Shadow financial services and firm performance in South Africa

Zhou, Sheunesu; Tewari, Devi D. Article Shadow financial services and firm performance in South Africa Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Zhou, Sheunesu; Tewari, Devi D. (2019) : Shadow financial services and firm performance in South Africa, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 7, Iss. 1, pp. 1-18, https://doi.org/10.1080/23322039.2019.1603654 This Version is available at: https://hdl.handle.net/10419/245229 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 Shadow financial services and firm performance in South Africa Sheunesu Zhou & D. D. Tewari | To cite this article: Sheunesu Zhou & D. D. Tewari | (2019) Shadow financial services and firm performance in South Africa, Cogent Economics & Finance, 7:1, 1603654, DOI: 10.1080/23322039.2019.1603654 To link to this article: https://doi.org/10.1080/23322039.2019.1603654 © 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 22 Apr 2019. Submit your article to this journal Article views: 1093 View related articles View Crossmark data FINANCIAL ECONOMICS | RESEARCH ARTICLE Shadow financial services and firm performance in South Africa Sheunesu Zhou 1 *and D. D. Tewari 2 Abstract: The last two decades have seen a sharp increase in shadow banking activities in both advanced and emerging economies. Shadow banks have therefore become an important part of financial markets due to their credit creation and capital allocation roles. This study investigates the impact of shadow banking on firm profitability in South Africa and evaluates the linkages between shadow banking and real economic activity. We employ single-equation cointegration methods and three measures of firm profitability in our analyses, and several macroeconomic and bank-specific variables are used as control variables. Our results are mixed showing that shadow banking has a negative impact on traditional banks’profitability whilst on the other hand it positively impacts non-financial firms and the overall measures of firm profitability. Our results indicate that both non-financial firms and non-bank financial institutions could be benefiting from the expansion in shadow banking activities. Targeted, functional regulation is suggested in order to promote economic activities in the shadow banking sector whilst at the same time limiting possible risks that may arise. Sheunesu Zhou ABOUT THE AUTHORS Sheunesu Zhou is a PhD Candidate at the University of Zululand in South Africa. He is a specialist in Financial Economics and Macroeconomics. Mr Zhou has wider research interests in financial markets and macroeconomic policy formulation, and the application of econometric methods in economic policy analysis. He holds a BSc. Economics degree from the University of Zimbabwe and an MCom. Financial Economics degree from Great Zimbabwe University. He is currently Lecturing undergraduate courses in Economics and Finance. D. D. Tewari is Dedicated Economist, having more than 25 years of teaching, research, consulting and managerial experience. Has taught at the Indian Institute of Management, Ahmedabad, India; University of Natal and University of KwaZulu-Natal, Durban, University of Zululand, South Africa; HEC- Montreal, Canada; and, School of Economics at the University of Shangdong, China. Major areas of research include among others natural resource economics; educational economics; financial economics and monetary economics. PUBLIC INTEREST STATEMENT The shadow banking sector is an important component of financial markets globally and its growth has accelerated in the past decades. Shadow banks, which are financial institutions outside the formal banking sector involved in credit extension, play an important role in increasing finance available to borrowers. Shadow banks provide financing at a lower cost and on special contractual agreements, which are usually less stringent than formal banks. Therefore, shadow bank credit is expected to increase the profitability of non-financial firms. On the other hand, the participation of formal banks in shadow banking activities leads to a trade-off between formal banking and shadow banking activity. Thus, increased shadow banking should decrease bank profitability. The findings of this study show that these two propositions hold for South Africa. Shadow banking positively influence non-financial firm profitability and negatively impacts bank profitability. Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1603654 https://doi.org/10.1080/23322039.2019.1603654 © 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Received: 25 July 2018 Accepted: 02 April 2019 First Published: 07 April 2019 *Corresponding author: Sheunesu Zhou, University of Zululand, Empangeni, South Africa E-mail: [email protected] Reviewing editor: Christian Nsiah, School of Business, Baldwin Wallace University, USA Additional information is available at the end of the article Page 1 of 18 Subjects: Economics; Monetary Economics; Econometrics; International Finance; Development Economics; Corporate Finance; Banking Keywords: shadow banking; firm profitability; cointegration; macro-economic variables JEL classification: C33; E44; G23 1. Introduction The main argument for proliferation and growth of shadow financial services is that financial innovation promotes economic activity by enabling economic agents to ameliorate financial market imperfections (FSB, 2013; Henderson & Pearson, 2011). Any kind of financial innovation should therefore improve efficiency and effectiveness of financial markets. Shadow banking 1 literature argue that capital can be sourced at a lower cost and efficiently from shadow banks (Tang & Wang, 2015). By construct, firms should find capital from shadow banks relatively cheaper compared to mainstream capital markets and banks. Theoretically, this provides an alternative capital source to the two most reviewed in literature, mainly bank based and market-based capital (Boot & Marinč, 2010). Following this argument, one is persuaded to conclude that firms’profitability increases with an up-surge in shadow banking activity (Lu, Guo, Kao, & Fung, 2015). We test this proposition in this paper for South Africa using a unique data set on the growth of Other Financial Intermediaries (OFI). Our choice of variable is necessitated by lack of data on the more relevant function based narrow measure of shadow banking that is constructed by the Financial Stability Board (FSB). The spirit of this paper is to provide empirical evidence on the impact of shadow banking on the real economy. Whilst several studies have investigated the relationship between firm profitability/performance and broad measures of financial development, no evidence is available that link shadow banking to firm performance. Studies by Lu, Guo, Kao, and Fung (2015) and Acharya, Khandwala, and Öncü (2013) argue that shadow banks have the propensity to finance non-financial firms and hence enhance the profitability of such firms. On the contrary, Pozsar and Singh (2011)establishthat shadow banking is only an activity between shadow banks and traditional banks for the United States of America (US). We submit, therefore, that differences in structure of financial markets and regulatory environment are important determinants of the effect that shadow banking has on the economy. It is on this backdrop that this study analyses the impact of shadow banking on firm profitability in South Africa. Studies closer to ours investigate the impact of macroeconomic factors on firm performance (Francis, 2013; Hirsch, Schiefer, Gschwandtner, & Hartmann, 2014;Kandir,2008; McNamara & Duncan, 1995). Whilst these studies provide linkages between firm profitability and several macroeconomic and financial variables, no evidence exists that link shadow banking to firm performance. Our contribution is twofold, firstly, we analyse the impact shadow banking has on firm profitability. Furthermore, we disaggregate firm profitability in South Africa by considering banks and non-financial firms separately. Thus, we use industry data on banks and non-financial firms to reveal whether shadow banking benefits non-financial firms or banks only. To the extent of our knowledge, this is the first study that provides an analysis of the relationship between shadow banking and various measures of profitability. This section is of an introductory nature. The rest of the paper proceeds as follows: Section 1.1 provides a concise description of shadow banking activities, with a special reference to South Africa. Section 2provides empirical literature on the determinants of firm profitability and illustrate theoretically linkages between shadow banking and firm profitability. Measures of firm profitability are also reviewed. Sections 3and 4provides the methodology used in the study and estimation results, respectively. The paper concludes in Section 5. 