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10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
10120140503001
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10120140503001

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  • 1. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 1 CAPITAL STRUCTURE AND ECONOMIC PERFORMANCE OF THE FIRM: EVIDENCE FROM ITALY PASQUALE DE LUCA PhD, Researcher, Faculty of Economics at Sapienza University in Rome (Italy) ABSTRACT Financing choices are one of the most critical decision for management by influencing the firm’s behaviour as well as its economic performance and value. Based on the researches of Abor (2005) and Gill, Biger and Mathur (2011) the paper is a moderate attempt to understand the relationship between capital structure and economic performance of Italian large, medium and small firms in manufacturing and service industry listed in Italian Stock Exchange in a period of 5 years (from 2007 to 2011). The analysis found a significant relationship between economic performance of the firm and its financial debt but with non-unique direction. A positive correlation was found in the medium manufacturing firms (between i) ROE and total, long-term and short-term financial debt to total assets; ii) ROA and total and short-term financial debt to total assets; iii) ROI and short-term financial debt to total assets), in the large service firms (between i) ROA and total financial debt to total assets; ii) ROI and long-term financial debt to total assets) and in the small service firms (between ROE and total and short-term financial debt to total assets). Differently a negative correlation was found in the large manufacturing firms (between i) ROE, ROA and short-term financial debt to total assets; ii) ROI and total and long-term financial debt to total assets), in the small manufacturing firms (between ROE and total and short-term financial debt to total assets) and in the large and small service firms (between ROI and short-term financial debt to total assets). In medium service firms were not found correlations. 1. INTRODUCTION The capital structure choices are relevant for the firm not only in order to maximize the returns on equity, but also because they have effects on firm’s ability to deal to its competitive environmental. The financing choices can influence the firm’s behaviour as well as its performance, survivability, business perspectives and market value. Thus, they are one of the most critical decision for management. The capital structure basically refers to the combination of equity and debt a firm uses to INTERNATIONAL JOURNAL OF MANAGEMENT (IJM) ISSN 0976-6502 (Print) ISSN 0976-6510 (Online) Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME: www.iaeme.com/ijm.asp Journal Impact Factor (2014): 7.2230 (Calculated by GISI) www.jifactor.com IJM © I A E M E
  • 2. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 2 finance its operations and investments. Equityholders and debtholders have different level of the risk, benefit and control. Debtholder bears the default risk, earns a fixed rate of return, and exerts a lower and indirect control on firm's activities. Differently equityholder bears the most of the risk, his gain are uncertainly and variables depending to the firm’s performance and its self-financing perspectives, and exerts a full control on the firm's activities. More than 50 years later Modigliani and Miller’s (1958) celebrate paper and despite the theoretical models and empirical researches developed over the following years, the problem of “optimal” capital structure is still open. Researchers continue to analyse capital structure trying to determinate whether “optimal” capital structure exists, what are the determinates and in which way they combine. Theories and empirical researches seem to explain some aspects under certain condition of the firm’s behaviour. Actually there is still no theory can fully explain the firm’s behaviour on capital structure decision or, even more, be able to define the optimal capital structure. The problem is still open. In this paper the relationship between capital structure and economic performance of the firm was investigated by integrating studies and researches in strategic management and corporate finance inside in the framework of the economic theoretical premises. Traditionally strategic management and corporate finance are considered separately. It is argued they are based on different paradigms not related and sometimes in opposition with difficulty for manager to find a linkages (Bettis, 1983; Ward and Grundy, 1996). Today, the high complexity of the firm and the relationship with its dynamic environment require a strong integration between skills in corporate strategy and in corporate finance. Finance plays central role in the government strategic of the firm. It is a relevant player in all business decisions and influences the system of strategies. Every decision, operational or strategic, has financial implications that relate to its size. Finance, therefore, has full and rightful place in the strategic government of the firm. One of the most critical decision for finance in its role of relevant player in the strategic government of the firm are the capital structure choices. The optimization of capital structure is relevant since that good performances, the future survivability and business prospects of the firm depend also on financing decisions. The leverage has an impact on competitive strategy of the firm and therefore on its economic performance (Barton and Gordon, 1987 and 1988; Hitt et al., 1991; Kester and Luehrman, 1992; Balakrishnan and Fox, 1993; Short, 1994; Barclay and Smith, 1995; Kochhar, 1996; Stohs and Mauer, 1996; Shleifer and Vishny, 1997; Kochhar and Hitt, 1998; Simerly and Mingfang Li, 2000; Ozkan, 2002). The relationship between capital structure and economic performance of the firm is confirmed by several empirical researches although with non-unique results. Under different perspectives and assumptions, some researches found a positive relationship while others found a negative relationship. The aim of this paper is a moderate attempt to understand the relationship between capital structure and economic performance of the Italian large, medium and small firms in manufacturing and services industry listed in Italian Stock Exchange in a period of 5 years (from 2007 to 2011). Although there have been several researches on the relationship between capital structure and firm performance, this paper is interesting by considering the particularity of the Italian firms and their economic and environmental system. In this sense it is sufficient to consider three mainly aspects. First, the Italian production is made up by small and medium firms run mostly family. Second, the capital market is largely inefficient and profoundly bank-oriented. Third, there is a high corruption and illegality at all level. The paper is organized as follows. The Section-2 presents a review of literature. The Section- 3 discusses the research methodology by defining the data collection, the variables, the hypothesis and the empirical analysis model. The Section-4 discuss empirical results. Finally Section-5 summarizes the finding of the research and concludes the discussion.
