The relationship between goodwill and capital structure and the moderating effect of financial market development

Thakur, Oli Ahad; Matemilola Bolaji Tunde; Bany Ariffin Bin Amin Noordin; Alam, Md. Kausar; Prabowo, M. Agung Article The relationship between goodwill and capital structure and the moderating effect of financial market development Journal of Economics, Finance and Administrative Science Provided in Cooperation with: Universidad ESAN, Lima Suggested Citation: Thakur, Oli Ahad; Matemilola Bolaji Tunde; Bany Ariffin Bin Amin Noordin; Alam, Md. Kausar; Prabowo, M. Agung (2024) : The relationship between goodwill and capital structure and the moderating effect of financial market development, Journal of Economics, Finance and Administrative Science, ISSN 2218-0648, Emerald Publishing Limited, Bingley, Vol. 29, Iss. 57, pp. 121-145, https://doi.org/10.1108/JEFAS-04-2022-0107 This Version is available at: https://hdl.handle.net/10419/289643 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. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. 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Kausar Alam Brac Business School, Brac University, Dhaka, Bangladesh, and Muhammad Agung Prabowo Faculty of Economics and Business, Universitas Sebelas Maret, Surakarta, Indonesia Abstract Purpose –This study empirically investigates the relationship between goodwill assets and capital structure (i.e. debt ratio) of firms and the moderating effect of financial market development on the relationship between goodwill assets and capital structure. Design/methodology/approach –This research applied a quantitative method. The article collects large samples of listed firms from 23 developing and nine developed countries and applied the panel data techniques. This research used firm-level data from the DataStream database for both developed and developing countries. The study uses 4,912 firm-level data from 23 developing countries and 4,303 firm-level data from nine developed countries. Findings –The findings reveal a significant positive relationship between goodwill assets and capital structure in developing countries, but goodwill assets have a significant negative relationship with capital structure in developed countries. Moreover, financial market development positively moderates the relationship between goodwill assets and the capital structure of firms in developing countries. The results inform firm managers that goodwill assets serve as additional collateral to secure debt financing. Moreover, policymakers should formulate a debt market policy that recognizes goodwill assets as additional collateral for the purpose of obtaining debt capital. Research limitations/implications –The study has several implications. First, goodwill assets are identified as a factor of capital structure in this study. Fixed assets have been identified as one of the drivers of capital structure in previous research, although goodwill assets are seldom included. Second, this article shows that along with demand-side determinants, supply-side determinants also play an important role in terms of the Financial market development 121 JEL Classification —G32, C33, C52 © Oli Ahad Thakur, Matemilola Bolaji Tunde, Bany-Ariffin Amin Noordin, Md. Kausar Alam and Muhammad Agung Prabowo. Published in Journal of Economics, Finance and Administrative Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons. org/licences/by/4.0/legalcode Since submission of this article, the following author(s) have updated their affiliations: Oli Ahad Thakur is at the Southeast Business School, Southeast University, Dhaka, Bangladesh. Data availability statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. Conflict of interest: The authors declare that they have no competing interests. The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2077-1886.htm Received 26 April 2022 Revised 6 February 2023 18 September 2023 Accepted 30 October 2023 Journal of Economics, Finance and Administrative Science Vol. 29 No. 57, 2024 pp. 121-145 Emerald Publishing Limited 2077-1886 DOI 10.1108/JEFAS-04-2022-0107 firms’choice about the capital structure. Therefore, firms should take both the demand-side and supply-side factors into consideration when sourcing for external financing (i.e. debt capital). Originality/value –The study considered goodwill as a component of capital structure. The study analysis includes a large sample of enterprises, including 4,912 big firms from 23 developing countries and 4,303 large firms from nine industrialized or developed countries, which adds to the current capital structure information. Furthermore, a large sample size increases the results’robustness and generalizability. Keywords Capital structure, Goodwill, Information asymmetry, Financial market development, Panel data analysis Paper type Research paper 1. Introduction Firms have been working harder throughout the time to raise their goodwill assets in order to produce market value that is greater than the book value of their assets (Widnyana et al., 2020). Keeping an eye on goodwill is actually vital because it represents the sum of money paid beyond the assets’book value (Widnyana et al., 2020). Although the loss of goodwill generally lowers the firm value, goodwill loss can occasionally indicate overspending and careless spending. Lenders of money-to-businesses are aware of this issue and may think about including goodwill when lending borrowed financing to businesses. However, there is not much research that can confirm whether raising goodwill assets is a wise decision. Capital structure which is the mixture of debt and equity to finance firms’operations has been extensively researched, but the fundamental question raised by Myers (1984),“how do firms choose their capital structure or what are the precise determinants of capital structure?”