Are the determinants of banks' and insurers' capital structures homogeneous? Evidence using South African data

Sibindi, Athenia Bongani; Makina, Daniel Article Are the determinants of banks' and insurers' capital structures homogeneous? Evidence using South African data Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Sibindi, Athenia Bongani; Makina, Daniel (2018) : Are the determinants of banks' and insurers' capital structures homogeneous? Evidence using South African data, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 6, Iss. 1, pp. 1-21, https://doi.org/10.1080/23322039.2018.1519899 This Version is available at: https://hdl.handle.net/10419/245167 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 Cogent Economics & Finance ISSN: (Print) 2332-2039 (Online) Journal homepage: https://www.tandfonline.com/loi/oaef20 Are the determinants of banks’ and insurers’ capital structures homogeneous? Evidence using South African data Athenia Bongani Sibindi & Daniel Makina | To cite this article: Athenia Bongani Sibindi & Daniel Makina | (2018) Are the determinants of banks’ and insurers’ capital structures homogeneous? Evidence using South African data, Cogent Economics & Finance, 6:1, 1519899, DOI: 10.1080/23322039.2018.1519899 To link to this article: https://doi.org/10.1080/23322039.2018.1519899 © 2018 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 27 Sep 2018. Submit your article to this journal Article views: 1024 View related articles View Crossmark data Citing articles: 1 View citing articles FINANCIAL ECONOMICS | RESEARCH ARTICLE Are the determinants of banks’and insurers’ capital structures homogeneous? Evidence using South African data Athenia Bongani Sibindi 1 *and Daniel Makina 2 Abstract: This paper investigates the factors that determine capital structures of financial firms using two separate samples of banks and insurance companies and draws comparisons therefrom. It utilizes two samples of 16 South African banks and 26 South African insurance companies for the period 2006–2015. The relationship between leverage and firm-level determinants of capital structure is tested for each sample. The results show that the standard firm-level determinants of capital structure empirically observed on non-financial firms also apply for banks and insurers. Confirming the fundamental differences between banks and insurance companies, the study observed that the 2007–2009 global financial crisis (GFC) have a negative impact on capital structures of banks (meaning that they deleverage during crises). In contrast, the GFC was found to have a positive impact on capital structures of insurance companies (meaning, unlike banks, they leverage during crises). We find that banks and insurers have target capital structures. Banks adjust to this target at an adjustment speed of 44%, whereas insurers adjust at a lower rate of 21%. In conclusion, the paper finds ABOUT THE AUTHORS Athenia Bongani Sibindi is a senior lecturer in finance and insurance. He holds a PhD degree in finance. Athenia also holds the fellowship of the Insurance Institute of South Africa (FIISA) and Certified Risk Management Practitioner (CRM Prac) professional designations. He currently teaches undergraduate and postgraduate student in finance and insurance. Athenia also supervises several PhD and masters’students. His research interests include financial market stability, InsurTech, and microinsurance. Daniel Makina is a professor in finance. He completed his PhD degree at the University of Witwatersrand in the field of financial markets. He also holds an MSc degree in financial economics from the London School of Economics. He has published widely on various aspects of financial markets, banking and migration economics. He supervised to completion many students at both PhD level and master’s degree level. His broad areas of research interests include FinTech, financial inclusion, and migration economics. PUBLIC INTEREST STATEMENT The effects of the 2007–2009 global financial crises which mutated from the financial sector and afflicted many economies are still being experienced to date. Banks and insurance companies were the chief architects of this crisis. This was occasioned by the erosion of capital levels to sustain these institutions as a result of sub-prime lending. As such, it has become a policy imperative more than ever before to secure the financial sector. Although, the South African financial sector was largely insulated from the effects of the financial crisis due to the good regulatory architecture in place, it also experienced challenges due to economic downturn. Against this backdrop, this paper examines the financing policies of banks and insurance companies in order to establish the factors that drive their capital structures. This will also aid researchers to understand better what drives the capital structure policies of banks and insurers, which have remained a “grey area”in capital structure. Sibindi & Makina, Cogent Economics & Finance (2018), 6: 1519899 https://doi.org/10.1080/23322039.2018.1519899 © 2018 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Received: 