1.1. Shadow banking activities in South Africa Shadow banking as a term only became popular after McCulley (2009)’s paper in which he referred to financial activities done outside the normal banking sector. Several other studies have explored the growth and characteristics of shadow banking activities, with more literature focusing on Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1603654 https://doi.org/10.1080/23322039.2019.1603654 Page 2 of 18 advanced economies (Meeks, Nelson, & Alessandri, 2017; Pozsar, Adrian, Ashcraft, & Boesky, 2013; Pozsar & Singh, 2011; Xiang & Qianglong, 2014). Recent studies have however centred on emerging markets as there has been a surge in shadow banking activities in these markets in the aftermath of the Global Financial Crisis (GFC). The Financial Stability Board (FSB) reports that shadow banking grew by an average of 10% between 2016 and 2017 compared to an increase averaging 6% in advanced economies (FSB, 2017). In South Africa specifically, shadow banking assets grew by a staggering R4.5 billion during the same period. Figure 1shows that there has been a gradual increase in shadow banking assets from the year 2002. From Figure 1one can see that during the crisis, there was a decrease in shadow banking growth but after the crisis, there is an upward trend again. Of importance is the growth in assets of shadow banks relative to the growth rate of formal banks’assets. Prior to 2003, banks’share of financial assets has always been higher and growing compared to other financial institutions. Available data, however, shows that from 2003 the proportion of bank assets to total assets of the financial services sector has gradually dropped. Contrary to this is a gradual increase in the proportion of shadow banks assets. Is there a trade-off between shadow banking and formal banking or it is only a coincidence? Theoretically, a trade-off should exist between shadow banking and formal banking (Meeks et al., 2017). Two explanations support this trade-off, firstly, when there is shortage in funding, innovative financial agents introduce new ways of providing finance, usually outside mainstream banking activities (Adrian & Ashcraft, 2016). This could be a result of regulatory arbitrage or new technology. The second explanation hinges on profit incentive where formal banks are driven to engage in shadow banking activities, including securitisations and other forms of credit creation in expectation of higher earnings (Meeks et al., 2017; Tang & Wang, 2015). Formal banking institutions may direct more of their assets toward shadow banking activities with the expectation of earning higher profits whilst concurrently reducing assets for mainstream banking activities. Figure 2illustrates the growth of shadow banking assets compared to other assets in the South African financial sector. One can clearly see from the diagram that as the proportion of assets held by traditional banks decreases, there is an increase in assets owned by shadow banks. Furthermore, the pool of activities classified as shadow banking is wide and heterogeneous across countries owing to differences in the regulatory environment (FSB, 2017). Pozsar et al. (2013) undertake a comprehensive analysis of activities and firms classified under shadow banking in the US. They compare shadow banking to commercial banks of the early 1900, which operated without a public backstop and argue for possible benefits that can be derived from shadow banking. Shadow banking activities include asset securitisation, credit from non-bank firms, wholesale funding, enhanced credit intermediation and direct lending from finance companies (Acharya et al., 2013; 0 5 10 15 20 25 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 OFIs Figure 1. Growth of assets of other financial intermediaries. Source: South African Reserve Bank Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1603654 https://doi.org/10.1080/23322039.2019.1603654 Page 3 of 18 Pozsar et al., 2013). In addition, shadow banking institutions can also be identified including finance companies, money market funds (MMFs), hedge funds, other investment funds, real estate investment trusts and real estate funds (hereafter REITS), central counterparties, money lenders, structured finance vehicles, trust companies, and captive financial institutions and broker-dealers (FSB, 2017). The FSB report (FSB, 2017) shows that in South Africa major shadow banking activities consists of multi-asset funds, funds of funds, money market funds, vehicle financing and securitisations amongst others. These are illustrated by importance in Figure 3. Shadow banks also undertake the three main functions of banks; namely, maturity, liquidity and credit transformation, albeit without the use of any government backstop or deposit insurance (Pozsar et al., 2013). In addition, shadow banks employ high levels of leverage. There is no homogeneity however, in the extent to which a particular type of shadow bank can undertake these functions. Under its economic function 1 category, for instance, the FSB (2016) reports that Shadow Banking in South Africa MMFs Fixed income Multi asset Fund of funds Hed g e funds Finance com p anies Insurance Securitisation Figure 3. Composition of shadow banking in South Africa. Source: South African Reserve Bank 0 20 40 60 80 100 120 