– remains a puzzle to date (DeAngelo, 2022). However, researchers have made an attempt to answer this question in several ways. Firstly, Myers (1984) developed a version of the trade-off and pecking order theories and Baker and Wurgler (2002) established the market timing theory to answer this question and form the theoretical basis to explain capital structure determinants. Secondly, Guizani (2020) and Frank and Goyal (2009) and other earlier researchers, used the above theories and conducted empirical studies which identified some firm-specific factors like firm size, profitability, growth opportunity, non-debt tax-shield and fixed assets as determinants of capital structure but these studies excluded goodwill assets as determinants of capital structure. In a closely related empirical study, Matemilola and Ahmad (2015) focused on the goodwill assets as determinant of capital structure butthey used only 100 listed firms from South African countries. In another related paper, Chatterjee et al. (2022) focused on the value-relevance of goodwill assets and they find that in the post-period, firms that include goodwill in their debt agreements seems to enjoy increase debt and value. Moreover, Çam and € Ozer (2022) and AcedoRam ırez and Ruiz-Cabestre (2014) identified financial market development –a supply-side determinant of capital structure, which might indirectly affect the relationship between capital structure and goodwill assets and this is overlooked in the capital structure literature. In today’s intangible asset-driven economies, goodwill assets are also viewed as an intriguing research topic. They are significant in both developed and developing nations with underdeveloped capital markets that are catching up to developed nations in terms of financial market development. This study offers a chance to advance our understanding of goodwill assets and how financial market growth modifies the relationship between goodwill assets and capital structure because goodwill assets are remarkably understudied (Thakur et al., 2020). The study has three important contributions: first, goodwill assets are identified as a factor of capital structure in this study. Fixed assets have been identified as one of the drivers of capital structure in previous research, although goodwill assets are seldom included. Second, this article shows that along with demand-side determinants, supply-side determinants also play an important role in terms of the firms’choice about the capital structure. Therefore, firms should take both the demand side and supply-side factors into JEFAS 29,57 122 consideration when sourcing for external financing (i.e. debt capital). Finally, this analysis includes a large sample of enterprises, including 4,912 big firms from 23 developing countries and 4,303 large firms from nine developed countries. Furthermore, a large sample size increases robustness and generalization of the results. Our results show that goodwill assets are connected to the capital structure of firms in developing countries in a major and productive way. However, it is both positively and adversely connected to the capital structure of firms in developed countries. These direct effect results for developing countries suggest that markets in developing countries are considering goodwill as collateralizable assets along with fixed assets. Moreover, we found evidence that financial market development positively moderates the relationship between goodwill assets and the capital structure of firms in developing countries. Equally, most of the supply-side determinants significantly impact the capital structure of firms in both developing and developed countries. Thus, we conclude that the capital structure choice of firms is affected by both the demand-side determinants and supply-side determinants such as financial market development, inflation rate, the growth rate in gross domestic product (GDP) and interest rate. 2. Literature review 2.1 Theoretical framework Azad et al. (2014) theorize that financial market development in less developed and developing economies appears inefficient. If the financial market is inefficient, both insiders and outsiders of a firm will have unequal information. In other words, there will be asymmetry of information (Chakraborty, 2010). Prior studies (e.g. Sharma, 2017;Wurgler, 2000) also show that financial market development has a strong link with the level of asymmetry in the market, which ultimately affects the supply and allocation of capital. Wurgler (2000) findings reveal that the financial market’s degree of development is favorably related to an improved allocation of capital. Moreover, Chatterjee et al. (2022) and Hulten and Hao (2008) have shown that goodwill is recognized in the market, but it has no adequate accounting recognition. Also, firms are reluctant to share information on activities which generate the value of goodwill in the market, and it may lead to loss of competitive advantage. As a result, goodwill assets suffer from information asymmetry problems. However, as the financial market develops, information asymmetry reduces and recognition of goodwill assets as collateral to secure debt capital increases (Thakur et al., 2020). This reasoning explains why financial market development positively moderates the relationship between goodwill assets and firms’ capital structure. 2.2 Demand-side determinants of capital structure Capital structure is usually proxy as the ratio of total debt to total assets because emphasis is placed on the debt component of the capital structure. The determinants of capital structure remain a puzzle to date even after over 60 years of research on capital structure (DeAngelo, 2022). The majority of capital structure research (e.g. Jaworski and Santos, 2022;Touil and Mamoghli, 2020;Bilgin and Dinc, 2019;Matemilola and Ahmad, 2015) has concentrated on firm-specific variables or determinants. They are also called demand-side determinants as they control the firms’demand for external funds. This study has conducted a literature survey (see Table 1) covering the articles published in the last 10 years. Based on our literature search (Table 1), the study found some core firm-specific factors, which are firm size, fixed assets, profitability and growth opportunities. Employing meta-regression analysis and covering the results of 100 recently published articles, Hang et al. (2018) found Financial market