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Efficiency and stock returns: evidence from the insurance industry

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Abstract

This study investigates whether the capital market values the efficiency of firms. After tracing stock returns and efficiency changes of 399 listed insurance firms in 52 countries during the 2002–2008 period, the paper reports a positive and statistically significant relationship between profit efficiency change and market adjusted stock returns. However, there is no robust evidence that cost efficiency change is associated with stock returns.

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Notes

  1. In recent years, studies on the efficiency of insurance firms have examined numerous issues, such as regulations (e.g., Weiss and Choi 2008), initial public offerings (e.g., Xie 2010), organizational structure (e.g., Brockett et al. 2005), competition (e.g., Bikker and van Leuvensteijn 2008), bank-assurance (e.g., Fiordelisi and Ricci 2011), risk management (Cummins et al. 2009), and the relationship between cost efficiency and profitability (Greene and Segal 2004; Karim and Jhantasana 2005). Most of these studies focus on single countries, while a few others provide cross-country evidence (e.g., Eling and Luhnen 2010a).

  2. Greene and Segal (2004) find that cost inefficiency in the US life insurance industry is substantial relative to earnings, and that inefficiency is negatively associated with profitability measures such as the return on equity. Karim and Jhantasana (2005) also report a negative association between cost inefficiency and profitability ratios in the case of Thai life insurance firms. On the basis of these results, one could expect a statistically significant association between stock returns and cost efficiency. However, we fail to find such evidence.

  3. The first advantage of estimating a global frontier is that it increases the number of available observations. Berger and Humphrey (1997) argue a second advantage: “a frontier formed from the complete data set across nations would allow for a better comparison across nations, since the banks in each country would be compared against the same standard” (pp. 187–188). As mentioned by an anonymous referee, an alternative methodology would be the construction of meta-frontiers that allow the calculation of technology gaps across countries and the estimation of adjusted efficiency scores (e.g., Battese et al. 2004; Bos and Schmiedel 2007; O'Donnell et al. 2008; Kontolaimou and Tsekouras 2010). This approach is quite interesting; however, it can be applied only to those countries for which a sufficiently large number of observations are available (Bos and Schmiedel 2007; p. 2088). In the present study we focus on listed insurance firms, which reduces the number of firms per country and rules out the possibility of using meta-frontiers. Nonetheless, as we discuss in the text, in an attempt to control for broad differences in technology we include in our global frontier a dummy variable that distinguishes between developed and developing countries.

  4. This approach originates from stochastic frontier analysis, or SFA (Aigner et al. 1977; Meeusen and van den Broeck 1977). In general, SFA assumes that inefficiencies follow an asymmetric half-normal distribution and that random errors follow a symmetric standard normal distribution; however, other distributions such as the truncated normal or the gamma distributions can also be assumed (e.g., Cummins and Zi 1998). Studies on insurance efficiency have also employed alternative techniques like the distribution free approach, or DFA, the thick frontier approach, or TFA, and data envelopment analysis, or DEA. The DFA assumes that the efficiency of firms is stable over time, whereas random error tends to average out to zero over time. The TFA assumes that deviations from predicted costs within the lowest average cost quartile in a size class represent random error, whereas deviations in predicted costs between the highest and lowest quartiles represent inefficiencies. In contrast to the other three techniques, DEA is a non-parametric method that is based on mathematical programming. The advantage of DEA is that it does not require any assumption to be made about the distribution of inefficiency, and it does not require a particular functional form of the data in determining the most efficient firms. However, one of its disadvantages is that it assumes that data are free of measurement error.

  5. Following past studies (e.g., Hao and Chou 2005; Hao 2007), we define total operating cost as the summation of management expenses, commission expenses, and claims (non-life) or benefits (life).

  6. See Battese and Coelli (1995) for further details.

  7. Berger and Mester (1997) and DeYoung and Hasan (1998) outline a number of cases under which the alternative profit function may be more appropriate than the standard one. Furthermore, based on these arguments, Maudos et al. (2002) and Kasman and Yildirim (2006) point out that in international comparisons with a diverse group of countries and competition levels it seems more appropriate to estimate an alternative rather than a standard profit function.

  8. Yuengert (1993) suggests the use of additions to reserves as an alternative output. However, Greene and Segal (2004) point out that the major problem with this measure is that reserves change when policies age, regardless of whether new policies are sold. In addition, the change in reserves measures the change in liabilities, rather than the output of the selling effort.