2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008 2008 2009 2009 2010 2010 2011 2011 2012 2012 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 Proportion of Total assets % South African Reserve Bank Banks Insurance companies Pension funds Public financial enter p rises Other Financial Intermediaries Figure 2. Proportion of OFIs to total assets of the financial sector. Source: South African Reserve Bank Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1603654 https://doi.org/10.1080/23322039.2019.1603654 Page 4 of 18 credit intermediation is high for fixed income funds, MMFs and Mortgages. Liquidity transformation is also high for fixed income and MMFs whilst leverage is low for these categories. Thus, different types of risks could be associated with each type of shadow bank. Literature also offers several arguments for the growth of shadow banking with regulatory arbitrage being the main explanation (Barbu, Boitan, & Cioaca, 2016; Pozsar et al., 2013; Pozsar & Singh, 2011). Limits to growth imposed by regulation often drive market participants to find ways of expanding income that by-pass regulations. Tang and Wang (2015) also suggest a profit motive. The profit incentive as suggested in Tang and Wang (2015) implies that financial institutions including commercial banks prefer shadow banking activities to traditional banking because it is highly profitable. Due to lack of regulation, shadow banking markets are deemed more efficient and allow capital to be availed to investors at low cost, however at high risk (Pozsar et al., 2013). Other reasons for the growth of shadow banking includes worsening liquidity conditions, increased risk appetite and flight to safe assets (Barbu et al., 2016). Considering the reasons behind the growth of shadow banking, several authors have argued that benefits of shadow banking may surpass the risk associated with its growth (Adrian & Ashcraft, 2016; Claessens, Ratnovski, & Singh, 2012). Thus, the growth of shadow banking activities and assets should have a positive impact on both financial firm’s profit and profitability of non-financial firms. 1.2. Determinants of firm profitability In this section, we briefly review the literature on the determinants of firm performance and link it to shadow banking. The basic premise of this relationship is that shadow banking increases credit available to firms, both in the financial sector and to non-financial sectors (Adrian & Ashcraft, 2016; FSB, 2017). This claim can only be robust if two central theories of finance hold, the perking order theory and Modigliani Miller capital structure proposition. The pecking order theorem suggests that firms prefer debt to equity. Thus, in the absence of internally generated funds, there is an incentive to increase debt and shadow bank credit provides a cheaper source of debt. Modigliani and Miller proposition argues that in the presence of taxes and other constraints capital structure does have an impact on firm performance. Increased access to debt through shadow banks should positively impact firm profitability. Three sets of factors are used to account for changes in firm profitability, firm-specific factors, industry factors and macroeconomic factors (Hirsch et al., 2014; Issah & Antwi, 2017). Stylised macroeconomic determinants of firm profitability include money supply growth, inflation rate, interest rate, saving and investments and exchange rate changes (Broadstock, Shu, & Xu, 2011; Issah & Antwi, 2017; Zeitun, Tian, & Keen, 2007). Issah and Antwi (2017), McNamara and Duncan (1995) and Broadstock et al. (2011) derive macroeconomic factors using principal component analysis (PCA) from a range of macroeconomic variables covering business cycle indicators, monetary variables, financial factors and supply factors. All three studies find that the derived macroeconomic factors have statistical significance in determining firm profitability when employed in regression models. Other studies use specific macroeconomic variables to explain the variation in firm profitability. Zeitun et al. (2007) use several macroeconomic aggregates for a panel sample of 167 firms. Macroeconomic variables used include the nominal interest rate, changes in money supply, the production manufacturing index, inflation, exports and availability of credit. Their results show that unexpected changes in the interest rate have a significant negative effect on profitability. Production manufacturing index and Islamic credit have a positive and significant effect on firm profitability. Inflation, money supply and other commercial bank credit do not have a significant effect on profitability. Asma’Rashidah Idris et al. (2011) investigates the determinants of banks’profitability in the case