development 123 Study Sample period Country of study Dependent variable Independent/Control variables Sz Pr Tg Gr FR IL Lq Id TR NDTS EFC Ag MC GDP GDPG INF Frank and Goyal (2009) 1950–2003 USA TDTAM þ a  a þ a  a þ a þ a Vo and Ellis (2017) 2006–2015 Vietnam LDTA þ b þþ a þþ Kh emiri and Noubbigh (2018) 2006–2016 5 Sub-Saharan countries LDTA þ a  a þ a  a  a  a þ a  a þ a  a þ a  a  a þ a Chakrabarti and Chakrabarti (2019) 2006–07 to 2016–17 India TDTA þ a þ  b  a Jermias and Yigit (2019) 1989–2012 Turkey TDTA þ a  a  a  b þ a Haron (2018) 2000–2014 Indonesia TDTA  a  b þ a þ b  a M’ng et al. (2017) 2004–2013 3 Asian countries TDC þ a  a þ a  a þa Alves and Ferreira (2011) 1991–2001 31 countries  a  a  a þ a Matemilola et al. (2019) 2008–2012 Malaysia TDTA þ a þ a þ a  a  a Bilgin and Dinc (2019) 2012–2017 Turkey TDTC þ a  c þ b þ a  a þþ Rajan and Zingales (1998) 1987–1990 G7 countries –þ a  a þ a  a Harrison and Wisnu Widjaja (2014) 2004–2011 USA LTD þ a þ a  a  c AlNajjar and Hussainey (2011) 1991–2002 UK TDC* þ c  c  c  c þ AcedoRam ırez and RuizCabestre (2014) 1998–2008 5 European TDTA þ a þ a  Matemilola and Ahmad (2015) 2004–2009 South Africa TDTA*  a þ b Study Independent/Control variables INT OWS ATR DSC Ig SPP OC Uf IdD IdC LS CR SR EFWA ME Ft FDC Inv CF GA Frank and Goyal (2009) Vo and Ellis (2017) Kh emiri and Noubbigh (2018) þ a  a Chakrabarti and Chakrabarti (2019)  a  Jermias and Yigit (2019) Haron (2018)  b  b  a  b  M’ng et al. (2017) Alves and Ferreira (2011)  a þ a  a Matemilola et al. (2019) þ b Bilgin and Dinc (2019) þa Rajan and Zingales (1998) (continued) Table 1. Review of past studies on determinants of capital structure JEFAS 29,57 124 three major capital structure determinants, which are fixed assets, growth and profitability. Applying similar techniques and data collection strategies, Kumar et al. (2017) found a slightly longer list of determinants, which are, profitability, fixed assets, size, firm age, growth opportunities, liquidity, non-debt tax shield and risk. However, the nature of the relationship and the level of significance are not consistent among these literature surveys and within the results of developed and developing countries. Focusing on goodwill assets and fixed assets as a demand side factor, Matemilola and Ahmad (2015) applied the trade-off theory to investigate the impact of goodwill assets and fixed assets on debt ratios of South African firms from 2004 to 2009. They found that goodwill assets and fixed assets increase debt ratios of firms in South Africa and concluded that goodwill assets serve as additional collateral to secure debt capital in South Africa. Unlike Matemilola and Ahmad (2015) which focused on a single country and used 100 listed firms, our study focuses on the impact of goodwill assets on debt ratios using 4,912 listed firms from 23 developing countries and 4,303 listed firms from nine developed countries. Recently, Chatterjee et al. (2022) applied the debt contracting theory and discovered that for businesses that include goodwill in debt agreements in the post-new law period, the valuerelevance of goodwill is higher. Additionally, they discover that companies that include goodwill in their debt arrangements seem to benefit from increased debt and value in the post-period. The first hypothesis of this study in alternative form is: H1. Goodwill assets have a significant positive impact on the capital structure (debt ratio) of firms in developing and developed countries. 2.3 Supply-side determinants of capital structure Along with the factors which affect the demand for the debt capital of a firm, some supplyside considerations may also help to determine a company’s capital structure. However, studies on the supply-side determinants are less common compared to the studies on firmspecific or demand-side determinants. Among the few studies are (Demirguç-Kunt and Maksimovic, 1999;De Jong et al., 2008;Fan et al., 2012;Acedo-Ram ırez and Ruiz-Cabestre, 2014;Mc Namara et al., 2017;Thakur et al., 2020,2022,2023;Henrique et al., 2021). They have considered supply-side factors and have mostly looked at the direct link between supply-side Study Independent/Control variables INT OWS ATR DSC Ig SPP OC Uf IdD IdC LS CR SR EFWA ME Ft FDC Inv CF GA Harrison and Wisnu Widjaja (2014) AlNajjar and Hussainey (2011) Acedo-Ram ırez and RuizCabestre (2014)  b þ c  a Matemilola and Ahmad (2015) þ þ b Note(s): SZ5Firm size, Pr5Profitability, Tg 5Tangibility, FR5Firm risk, IL5Industry leverage, Lq 5Liquidity, TR5Tax rate, NDTS5Non-debt tax shield, EFC 5External financing cost, Ag5Firm age, MCGDP 5Market capitalization to GDP(Capital Market Development), GDPG 5GDP growth rate, INF5Inflation Rate, INT5Interest rate, OWS5Ownership structure, ATR5Asset turnover ratio, DSC 5Debt service capacity, Ig 5Intangibility, SPP5Share price performance, OC5Ownership concentration, Uf 5Munificence, IdD 5Industry dynamism, IdC5Industry concentration, LS 5Legal system, CR5Creditors’Right, SR5Shareholders’Right, EFWA 5External Finance Weighted Average M/B, ME 5Managers’Experience, Ft 5Factoring, FDC5Financial distress cost, Inv 5Investment, CF5Cash flow, GA 5Goodwill assets. TDTA 5Total debt to total assets, LDTA 5Long term debt to total assets, TDC 5Total debt to total capital, LTD 5Long term debt, TDTAM 5Total debt to total asset based on market values. a, b and c represent level of significance at 1, 5 and 10%, respectively Source(s): Table by Thakur Table 1. Financial market development 125 factors and capital structure. Following that, Demirguç-Kunt and Maksimovic (1999) attempted to investigate the effect of supply-side determinants on the maturity of debt used by enterprises in both developed and developing nations. Results of their study show that (1) in contrast to the general expectation, in countries where equity markets are more developed, large firms have higher debt ratios, (2) size of the banking sector in a country does not have a significant impact on the debt ratios of large firms in that country and (3) firms in developed as well as developing countries significantly differ in terms of their use of long-term debt. This