator uses accounting data, its results are easily verifiable, providing insurance regulators with a better understanding of risk factors for both listed and unlisted insurance companies [15]. Nevertheless, the use of an accounting measure has some disadvantages that need to be considered. First, its reliability depends on the quality of the underlying accounting and auditing framework. Second, the Zscore has limitations in the macroprudential monitoring framework because accountingbased measures do not capture systemic risk, although this element may be less relevant in the insurance sector. Notes1. “In order to promote good risk management and align regulatory capital requirements with industry practices, the Solvency Capital Requirement should be determined as the economic capital to be held by insurance and reinsurance undertakings in order to ensure that ruin occurs no more often than once in every 200 cases or, alternatively, that those undertakings will still be in a position, with a probability of at least 99.5%, to meet their obligations to policyholders and beneficiaries over the following 12 months. That economic capital should be calculated on the basis of the true risk profile of those undertakings, taking account of the impact of possible riskmitigation techniques, as well as diversification effects”. [Directive 2009/138/EC of the European Parliament and of the Council of 25 November 2009 on the takingup and pursuit of the business of insurance and reinsurance (Solvency II) (recast)]. 2. Chen and Wong (2004), Sharpe and Stadnik (2007) and Caporale et al. (2017) focus on the particular event of insolvency or bankruptcy and propose models to identify or predict insurers experiencing financial distress. However, researchers undertaking this kind of analysis face the difficulty of finding data on the insolvency of insurance firms—the majority of these companies decide to transfer their business to other insurance firms or to just stop underwriting new business instead of becoming “insolvent” (Caporale et al., 2017). 3. Bongini et al. (2018) test the reliability of seven different versions of the Zscore in detecting periods of banking crisis on a sample of 20 Central, Eastern and Southeastern European (CESEE) countries during 1995–2014. 4. However, unlike banks, insurers are not the primary drivers of systemic risk. They are not part of the financial payment system and rarely interact with other insurers (except through reinsurance). This contrasts with banks, as they are primary lenders to other banks and thus are highly interconnected (Rudolph, 2017). 5. The threeyear rolling window in the SD calculation avoids the problem that Zscores are exclusively driven by changes in ROA and Eq/TA (Schaeck et al., 2012). Although the use of a longer period for the calculation of the SD could result in more reliable Zscores, we must consider the loss of observations created by imposing a stronger requirement (Pasiouras and Gaganis, 2013). 6. Gaganis et al. (2019) use this Zscore metric to investigate the interplay between national culture and risk in insurance firms. 8. This restriction does not apply if we estimate the Zscore by combining the values of ROA and capital in current period t with the SD of ROA calculated over the full period (2008–2017). However, because we want to compare different Zscore measures, we use the same observations in all of the analyses. 10. We use the same criteria as the DGSFP (2018) to differentiate between life and nonlife specialized insurers. 11. The only variable that has a VIF higher than 4 is the HHI, which is used to account for industry concentration (and has a value close to 6). We regress our models with alternative variables (e.g. the concentration ratio CR5) and even without including that variable, each of which leaves our conclusions practically unchanged. 13. This is a required condition to test for the absence of secondorder serial correlation. 14. We also estimate regressions in which the organizational form and/or the specialization are considered endogenous variables. The results barely differ from those previously obtained. 15. Plantin and Rochet (2007) state that “prudential ratios should be defined simply and derived from public accounts, because these accounts are easily verifiable”. ReferencesAlhassan, A.L. and Biekpe, N. 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Corresponding authorHow do you assess financial soundness?How to Determine the Financial Health of a Company. Analyze the Balance Sheet. The balance sheet is a statement that shows a company's financial position at a specific point in time. ... . Analyze the Income Statement. ... . Analyze the Cash Flow Statement. ... . Financial Ratio Analysis.. Which indicates financial soundness of the company?A company's bottom line profit margin is the best single indicator of its financial health and longterm viability.
How do you evaluate a company's financial stability?Measuring a Company's Stability and Success. Quick Ratio = (Cash + Accounts Receivable + Other Easily Liquidated Assets) / Current Liabilities. ... . Stable Current Ratio = Total Current Assets / Total Current Liabilities. ... . EBIT/Interest= Earnings Before Interest & Taxes / Interest Expense.. Under Which type of agreement does a business seller agree not to open a competing business within a specified time period and geographic area of the existing one?A noncompete agreement involving the sale of a business typically provides that, in exchange for a specified payment (which may be part of the sales price), the seller will promise not to go into a similar type of business within a certain geographic area for a specified period of time.