  • 3. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 3 2. LITERATURE REVIEW 2.1. Capital structure theories The relationship between capital structure and firm’s performance is the subject of considerable debate. It is focused on the question about the real existence of the “optimal” capital structure as the mix of equity and debt that maximizes the firm’s market value. The Modigliani-Miller’s theory (1958) is considered the starting point of the modern theory of the capital structure. Based on strong restrictive assumptions structured on perfect capital market, MM argued that capital structure choices has irrelevant both on the value of the firm than on its cost of capital (Proposition I and II). The MM’s Propositions was based on very restrictive assumptions and do not hold in the real word. Over the years many theories and empirical researches have been developed by removing the restrictions assumptions used by MM. The introductions in the models of many variables lead to postulate the relevance of capital structure on firm’s performance and its market value. Thus it is possible to postulate the existence of “optimal” capital structure as the mix of equity and debt that minimizes the cost of capital while maximizing firm's value. The main theoretical models about capital structure are the trade-off, the agency costs and the pecking order. The trade-off theory (Kraus and Litzenberger, 1973) tries to find “optimal” capital structure balancing benefits and costs of debt as derived by taxation and financial distress and bankruptcy costs. Taxation generates a positive effect of debt on firm performance in order to the tax shield. They were just Modigliani and Miller (1963), in a subsequent paper, to revise their earlier Propositions (1958) by incorporating corporate tax other Proposition’s assumptions fixed. They argued that under capital market imperfection where interest rate on debt are tax deductible, firm value increases with higher financial leverage. Miller (1977), in a subsequent further study, introduces the personal tax imposed to individuals in addition to corporate taxation on the firm. Based on USA tax legislation, Miller identifies three taxes rate that could impact on firm’s value in order to the relative level and connections: corporate tax rate, shareholders tax rate (imposed on the income of the dividends) and debholders tax rate (imposed on the income of interest inflows). In presence of default risk, debt can generate financial distress and bankruptcy costs (Myers, 1984; 2001; Ross, 1977). The use of the debt, above certain levels, increase the operational and financial risk of the firm, increasing its default probability and bankruptcy and reduce the firm’s value. Thus it has a negative effect on firm performance and profitability (Johnson, 1997; Bradley et al., 1984; Titman and Wessels, 1988; Friend and Lang, 1988; MacKie-Mason, 1990; Kale et al., 1991; Kim et al., 1998; Jordan et al., 1998; Michaelas et al., 1999; Wald, 1999; Esperanca et al., 2003). The risk of financial distress is mainly due to the fact that firm might not be able to generate enough profits to repay interest on debt and return back the debt. Greater is the earning volatility of the firm, greater is the probability of reduction of the cash flow, greater is the probability of inability of the firm to fulfil the commitments on debt. Firm with a high business risk has less capacity to sustain financial risks and has to use less debt (Kim and Sorensen, 1986; Titman, 1984). Trade-off theory claims that the optimal capital structure is the point at which the marginal cost of debt, due its negative effects, is equal to its marginal benefits due to its positive effects. At this debt level the levered firm value is maximized. The agency costs theory can be use to investigate the effect of debt on performance of the firm (Jensen and Meckling, 1976; Jensen, 1986).
