Loss Reserve Error Volatility and Internal Capital Markets. James M. Carson Daniel P. Amos Distinguished Professor of Insurance

Transcription

1 Loss Reserve Error Volatility and Internal Capital Markets James M. Carson Daniel P. Amos Distinguished Professor of Insurance Terry College of Business The University of Georgia Athens, GA Phone: David Eckles Associate Professor of Risk Management and Insurance Terry College of Business The University of Georgia Athens, GA Phone: In Jung Song (contact author) Terry College of Business The University of Georgia Athens, GA Phone: July 1, 2015 Working Paper Do not cite or circulate without permission 1

2 Loss Reserve Error Volatility and Internal Capital Markets Abstract The property-liability insurance business is inherently risky and the estimation process of reserves is imperfect, making variability in loss reserves inevitable. This paper investigates the relation between loss reserve error volatility and internal capital markets (ICMs). High loss reserve error volatility suggests inconsistency of error and thus high information risk. We first look at certain firm characteristics that are associated with high loss reserve error volatility such as group membership. Then we further inspect the relation between group loss reserve error volatility with ICMs activities. We find that affiliated insurers have lower individual firm-level loss reserve error volatility than single unaffiliated insurers. We hypothesize that unaffiliated single insurers have their own authority to manage reserves whereas affiliated insurers can transfer capital internally to other insurers in the group via internal capital markets. We show that affiliated firms with more ICMs tend to have more consistent loss reserve errors which result in low loss reserve error volatility and correspondingly low information risk. Keywords: Earnings Management, Information Risk, Loss Reserve Error Volatility, Information Risk, Accounting Discretion, Insurance JEL classification: G34, G32; G22 1. Introduction Loss reserves, the insurer s estimated liability for unpaid claims, generally are the largest and most important liability on a property-liability insurer s balance sheet. Insurance companies need to estimate the proper amount of reserves to meet future liabilities from losses. However, the insurance business is inherently risky and the estimation process is imperfect; thus, some variability in loss reserves is unavoidable. Estimation of loss reserves is not a formulaic process. Rather it requires quantitative methods and subjective consideration and managerial judgment. Frequently, an insurer s loss reserve errors are used as a measure of managerial discretion (Weiss, 1985; Petroni, 1992; Beaver, McNichols, and Nelson, 2003; Eckles and Halek, 2010; Grace and Leverty, 2010, 2012). Whether loss reserves are properly estimated can only be determined over time. Comparing previous loss 2

3 reserves for certain claims with the actual dollars paid for those claims shows the degree to which the reserves were correctly set. Each year, insurers reestimate prior years loss estimates to reflect actual payments made and changes in estimates. Estimations must be made in advance of ultimate settlement and thus the accuracy of an insurer at setting loss reserve levels is important. The accuracy of an insurer can be calculated by loss reserve error volatility. This volatility can be considered information risk according to Eckles, Halek and Zhang (2014). 1 Information risk refers to the potential for inaccurate or incomplete firm information. High loss reserve error volatility suggests inconsistency of error and uncertainty of firm information and thus relatively high information risk. On the other hand, low loss reserve error volatility suggests consistency of error and more certainty of firm information therefore relatively low information risk. Accounting measures such as loss reserves are widely used for a firm s information dissemination to managers, shareholders, regulators and rating agencies. These parties all have an interest in knowing the magnitude and variability of these potential deviations in loss reserves over the time period. Managers often alter loss reserve estimates for many reasons including income smoothing, tax avoidance, regulatory scrutiny avoidance, and executive compensation incentives. Shareholders and regulators may care about loss reserves in terms of financial weakness and solvency. Insurers may make sure to keep their values in line with expectations because variability can be seen as an indicator of a firm s risk. Also, rating agencies consider information risk as uncertainty of the firm when they rate the firm (Carson, Eastman and Eckles, 2015). The insurance industry is a perfect laboratory to study the loss reserves not only because it provides a homogeneous sample of firms but also insurer-specific accruals, loss reserves, can be used to measure the actual error in accruals unlike other measures used in many accounting and 1 Eckles, Halek and Zhang (2014) utilize standard deviation of insurer loss reserve error as well as conventional accruals measures to measure accruals quality, also known as information risk. 3

4 finance studies examining non-insurance firms. In addition to the unique ability to witness the loss reserve errors, the insurance industry is also comprised of two distinct structures; group affiliated and single unaffiliated firms. 2 The group structure allows for a potentially active internal capital market (ICM) with corporate headquarters allocating capital among the group members in the form of reinsurance ceded to affiliates (William, 1975; Stein, 1997; Houston, James, and Marcus, 1997). In this paper, we investigate insurers loss reserve error volatility both at an individual single unaffiliated and aggregate group affiliated level. This paper will extend the existing literature in three ways. First, we look at the characteristics of insurers as they relate to loss reserve error volatility, in particular whether or not the insurer is affiliated with an insurer group. Second, we compare the loss reserve error volatility both at the individual firm level and aggregate group level. Here we also derive a new unique measure of group level loss reserve error and its volatility. Third, we further inspect the relation between group loss reserve error volatility and internal capital market transactions. Prior literature provides support for the existence of ICMs within group insurers but has not incorporated loss reserve error nor its volatility. We find evidence that affiliated insurance companies have lower individual firm-level loss reserve error volatility than single unaffiliated insurers and that internal capital market activities are associated with lower aggregate group-level loss reserve error volatility. The remainder of this paper proceeds as follows. The second section summarizes prior literature on loss reserve errors and internal capital markets. The third section discusses conceptual background and hypotheses. The following section presents the model and data followed by the 2 Insurance regulation has focused on the operations and financial strength of insurers on a legal entity basis. Propertyliability insurers have the ability to operate in groups and they are required to prepare statutory filings at the group level. Additionally, model laws specifically states as insurance holding companies and regulate transactions within the insurance group. Single unaffiliated firms are defined if no other firms on the National Association of Insurance Commissioner (NAIC) property-liability database shares the same group code and group affiliated firms are the opposite. 4