study suggests that factors such as stock market and banking sector developments, legal systems and degree of government subsidies are responsible for relatively lower use of debt by firms in developing countries. Subsequently, Fan et al. (2012), taking a sample from many countries (39) and covering both developed and developing economies, showed that more influence has the macroeconomic factors on a firm’s capital structure than industry-related factors. This study examined a wide range of institutional factors, some of which were not taken into consideration by previous researchers. This study found that a country’s level of corruption, its legal system and tax codes and the preferences of the suppliers of the fund have a tremendous impact on the firms’capital structure and debt maturity choice. Later, Acedo-Ram ırez and Ruiz-Cabestre (2014) tried to examine how supply-side determinants of capital structure affect firms’debt ratios through company-specific factors. Taking a sample from five major European countries and applying GMM technique, this study showed that significant differences exist in the debt ratios of firms across these countries. Like De Jong et al. (2008), this study also provides evidence that supply-side factors have an indirect effect on the interrelationship between debt ratios and firm-specific factors. One of the recent studies, Antzoulatos et al. (2016), tried to find out the impact of financial development on the debt ratios of firms. This study is unique in terms of the analytical method used. While using the data convergence technique, this study showed that most of the firms converged in terms of the debt ratio, and this convergence is affected by financial development. However, they also found another convergent group whose debt ratios are not affected by financial development since firms in this group do not have adequate access to capital markets. Most of the studies reviewed here show that supply-side factors have a significant impact on firms’debt ratios or capital structure. Supply-side factors include capital market development, the legal system, GDP growth, creditors’protection, legal enforcement, interest rate and inflation rate. 2.4 Financial market development, information asymmetry and supply of capital (debt) Recent studies (e.g. Ojah and Karemera, 1999;Azad et al., 2014) on capital market development show that capital markets in less developed and developing economies are inefficient. If the capital market is efficient, both insiders and outsiders of a company will have the same information; in other words, there will be no asymmetry of information (Chakraborty, 2010). Past studies also show that capital market development, one of the supply-side factors, has a strong connection with the level of asymmetry in the market, which ultimately affects the supply and allocation of capital. One of the very early studies, Akerlof (1970), has shed light on the information asymmetry problem. He suggested that asymmetry in information in the capital market can seriously affect its smooth functioning and in extreme cases, it may even lead to a breakdown. Afterward, Sufi (2007) using 12,672 USA syndicated loan data shows evidence that information asymmetry in the syndicated loan market affects the composition of the members of a syndicate and the structure of it. The results suggest that information asymmetry has a significant impact on the channeling of funds from savers to ultimate investors. Finally, Pang and Wu (2009) explored a JEFAS 29,57 126 particular channel through which finance stimulates growth, i.e. capital allocation. While using international industrial data, this study finds that countries, where the capital market is developed, attract more funds. According to Sharma (2017), a high level of information asymmetries still exists in the credit markets, which creates opportunities for the borrower to act immorally. This risk of immoral behavior can result in undesirable market outcomes, such as poor resource allocation and market failure (Akerlof, 1970;Atakan and Ekmekci, 2014). Past studies (e.g. Black et al., 2000;Hulten and Hao, 2008) have shown that goodwill is recognized in the market, but it has no adequate accounting recognition. Also, companies are reluctant to share information on activities or conditions (such as R&D, development of human capital, improved process and loyal customer base), which generate the value of goodwill in the market as sharing that information may lead to loss of competitive advantage and further, there is no regulatory requirement to disclose that information. Due to these reasons, goodwill assets and factors leading to the generation of it, both suffer from information asymmetry problems. Despite these characteristics, if a higher level of market efficiency exists in the financial market, this information asymmetry problem may be reduced. As the financial market develops, information asymmetry reduces and recognition of goodwill assets as collateral to secure debt capital increases. Therefore, financial market development should positively moderate the relationship between goodwill assets and firms’capital structure. The study’s second alternate hypothesis is the following: H2. Financial market development moderates the relationship between goodwill assets and capital structure (debt ratio) of firms in developing and developed countries. Goodwill Assets (Independent Variable) Capital Structure [Debt ratio] (Dependent Variable) Financial Market Development (Moderating Variable) Control Variables: Sales, Market to book ratio, Tangible Assets (PPE), Depreciation, EBIT, Payout ratio, Firm-Age, Growth rate of GDP, Inflation 3. Method 3.1 Data This research used two data sets: first, the firm-level data from the DataStream database for developed countries and at a time for developing countries. In total, the firm-level data were collected from 23 developing countries and nine developed countries. Bangladesh, Brazil, Chile, Egypt, Ghana, Kenya, Malaysia, Mauritius, Mexico, Morocco, Nigeria, Pakistan, Peru, the Philippines, Poland, South Africa, Sri Lanka, Thailand, Tunisia and Turkey are among