23 May 2018 Accepted: 29 August 2018 First Published: 12 September 2018 *Corresponding author: Athenia Bongani Sibindi, Finance, Risk Management and Banking, University of South Africa, South Africa, E-mail: [email protected] Reviewing editor: Andrew Vivian, Loughborough University, UK Additional information is available at the end of the article Page 1 of 21 both commonalities and fundamental differences between the capital structures of banks and insurers. Subjects: Corporate Finance; Banking; Insurance Keywords: Banks; insurers; capital structure; firm level; regulation; South Africa Subjects: G01; G21; G32 1. Introduction Capital structure theory is firmly premised on the seminal works of Modigliani and Miller (1958, 1963)). They contended that firm value is invariant to capital structure choices. Sub-sequent studies have proven to the contrary, that capital structure does matter (see for instance Berger, Herring, & Szegö, 1995; DeMarzo & Duffie, 1995; Froot & Stein, 1998; Miller, 1995; Smith & Stulz, 1985). Despite banks and insurance companies occupying center stage in the economy, extant studies on capital structure have generally excluded financial firms from their analysis. This has been premised on the notion that financial firms have peculiar firm characteristics. For instance, in the context of banking and insurance institutions, the deposit taking and premium taking abilities, respectively, set them apart from other non-financial firms. This ability to generate deposits and premiums lends them an extra source of finance not ordinarily available to other firms. The second peculiar feature of banks and insurers is that they are subject to capital regulation which could also have a bearing on their capital structure choices. The standard view is that capital regulation constitutes an additional overriding departure from the Modigliani–Miller irrelevance proposition. Though few studies have been conducted in the realm of financial firms, two dominant schools of thoughts have emerged with regard to banks’and insurers’capital structures. First, there is the “standard corporate finance”view, which proposes that bank and insurer financing patterns are similar to those of non-financial firms. The second school of thought is the “regulatory view”of capital, which argues that capital regulation is binding and solely determines banks’and insurers’capital structures. Among others, Teixeira, Silva, Fernandes, and Alves (2014), Jucá, de Sousa, and Fishlow (2012), Ahmad and Abbas (2011), and Gropp and Heider (2010) demonstrated that there are striking similarities between the capital structures of banks and those of non-financial firms. However, there are bank-specific fixed factors that also come into play in the determination of the capital structures of banks. These include banking regulation, credit risk management and regulatory capital arbitrage opportunities. Comparatively, the research on the determinants of capital structure of insurance companies is still in its infancy stage (refer to among others Cheng & Weiss, 2012; Ahmed & Shabbir, 2014). The few studies have mainly been confined to investigating the determinants of solvency rather than capital structure. More recently, Moyo (2016) utilized a heterogeneous panel of South African insurance firms and banks institutions to test for the validity of the market timing, perking order and dynamic trade-off theories in explaining the financing behavior of financial services firms. He established that their financing behavior was consistent with the dynamic trade-off theory and contrary to the pecking order and market timing theories. However, his results did not test and correct for cross-sectional dependency inherent in the financing of financial firms; moreover, he treated the banks and insurers as homogenous entities. This study contributes to the existing literature in a number of ways. Principally, it investigates the determinants of capital structure of banks and insurers by factoring in the cross-sectional dependence inherently present in the financing behavior of such panels. The study also extends the work of Cebenoyan and Strahan (2004) and Gropp and Heider (2010) in three ways. First, a different proxy for risk that captures credit risk is employed to examine the dynamics of bank financing. Second, the study also investigates how financial firm leverage is impacted upon by business cycles such as the global financial crisis (GFC). Lastly, the study estimates the true speed of adjustment towards the target capital structure by employing LSDV with Kiviet (1995) correction estimators. The rest of the Sibindi & Makina, Cogent Economics & Finance (2018), 6: 1519899 https://doi.org/10.1080/23322039.2018.1519899 Page 2 of 21 paper is organized as follows. Section 2reviews relevant literature. Section 3describes research methodology followed. Section 4presents and discusses the results, and finally, section 5concludes. 