of Malaysia. Their study uses return on assets as a measure of profitability and bank-specific variables as regressors. They employ a panel (GLS) technique and find that only bank size has Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1603654 https://doi.org/10.1080/23322039.2019.1603654 Page 5 of 18 a statistically significant influence on bank profitability. Other variables considered are liquidity, capital adequacy, credit risk and expenses management. Ali, Akhtar, and Ahmed (2011) and Panayiotis, Athanasoglou, and Delis (2008) consider bank-specific, industry-specific and macroeconomic factors as determinants of banking firm profitability. Inflation rate and GDP are used as macroeconomic factors. Panayiotis et al. (2008) find that surprise inflation and the output gap both positively impact the output gap. These findings are supported by Ali et al. (2011) who find a positive relationship between economic growth and profitability. Contrary to this, however, Naceur (2003) does not find a significant relationship between profitability and both inflation and growth for Tunisia. Literature that links shadow banking to economic performance is still in its infancy, mostly as a result of the unavailability of data for shadow banking in Emerging markets and even in advanced economies (Adrian & Ashcraft, 2016). However, several studies have analysed the growth and impact of shadow banking on financial stability stemming from the role shadow banks played during the GFC (Bengtsson, 2013; Hsu, Li, & Qin, 2013; McCulley, 2009; Meeks et al., 2017). More so only a handful of studies have linked shadow banking to macroeconomic or firmspecific variables, although shadow banking is encouraged on the premise that it affords firms to acquire capital at low cost (Barbu et al., 2016; Tang & Wang, 2015). This is due to reduced transaction and finance costs associated with shadow bank financing. Lumpkin (2010) posits that financial innovations are neither totally harmful or absolutely beneficial. Thus, whilst shadow banking has been blamed for its role in the GFC, others have argued for growth and proper regulation of shadow banks to allow market agents to derive economic benefits stemming from shadow banking activities (Claessens et al., 2012;Pozsaretal.,2013). The study by Tang and Wang (2015) investigates the effect of shadow banking on Chinese banks’return and risk-adjusted return. Their study employs return on average assets (ROAA) and the Sharpe ratio as measures of return and riskadjusted return respectively within Ordinary least squares (OLS) and Generalised least squares (GLS) regressions. They find that shadow banking activities increase commercial banks’return. Their finding supports earlier literature on financial innovation that argues for the positive effect of financial innovations on the economy (Beck, Chen, Lin, & Song, 2016; Boot & Marinč,2010). According to this strand of literature, shadow banking’s higher returns come on the backdrop of higher risk and regulation is required to ensure that the benefits of shadow banking are not eroded by costs from heightened risks. Agostino and Mazzuca (2011) and Barbu et al. (2016) empirically analyse the determinants of securitisation and shadow banking, respectively. Agostino and Mazzuca (2011) consider bankspecific and market-related ratios as influences of the decision for a bank to securitise in a - given year. Securitisation is measured with a dummy variable and the authors employ probit regressions. They find that Italian banks securitise as way of diversification, funding and capital arbitrage. In Barbu et al. (2016) macroeconomic determinants of shadow banking are analysed using quarterly data for 15 countries covering 2008 to 2015. Their study uses panel Generalised method of moments technique and find a negative relationship between shadow banking and GDP growth, short-term interest rates and money supply. On the other hand, stock index and long-term interest rates positively influence shadow banking. 1.3. Interconnectedness of shadow banking with the corporate sector Whilst proponents of the financial instability view of shadow banking concentrate on instability channelled through interconnectedness of shadow banks with the traditional banking sector, literature shows three ways in which shadow banking can be linked to the corporate sector profitability. The first channel is through traditional banks. Harutyunyan, Massara, Ugazio, Amidzic, and Walton (2015) suggest that banks in the formal system also engage in shadow banking activities such as securitisations. By removing a bank’s assets from its balance sheet, banks can be provided with more capacity to issue new credit and hence allow more non-bank corporates to access loans. This