the developing nations. Canada, Denmark, France, Germany, Italy, the Netherlands, Singapore, South Korea and the UK make up the developed countries. The 23 developing countries selected have functioning financial markets, where forces of demand determine the prices of financial assets. Moreover, the nine developed countries selected have strong functioning financial markets and are mostly used in the capital structure literature (Antoniou et al., 2008;Matemilola et al., 2019). A total of 4,912 and 4,303 exchange-listed firms were selected from the developing and developed countries, respectively. To avoid the impact of the 2008 financial crisis, the starting year of data collection was 2010 and covered up to the latest available year 2018. After the final screening process, this study succeeded in having a balanced panel of 9,212 firms for the developed countries and developing countries over nine Financial market development 127 years. Two sources of macroeconomic information were used to compile the statistics: the World Bank’s open database and the IMF’s World Economic Outlook data. As in previous studies (e.g. Gaud et al., 2005) on capital structure, we excluded financial firms such as a bank, insurance and leasing firms as they have quite different financing patterns from the nonfinancial firms. Moreover, the study excluded both financial and utility firms from our sample because they are highly regulated and exhibit a different pattern of capital structure from the rest of the firms. Further, the firms with missing values during the study period were excluded. Thus, the total final sample for this study was 9,215 firms from 32 countries. The selection of countries was based on data availability and the presence of well-functioning capital markets that determine the prices of financial assets. The sample size of the study is sufficiently large for both the developing and developed countries as there are over 4,000 firms in each category. Specifically, a total number of 4,912 listed firms from developing countries are used while a total of 4,303 listed firms are used from developed countries. Stata software is used to analyze the data. 3.2 Econometric model To examine the impact of goodwill assets on capital structure and the moderating effect of banking sector development on the relationship between capital structure and goodwill assets, the following two models were run. Equation (1) and (2) are the fixed-effect models as in Jermias and Yigit (2019). However, using the fixed-effect model is not automatic. The Hausman test is used to choose between the fixed-effect model and the random-effect model (Luo, 2015). TDTAijt ¼β1þβ2LEVTAijt þβ3LSLijt þβ4MTBijtþβ5PPETAijt þβ6DEPTAijtþβ7EBITAijtþβ8Fageijt þβ9PORijt þβ10GDPGjt þβ11INFjt þβ12INTjt þβ13MCGDPjt þ η iþ α tþ μ ijt (1) TDTAijt ¼β1þβ2LEVTAijt þβ3PCDBMjt þβ4ðLEVTA*PCDBMÞijt þβ5LSLijt þβ6MTBijt þβ7PPETAijtþβ8DEPTAijtþβ9EBITAijt þβ10Fageijt þβ11PORijt þβ12GDPGjt þβ13INFjt þβ14INTjt þ η iþ α tþ μ ijt (2) where: TDTA 5Total debt to total assets ratio; LEVTA 5Excess of enterprise value over total assets; LSL 5Log of sales; MTB 5Market-to-book ratio; PPETA 5Property, plant and equipment, scaled by total assets; DEPTA 5Depreciation to total assets; EBITA 5Earnings before interest and tax scaled by total assets; FAge 5Current year minus year of establishment; POR 5Payout ratio; GPDG 5Growth rate of GDP (%); JEFAS 29,57 128 the data from industrialized countries using the findings of the Breusch and Pagan Lagrangian Multiplier (LM) test. Because the results justify the adoption of a random-effect model rather than a pooled OLS and the test p-values are less than 0.01. For greater certainty, we determined whether to use a fixed-effect model or a random-effect model using the Hausman Tests on both datasets. Additionally, a fixed-effect model appears to be sufficient according to the Hausman Tests. The issue of heteroscedasticity and serial correlation in the datasets was discovered using the Modified Wald test for group-wise heteroscedasticity and the Wooldridge test for autocorrelation once more. The results of these tests show both autocorrelation and heteroscedasticity. So, using pooled OLS with heteroscedasticity and serial correlation adjusted standard error, the fourth model was created (OLSHSCSE). The regression analyses (Tables 8 and 9) based on data from developing and developed countries reveal that goodwill (LEVTA) has a considerable positive influence in all four models as a demand-side predictor. Some control variables, such as business size, asset tangibility, profitability and dividend payout ratio, are remarkably constant across models and datasets. Other variables produce inconsistencies in outcomes, either among models or across datasets. However, in both emerging and established countries, the bulk of the variables have a strong link with debt ratio. Private Credit by Deposit Money Bank (PCDBM): Results based on the developing countries’data show that financial market development measured by private credit by deposit money bank as a percentage of GDP has a significant positive impact (except in RE model) on debt ratio. However, in the developed countries’sample, PCDBM has significant negative impact on debt ratio (except in the FE model). 4.3.2 Moderating impact of financial market development on the relationship between goodwill-assets and capital structure. The fixed-effect (FE) model’s output (Table 10, Column 4) based on samples from emerging countries demonstrates that the development of financial markets significantly modifies the link between goodwill assets and firms’debt ratios. In other words, the development of the financial industry affects the effect of goodwill assets on debt ratio in the sample of emerging countries. The growth of the financial markets has a sizable moderating influence on the relationship between goodwill assets and firms’debt ratio, according to the ordinary least squares (OLS) with heteroscedasticity and serial correlation adjusted. While the OLS with heteroscedasticity and serial correlation corrected results show that financial market development has an insignificant moderating effect on debt ratio in the sample of developed countries, the results for the fixed-effect model show that the relationship between goodwill assets and firms’debt ratio is negatively moderated by the development of financial markets. This suggests that the impact of goodwill assets on firms’debt ratio is unconditional on the financial market. 