2. Review of related literature A number of theories have been advanced to explain firm financing in the aftermath of the M and M irrelevance propositions. Notwithstanding, extant studies on the determinants of capital structure have employed the pecking order and trade-off theories in the interpretation of their results. Suffice to say that this paper takes cue from such studies and relies on the trade-off and pecking order theories to explain the capital structure choices of financial firms. 2.1. Trade-off theory The trade-off theory is associated with Kraus and Litzenberger (1973). They postulated that firms desire to attain an optimal leverage which reflects a trade-off between the tax benefits of debt and the deadweight costs of bankruptcy. Myers (1984) further advanced this theory in his static tradeoff framework in which he asserted that firms set a target debt-to-value ratio and gradually moves towards it, the same way that firms adjust dividends to move towards a target dividend pay-out ratio. Quintessentially, a firm has two sources of financing at its disposal, namely; debt and equity. Within a trade-off framework, the firm will utilize more debt in order to benefit from the debt interest tax shield up to a point where the bankruptcy costs outweigh the present value of interest tax shield. Beyond this point it is prudent that the firm finances out of equity. 2.2. Pecking order theory The pecking order theory was advanced by Myers and Majluf (1984). They postulated that it is generally better to issue safe securities than risky ones. Firms should go to bond markets for external capital, but raise equity by retention if possible. In other words, external financing using debt is better than financing by equity. In this pecking order model, a financial hierarchy descends from internal funds, to debt, to external equity (Chirinko & Singha, 2000, 418). Put in other words, managers will tend to have the priority to fund projects by using retained earnings, and issue debts when the retained earnings are exhausted, and lastly they will only turn to the issuance of equity when it is not sensible to issue any more debts (Rasiah & Kim, 2011, 151). Within a pecking order framework, the firm has no well-defined target debt-to-equity ratio (Myers, 1984). This theory implies that corporate managers making financing decisions are not really thinking about a longrun target debt-to-equity ratio. Instead, they take the path of least resistance and choose what at the time appears to be the lowest-cost financing vehicle—generally debt—with little thought about the future consequences of these choices (Barclay & Smith, 2005). The pecking order theory is classified as an information cost theory. Implicit in the pecking order theory is information asymmetry. Information asymmetry arises as a result of managers (insiders) having more information than investors (outsiders), which they use to their advantage. As such, within this setting long term debt is considered as a last resort in financing. 2.3. Standard firm-level determinants of capital structure Extant studies on capital structure have isolated a number of factors to have an effect on firm leverage (see for instance, Al-Najjar & Hussainey, 2011; Frank & Goyal, 2009;Gropp&Heider,2010; Rajan & Zingales, 1995; Shyam-Sunder & Myers, 1999; Titman & Wessels, 1988, among other studies). These are profitability, asset tangibility (collateral), size, market-to-book value (growth) and risk. 2.3.1. Profitability The pecking order predicts a negative relationship between profitability and firm leverage. Indeed, most empirical studies have confirmed this prediction (see Bartoloni, 2013; Booth, Aivazian, Demirguc-Kunt, & Maksimovic, 2001, 117; Rajan & Zingales, 1995, 1457; Shyam-Sunder & Myers, 1999: 221, among other studies on non-financial firms). Similarly, studies on financial firms have also bolstered this prediction (refer to Ahmad & Abbas, 2011, 211; Gropp & Heider, 2010, 598 and Jucá et al., 2012, 23, among other studies). On the contrary, the trade-off theory predicts a positive association between firm profitability and firm leverage. The trade-off theory predicts that highly Sibindi & Makina, Cogent Economics & Finance (2018), 6: 1519899 https://doi.org/10.1080/23322039.2018.1519899 Page 3 of 21 profitable firms are more likely to finance out of debt in order to enjoy the benefits of debt taxdeductibility. However, this benefit seems to accrue the most to large and very large firms, who have generated goodwill on the debt market and as such are rated favorably and can access debt at preferential terms. Danis, Rettl, and Whited (2014, 424) lend credence to the trade-off theory positive leverage-profitability prediction as they establish that at times when firms are at or close to their optimal level of leverage, the relationship is positive. Furthermore, their results document that the relationship is negative at other times. Notwithstanding, the pecking order prediction seems to be the most plausible one and most empirical studies seem to lend credence more to the negative prediction. 