is supported by evidence from the FSB (FSB, 2017) showing that OFIs account for higher shares Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1603654 https://doi.org/10.1080/23322039.2019.1603654 Page 6 of 18 in formal banking sector liabilities. In addition, net positions in the wholesale market have tilted towards OFIs, who have a positive net position in the repo market, signifying that they are net suppliers of financing to the rest of the financial system. This suggests shadow banks directly supply credit to other financial institutions who in turn fund non-financial firms. Both explanations lead to higher access to credit by non-financial firms. Secondly, shadow banks have linkages with the non-financial corporates through direct lending to non-financial firms. Barbu et al. (2016) show that MMFs pool financial resources, which can be directly channelled to the real sector. In this case, MMFs can finance firms directly and therefore contribute more to money supply in the economy. In the FSB report (FSB, 2017) loans extended by shadow banks (OFIs) increased with more than 10% between 2011 and 2015 in South Africa and other countries. Large public and private non-financial corporates also participate in the wholesale market directly through treasuries. For instance, participation of non-financial corporates in the repo market is acknowledged in South Africa and other countries (Pozsar et al., 2013). The third channel can be termed the “asset”channel where firms are holders of financial assets issued by shadow banks. Using the FSB Economic Function 3 (EF3) measure, activities dependent on short-term funding such as short-selling securities and financing client securities are undertaken by shadow banks. These could be important in determining asset value of securities held by non-financial firms, resulting in changes in firm profitability. In addition, shadow banks can issue Asset-Backed Securities (ABS) in tranches, which investors, including non-financial firms purchase. The impact on net income of this channel will however depend on the accounting treatment of the asset, where recognition in the accounting Income Statement (profit and loss) could result in a higher net income for the firm. On the other hand, if the investment returns are recognised in Other Comprehensive Income, the holding may nothaveasignificanteffectoneitherreturnonassets(ROA)orreturnonequity(ROE). 1.4. Measuring firm performance ROA is the most widely applied measure of profitability in firm-level studies (Issah & Antwi, 2017). ROA is an accounting profitability ratio computed by dividing a firm’s net income by its total assets. It measures the ability of the firm to generate income using its assets and may demonstrate management’s efficiency. ROA ¼Net Income Total Assets The current paper uses the ratio of net profit before tax to book value of fixed assets as reported by Statistics South Africa (Statssa) as a proxy for ROA of non-financial firms. ROA for banks follows the above definition. Unlike McNamara and Duncan (1995)’s assertion that ROA has limited the effects of earnings management, ROA is susceptible to earnings smoothing by the management of the firm. For instance, off-balance sheet activities can result in an overstated ROA ratio. However, it is preferable for analysis as it is the most popular and available measure in terms of data availability (Issah & Antwi, 2017). Other variables in use for measuring firm performance include ROE, net profit margin and Earnings per share. ROE is the net income of the firm expressed as a percentage of a firm’s equity capital. It signifies the return to the suppliers of equity share capital (Panayiotis et al., 2008). ROE ¼Net Income Equity capital The present study uses ROA as a measure of profitability for both banks and non-financial firms. Further, the study also employs the stock market index, Johannesburg Stock Exchange All-Share Index (JSEASLI) to measure the profitability of all listed firms (financial and non-financial). Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1603654 https://doi.org/10.1080/23322039.2019.1603654 Page 7 of 18 commercial banks. International Journal of Financial Research,7(1), 207. doi:10.5430/ijfr. v7n1p207 Wamucii, J. C. (2010). The relationship between inflation and financial performance of commercial banks in Kenya (Unpublished MBA Thesis). University of Nairobi. Retrieved from http://erepository.uonbi.ac.ke/bit stream/handle/11295/95883 Xiang, Q., & Qianglong, Z. (2014). Shadow banking and monetary policy transmission. Economic Research Journal,5, 008. Zeitun, R., Tian, G., & Keen, S. (2007). Macroeconomic determinants of corporate performance and failure: Evidence from an emerging market the case of Jordan. Corporate