  • 4. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 4 Considering agency costs of equity and debt, the effect of debt on firm performance is non-unique. Debt has positive effect on agency cost of equity reducing the conflicts between shareholders and management due to difference in their utility functions, with consequently different behaviour, targets, information and operating decisions. While the first want to maximize equity value, the second have incentive to maximize firm value because it increases their control of the resources, their power and their compensation. Debt has a discipline function for managers (Jensen, 1986) because it increase the default risk in order to the cash-out relate debt payment, requiring to maximize efficiency, thus increasing equity value (Amihud and Lev, 1981; Jensen, 1986; Friend and Hasbrouck, 1988; Stulz, 1990; Harris and Raviv, 1990 and 1991; Bethel and Liebeskind, 1993; Hoskisson et al., 1994; Bergh, 1995; Noe and Rebello, 1996; Lane et al., 1998). On the contrary debt has a negative effect on agency cost of debt increasing the conflicts between shareholders and debtholders in order to the different claims on the firm (Jensen, 1986). Equity offers to holders residual claim on firm’s cash flow; debt offer to holders a fixed claim over a borrowing firm’s cash flow. Thus, it is possible shareholders’ moral hazard and asset-substitutions (Jensen and Meckling, 1976; Diamond, 1989; Harris and Raviv, 1991). Debtholder try to anticipate the shareholder’s opportunistic behaviour by lending less debt or increasing its cost. The packing order theory (Myers, 1984; Myers and Majluf, 1984; Fama and French, 1998 and 2002) based on asymmetric information in a market and postulates a hierarchy of the finance sources and not define a “optimal” capital structure. In asymmetric information contest, insiders are assumed to possess private information about the firm. The presence of private information influence the perception of the investors about the firm’s risk with consequent impact on the cost of capital. Thus, the costs of finance vary among different financial sources (Myers, 1977 and 1984; Brennan and Kraus, 1987; Noe, 1988; Constantinides and Grundy, 1989; Rajan and Zingales, 1995; Chittenden et al., 1996; Abor, 2005). The direction of the relationship between capital structure choices and asymmetric information is not clear (Harris and Raviv, 1991). On one hand, it has been hypothesized that the capital structure choices can mitigate inefficiencies in the firm’s investment policy due to asymmetric information reducing over-under investment problems (Myers and Majluf 1984; Myers, 1984; Narayanan, 1988; Heinkel and Zechner, 1990; Brennan and Kraus, 1987; Noe, 1988; Constantinides and Grundy, 1989). On the other hand, it has been hypothesized that the capital structure choices signals to investors private information about firm perspectives reducing asymmetric information (Ross, 1977; Leland and Pyle, 1977; Heinkel, 1982; Poitevin, 1989). The pecking order theory does not define a “optimal” capital structure but assume that firm defines its capital structure basing on a finance sources hierarchy. The theory suggests that firm prefers internal sources of finance instead of external source. Firm prefers first internal source of finance due to self-finance adapting the dividend policy (payout ratio) to the investment opportunities. If external source of finance are required, the firm prefers to resort first to the debt, then hybrid instruments and only finally to equity. Thus there are two kinds of equity: internal, regarding self-financing, that is at the top of the packing order; external, regarding the issue of new shares, that is at the bottom of the packing order. 2.2. Capital structure and economic performance of the firm The empirical researches have highlighted other determinants, in addition to the models, that could affect the capital structure choices. Among these the main are profit, age, size, growth, industry, asset structure, managerial characteristics and ownership, institutional and macroeconomic environment of the firm. In this paper the attention was focused on the firm profitability by investigating the relationship between capital structure choices and economic performance of the firm.