2.3.2. Asset tangibility On the one hand, the trade-off theory predicts a positive relationship between asset tangibility and firm leverage. Among other studies on financial firms, Gropp and Heider (2010, 598) and Jucá et al. (2012, 23) found a positive relationship between asset tangibility and firm leverage. Yet, on the other hand, the pecking order theory predicts an inverse relationship between asset tangibility and firm leverage. This can be attributed to low information asymmetry associated with tangible assets, making equity issuances less costly. Empirical support of this prediction can be found in Bradley, Jarrell, and Kim (1984, 874), Ahmad and Abbas (2011, 208), and Al-Najjar and Hussainey (2011, 333), for instance. Nonetheless, the positive prediction is the most persuasive. 2.3.3. Size The effect of size on financial leverage can be twofold. From the pecking order theory vantage point, as firms grow, they are bound to generate more retained earnings. As such, they should be in a position to fund their operations more out of retained earnings and hence substitute debt. Therefore, a negative relationship is predicted to exist between firm leverage and size, whereas the trade-off theory predicts that large firms should be highly leveraged as compared to small firms as they stand to enjoy the benefits of debt interest tax shields. As such, from the trade-off theory point of view the prediction is that firm leverage is positively associated with size. Notwithstanding, empirical support for the positive firm leverage and size relationship is overwhelming (see, for instance, Ahmed, Ahmed, &Ahmed,2010, 9; Antoniou, Guney, & Paudyal, 2008, 73; Al-Najjar & Hussainey, 2011, 334; Bartoloni, 2013,142;Lemma&Negash,2014,81;andLim,2012, 197, among other scholars). 2.3.4. Growth Frank and Goyal (2009, 8) contend that growth increases the costs of financial distress, reduces free cash flow problems and exacerbates debt-related agency problems. Growing firms place a greater value on stakeholder co-investment. Therefore, the trade-off theory predicts that growth reduces leverage. Antoniou et al. (2008) contended that internal resources of growing firms may not be sufficient to finance their positive NPV investment opportunities and, hence, they may have to raise external capital. In essence, if firms require external finance, they issue debt before equity according to the pecking order theory. Therefore, growth opportunities and leverage are positively related in terms of the pecking order theory. 2.3.5. Risk The trade-off theory predicts a negative relationship between firm leverage and risk. In other words, a firm that has highly volatile cash flows must avoid debt financing. The intuition behind this is that highly volatile cash flows could result in financial distress. As such, to avoid going bankrupt, firms with high levels of volatile cash flows must desist from debt financing. According to Antoniou et al. (2008, 64), firms with high earnings volatility carry a risk of the earnings level dropping below their debt-servicing commitments. The pecking order theory, however, predicts a positive relationship between firm leverage and risk. This ought to be premised on the notion that the volatility of cash flows implies the volatility of earnings. As such, the firm becomes constrained to finance out of retained earnings. It would therefore have to seek funding from the external markets, starting off with the debt market, to Sibindi & Makina, Cogent Economics & Finance (2018), 6: 1519899 https://doi.org/10.1080/23322039.2018.1519899 Page 4 of 21 avoid the problem of adverse selection. In synch with this view, Frank and Goyal (2009, 9) assert that firms with volatile shares are expected to be those about which beliefs are quite volatile. It would seem plausible that such firms suffer more from adverse selection. 2.4. Empirical evidence on the financial firms’determinants of capital structure The empirical studies carried out in the insurance realm are very scant. Amongst others, Ahmed and Shabbir (2014) tested the pecking order theory by employing financial data of insurance companies of Pakistan over a 5-year period from 2007 to 2011. Their empirical results indicate that size, profitability, liquidity, tangibility and risk are important determinants of the capital structure of insurance companies of Pakistan. Furthermore, they report that Pakistani insurers seem to follow a pecking order pattern of financing in terms of profitability, risk, tangibility and liquidity, as all the coefficients are negative. However, with regard to size, a positive relationship subsists, which is consistent with the trade-off theory. Cheng and Weiss (2012) conducted tests of the trade-off and pecking order theories within the US property-liability insurance industry. Their sample period for the study, 1994–2003, coincided with the institution of risk-based capital requirements in this industry. They estimated a partial adjustment model to determine whether firms have an optimal capital structure and how quickly firms adjust to the optimum when deviations from the optimum occur. The results of their research indicate that the trade-off theory dominates the pecking order theory for property-liability insurers. There is a growing body