Ownership and Control,5(1), 179–194. doi:10.22495/cocv5i1c1p2 Appendix Table A1. Unit root tests Variable ADF statistic PP Statistic Breakpoint Test statistic Overall Decision ROA bnks −2.829181 −2.861533 −6.3505*** Non-stationary LA -liquidity −1.971224 −2.001135 −2.9170 Non-stationary OFI −2.048543 −2.115549 −3.1549 Non-stationery Lnlp −0.516984 0.151419 −3.5479 Non-stationary Unempl −2.120973 −4.775994** −2.5127 Non-Stationary Intspr −1.633763 −1.419061 −4.7628** Non-stationary ROA nonfin −1.927473 −3.345827** −3.6350 Non-Stationary ljsealsi −3.125611 −2.244967 −3.9381 Not-stationary TA—size −1.893555 −2.012227 −4.0133 Non-stationary LGDP −0.921866 −0.921866 −5.5549** Non-stationary LCPI −3.415133* −1.650872 −4.0502 Non-stationary LCREDIT −1.865791 −1.865791 −4.4963 Non-stationary lM2 −3.272219* −3.161027 −3.6560 Non-stationary ΔROAbnks −4.475975*** −3.576334** Stationary Δlnlp −1.838436* −3.357227*** −5.5014*** Stationary ΔOFI −6.593332*** −6.594361*** −7.0706*** Stationary ΔLA −5.920028*** −5.920028*** −6.5985*** Stationary ΔLTA −4.346587*** −5.632513*** −5.8365*** Stationary ΔLGDP −7.063738*** −7.105128*** Stationary ΔLCPI −4.805467*** −7.180994*** −4.9231** Stationary Δintspr −5.025978*** −4.988335*** −4.7628*** Stationary Δljsealsi −4.270336*** −3.773391** −4.3603*** Stationary Δlcred$−6.591382*** −6.595619*** −7.0102*** Stationary ΔUnempl −9.098188*** −9.300893*** −9.3524*** Stationary ΔProfitall −8.776279*** −8.856430*** −8.7399*** Stationary Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1603654 https://doi.org/10.1080/23322039.2019.1603654 Page 14 of 18 Table A2. Descriptive statistics GROWTH INFLATION OFI INTSPR CREDIT LTA UNEMP ROANONFIN LALSIJSE Mean 0.388889 5.647222 17.65748 1.769444 227,852.7 15.09530 24.74167 0.070539 10.49941 Skewness −0.970796 0.217487 0.202628 −1.655125 0.137314 0.372148 −0.417962 1.499741 −0.235105 Kurtosis 4.372449 3.322967 1.699881 4.512447 2.275865 1.877506 3.227514 5.155234 1.886443 Jarque–Bera 8.480098 0.440266 2.781811 19.86788 0.899689 2.720952 1.125797 20.46288 2.191658 Probability 0.014407 0.802412 0.248850 0.000049 0.637727 0.256539 0.569556 0.000036 0.334262 Sum 14.00000 203.3000 635.6693 63.70000 8,202,698. 543.4308 890.7000 2.539400 377.9788 Sum Sq. Dev. 13.07556 75.20972 134.2681 79.04579 2.93E+10 1.086975 52.20750 0.016805 3.133741 Observations 36 36 36 36 36 36 36 36 36 Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1603654 https://doi.org/10.1080/23322039.2019.1603654 Page 15 of 18 Table A3. Correlations Covariance Analysis: Ordinary Sample: 2008Q1 2016Q4 Included observations: 36 Correlation GROWTH INFLATION OFI INTSPR CREDIT LTA UNEMP ROANONFIN LALSIJSE GROWTH 1.0000 INFLATION −0.5488 1.0000 OFI −0.0177 −0.1602 1.0000 INTSPR 0.3557 −0.7238 0.4711 1.0000 CREDIT 0.4960 −0.6057 −0.4038 0.4679 1.0000 LTA −0.2131 −0.0140 0.9540 0.3435 −0.5435 1.0000 UNEMP 0.1081 −0.5585 0.7405 0.6233 0.0084 0.6702 1.0000 ROANONFIN 0.1008 0.3598 −0.5545 −0.6312 −0.0366 −0.5292 −0.7097 1.0000 LALSIJSE 0.1035 −0.1973 0.9579 0.4770 −0.3382 0.8893 0.6898 −0.4563 1.0000 Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1603654 https://doi.org/10.1080/23322039.2019.1603654 Page 16 of 18 Model 1 -16 -12 -8 -4 0 4 8 12 16 2009 2010 2011 2012 2013 2014 2015 2016 CUSUM 5% Significance Model 2 -16 -12 -8 -4 0 4 8 12 16 2010 2011 2012 2013 2014 2015 2016 2017 CUSUM 5% Significance Model 3 -16 -12 -8 -4 0 4 8 12 16 2009 2010 2011 2012 2013 2014 2015 2016 CUSUM 5% Significance Figure A1. Parameter stability tests (CUSUM test). Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1603654 https://doi.org/10.1080/23322039.2019.1603654 Page 17 of 18 © 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. You are free to: Share —copy and redistribute the material in any medium or format. Adapt —remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution —You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. No additional restrictions You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Cogent Economics & Finance (ISSN: 2332-2039) is published by Cogent OA, part of Taylor & Francis Group. Publishing with Cogent OA ensures: •Immediate, universal access to your article on publication •High visibility and discoverability via the Cogent OA website as well as Taylor & Francis Online •Download and citation statistics for your article •Rapid online publication •Input from, and dialog with, expert editors and editorial boards •Retention of full copyright of your article •Guaranteed legacy preservation of your article •Discounts and waivers for authors in developing regions Submit your manuscript to a Cogent OA journal at www.CogentOA.com Zhou & Tewari, Cogent Economics & Finance (2019), 7: 1603654 https://doi.org/10.1080/23322039.2019.1603654 Page 18 of 18