  9. Yao et al. (2007) mention that even if payment and benefits are to be seen as an output variable, they actually constitute a bad output and consequently should be treated as an input.

  10. Ideally, we would divide the commission and management expenses by the number of employees. However, such data were not available in our case. Therefore, we follow many banking studies and divide management and commission expenses by total assets (e.g., Maudos et al. 2002; Lozano-Vivas and Pasiouras 2010) as the best alternative proxy.

  11. Numerous studies in banking also use equity and/or loan loss provisions as quasi fixed inputs (e.g., Altunbas et al. 2000; Hasan and Marton 2003; Lozano-Vivas and Pasiouras 2010).

  12. The use of industry/sector dummies to account for different characteristics in production technology has been employed in various studies. For example, Rai (1996) uses this approach in the case of insurance firms. Bos et al. (2009) use it to account for heterogeneity across different types of banks, whereas Hu et al. (2005) rely on this approach in the case of non-financial firms drawn from various industries. As discussed in Lovell (1993), the inclusion of dummies in the frontier model “…allows the comparison of performance across categories and also permits a determination of the ability of members of each category to keep up with best practice in their own category” (p. 7). In the robustness analysis in Sect. 3.2.2. We take this further by including these dummies simultaneously in the frontier and the inefficiency term. Bos et al. (2009) follow a similar approach in their banking study.

  13. Insurance studies that use the translog function include Bikker and van Leuvensteijn (2008), Eling and Luhnen (2010a), and Fiordelisi and Ricci (2011).

  14. For example, a score of 100 indicates that: independent central bank supervision and regulation of financial institutions are limited to enforcing contractual obligations and preventing fraud; credit is allocated on market terms; the government does not own financial institutions; financial institutions may engage in all types of financial services; banks are free to issue competitive notes, extend credit and accept deposits, and conduct operations in foreign currencies; and foreign financial institutions operate freely and are treated the same as domestic institutions. In contrast, a score equal to zero indicates, among other factors, that: credit allocation is controlled by the government; bank formation is restricted; foreign financial institutions are prohibited; supervision and regulation are designed to prevent private financial institution; the central bank is not independent; and so on.

  15. The market wide return refers to the return of the general or the most representative index of the stock exchange where the insurer is listed (e.g., UK: FTSE All Share index; Germany: DAX Index; USA: Dow Jones Industrial or Nasdaq Composite Index).

  16. The simultaneous inclusion of EFCH and ROECH provides a strong test for our hypothesis, since it assesses whether efficiency changes provide any valuable information when traditional financial performance indicators are already included in the analysis.

  17. Cummins and Xie (2009) take this argument even further and mention that in cases where the interest lies on traded firms, one should also include non-traded firms to obtain more representative estimates of efficiency. While such an exercise could form an interesting robustness test, OSIRIS contains information only for publicly listed (and delisted) firms.

  18. The sample consists of 433 yearly observations from life insurers, 1251 observations from non-life insurers, and 385 from combined firms. In terms of country coverage, the distribution of the yearly observations is as follows: Australia (28), Austria (15), Bahrain (12), Canada (81), Chile (7), China (15), Croatia (2), Cyprus (5), Denmark (13), Egypt (5), Finland (2), France (22), Germany (87), Greece (12), Hong Kong (2), Iceland (5), Indonesia (21), Ireland (5), Israel (15), Italy (49), Japan (61), Jordan (31), Korea (58), Kuwait (24), Luxembourg (5), Malaysia (53), Malta (5), Morocco (9), Netherlands (4), New Zealand (4), Norway (10), Oman (9), Pakistan (5), Peru (7), Philippines (5), Poland (14), Portugal (6), Qatar (15), Russia (2), Singapore (11), South Africa (45), Spain (10), Sri Lanka (7), Sweden (3), Switzerland (43), Taiwan (41), Thailand (122), Tunisia (10), Turkey (36), United Arab Emirates (74), UK (115), USA (822).

  19. All the variables used in the second stage regressions were capped at the 5th and 95th percentile to reduce the impact of outliers, while keeping all the observations in the sample. The results remain the same when we cap the variables at the 1st and 99th percentile.

  20. The Battese and Coelli (1995) model utilizes the parameterization of Battese and Corra (1977), who replace σ 2V and σ 2U with σ2 = σ 2V  + σ 2U and γ = σ 2U /(σ 2V  + σ 2U ).