of literature corroborating that the standard non-financial firm’s determinants of capital structure also apply to the banking sector (Teixeira et al., 2014; Jucá et al., 2012; Ahmad & Abbas, 2011; and Gropp & Heider, 2010). More recently, Sorokina, Thornton, and Patel (2017, 51) extend the work of Gropp and Heider (2010) by examining the financing behavior of 1700 publicly traded US banking during the period 1973–2012. Their results document that a vast majority of the banks hold equity capital above the prescribed regulatory minimum levels. They reason that, the capital structure of banks is determined independently from regulatory requirements, until they fall within very close proximity to the required minimum and regulators explicitly force capital-related decisions upon them. Furthermore, Sorokina et al. (2017, 51) document that at other times, general market factors, market-to-book ratio, profitability, size, collateral, dividends, risk and some other macro- and micro-economic parameters drive capital structure of banks. Table 1documents the findings of select studies that have been conducted on banking firms. 3. Research methodology 3.1. Sources of data The sample selected for investigated consisted of all South African banks and insurance companies both listed and unlisted with complete data sets for the 10-year period running from 2006 to 2015. Data were obtained from the Bureau van Dijk Bankscope and Orbis databases. The banking panel comprised of 16 banks, whilst the insurance panel comprised of 26 insurers. The sample of banks and insurance companies is almost representative of the entire population of banks and insurance companies. This is so because the five big banks included among the total of the 16 investigated account for over 90% of bank assets, and similarly, five insurance companies included in the sample of 26 account for over 80% of insurance assets. The list of the banks and insurance companies and their sub-sectors is documented under Appendices A and B. 3.2. Variables Standard corporate finance regression analysis has been employed in extant studies to analyze the relationship between capital structure and its determinants. It is therefore imperative that the proxies employed for the dependent variable as well as the independent variables in this study are defined. Sibindi & Makina, Cogent Economics & Finance (2018), 6: 1519899 https://doi.org/10.1080/23322039.2018.1519899 Page 5 of 21 3.2.1. Dependent variables In this study, three dependent variables were employed to test the relationship between leverage and its determinants. The primary dependent variable employed for this study was book leverage. The book leverage measure (BLE) is a broad measure of leverage, defined as one minus the ratio of book value of equity to book value of assets. This follows from Gropp and Heider (2010), it is suffice to highlight that many proxies have been employed to define the leverage variable. There are three strands of literature in this regard. In the first instance, scholars rely on one measure of leverage. In the second strand, scholars rely on two measures of leverage, namely book leverage and market leverage. In the last strand of literature, scholars rely on the broadest measure of leverage and have three proxies for leverage, namely total debt ratio, short-term debt ratio and long-term debt ratio. The major contestation has been whether to employ book leverage, market leverage or both. Notwithstanding that the conundrum remains unresolved; studies that have employed both measures demonstrate that the results are robust to either proxy adopted. Therefore, the inference is largely the same irrespective of whether book leverage or market leverage was employed (see, for instance, Antoniou et al., 2008; Frank & Goyal, 2004; Gropp & Heider, 2010; Hovakimian, Opler, & Titman, 2001; Rajan & Zingales, 1995; Titman and Wessels, 1988, among others). Moreover, the justification for using book value leverage is premised on other considerations. First, capital regulation of banks is imposed on book values and not market values and hence Table 1. Empirical studies on bank capital structure Firm-level determinant Expected sign Empirical evidence from the banking firms Pecking order theory Trade-off theory Size Positive Positive Positive Ahmad and Abbas (2011, 211) Baltaci and Ayaydin (2014, 46) Gropp and Heider (2010, 598) Jucá et al. (2012, 23) Negative Teixeira et al. (2014, 56) Profitability Negative Positive Positive Teixeira et al. (2014, 56) Berger and Di Patti (2006, 21) Negative Ahmad and Abbas (2011, 211) Baltaci and Ayaydin (2014,46) Gropp and Heider (2010, 598) Jucá et al. (2012, 23) Asset tangibility Negative Positive Positive Gropp and Heider (2010, 598) Jucá et al. (2012, 23) Negative Teixeira et al. (2014) Risk Positive Negative Positive Teixeira et al. (2014, 56) Negative Gropp and Heider (2010, 598) Jucá et al. (2012, 23) Growth Positive Negative Positive Ahmad and Abbas (2011, 211) Teixeira et al. (2014, 56) Negative Gropp and Heider (2010, 598) Jucá et al. (2012, 23) Source: Sibindi (2017, 97). Sibindi & Makina, Cogent