5 empirical results. The last section concludes. 2. Literature Review There is a large body of literature devoted to evaluating earnings management via loss reserves in the property-liability insurance industry. Prior studies in this area have examined loss reserve practices based exclusively on individual insurance company data. Prior studies have not focused on the characteristics or conditions of the groups with which affiliated insurers are associated. In this paper, we investigate insurers loss reserve error volatility both at the single unaffiliated, the single affiliated and aggregate group affiliated levels. 2.1 Loss Reserve Error Property-liability insurer reserve errors have been discussed extensively in the existing literature. However insurer reserve error volatility has been largely ignored. Most previous studies examine earnings management via loss reserve manipulation and have shown that the manipulation is driven by a number of incentives. First, financial weakness of a firm is examined as an inducement to manipulate loss reserves. Petroni (1992) shows evidence that financially weak insurers tend to underestimate loss reserves to improve Insurance Regulatory Information System (IRIS) ratios. 3 Petroni (1992) shows not only that financially weak firms understate their reserve estimates to avoid regulatory attention but also that managers of insurers close to receiving regulatory attention understate reserve estimates. Studies also find that financially weak firms tend to under-reserve to a greater extent than financially strong firms (Petroni, 1992; Petroni and Beasley, 1996; Penalva, 1998; Gaver and Paterson, ). 3 Insurance Regulatory Information System (IRIS) ratio is a typical industry-specific ratios which are designed by regulators to identify financially weak insurers (Petroni 1992, Harrington and Danzon 1994, Nelson 2000, Beaver, McNichols, and Nelson 2003, Gaver and Paterson 2004, Grave and Leverty 2012) 5

6 Building upon Petroni (1992), Gaver and Patterson (2004) also study whether insurers manage loss reserves to avoid regulatory intervention. Along with Petroni (1992), they assert that financially strong firms and tend to over-reserve and financially weaker firms tend to underreserve. Moreover, insurers manage loss reserves to reduce the number of IRIS ratio violations. Regulatory scrutiny is a crucial motivation of insurers to manipulate their reserves. Earnings management by property-liability insurers via loss reserve manipulation has been discussed frequently in the context of insurers manipulating their loss reserves to smooth their income. Weiss (1985) supports the hypothesis that loss reserves are used as a tool in smoothing underwriting results. Theoretical analysis with respect to income smoothing is also introduced by Grace (1990). Aiuppa and Trieschmann (1987) empirically incorporate size, organizational structure, and product mix of the firm. They show evidence that small insurers experience relatively more reserve error and stock insurers experience relatively more accurate reserves. Insurers also have an incentive to smooth earnings in order to minimize their associated tax liability. By overestimating losses relative to current premiums, the insurer reduces its current tax liability and can also postpone the tax payments until future periods. Grace (1990) hypothesizes that overestimating reserves gives insurers an opportunity to shelter their earnings and finds evidence that insurers over-reserve as taxable income increases. However, Petroni (1992) finds no evidence of earnings management to minimize taxes and Grace and Leverty (2012) shows mixed results. Also, Beaver, McNichols, and Nelson (2003) examine the effect of reserve bias on firm s earnings and find that less profitable firms understate reserves relative to more profitable firms. Specifically for public and mutual firms, small firms with positive earnings understate loss reserves relative to insurers with small negative earnings. They conclude that insurers manage reserves across the distribution of earnings. When it comes to managerial discretion, executive compensation can be another reason why 6

7 insurers manipulate loss reserve. Eckles and Halek (2010) find that larger proportions of managerial compensation from bonus payments and restricted stock have a positive relationship with earnings management. Along with this, Eckles, Halek and Zhang (2011) combine the effect of managerial compensation with corporate governance board structure on firm s earnings management with a group level data. Grace and Leverty (2012) consider the income smoothing, financial weakness, tax, and rate regulation motives together. They conclude that a firm s incentive to over-reserve is higher when there is potential tax savings from reserving. Firms smooth earnings to follow the expectations of stakeholders and regulators. Further, firms with more than four IRIS ratios outside acceptable ranges under-reserve to avoid regulatory attention. Although earnings management via loss reserve manipulation in property-liability insurers is thoroughly examined in the existing literature, loss reserve error volatility is less so. Recently, Eckles, Halek, and Zhang (2014) use loss reserve error volatility to measure the accruals quality (AQ), also called information risk, which refers to the potential for inaccurate or incomplete firm information. They find a negative relation between the quality of accruals and the cost of debt. In this paper, we follow a similar technique to compute the volatility of loss reserve error. We further examine the characteristics of insurers experiencing high loss reserve error volatility with respect to group membership and integrate loss reserve error volatility with internal capital markets activities among the group subsidiaries. 2.2 Internal Capital Markets (ICMs) The property-liability insurance industry also provides the ability for insurers to operate in groups which allows for ICM transactions. A substantial theoretical and empirical literature on ICM activities and efficiencies has developed. The literature generally shows that conglomerates have the benefit of ICMs since the insurers within the group structure can allocate internal capital 7