5. Discussion of results The findings indicate that debt ratios in developing nations are positively correlated with the growth of financial markets. In contrast, the growth of the financial markets has little bearing on the debt levels of wealthy nations. Matemilola et al. (2019) demonstrate a favorable impact of financial market growth on debt ratios of enterprises in developing nations, which is consistent with the findings for developing countries. Debt ratios of businesses are impacted by the growth of financial markets because financing methods through banks or stock markets suit the governance structure in that those individuals receive the greatest ability to influence business strategies and are also the largest capital suppliers (Matemilola et al., 2019; Fan et al., 2012). As financial markets (i.e. the banking sector) expand, borrowing costs decline and businesses are more inclined to raise debt through banks, which results in a rise in debt consumption. The results are in line with Antoniou et al. (2008) findings that macroeconomic factors and whether firms operate in bank-based economies or capital-based economies affect their debt financing decisions. Antoniou et al. (2008) note that empirical studies generally Financial market development 135 focus on the firm specific factors or demand side factors that financial managers should consider when making debt financing decisions but overlook the possible implications of macroeconomic factors that may affect the choice of debt financing mix. Moreover, as suggested by past researchers (Ojah and Karemera, 1999;Azad et al., 2014), inefficiencies still exist in the financial markets of developing countries. Due to this inefficiency in the developing markets, there is still room for improvement and thus, further Variables (Pooled OLS) (RE) (FE) (OLS with hetero and serial corr.) Two-step system GMM TDTA TDTA TDTA TDTA TDTA L.TDTA 0.736*** (0.012) LEVTA (Excess of enterprise value over total assets) 0.479*** 0.551*** 0.637*** 0.579** 0.638*** (0.111) (0.10) (0.109) (0.284) (0.107) PCDBM (Private credit by deposit money bank as % of GDP) 0.024*** 0.006 0.074*** 0.024*** 0.075*** (0.004) (0.007) (0.014) (0.009) (0.012) LSL (Log of sales) 2.780*** 1.782*** 1.273*** 2.780*** 1.275*** (0.067) (0.098) (0.120) (0.179) (0.118) MTB (Market-to-book ratio) 0.181*** 0.176*** 0.184*** 0.181* 0.185*** (0.048) (0.037) (0.038) (0.109) (0.037) PPETA (Property, plant and equipment, net, scaled by total assets) 15.550*** 15.310*** 14.990*** 15.550*** 14.992*** (0.430) (0.533) (0.602) (1.084) (0.599) DEPTA (Depreciation to total assets) 7.080** 1.012 0.852 7.080 0.853 (3.536) (3.043) (3.144) (7.747) (3.139) EBITA (Earnings before interest and tax to total assets) 47.200*** 26.530*** 23.520*** 47.200*** 23.518*** (1.165) (0.885) (0.904) (3.431) (0.899) Fage (Year of establishment) 0.052*** 0.048*** 0.081*** 0.052*** 0.083*** (0.004) (0.009) (0.016) (0.011) (0.013) POR (Payout ratio) 0.137*** 0.073*** 0.060*** 0.137*** 0.061*** (0.004) (0.003) (0.003) (0.008) (0.001) GDPG (Growth rate of GDP) 0.485*** 0.075** 0.115*** 0.485*** 0.117*** (0.041) (0.035) (0.039) (0.077) (0.034) INF (Inflation rate) 0.369*** 0.252*** 0.236*** 0.369*** 0.239*** (0.029) (0.020) (0.021) (0.047) (0.019) INT (Banks’lending rate) 0.294*** 0.104*** 0.0368 0.294*** 0.039 (0.015) (0.024) (0.032) (0.035) (0.028) Constant 1.014 11.120*** 12.940*** 1.014 12.938** (0.716) (1.032) (1.292) (1.700) (1.290) Observations 43,866 43,866 43,866 43,866 43,866 R-squared 0.163 0.052 0.163 Number of id 4,906 4,906 Breusch–Pagan LM test (0.000) Hausman test (0.000) Multicollinearity (VIF) 1.25 Heteroscedasticity ( χ 2 stat) (0.000) Serial correlation (F-stat) (0.000) AR (2) 0.235 Hansen test 0.493 Note(s): (a) See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1 (***), 5 (**) and 10% (*) (b) The standard errors are reported in parentheses Source(s): Table by Thakur and modified by Matemilola Table 8. Regression results based on 4,912 nonfinancial listed firms from 23 developing countries JEFAS 29,57 136 development in the financial market has the scope to enhance the impact of goodwill assets on firms’debt ratio. Conversely, financial markets in the developed countries are much more efficient compared to the developing countries’counterparts (Bas et al., 2009). In addition, due to favorable property rights (Claessens and Laeven, 2003) and better protection of creditors’ rights (Giannetti, 2003), the collaterizability of intangible assets (goodwill assets) is more (Pooled OLS) (RE) (FE) (OLS with hetero and serial corr.) Two-step System GMM Variables TDTA TDTA TDTA TDTA TDTA L.TDTA 0.639*** (0.009) LEVTA (Excess of enterprise value over total assets) 2.865*** 1.614*** 1.447*** 2.865*** 1.450*** (0.110) (0.095) (0.100) (0.198) (0.099) PCDBM (Private credit by deposit money bank as % of GDP) 0.056*** 0.022*** 0.011** 0.056*** 0.012** (0.003) (0.003) (0.005) (0.007) (0.003) LSL (Log of sales) 2.080*** 1.896*** 2.257*** 2.080*** 2.260*** (0.051) (0.082) (0.117) (0.117) (0.115) MTB (Market-to-book ratio) 0.323*** 0.0157 0.0597 0.323*** 0.0600** (0.045) (0.035) (0.036) (0.094) (0.006) PPETA (Property, plant and equipment, net, scaled by total assets) 19.930*** 18.310*** 17.550*** 19.930*** 17.552*** (0.426) (0.535) (0.612) (1.121) (0.609) DEPTA (Depreciation to total assets) 4.498 6.483*** 9.134*** 4.498 9.136*** (2.795) (2.365) (2.444) (5.794) (2.439) EBITA (Earnings before interest and tax to total assets) 11.580*** 9.992*** 10.040*** 11.580*** 9.041*** (0.581) (0.433) (0.442) (1.094) (0.438) Fage (Year of establishment) 0.006 0.005 0.095*** 0.006 0.096*** (0.004) (0.009) (0.034) (0.008) (0.029) POR (Payout ratio) 0.053*** 0.038*** 0.035*** 0.053*** 0.037*** (0.004) (0.003) (0.004) (0.008) (0.002) GDPG (Growth rate of