  21. Fiordelisi and Ricci (2011) find that joint venture insurers are the most cost efficient but less profit efficient types of firms in their sample. Similarly, Guevara and Maudos (2002) estimate cost and profit efficiency in EU banking sectors, showing that the “other bank institutions” group is the most cost efficient but also the most profit inefficient. Furthermore, Berger and Mester (1997) and Rogers (1998) report a negative correlation between cost efficiency and profit efficiency in the US.

  22. The Pearson’s correlation coefficient equals −0.005 while the Spearman’s rho equals 0.016.

  23. We drop solvency change from the regressions with cost efficiency change due to a positive and statistically significant correlation between the two variables (correlation coefficient of 0.371, statistically significant at the 1 % level) that appears to distort the results. To be more detailed, when we include both variables in the analysis, CEFCH carries a negative coefficient that is statistically significant at the 10 % level. A univariate regression confirms that CEFCH has a positive but insignificant impact on market adjusted returns that is consistent with the results presented in Table 4.

  24. Destefanis and Sena (2007) offer additional explanations as to why efficiency indicators may be of interest to shareholders. First, profitability ratios like the return on equity may under represent the value of the firm due to the investment myopia problem, which is not the case for efficiency estimates. Additionally, when managers engage in myopic behavior, long-term investment should be expected to decrease, leading to lower efficiency. Finally, due to the separation between management and ownership, managers may have incentives to invest in projects that grant power and prestige but that do not result in an improvement in efficiency and productivity.

  25. Including the same variables in the inefficiency model (i.e., Eq. 4) and in the stochastic frontier (i.e., Eq. 3) does not violate the assumption of the independence when the equations are estimated simultaneously as in the Battese and Coelli (1995) model. As Battese and Coelli (1995) note, “The explanatory variables in the inefficiency model may include some input variables in the stochastic frontier” (p. 327). It should be mentioned that the idea of using common variables in the stochastic frontier and in the inefficiency model is not unique to this paper (e.g., Coelli and Battese 1996; Bos et al. 2009; Lozano-Vivas and Pasiouras 2010, among others).

  26. Considering the small sample of life (433 yearly observations) and combined firms (385 yearly observations) we pool these two types together and estimate a common frontier with a dummy variable that distinguishes between these firms.

  27. The correlation coefficient between the current and lagged efficiency change is 0.150 in the case of profit and −0.033 in the case of cost. Thus, there is no problem with their simultaneous inclusion in the model.

  28. The reported figures were obtained using the same values as the ones used in the regressions (i.e., capped at 5th and 95th percentiles). We also calculated the corresponding figures using the original values (i.e., non-capped). In this case, the average market adjusted return for top performers and bottom performers are: 5.654 and 0.158 in the case of profit efficiency, and 10.419 and 0.481 in the case of cost efficiency. The p value equals 0.055 and 0.021 in the case of profit and cost efficiency change, respectively. However, these figures should be treated with caution as they include some extreme values. Profit efficiency change varies from −100 to 332 % and cost efficiency change varies from −50.64 to 132.19 %. The market adjusted returns that were included in the portfolios range between −113.44 and 94.62 % in the case of profit efficiency and between −96.78 and 253.17 % in the case of cost efficiency.

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Acknowledgments

We would like to thank the two reviewers, the Guest Editor and the Associate Editor. We would also like to thank the participants at the 2010 International Workshop on Efficiency and Productivity in honor of Professor Knox Lovell and the 2010 Conference of the EURO Working Group on Efficiency & Productivity Analysis for valuable comments that helped improve earlier versions of this manuscript.

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Authors and Affiliations

  1. Department of Economics, University of Crete, Crete, Greece

    Chrysovalantis Gaganis

  2. Surrey Business School, University of Surrey, Guildford, UK

    Fotios Pasiouras

  3. Schools of Business, Fordham University, New York, USA

    Iftekhar Hasan

  4. Bank of Finland, Helsinki, Finland

    Iftekhar Hasan

  5. Financial Engineering Laboratory, Technical University of Crete, Crete, Greece

    Fotios Pasiouras

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  1. Chrysovalantis Gaganis
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  2. Iftekhar Hasan
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Correspondence to Iftekhar Hasan.

Appendices

Appendix 1

See Table 6.

Table 6 Descriptive statistics by firm type and country development status
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Appendix 2

See Table 7.

Table 7 Parameters of the cost and profit function
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Gaganis, C., Hasan, I. & Pasiouras, F. Efficiency and stock returns: evidence from the insurance industry. J Prod Anal 40, 429–442 (2013). https://doi.org/10.1007/s11123-013-0347-x

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