8 across the group members (Gertner, Scharfstein and Stein, 1994; Stein, 1997; Houston and James, 1998; Maksimovic and Phillips, 2002,2006; Powell and Sommer, 2007; Powell, Sommer, and Eckles, 2008; Fier, McCullough, and Carson, 2012) though empirical results for ICM efficiency are mixed (Shin and Stulz, 1998; Rajan, Servaes, and Zingales, 2000; Powell and Sommer, 2007). Stein (1997) develops a model in which headquarters creates value by engaging in winner picking and allocating capital to projects having the highest expected returns. 4 On one hand, headquarters could raise more than stand-alone firms because of fewer credit constraints. On the other hand, there involves high expense in monitoring headquarters. A number of studies find internal capital markets play a significant role in the operations of financial intermediaries (Houston, James, and Marcus, 1997; Shin and Stulz, 1998), the nonfinancial industry (Khanna and Tice 2001), and the property-liability insurance industry (Powell and Sommer, 2007; Powell, Sommer and Eckles, 2008; Fier, McCullough and Carson, 2012; Niehaus 2014). In Powell, Sommer, Eckles (2008), the authors model a change in an affiliated insurer s capitalization through ICM transactions by transferring capital to another affiliated insurer through reinsurance. They distinguish between affiliate and non-affiliate uses of capital and reinsurance and find that ICM transactions in group insurers stimulate growth in affiliate s investment. The common proxy for ICMs used in many studies is reinsurance which can be measured as the changes in net reinsurance ceded or the difference in the ratio of net reinsurance premiums ceded to total premiums written. Both measures affect the internal capital structure since increasing the premium ceded to reinsurers allows an insurer to write more insurance. Recently, Niehaus (2014) shows that a profit maximizing group may want to recapitalize subsidiaries after negative capital shocks. Recapitalizing weak subsidiaries allows the group to continue to earn rents from previous investments in the subsidiary s reputation. He examines 4 Winner picking represents headquarters having the ability to reallocate capitals from divisions where returns are low (losers) to divisions where returns are high (winners) (Stein, 1997; and Gertner, Scharfstein and Stein, 1994) 8

9 insurers receiving internal capital contributions as well as insurers paying internal shareholder dividends as proxies for internal capital markets. He finds evidence that insurance groups manage capital with respect to insolvency risk and concludes that ICMs play a crucial role within groups for life and health insurers. In contrast, other studies indicate that internal capital market activities are not a significant factor in the investment strategy of conglomerate firms and are associated with less efficiency (Shin and Stulz, 1998; Scharfstein and Stein, 2000; Rajan, Servaes, and Zingales, 2000). Scharfstein and Stein (2000) introduce the agency problems among division managers and conclude the hierarchies lead to misallocation of internal capital. Further, Rajan, Servaes, and Zingales (2000) argue diversified firms may invest inefficiently since ICMs might in fact hinder investment efficiency by allocating resources more poorly than would external capital markets. Theory indicates that diversification via ICMs is associated with both costs and benefits. In general, benefits associated with diversification include economies of scope (Teece, 1980), internal capital markets without information asymmetries (Stein, 1997), as well as risk pooling (Cummins, Phillips, and Smith, 2001). Gertner, Scharfstein and Stein (1994) compare internal capital markets with external capital markets via bank lending and find that ICMs are less costly and more efficient than external capital markets since they reduce agency costs as well as information asymmetries. Gertner, Scharfstein and Stein (1994) and Stein (1997) both find that ICMs play a significant role as conglomerates allocate capital to the divisions with the best expected returns. Corporate headquarters creates value in a way that headquarters has the ability to reallocate capital from divisions where expected returns are low to divisions where expected returns are high. On the other hand, transferring capital among affiliates can create an information asymmetry between insurance groups and other parties such as shareholders, policyholders, and regulators. Diversification via internal capital markets may magnify agency costs (Harris, Kriebel, 9