Economics & Finance (2018), 6: 1519899 https://doi.org/10.1080/23322039.2018.1519899 Page 6 of 21 this became the variable of interest for the purposes of this study. Second, as the sample of financial firms included firms that were not listed on the JSE, there was scant availability of market value data. Because banks have an additional source of financing, in the form of deposits, leverage was also decomposed to analyze the dynamics of deposit financing. The secondary measures of leverage employed for banks were deposit leverage (deposit liabilities) and non-deposit leverage (nondeposit liabilities). Deposit leverage (DEPOSIT) equals the ratio of total deposits to total assets. Non-deposit leverage (NON-DEP) is the difference between book leverage and deposit leverage. For the insurance sector, this study also employed secondary measures of leverage. These were non-premium liabilities (“non-premium leverage”) and premium reserves (“premium leverage”). Premium leverage (PRL) equals the ratio of total gross provisions to total assets. Non-premium leverage (NON-PREM) is equal to book leverage minus premium leverage. 3.2.2. Independent variables The independent variables consist of the firm-level determinants of capital structure as well as dummy variables. The firm-level determinants of capital structure considered were size, growth, asset tangibility, profitability, risk and reinsurance. The dummy variables employed captured the effects of the 2007–2009 GFC as well as a dummy variable to capture payment and non-payment of dividends. ●Size To measure size, the natural logarithm of total assets was employed as in other studies (Al-Najjar & Hussainey, 2011; Antoniou et al., 2008; Booth et al., 2001; Frank & Goyal, 2009; Mukherjee & Mahakud, 2010; and Öztekin & Flannery, 2012). There is a direct relationship between size and the value of assets held. Other studies have employed the logarithm of sales or net sales to capture the effect of size (see, for instance, Barclay & Smith, 2005; Rajan & Zingales, 1995; Titman and Wessels, 1988). We preferred the total assets variable because it proxies, on the one hand, both the loan activity and investment activities of banks and, on the other hand, the underwriting and investment activities of insurance companies. ●Growth The growth variable is defined as the annual growth rate of total assets. Taking cue from Titman and Wessels (1998) and Anarfo (2015), the higher the growth rate, the higher the growth prospects of the firm. The alternative definition that has also been used widely in empirical studies would have been to proxy growth prospects with the market-to-book value ratio (see, among others, Booth et al., 2001; Frank & Goyal, 2009; and Teixeira et al., 2014). However, we could not use this proxy because of non-availability of market value data. ●Asset tangibility Asset tangibility is defined as the ratio of fixed assets to total assets, which ratio expresses the collateral value. If the collateral value is high, the firm would be viewed in good light in the debt market, enabling it to access loans at concessionary rates. ●Profitability While recognizing that profitability is defined in several ways, in the study we employed the return on assets (ROA) measure as the proxy for profitability. In the case of the banking sample, this was defined as the return on average assets (ROAA). Sibindi & Makina, Cogent Economics & Finance (2018), 6: 1519899 https://doi.org/10.1080/23322039.2018.1519899 Page 7 of 21 5. Conclusion This paper offers new insights in several respects. First, the paper recognizes that banks and insurance companies are fundamentally different with regard to capital structure and regulation and so warranted separate treatment in studies. This is in contrast with recent studies that do not recognize the heterogeneity of the two types of firms. Second, to the best of our knowledge the study is the first to examine the impact of business cycles/financial crises on the financing patterns of financial firms. Confirming the fundamental differences between banks and insurance companies, the study observed that financial crises have a negative impact on capital structures of banks (meaning that they deleverage during crises). In Table 6. Robustness checks of the leverage variable for insurers Dependent variable Book leverage Non-premium leverage Premium leverage Growth 0.050*** 0.066** −0.014 Profit −0.288*** −0.027 −0.254** Asset tangibility 0.010 −0.255*** 0.271*** Risk 0.171*** 0.112* 0.059 Size 0.068*** 0.124*** −0.062 Reinsurance 0.116*** 0.010 0.099** (*)/(**) and (***) indicate the (10%), (5%), and (1%) level of significance, respectively. Table 7. Panel regression results to determine a target capital structure for banks Difference-GMM (one-step) System-GMM (one-step) FGLS (Parks, 1967; Kmenta, 1971) LSDV with Kiviet (1995) correction Leverage (−1) 0.554*** 0.524*** 0.790*** 0.558*** (3.88) (3.97) (20.78) (7.98) Growth 0.101*** 0.067*** 0.092*** 0.080*** (4.36) (3.00) (13.71) (35.86) Profit −0.706*** −1.045*** −0.762*** −0.677*** (−5.62) (−4.76) (−13.20) (−47.86) Asset tangibility −1.294*** −1.000 −0.273 0.568 (−2.28) (−1.39) (−0.81) (1.00) Risk 0.257*** −0.016 0.071* 0.211*** (4.72) (−0.19) (1.78) (8.47) Size 0.007 0.001 −0.003* 0.013*** (0.54) (0.95) (1.86) (19.19) Dividend −0.011 0.021** 0.007*** 0.003 (−1.00) (2.09) (3.48) (0.46) GFC 0.013 0.016** 0.006** 0.013*** (2.19) (2.10) (2.08) (23.49) AR(1) statistic −1.75* −1.17 AR(2) statistic 0.846 0.965 Sargan 7.12 27.9** LM-statistic 917*** (*)/(**) and (***) indicate the (10%), (5%), and (1%) level of significance, respectively. The t-statistics are reported in parentheses. The above table shows the results of estimating the following regression for the sample of 16 South African banks for the period 2006–2015. Sibindi & Makina, Cogent Economics & Finance (2018), 6: 1519899 https://doi.org/10.1080/23322039.2018.1519899 Page 14 of 21 contrast, financial crises have a positive impact on capital structures of insurance companies (meaning, unlike banks, they leverage during crises). The results of this study also demonstrate that the financing behavior of financial firms mirrors that of non-financial firms. This corroborates the findings of Gropp and Heider (2010), Ahmad and Abbas (2011), and Jucá et al. (2012), among the few studies on the subject matter. The standard firm-level determinants of capital structure showed significant explanatory power in terms of the leverage variable. Crucially, a negative relationship was found to exist between profits and bank leverage demonstrating that the pecking order theory can be relied on in explaining the financing behavior of South African banks. Similarly, for the insurance sector, strong evidence was found that validates the hypothesis that the firm-level determinants of capital structure have a predictive power in insurer leveraging. As in banks, a negative relationship was found to exist between profits and insurer leverage. The finding also demonstrates that the capital structure of South African insurance companies can be explained in terms of the pecking order theory. The salient feature of the estimated results of the banking sector and insurance sector is that they bear striking uniformity. As such, this study validates the generalization that the financing behavior of financial firms mirrors that of non-financial firms. The other significance of the “standard corporate finance view”finding is that it relegates capital regulation to be of secondary importance in the determination of the capital structure of financial firms. Table 8. Panel regression results to determine a target capital structure for insurers Diff-GMM (one-step) LSDV with Kiviet (1995) correction FGLS (Kmenta, 1971; Parks, 1967) Leverage (−1) 0.797*** 0.754*** 0.794*** (2.92) (71.75) (15.78) Growth 0.043** 0.033*** 0.067*** (2.58) (3.04) (4.90) Profit −0.314*** −0.361*** −0.313*** (−3.16) (12.89) (−5.61) Asset tangibility 0.012 0.052 0.024 (0.14) (1.12) (0.31) Risk 0.029 0.041 0.062 (0.57) (1.40) (0.21) Size 0.053* 0.036*** 0.080** (1.89) (8.63) (2.18) Reinsurance 0.122*** 0.059 0.041 (3.16) (1.02) (1.03) GFC 0.032** 0.028*** 0.008 (2.33) (7.39) (1.09) AR(1)-statistic −1.60 AR(2)-statistic −0.85 Sargan 31.51 LM-statistic 550*** Number of observations 260 260 260 (*)/(**) and (***) indicate the (10%), (5%), and (1%) level of significance, respectively. The t-statistics are reported in parentheses Sibindi & Makina, Cogent Economics & Finance (2018), 6: 1519899 https://doi.org/10.1080/23322039.2018.1519899 Page 15 of 21 Unlike previous studies on financial firm capital structure such as Gropp and Heider (2010)andDe Haan and Kakes (2010), this study estimated the true speed of adjustment by utilizing FGLS (Parks– Kmenta) and LSDV with Kiviet (1995) correction estimators, which are most suitable to estimate capital structure partial adjustment models in the presence of heteroskedasticity and cross-sectional dependence. For the banking panel, it was demonstrated that banks have a target capital structure that they seek to achieve in their financing and adjust towards this target faster compared to nonfinancial firms. It was found that the speed of adjustment of South African banks is 44% (half-life of 2.3 years) with respect to total debt. Furthermore, the speed with which South African banks adjust to attain their target level is comparable to that of banks in the developed world and is reflective of low adjustment costs. South African insurance companies adjust at a lower rate comparable to the banking sector. They adjust at a rate of 21% (half-life of 4.76 years). This could be attributable to the heterogeneity of the balance sheets of the banking and insurance panels. In essence, the profile of their liabilities is different from one another. This finding is also inconsistent with capital regulation being of first-order importance in the determination of the capital structure of financial firms. It also leads to the generalization that financial firms seek to achieve optimality in their financing behavior in the same manner as non-financial firms. The financing behavior (capital structure targeting) of banks and insurance companies is inconsistent with those