GDP) 0.547*** 0.308*** 0.135*** 0.547*** 0.137*** (0.054) (0.036) (0.039) (0.074) (0.032) INF (Inflation rate) 0.137* 0.028 0.159*** 0.137 0.161*** (0.083) (0.053) (0.057) (0.092) (0.053) INT (Banks’lending rate) 0.182*** 0.496*** 1.544*** 0.182 1.545*** (0.057) (0.082) (0.119) (0.127) (0.114) Constant 13.680*** 7.553*** 2.512* 13.680*** 2.514* (0.525) (0.780) (1.308) (1.173) (1.301) Observations 38,075 38,075 38,075 38,075 38,075 R-squared 0.142 0.062 0.142 Number of id 4,297 4,297 Breusch–Pagan LM test (0.000) Hausman test (0.000) Multicollinearity (VIF) 1.50 Heteroscedasticity ( χ 2 stat) (0.000) Serial correlation (F-stat) (0.000) AR (2) 0.219 Hansen test 0.382 Note(s): (a) See Table 1 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**) and 10% (*) (b)The standard errors are reported in parentheses Source(s): Table by Thakur and modified by Matemilola Table 9. Regression results based on 4,303 nonfinancial listed companies from 9 developed countries Financial market development 137 established in the developed markets. Thus, there is a little absorption of the financial market development effect on the goodwill-capital structure relationship in the developed countries. This reasoning explains why goodwill has insignificant moderating impact on the relationship between goodwill assets and firms’debt ratios in developed countries. (Pooled OLS) (RE) (FE) (OLS with hetero and serial corr) Two-step system GMM Variables TDTA TDTA TDTA TDTA TDTA L.TDTA 0.725**^ (0.007) LEVTA (Excess of enterprise value over total assets) 1.538*** 0.308 0.481** 1.538*** 0.482** (0.221) (0.226) (0.241) (0.525) (0.237) PCDBM (Private credit to deposit money bank as % of GDP) 0.034*** 0.009 0.074*** 0.034*** 0.076*** (0.004) (0.007) (0.014) (0.009) (0.010) LEVTAPCDBM (LEVTA*PCDBM) 0.0387*** 0.012*** 0.006* 0.039*** 0.029*** (0.004) (0.004) (0.004) (0.008) (0.004) LSL (Log of sales) 2.771*** 1.770*** 1.265*** 2.771*** 1.264*** (0.067) (0.099) (0.120) (0.178) (0.116) MTB (Market-to-book ratio) 0.162*** 0.176*** 0.184*** 0.162 0.187*** (0.048) (0.037) (0.038) (0.108) (0.031) PPETA (Property, plant and equipment, net, scaled by total assets) 15.490*** 15.300*** 14.990*** 15.490*** 14.991*** (0.429) (0.533) (0.602) (1.083) (0.598) DEPTA (Depreciation to total assets) 7.048** 0.797 0.727 7.048 0.727 (3.532) (3.044) (3.144) (7.707) (3.144) EBITA (Earnings before interest and tax scaled by total assets) 47.430*** 26.530*** 23.510*** 47.430*** 23.499*** (1.163) (0.885) (0.904) (3.413) (0.899) Fage (Year of establishment) 0.050*** 0.048*** 0.080*** 0.050*** 0.083*** (0.004) (0.009) (0.016) (0.011) (0.009) POR (Payout ratio, in %) 0.139*** 0.074*** 0.060*** 0.139*** 0.059*** (0.004) (0.003) (0.003) (0.008) (0.005) GDPG (Growth rate of GDP, in %) 0.501*** 0.069** 0.113*** 0.501*** 0.109*** (0.041) (0.035) (0.039) (0.077) (0.029) INF (Inflation rate, in %) 0.355*** 0.250*** 0.236*** 0.355*** 0.234*** (0.029) (0.020) (0.021) (0.047) (0.018) INT (Banks’lending rate, in %) 0.296*** 0.105*** 0.036 0.296*** 0.042^ (0.015) (0.024) (0.032) (0.036) (0.023) Constant 0.628 11.030*** 12.900*** 0.628 12.903*** (0.717) (1.031) (1.292) (1.705) (1.288) Observations 43,866 43,866 43,866 43,866 43,866 R-squared 0.165 0.052 0.165 Number of id 4,906 4,906 Breusch–Pagan LM test (p-value) 0.000 Hausman test (p-value) 0.000 Multicollinearity (mean VIF) 2.05 Heteroscedasticity test (p-value) 0.000 F-statistics (p-value) 0.000 AR (2) 0.316 Hansen test 0.471 Note(s): (a) See Table 2 for the definition of variables and measurements. Asterisks indicate significance at 1% (***), 5% (**) and 10% (*) (b) The standard errors are reported in parenthesis Source(s): Table by Thakur and modified by Matemilola Table 10. Regression results (moderating effect), based on 4,912 nonfinancial listed companies from 23 developing countries JEFAS 29,57 138 Besides, demand side factors such as PPETA (property, plant and equipment to total assets), LSL (log of sales), earnings before interest and tax to total assets (EBITA), market-to-book ratio (MTB), firm age, pay-out ratio and depreciation to total assets (DEPTA) consistently predict debt ratios in both the developed and developing countries. These outcomes align with the outcomes of Henrique et al. (2021) and Kumar et al. (2017) who reported EBITA, PPETA, LSL, firm age, MTB and DEPTA, as determinants of firms’debt ratios in developing and developed countries. The findings of Hang et al. (2018), who found PPETA, MTB and EBITA as drivers of enterprises’debt ratios, are also supported by these data. In both the emerging and developed countries, the bulk of the variables have a strong link with debt ratio. These results are consistent with the findings of Matemilola and Ahmad (2015), who reported positive impact of goodwill assets on capital structure of South African firms. These results are consistent with the findings of Matemilola et al. (2019) and Jaworski and Santos (2022) who reported that firm size, asset tangibility, profitability and dividend payout ratio are consistent determinants of debt ratio. In the developing countries’results, the private credit by deposit money bank variable reveal that financial market development positively impact the debt ratio, except in random-effects model. These results are consistent with the findings of Çam and € Ozer (2022) and Matemilola et al. (2019), who also reported that financial market development has positive impact on debt ratio. But, in the developed countries’sample, financial market development significantly and negatively impacts the debt ratio (except in the FE model). This result is consistent with the findings of Kh emiri and Noubbigh (2018), who reported negative impact of financial market development on debt ratio of firms. 