10 and Raviv, 1982; Rotemberg and Saloner, 1994) and agency problems can increase among division managers which may lead to misallocation of internal capital according to Scharfstein and Stein (2000). Likewise, it may allow inefficient cross-subsidization of poorly performing business units (Rajan, Servaes, and Zingales, 2000). As discussed above, the prior literature provides support for the existence of internal capital markets within group insurers and various incentives for earnings management by insurers via loss reserve error but has not incorporated loss reserve error volatility with internal capital markets. Some potential benefits associated with ICMs include lower monitoring costs, reduced agency problems, greater efficiency of capital allocation and lower cost of internal capital compared to external capital. The lower cost of capital drives the reduced information asymmetries and lower agency costs within the internal capital markets. 3. Theoretical Background and Hypotheses Development 3.1. Loss Reserve Error Volatility at the Firm-level Theoretically, under the assumption that all firms and managers are profit maximizers, we would expect unaffiliated single firms and affiliated group firms to behave the same. However, the argument that these two types of firms differ in their ability to exploit market opportunities is not new (Maksimovic and Phillips, ). 5 In our first analysis, we investigate insurers loss reserve error volatility at the firm-level. We look at the characteristics of insurers as they relate to loss reserve error volatility, in particular whether or not the insurer is affiliated with a group of insurers. Unaffiliated single firms have their own authority to manage their reserves to meet 5 Maksimovic and Phillips (2002) develop a neoclassical model of profit maximizing firms with heterogeneous industry-specific productivity. In equilibrium, the goal is to maximize firm profit for both unaffiliated single firms and affiliated group firms. However, since they have different market opportunities, firms produce in industries in which they have a comparative advantage and firms with lower industry-specific productivity diversify more. 10

11 corporate goals such as income smoothing (Weiss, 1985; Grace, 1990; Beaver, McNichols, and Nelson, 2003), regulatory scrutiny aviodance (Petroni, 1992; Harrington and Danzon, 1994; Gaver and Paterson, 1994; Grace and Leverty, 2012), tax minimization (Grace, 1990; Petroni, 1992; Nelson, 2000; Grace and Leverty, 2012), executive compensation (Eckles and Halek, 2010; Eckles et al, 2011) and external monitoring (Gaver and Patterson 2001). Therefore unaffiliated firms may exhibit more inconsistency of error and uncertainty of firm information which corresponds with relatively high information risk. In other words, unaffiliated firms are formed by themselves and are not part of a group with other subsidiaries. Thus, it is completely up to the unaffiliated insurer to manage their loss reserve to achieve corporate objectives as discussed above. Further, single unaffiliated firms do not have the ability to transfer capital to affiliates. On the other hand, group affiliated insurers are less likely to need to overstate or understate the loss reserve than single unaffiliated because an affiliated insurer is a member of a group comprised of one or more subsidiaries where group members can transfer capital internally to the rest of the group. Consequently, we hypothesize that affiliated firms will tend to have more consistent loss reserve errors which result in low loss reserve error volatility and more certainty of firm information therefore correspondingly low information risk. As in Eckles, Halek and Zhang (2014), information risk in this paper refers to the potential for inaccurate or incomplete firm information. With this theoretical background, we inspect the characteristics of insurers experiencing high or low loss reserve error volatility with respect to group insurer. Thus we propose the following hypothesis: Hypothesis 1: Loss reserve error volatility for affiliated insurers is lower than that for unaffiliated insurers Ultimately unaffiliated insurers are largely constrained to manage capital by managing 11

12 their reserves which results in high loss reserve error volatility. However, affiliated insurers have ICM opportunities among the group members which results in less need to manage capital via their reserves. Hence, we anticipate that the insurers that are part of a group have less volatility than unaffiliated insurers Loss Reserve Error Volatility at the Group-level The NAIC notes that that analysis of an individual company may not be complete without understanding the context of the group and the markets in which the company operates (NAIC, 2002, p.2). Thus it is important to investigate loss reserve error volatility not only at the individual firm level but in the context of insurers groups. In our second analysis, we investigate an insurer s loss reserve error volatility at the aggregate affiliated level. The first hypothesis tested in this paper is that certain firm characteristics are associated with high loss reserve error volatility, such as whether the insurer is part of a group or not. Now we focus on the loss reserve error volatility at group level. With a new unique measure of group level loss reserve error and its volatility, we further analyze the relation between group loss reserve error volatility with internal capital markets. In this context, we only consider grouplevel data. 6 Insurance regulation has focused on the operations and financial strength of insurers on a legal entity basis. Property-liability insurers have the ability to operate in groups and are required to prepare statutory filings at the group level. Certain model laws specifically relate to insurance holding companies and regulate transactions within the insurance group. 7 The majority of 6 Under National Association of Insurance Commissioner s (NAIC) consolidation, all assets and liabilities of a subsidiary are brought into the parent s financial statements. 7 For example, model Law (ML) 440, Insurance Holding Company System Regulatory Act, and ML 450, Insurance Holding Company System Model Regulation with Reporting Forms and Instructions. A new proposed model law with implications for group supervision, the Risk Management and Own Risk and Solvency Assessment (RMORSA) Model Law is also under consideration. 12