seeking to observe the minimum regulatory requirement. This finding demonstrates that at the worst case, capital regulation is not binding and may be ineffectual. This could be attributable to the individual effects banks and insurers. The policy implication that flows from these findings is that, it could be prudent for regulatory authorities to consider instituting some variant of financial firm-specific capital regulations as opposed to sector-wide (one-size-fits-all) capital regulations. This study was undertaken during a transition period when Basel III and SAM capital regulation standards were being implemented. As such, future studies could examine the impact of the implementation of these capital standards on the financing patterns of banks and insurance companies, respectively. It could be that in future the capital regulations will become binding. Acknowledgments We would like to acknowledge the valuable comments and suggestions on how to improve this paper from the anonymous referees of the Journal of Cogent Economics and Finance as well as the participants of the 30th Australasian Finance and Banking conference held in Sydney in December 2017. Funding The authors received no direct funding for this research. Author details Athenia Bongani Sibindi 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0003-0953-8424 Daniel Makina 2 E-mail: [email protected] 1 Department of Finance, Risk Management and Banking, University of South Africa, UNISA Main Campus, 5-84 AJH van der Waalt Building, P.O. Box 392, Pretoria 0003, South Africa. 2 Department of Finance, Risk Management and Banking, 5-103 AJH van der Waalt Building, University of South Africa, UNISA Main Campus, P.O. Box 392, UNISA 003, Pretoria 0003, South Africa. 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Sibindi & Makina, Cogent Economics & Finance (2018), 6: 1519899 https://doi.org/10.1080/23322039.2018.1519899 Page 18 of 21 Appendix A1: Sample of banks Name of bank Specialization Total assets in 2015 (R’mil) Net income in 2015 (R’mil) World rank Country rank 1 ABSA Bank Limited Commercial Bank 936,141 10,047 350 3 2 African Bank Limited Commercial Bank 50,679 (7,212) 2430 8 3 Albaraka Bank Limited Islamic Bank 4,814 40 7456 18 4 Bidvest Bank Limited Investment Bank 6,201 263 6800 17 5 First Rand Bank Limited Commercial Bank 851,200 12,750 270 2 6 GBS Mutual Bank Commercial Bank 1,085 8 12,726 24 7 Grindrod Bank Limited Commercial Bank 9,256 105 5441 12 8 Habib Overseas Bank Limited Commercial Bank 1,207 16 12,284 23 9 HBZ Bank Limited Commercial Bank 2,475 38 8253 19 10 Investec Bank Limited Investment Bank 332,706 3,128 682 5 11 Mercantile Bank Limited Commercial Bank 9,640 140 6206 15 12 Nedbank Limited Commercial Bank 319,135 757 377 4 13 Real People Investments Holdings Pty. Limited Investment Bank 3,755 (333) 8506 20 14 Sasfin Bank Limited Commercial Bank 8,429 137 5861 14 15 South African Bank of Athens Limited Commercial Bank 2,284 (58) 10,084 21 16 Standard Bank of South Africa Limited Commercial Bank 1,276,953 12,479 266 1 Sibindi & Makina, Cogent Economics & Finance (2018), 6: 1519899 https://doi.org/10.1080/23322039.2018.1519899 Page 19 of 21 Appendix B: Sample of insurance companies Name of insurance company Specialization 1 African Reinsurance Corporation Reinsurance company 2 AIG South Africa Limited Short-term insurance company 3 Allianz Insurance Limited Short-term insurance company 4 Clientele Limited Long-term insurance company 5 Credit Guarantee Insurance Corporation of Africa Limited Short-term insurance company 6 Discovery Life Limited Long-term insurance company 7 Export Credit Insurance Corporation of South Africa Limited Short-term insurance company 8 Federated Employers Mutual Assurance Company Limited Short-term insurance company 9 General Re Africa Limited Reinsurance 10 Guardrisk Insurance Company Limited Short-term insurance company 11 HDI-GERLING Insurance of South Africa Short-term insurance company 12 Hollard Insurance Company Limited Short-term insurance company 13 Hollard Life Assurance Company Limited Long-term insurance company 14 Liberty Holdings Limited Long-term insurance company 15 Lion of Africa Insurance Company Limited Short-term insurance company 16 Munich Reinsurance Company of Africa Limited Reinsurance 17 New National Assurance Company Limited Short-term insurance company 18 Old Mutual Life Assurance Company Limited Long-term insurance company 19 Professional Provident Society Long-term insurance company 20 Regent Insurance Company Limited Short-term insurance company 21 Regent Life Assurance Company Limited Long-term insurance company 22 Renasa Insurance Company Limited Short-term insurance company 23 Sanlam Life Insurance Limited Long-term insurance company 24 Santam Limited Short-term insurance company 25 Sasria Limited Short-term insurance company 26 Zurich Insurance Company South Africa Limited Short-term insurance company Sibindi & Makina, Cogent Economics & Finance (2018), 6: 1519899 https://doi.org/10.1080/23322039.2018.1519899 Page 20 of 21 © 2018 The Author(s). 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