5.1 Theoretical implications Azad et al. (2014) theorize that financial market development in less developed and developing economies appear inefficient because the problem of information asymmetry exists. Our results that financial market development positively moderates the link between goodwill assets and firms’debt capital ratio in developing countries contribute to the literature that argues that financial market development is strongly linked to the level of information asymmetry in the markets which ultimately influence the supply and allocation of capital (Sharma, 2017). The financial market development lowers the information asymmetry problem inherent in goodwill assets and improves it as collateral to secure debt capital. Moreover, our results that goodwill assets are positively related to firms’debt capital ratio in the developing and developed countries extends the trade-off theory of capital structure, which traditionally identifies fixed assets as one of the major factors that support firms’ability to secure debt capital from creditors. In other words, we introduced goodwill assets as an added factor to the traditional factors identified by the trade-off theory of capital structure. 5.2 Policy and managerial implications The findings of this research have policy implications. Firstly, the results inform firmmanagers that rather than focusing on only fixed assets as collateral, goodwill assets also serve as additional collateral to secure debt financing. The firm managers that have goodwill assets would benefit while formulating the financing policies of their firms. Second, creditors in the developed and developing markets will be more aware of the collaterizability of goodwill assets as they make decisions to provide credits to firms. Third, results on the impact of supply-side determinants on the capital structure will aid policymakers in both the developing and developed countries to design and implement macroeconomic policies to enhance the financing and investment decisions of firms. Finally, the study discovered that the expansion of financial markets has a moderating effect on the link between goodwill Financial market development 139 assets and firms’debt ratios which would help policymakers in developing countries to improve the collaterizability of goodwill assets through enhancing the financial market condition in those countries and easing access to debt capital (see Table 11). (Pooled OLS) (RE) (FE) (OLS with hetero and serial corr) Two-step system GMM Variables TDTA TDTA TDTA TDTA TDTA L.TDTA 0.624*** (0.006) LEVTA (Excess of enterprise value over total assets) 2.207*** 2.024*** 2.133*** 2.207*** 2.134*** (0.409) (0.332) (0.342) (0.693) (0.336) PCDBM (Private credit to deposit money bank as % of GDP) 0.055*** 0.022*** 0.011** 0.055*** 0.012** (0.003) (0.004) (0.005) (0.007) (0.003) LEVTAPCDBM (LEVTA*PCDBM) 0.005* 0.003 0.006** 0.005 0.005** (0.003) (0.003) (0.003) (0.006) (0.001) LSL (Log of sales) 2.080*** 1.893*** 2.253*** 2.080*** 2.255*** (0.051) (0.082) (0.117) (0.117) (0.114) MTB (Market-to-book ratio) 0.326*** 0.017 0.063* 0.326*** 0.064** (0.047) (0.036) (0.036) (0.094) (0.031) PPETA (Property, plant and equipment, net, scaled by total assets) 19.940*** 18.300*** 17.550*** 19.940*** 17.549*** (0.426) (0.535) (0.612) (1.121) (0.598) DEPTA (Depreciation to total assets) 4.465 6.488*** 9.153*** 4.465 9.155*** (2.795) (2.365) (2.444) (5.795) (2.403) EBITA (Earnings before interest and tax scaled by total assets) 11.610*** 9.982*** 10.030*** 11.610*** 10.029*** (0.582) (0.434) (0.442) (1.095) (0.425) Fage (Year of establishment) 0.006* 0.005 0.095*** 0.006 0.096*** (0.004) (0.010) (0.034) (0.008) (0.028) POR (Payout ratio, in %) 0.053*** 0.038*** 0.035*** 0.053*** 0.034*** (0.004) (0.003) (0.004) (0.008) (0.003) GDPG (Growth rate of GDP, in %) 0.546*** 0.308*** 0.134*** 0.546*** 0.136*** (0.054) (0.036) (0.039) (0.074) (0.035) INF (Inflation rate, in %) 0.152* 0.021 0.150*** 0.152* 0.148*** (0.083) (0.053) (0.057) (0.092) (0.050) INT (Banks’lending rate, in %) 0.180*** 0.502*** 1.565*** 0.180 1.566*** (0.057) (0.082) (0.120) (0.127) (0.118) Constant 13.630*** 7.602*** 2.499* 13.630*** 2.598** (0.526) (0.781) (1.308) (1.181) (1.239) Observations 38,075 38,075 38,075 38,075 38,075 R-squared 0.142 0.062 0.142 Number of id 4,297 4,297 4,297 Breusch-Pagan LM test (p-value) 0.000 Hausman test (p-value) 3.040 Multicollinearity (mean VIF) 0.000 Heteroscedasticity test (p-value) 0.000 F-statistics (p-value) 0.000 AR (2) 0.207 Hansen test 0.377 Note(s): (a) See Table 2 for the definition of variables and measurements. Asterisks indicate significance at 1 (***), 5 (**) and 10% (*) (b) The standard errors are reported in parenthesis Source(s): Table by Thakur and modified by Matemilola Table 11. Regression results (moderating effect) based on 4,303 nonfinancial listed companies from 9 developed countries JEFAS 29,57 140 5.3 Limitations and future research agenda This study has some limitations as it uses large firms only. Firstly, a similar study on small firms may give different results. But goodwill concept is currently based on the market value, and it is difficult to estimate the goodwill values of small firms because they are not publicly listed. The nature of the business or lack of data prevented all firms from being included, even in the major listed firm category. Future research on this subject can assess how many factors, such as the legal system, the state of law and order and corruption, affect the relationship between goodwill assets and capital structure. 6. Conclusions The effect of goodwill assets on demand-side factors affecting organizations’capital structures is examined in this article. Additionally, using two sizable samples of listed nonfinancial enterprises from both emerging and developed nations, panel data approaches are used to assess the moderating effect of financial market development on the link between goodwill assets and firms’capital structures. The findings of this panel regression demonstrate that whereas goodwill assets have a negative influence on enterprises’capital structures in rich nations, they have a considerably favorable impact on those in developing countries. The direct effect results for the developing countries are consistent with our expectation that markets in developing countries are considering goodwill as a collaterizable asset along with fixed assets. However, the results for the developed countries are inconsistent with our expectations. 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