13 property-liability insurers are affiliated with one or more other insurers in an insurer group. A number of studies examine group level information since characteristics or conditions of the groups with which affiliated insurers are associated would influence firms decisions (Harrington 1981; Houston, James and Marcus, 1997; Gaver and Potter 2005). Harrington (1981), in particular, looks at holding company s insolvency risk and claims that holding companies may have a lower insolvency risk than one of its subsidiary insurers would have as a stand-alone firm since group members can transfer capital to the other members of the group. The group structure creates ICMs which allows diversified firms to fund projects in one unit of the firm by reallocating capital from other units. That is, corporate headquarters are able to distribute capital among the group members (William, 1975; Stein, 1997; Houston, James, and Marcus, 1997). To measure ICMs, we use two proxies: ICM transactions and ICM participants. First, the most commonly used proxy for ICM transactions is internal reinsurance (Powell and Sommer, 2007; Powell, Sommer and Eckles, 2008; Fier, McCullough and Carson, 2012). The amount of reinsurance is calculated as premiums ceded to reinsurers scaled by total premiums written. A primary insurer may cede business to a reinsurer with which it is affiliated under the same parent company and/or to reinsurers with which they are not affiliated. For internal reinsurers, we calculate the net reinsurance ceded to affiliates. Next, as a second proxy for ICMs, we utilize the number of subsidiaries in a group that participate in ICMs. In general, the more lines of business of firms, the more diversified the firm is with more ICMs participants (Laeven and Levine, 2007; Liebenberg and Sommer, 2008; Schmid and Walter, 2009). Therefore, the more subsidiaries in a group, the more ICM participants. For example, a group insurer with ten insurance ICM participants has more Internal Capital Markets opportunities than a group with just two ICM participants. As a result, our second hypothesis on group loss reserve error volatility is as follows: 13

14 Hypothesis 2: Loss reserve error volatility is negatively related to Internal Capital Market activity We expect that as ICM activity increases, group-level loss reserve error volatility declines. Previous studies on property-liability insurers loss reserve do not distinguish between single unaffiliated and group affiliated companies, aside from the inclusion of a group dummy variable. 4. Model and Data 4.1 Hypotheses In order to calculate a property-liability insurer s loss reserve error, we follow the same measure of reserve error that has been widely used in the literature (Kazenski, Feldhaus, and Schneider, 1992; Petroni, 1992; Beaver, McNichols, and Nelson, 2003; Gaver and Paterson, 2004). 8 An excerpt from Schedule P can be found in Table 1. Specifically, we calculate the error to be the difference between the initial reserve plus the reserve set sometime in the future. Equation (1) formally shows our measure below: Reserve Error (RE) i,t=incurred Losses i,t Incurred Losses i,t+n (1) As shown in Table1, equation (1) utilizes the difference between total incurred losses for a firm i as of a given year t and a revised estimate of incurred losses in the future year t+n where 8 Another way to measure the reserve is discussed in Weiss (1985) and Grace (1990). They use the difference between total incurred losses for a firm i as of a given year t and cumulative developed losses paid in future year t+j where j also equals to five years. Unlike KFS (1992), reserve error is determined by comparing the initial estimate of reserves with claims paid. These claims paid are not ultimate claims paid since it is limited to just five years. It cannot guarantee all the claims are paid and thus very likely to overstate the reserve error. Thus we follow the measure used in KFS (1992) because it does not depend on the development of losses when losses are paid. This is the same measures of reserve error that has been widely used in the existing literature (Petroni 1992, Beaver, McNichols, and Nelson 2003, Gaver and Paterson 2004 and Grace and Leverty 2010). 14

15 we set n equal to five years. This loss reserve measure requires five years of data to calculate. The reserve Error is positive if the insurer over-reserved and negative if the insurer under-reserved. 9 The insurance literature has primarily used two loss reserve error scaling factors: total assets and developed reserves. Petroni (1992) and Beaver, McNichols, and Nelson (2003) scale by assets but report that their results are not sensitive to the scaling variable. Gaver and Paterson (2004) primarily scale by the developed reserve and some studies use both and find consistent results (Grace and Leverty 2010, 2012). Thus we use both scaling factors in this paper. RE1 represents reserve error scaled by total assets and RE2 represents reserve error scaled by developed reserve. Reserve Error (RE1) i,t= Reserve Error (RE2) i,t= (Incurred Losses i,t Incurred Losses i,t+5) Total assets i,t (Incurred Losses i,t Incurred Losses i,t+5) Developed Reserve i,t (2.1) (2.2) This paper also utilizes a unique measure of loss reserve error volatility both at the firm and a group level. As discussed in previous sections, loss reserves are insurer-specific accruals and reserve errors measure the actual error in accruals unlike the accruals used in many accounting and finance studies. Insurers need to re-estimate prior years loss estimates to reflect actual payments made and changes in estimates. Because reserve estimations must be made in advance of ultimate settlement, the accuracy of an insurer setting loss reserve levels is important. According to Eckles, Halek and Zhang (2014), we can calculate loss reserve error volatility which can be considered as information showing the potential for inaccurate or incomplete firm information. 9 In contrast, Petroni 1992, Beaver, McNicholas and Nelson (2003), Eckles and Halek (2010) switch the elements in the equation, Reserve Error (RE) i,t=incurred Losses i,t+j Incurred Losses i,t which only flips the sign of error. 15