  • 5. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 5 This relationship was confirmed by several empirical researches although with discordant results. Based on different point of view, some researches found a positive relationship between firm's performance and its capital structure. Roden and Lewell (1995) in a study of leveraged buyout transactions that took place in the Unites State in a period 1981-1990, found evidence about a systemic relationship between the proportion and the characteristics of the debt in the buyout financing package and the target firm’s earnings rate, earnings variability, growth prospects, and its tax and liquidity position. Nimalathasan and Valeriu (2010) in a study of 13 random selected on 31 listed manufacturing companies in Sri Lanka for a period 2003-2007, found a positive relationship between capital structure and firm’s profitability measured by gross profit ratio, operating profit ratio, net profit ratio of the firm. Majumdar and Sen (2010) in a study of Indian corporate firms in a period 1988-1993, found that firms which rely on arm’s-length debt have better performance and are more likely to engage in advertising and diversification than firms which rely on borrowing from institutional lenders such as term-lending institutions. On the contrary several studies found a negative relationship between capital structure and firm performance. Friend and Lang (1988) in a study of 984 firms listed in a New York Stock Exchange in a period 1979-1983 in order to test whether capital structure choices are at least in part motivated by managerial self interest, found that the level of debt is negatively related to the level of management investment. Also they found that if the firm has large non-managerial investors, the average debt ratio is significantly higher than in those with no principal stockholders. Rajan and Zingales (1995), analysing the determinants of the capital structure choices of the non-financial corporations of the G-7 countries in period 1987-1991, with regarding to the relationship between debt and firm’s profitability, found a negative relationship. Similar conclusions were found by Wald (1999) analysing the factors correlated with capital structure in different countries. Gleason, Mathur and Mathur (2000), in a study of 198 retailers in 14 European country retailers for 1994, found a negative relationship between capital strucurre and firm’s performance mainly due to agency conflict. Booth, Aivazian, Demirguc-Kant and Maksimovic (2001), analysing the capital structure choices of firms in 10 developing countries in comparison with developed countries, found a negative relationship between debt and firm’s profitability. Huang and Song (2006), analysing the determinants of capital structure choices of Chinese listed company, found a negative relationship between debt and firm profitability. Zeitun and Tian (2007), in a study of 167 Jordanian industrial firms (of which 47 were defaulted firms in the following year) listed in Amman Stock Exchange in 16 sectors in a period 1989-2003, found that a significantly negative relationship between capital structure and firm’s performance measures in both the accounting and market measures. Gi Shian Su and Hong Tam Vo (2010), in a study of listed firms in Vietnam, found a significantly negative relationship between return on equity and debt ratio. They show that combined effect of corporate strategy and capital structure explain well for the difference in firm performance. Bistrova, Lace and Peleckiene (2011), in a study of 36 blue-chip firms listed on the Baltic Stock Exchanges in a period 2007-2010, found that the firm pursues conservative capital management policy. They found that firms having lower debt levels on their accounts are able to demonstrate higher profitability. Thus, there is a negative relationship between debt and capital profitability. Gupta, Srivastava and Sharma (2011), in a study of 100 firms listed on National Stock Exchange of India in period 2006-2010, found a negatively correlation between firm performance and financial leverage. The firm that has high profitability and good performance have less debt.
  • 6. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 6 Muzir (2011), in a study based on 114 firms listed at the Istanbul Stock Exchange in a period 1994-2003, found that asset expansions through debt may contribute much to firms’ risk exposure in spite of tax benefits expected especially during economic downturns. Norvaisiene (2012) in a study of 70 non-financial listed Baltic firms (28 Lithuanian firms, 14 Estonian firms, 28 Latvian firms) for a period 2002-2011, found a negative correlation between debt level and profitability indicators for Lithuanian and Estonian firms and absence of correlation for Latvian firms. For firms in Baltic countries higher level of debt leads to lower profitability indicators and lower current solvency. Tsuji (2013), in a studies based on Japanese machinery industries firms listed on the Tokyo Stock Exchange in a period 1981-2011, found a negative relationship between capital structure and corporate profitability. Still other empirical researches found contemporary negative and positive relationship between capital structure and firm performance. Siemrly and Li (2000), in a study of 700 large U.S. firms in a variety of industry contexts integrating