16 We calculate the standard deviation of the reserve error to compute the volatility of the loss reserve error at individual firm-level by using both three-year and five-year volatilities based on the prior 3 (or 5) years, j, reserve errors. Firm Vol (RE1),j,i,t= σ i, t = (RE1i,t t 2 t (j 1) RE1i,t t t (j 1) ) j j 1 (3.1) Firm Vol (RE2),j,i,t= σ i, t = (RE2i,t t 2 t (j 1) RE2i,t t t (j 1) ) j j 1 (3.2) If a firm has high loss reserve error volatility, it suggests there is inconsistency of loss reserve error, thus relatively high uncertainty of firm information, which can be considered as high information risk. On the other hand, if a firm has low loss reserve error volatility, it indicates there is consistency of loss reserve error, and thus relatively low uncertainty of firm information, and low information risk. 10 Next, we further investigate a new and unique measure of group loss reserve error volatility to examine the relation between group loss reserve error volatility and internal capital markets. Group loss reserve error volatility can be calculated as a weighted average of each company s standard deviation of loss reserve error from equation (3.1) and (3.2). Equation (4.1) and (4.2) formally show our measures. n Group Vol (RE1) j,t= σ j, t = i,t Firm Vol (RE1)i, t % of NPW within a group (4.1) 10 We note that a firm can consistently be a poor reserve and have low information risk. That is, if a firm is always wrong, then decision makers will know to discount their reported reserves. 16

17 n Group Vol (RE2) j,t= σ j, t = i,t Firm Vol (RE2)i, t % of NPW within a group (4.2) This unique measure is designed to examine the relation between group loss reserve error volatility and internal capital markets. Just like firm level loss reserve error volatility, group level loss reserve error volatility has a similar meaning. High group level loss reserve error volatility suggests inconsistency of group members reserve errors after consideration of their net premium written relative to other members ultimately high uncertainty of firm information can be considered as high information risk. By the same token, low group level loss reserve error volatility suggests more consistency of group members reserve errors after consideration of their net premium written relative to other members ultimately low uncertainty of firm information can be considered as low information risk. 4.2 Model We use two parallel sets of variables. The first set is based on individual company-specific data. We include common variables used in the existing loss reserve literature. The second set, applicable to group-affiliated insurers only, is based on consolidated data for the group with which the insurer is affiliated. Two models and variables used in each are discussed below, along with the hypothesized association with loss reserve volatility. In order to test our first hypothesis, we employ the following Ordinary Least Square (OLS) regressions based on individual company-specific data. The regression includes firm and year fixed effects and standard errors are adjusted for heteroskedasticity. Firm Vol (REi,t) = β0 + β1 Groupi,t+ β2 Agei,t+ β3 Sizei,t+ β4 Netincomei,t + β5 Mutuali,t+ β6reinsurancei,t + β7growthi,t+ β8geo-herfi,t + β9 Product-Herfi,t+ β10 Longtaili,t + εi,t (5) 17

18 where i represents each individual insurer and t represents year. REi,t is insurer reserve error calculated from equation (1) divided by two scaling factors: total assets and developed reserve. Firm Vol (REi,t) is the firm level loss reserve error volatility which is shown in equation (3.1) and equation (3.2). Our main variable of interest is Groupi,t which is an indicator variable equal to one if firm i is a member of an insurer group and zero otherwise. This itests our first hypothesis on affiliation of firms to investigate the impact of group membership on the loss reserve error volatility at the firm level. We expect that reserve error volatility for affiliated vs. unaffiliated insurers is significantly different because of the group mechanism. We also include common variables used in the existing loss reserve literature. Agei,t represents the years a firm i operating businesses since established. Sizei,t is the natural logarithm of total assets in billions. Netincomei,t is firm i s net income in billions. Mutuali,t is a binary variable equal to one if the organizational structure form is a mutual and zero if stock. Reinsurancei,t represents reinsurance ceded divided by reinsurance assumed plus direct premiums. 11 Growthi,t is the one year percentage change in net premiums written. Geo-Herfi,t is a geographic Herfindahl index, which is the sum of the squared percentage of business written in each of the 50 states and the District of Columbia. Product-Herfi,t is a product Herfindahl index which is the sum of the squared percentage of premiums earned in each of the lines of propertyliability insurance. Longtaili,t is the proportion of a firm i s business written in long-tailed insurance lines. 12 εi,t is an error term. All of the above variables are defined in Table 2 and 11 This variable refers only to business with reinsurers with which they are not affiliated since group dummy variables already considers affiliates reinsurers. However, we will use ceded business to a reinsurer with which it is affiliated under the same parent company as one proxy for Internal Capital Markets in the next analysis. 12 Prior literature defines longtail lines as farm multi peril, homewoner s multi peril, commercial multi peril, medical malpractice, workers compensation, products liability, automobile liability, and other liability (Berger et al, 2000; Grace and Leverty, 2012; Eckles, Halek and Zhang, 2014). 18