elements from agency theory and transaction cost economics with strategic management, found that environmental dynamism moderate the relationship between leverage and performance of the firm. Leverage produces either positive and negative effects on performance depending on whether the firm is in stable or dynamic environments. Particularly the high leverage has a positive impact on performance for firms in stable environments while a negative impact in dynamic environments. Mesquita and Lara (2003), in a study on 70 Brazilian firms, found a positive correlation between short-term debt and firm profitability and a negative relationship between long-term debt and firm profitability. Abor (2005), in a study of 22 firms listed in Ghana Stock Exchange during the period 1998- 2002, found a positive relationship between total and short-term debt and firm’s profitability. On the contrary the results show a significantly negative relationship between long-term debt and firm’s profitability. San and Heng (2011), in a study of 49 construction firms listed in Main Board of Bursa Malaysia in a period 2005-2008, found a positive relationship between return on capital and debt to equity market value and between earnings per share and long-term debt to capital and a negative relationship between earnings per share and debt to capital for big firms. Also they found a positive relationship between operating margin and long-term debt to common equity for medium firms. Finally they found a negative relationship between earnings per share and debt to capital for small firms. Gill, Biger and Mathur (2011), in a study on 272 American service and manufacturing firms listed on New York Stock Exchange for a period of 3 years (2005-2007), found a positive relationship between total and short-term debt and profitability of the firm in a service industry. Also they found a positive relationship between total, long-term and short-term debt and profitability of the firm in manufacturing industry. Salehi and Manesh (2012), in a study based on 59 firms listed in Tehran Stock Exchange during a period 2004-2011, found a negative relationship between profitability and firm growth with capital structure and a positive relationship between firm size, market size, gross domestic product growth and inflation rate with capital structure. 3. RESEARCH METHODOLOGY 3.1. Data collection A database was built from selected firms, manufacturing and service, listed in the three main Index (FTSE Mib, FTSE Mid Cap, FTSE Small Cap) of Italian Stock Exchange in a period of 5
  • 7. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 7 years (2007-2011). Financial, insurance and real estate firms were excluded. The peculiarity of their operations, assets structure and liabilities could distort the analysis. Also firms listed after 2007 and firms which had made extraordinary operations during the observation period were excluded. Firms selected are 120 of which 79 are manufacturing firms and 41 are service firms. In this research book value was used. Data was obtained from balance sheet. Despite the market value expresses the real value of the firm, the book value was used for three main reasons. First, the market value of the firm is difficult to determinate, subject to the market volatility and the data choice by reference to the market value is arbitrary. Managers tend to think in term of book value rather than market value because it is more easily accessible, more accurately recorded and not subject to market volatility. Second, the measurement of the firm economic performance are usually based on income statements and the book value measure of leverage is considerate as best proxy of market value (Rajan and Zingales, 1995; Gupta et al., 2011). Finally, the main cost of debt is the expected cost of financial distress in the event of bankruptcy. In a financial distress situation, the value of the firm is near to its book value. Also if bankruptcy occurs, the accurate measure of debtholders’ liability is the book value of debt and not its market value. 3.2. Variables The relationship between capital structure and the economic performance of the firm was investigated by assuming the first as independent variable and the second as dependent variable. Financial debt was used to examine the capital structure choices. It was measured by using three indicators: total financial debt to total assets (TFD), short-term financial debt to total assets (SFD) and long-term financial debt to total assets (LFD). Economic performance of the firm was measured by using three classic indicators: return on equity (ROE), return on assets (ROA) and return on investments (ROI). Three control variables were used: firm size (SF), sales growth (SG) and firm industry (IF). The firm size variables was defined by using the Index of Stock Market as a proxy. Therefore all firms included in FTSE Mib (the index includes the top 40 firms by size and liquidity) were considered large firms (SF-L); similarly all firms includes in FTSE Mid Cap (the index includes the other 60 firms – excluding firms in FTES Mib – grater size, liquidity and capitalization) were considered medium firms (SF-M); and finally all firms includes in FTSE Small Cap (the index includes the other firms – excluding firms in FTSE Mib and FTSE Mid Cap – that meeting the minimum requirement of liquidity and capitalization) were considered as small firms (SF-S). The sales growth variable was defined by the percentage variation in revenue per year. The firm industry variable was defined by distinguishing all firms between manufacturing and service industry. Therefore it was assigned value one to manufacturing firm (IF(1)) and value zero to service firms (IF(0)). 