19 consistent with existing literature. To test our second hypothesis, we estimate a second Ordinary Least Square (OLS) regression for group level loss reserve error volatility. This second model, applicable to groupaffiliated insurers only, is based on consolidated data for the group with which the insurer is affiliated. The regression includes group and year fixed effects, and standard errors are adjusted for heteroskedasticity. Group Vol (REj,t) = β0 + β1 ICMsj,t+ β2 Agej,t+ β3 Sizej,t+ β4 Netincomej,t+ β4 Growthj,t+ β6 Geo-Herfi,t+ β7 Product-Herfj,t+ β8 Longtailj,t + εj,t (6) where j represents each group insurer and t represents year. REi,t is an insurer s reserve error calculated from equation (1) divided by two scaling factors: total assets and developed reserve. Group Vol (REj,t) is the group insurer loss reserve error volatility calculated from equation (4.1) and equation (4.2). All variables in the second regression are identical to the variables from the first regression except they are aggregated at the group level instead of individual firm level. Our variable of interest, ICMsj,t, has two proxies: ICM transactions and ICM participants. First, the most commonly used proxy for ICM transaction is internal reinsurance (Powell and Sommer, 2007; Powell, Sommer and Eckles, 2008; Fier, McCullough and Carson, 2012). In order to measure internal reinsurance, we only focus on ceded business to a reinsurer with which it is affiliated under the same parent company. This is calculated as affiliated reinsurance ceded minus affiliate reinsurance assumed divided by total premiums written. Next, we use the number of subsidiaries in a group that participate in ICM as a second proxy for internal capital markets. From equation (9), we examine the relation between group loss reserve volatility with internal capital markets activities. We expect to see as ICMs transactions and ICMs participants increase, grouplevel loss reserve volatility declines. 19

20 4.3 Data Under Statutory Accounting Principles (SAP), Schedule P of the National Association of Insurance Commissioner s (NAIC) annual statement requires property-liability insurers to disclose the gradual settlement of claims over time and record all changes of the loss reserve estimate. Those changes indicate whether the originally reported reserve was reported with error at the firm level. Our data is from all property-liability insurers in the United States. Insurer characteristics are from insurers annual statutory filings with the NAIC from 1996 to Our first analysis includes affiliated and unaffiliated single insurers whereas the second model is applicable to groupaffiliated insurers only which is based on consolidated data. In total there are 3,456 unique insurers and 294 groups. There are 15,483 firm-year observations and 4,912 group-year observations. Table 3 and 4 demonstrate the descriptive statistics for analysis one and two respectively. 14 Table 3 panel A shows summary statistics for affiliated insurers and panel B reflects summary statistics for unaffiliated firms only in accord with our first analysis. The majority of firms in the first sample over-reserve. The average magnitude of reserve error scaled by total assets (RE1) is 0.08% while reserve error scaled by developed reserve (RE2) is 2.81% for affiliated insurers. The average magnitude of RE1 is 2.50% while RE2 is 9.85% for unaffiliated firms. This indicates all firms overestimate their reserves but single unaffiliated generally over-reserve more than the affiliated insurers based on univariate comparison Since the reserve error calculation requires five year data, our final sample ranges from 1996 to 2006 for insurer reserve error. For volatility, we use three year (five year) standard deviations. Thus we further lose the first three (five) years in the beginning. 14 First analysis refers to Equation (5) where we examine the characteristics of high firm reserve error volatility insurers and the second analysis refers to Equation (6) where we incorporate ICMs with group insurer reserve error volatility. 15 We conduct difference in mean test as well as Wilcoxon rank sum test and find they are significantly different. Further, we conduct a permutation test on affiliation of firms (with other firm characteristics hold constant) to investigate the impact of group membership on the loss reserving practice. The null hypothesis, H0, is framed as group membership having no effect on loss reserve volatility. We reject the null with a signoficance level at 1%. 20

21 When it comes to the loss reserve volatility, for example, the average magnitude of three year volatility of reserve error scaled by developed reserve, Firm Vol(RE2_3), is 7.90% while the average magnitude of five year volatility of reserve scaled by developed reserve, Firm Vol(RE2_5), is 14.05% for affiliated insurers. However, for unaffiliated single, the average magnitude of three year volatility of reserve error scaled by developed reserve, Firm Vol(RE2_3), is 11.22% while the average magnitude of five year volatility of reserve scaled by developed reserve, Firm Vol(RE2_5), is 19.59% for unaffiliated frims. Consequently, the univariate comparison tells us that affiliated insurers have lower reserve error and lower volatility than unaffiliated single insurers regardless of scaling factors. Table 4 displays the descriptive statistics at the aggregated group level for our second analysis. Again, groups in the sample over-reserve on average. The average magnitude of group reserve error scaled by total assets, Group RE1, is 0.069% while the group reserve error scaled by developed reserve, Group RE2, is 1.59%. With respect to the loss reserve volatility, all four measures of group reserve error volatility, Group Vol(RE1_3), Group Vol(RE1_3), Group Vol(RE2_3), Group Vol(RE2_3), range from 3.85% to 8.69% which are relatively lower than the reserve volatility at the firm level. For data screening, we implement a screen similar to existing studies. We remove all insurers with negative total assets, and negative surplus. We also remove professional reinsurers defined as any insurer whose reinsurance assumed from unaffiliated firms is greater than 75% of the direct premiums written less reinsurance assumed from affiliated insurers. Also we only include either stock or mutual firms and exclude firms with values of Geo-Herfi,t, Product-Herfj,t, and Longtailj,t greater than one or less than zero. Finally, we eliminate firms which cede all premiums to reinsurers and write most of their premiums in workers compensation. 21

22 5. Empirical Results The regression result for the first analysis corresponding to equation (5) is reported in Table 5. The dependent variables are firm level loss reserve volatility. In the first column (1), the dependent variable is (Firm Vol (RE1_3)) firm level loss reserve volatility which is a three year standard deviation of reserve error scaled by total assets. In the second column, (2) displays the result for Firm Vol (RE1_5) which is the same as (1) except it is measured by a five year standard deviation. Likewise in (3), the response variable is (Firm Vol (RE2_3)) firm level loss reserve volatility calculated as a three year standard deviation of reserve error scaled by developed reserve and (4) indicates the result for Firm Vol (RE2_5) which is the same as (3) except it is computed as a five year standard deviation. Other explanatory variables are defined and explained in previous sections and Table 2. In all of the regressions in Table 5, the estimated coefficient of our variable of interest, Group, is strongly significant and negative with values ranges from -0.05% to -2.28%. This result illustrates evidence that firm level loss reserve error volatility for affiliated and unaffiliated insurers is significantly different. In fact, affiliated insurer loss reserve volatility is lower than that of unaffiliated insurers. As discussed earlier, unaffiliated single firms have more control over reserves and do not have access to internal capital markets. Therefore unaffiliated firms tend to have more inconsistent errors and uncertainty of firm information which leads to relatively high information risk. On the other hand, group affiliated insurers have less individual control and have access to internal capital markets. Our regression results support our first hypothesis and suggest that insurers that are part of group have less loss reserve error volatility than unaffiliated single insurers. Affiliated firms are more consistent with loss reserves and therefore have correspondingly low information risk. Additionally, we find that control variables such as Age, Size, Netincome, Mutual, and 22

23 Geo-Herf are associated with lower firm-level loss reserve error volatility as well. The estimated coefficients for Size and Netincome are negative and significant meaning that the bigger and financially stronger firms have lower volatility of reserve error. Sommer (1996) shows that smaller companies that write a few small lines of business have less ability to diversify variability than larger companies that write many lines of business which is consistent with the result in this paper. Moreover, stock firms engage in higher risk than mutual firms (Mayers and Smith, 1988; He and Sommer, 2010; Lamm-Tennant and Starks, 1993). Our result also demonstrates that volatility of loss reserves of stock firms is higher than mutual. Geographical diversification variable shows significant and negative relation with insurers reserve error volatility although product diversification and longtail lines seem to be associated with higher volatility. In our second analysis we examine the association between insurer group loss reserve volatility and internal capital markets. These results are presented in the next two tables. In this analysis, we only include group level data for affiliated insurers from our sample. To measure internal capital markets (ICMs), we use two proxies; ICM transactions and ICM participants. Table 6 shows the results for the first proxy while Table 7 represents the results for the second proxy. In both Table 6, and Table 7, dependent variables from (1) to (4) are group level loss reserve error volatility which is a sum of each insurer s standard deviation of loss reserve error divided by scaling factors multiplies weighted by the percentage of net premium written for a firm within a group as shown in equation (4.1) and (4.2). Colums (1)-(4) represent the same volatility measures as Table 5. All of other variables also have the same meaning as in Table 5 except they are now aggregated at the group level. Our first proxy for the ICMs is an internal reinsurance variable calculated as reinsurance ceded to affiliates minus reinsurance assumed from affiliates divided by total premiums written (Powell and Sommer, 2007; Powell, Sommer and Eckles, 2008; Fier, McCullough and Carson, 23

24 2012). The more internal reinsurance transactions, the more active their internal capital market is. In all of the regressions in Table 6, the estimated coefficient of our variable of interests, ICM transactions, is strongly significant and negative with values ranged from -0.08% to -0.34%. This suggests that affiliated insurers with access to more internal capital markets activities have lower group-level loss reserve error volatility than affiliated insurers with less active internal capital markets, supporting our second hypothesis. Next, we use our second proxy for internal capital market: ICM participants. More subsidiaries in a group represents more ICMs participants. In all of the regressions in Table 7, the estimated coefficient of our variable of interests, ICM transactions, is strongly significant and negative with values ranged from -0.02% to -0.04%. This also supports our second hypothesis that affiliated insurers with access to more internal capital markets activities have lower loss reserve error volatility than affiliated insurers with less active internal capital markets. From our second analysis, we also find some control variables such as Age, Size, Netincome, Geo-Herf, Prod-Herf, and Longtail are associated with lower group-level loss reserve error volatility as well. The bigger and financially stronger groups may have lower volatility of reserve error and the more geographically or product diversified groups experience lower group level loss reserve error volatility. One explanation is that more diversified firms have more ability to spread variability and larger risk pools exhibit lower volatility in underwriting results in property-liability insurers. Ultimately, we find evidence that as ICM transactions and participants increases, grouplevel loss reserve error volatility declines. No previous studies on property-liability insurers loss reserve have made any distinctions between single unaffiliated and group affiliated companies aside from the inclusion of a group dummy variable. Further, existing literature provides support for the existence of ICMs within group insurers but has not incorporated with loss reserve volatility (Houston, James, and Marcus 1997, Khanna and Tice 2001, Powell and Sommer 2007, 24