3.3. Hypothesis and empirical analysis model The aim of the paper is to investigate the relationship between financial debt, as a proxy of capital structure choices and measured by TFD, LFD, SFD, and economic performance, measured by ROE, ROA and ROI, for large, medium and small firms in manufacturing and service industry. The basic hypothesis was that financial debt has effects (positive or negative) on the economic performance of the firm. This basic hypothesis can be explicated for the large, medium, small manufacturing and service firms as following: Hp 1: There is a significant relationship between economic performance (measured by ROE, ROA and ROI) and the financial debt (measured by TFD, LFD, and SFD) in the large, medium and small manufacturing firms;
  • 8. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 8 Hp 2: There is a significant relationship between economic performance (measured by ROE, ROA and ROI) and the financial debt (measured by TFD, LFD, and SFD) in the large, medium and small service firms. The hypotheses were tested by regression analysis assuming the financial debt as independent variables and economic performance of the firm as dependent variables. Six dataset were defined by distinguishing between manufacturing and service industry and in each of them between large, medium and small firms. For each of them were realized the regression equations, as reported in Table 1. Table 1 – Regression equations for Large, Medium and Small Firms in Manufacturing and Service Industry Manufacturing Industry (IF(1)) – Large Firms (SF-L) Service Industry (IF(0)) – Large Firms (SF-L) uSGbSbLFDbbROI uSGbSbSFDbbROI uSGbSbTFDbbROI uSGbSbLFDbbROA uSGbSbSFDbbROA uSGbSbTFDbbROA uSGbSbLFDbbROE uSGbSbSFDbbROE uSGbSbTFDbbROE FLF FLF FLF FLF FLF FLF FLF FLF FLF +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= − − − − − − − − − 3210 3210 3210 3210 3210 3210 3210 3210 3210 )9 )8 )7 )6 )5 )4 )3 )2 )1 uSGbSbTFDbbROI uSGbSbLFDbbROI uSGbSbSFDbbROI uSGbSbLFDbbROA uSGbSbSFDbbROA uSGbSbTFDbbROA uSGbSbLFDbbROE uSGbSbSFDbbROE uSGbSbTFDbbROE FMF FLF FLF FLF FLF FLF FLF FLF FLF +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= − − − − − − − − − 3210 3210 3210 3210 3210 3210 3210 3210 3210 )9 )8 )7 )6 )5 )4 )3 )2 )1 Manufacturing Industry (IF(1)) – Medium Firms (SF-M) Service Industry (IF(0)) – Medium Firms (SF-M) uSGbSbLFDbbROI uSGbSbSFDbbROI uSGbSbTFDbbROI uSGbSbLFDbbROA uSGbSbSFDbbROA uSGbSbTFDbbROA uSGbSbLFDbbROE uSGbSbSFDbbROE uSGbSbTFDbbROE FMF FMF FMF FMF FMF FMF FMF FMF FMF +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= − − − − − − − − − 3210 3210 3210 3210 3210 3210 3210 3210 3210 )9 )8 )7 )6 )5 )4 )3 )2 )1 uSGbSbLFDbbROI uSGbSbSFDbbROI uSGbSbTFDbbROI uSGbSbLFDbbROA uSGbSbSFDbbROA uSGbSbTFDbbROA uSGbSbLFDbbROE uSGbSbSFDbbROE uSGbSbTFDbbROE FMF FMF FMF FMF FMF FMF FMF FMF FMF +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= − − − − − − − − − 3210 3210 3210 3210 3210 3210 3210 3210 3210 )9 )8 )7 )6 )5 )4 )3 )2 )1
  • 9. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 9 Manufacturing Industry (IF(1)) – Small Firms (SF-S) Service Industry (IF(0)) – Small Firms (SF-S) uSGbSbLFDbbROI uSGbSbSFDbbROI uSGbSbTFDbbROI uSGbSbLFDbbROA uSGbSbSFDbbROA uSGbSbTFDbbROA uSGbSbLFDbbROE uSGbSbSFDbbROE uSGbSbTFDbbROE FSF FSF FSF FSF FSF FSF FSF FSF FSF +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= − − − − − − − − − 3210 3210 3210 3210 3210 3210 3210 3210 3210 )9 )8 )7 )6 )5 )4 )3 )2 )1 uSGbSbLFDbbROI uSGbSbSFDbbROI uSGbSbTFDbbROI uSGbSbLFDbbROA uSGbSbSFDbbROA uSGbSbTFDbbROA uSGbSbLFDbbROE uSGbSbSFDbbROE uSGbSbTFDbbROE FSF FSF FSF FSF FSF FSF FSF FSF FSF +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= +⋅+⋅+⋅+= − − − − − − − − − 3210 3210 3210 3210 3210 3210 3210 3210 3210 )9 )8 )7 )6 )5 )4 )3 )2 )1 4. EMPIRICAL RESULTS 4.1. Descriptive statistics Descriptive statistics for the large, medium and small firms in manufacturing and service industry were reported in Tables 2. On average both in manufacturing and service industry, the large firms have the best economic performance followed by medium firms and small firms. In manufacturing industry on average: i) ROE is 15% for large firms, 11% for medium firms and -2% for small firms; ii) ROA is 11% for large firms, 5% for medium firms and 2% for small firms; iii) ROI is 4% for large firms, 3% for medium firms and 1% for small firms. In service industry on average: i) ROE is 11% for large firms, 6% for medium firms and 2% for small firms; ii) ROA is 8% for large firms, 3% for medium firms and 1% for small firms; iii) ROI is 3% for large firms, 2% for medium firms and 0.5% for small firms. In manufacturing industry on average the medium firms are the most indebted, where the TFD is approximately 29% (of which 14% is SFD and the 15% is LFD) followed by large firms with approximately 27% (of which 17% is LFD and 9% is SFD) and small firms with approximately 26% (of which 15% is SFD and 11% is LFD). On average large firms have the highest level of LFD, medium firms have the highest level of TFD, and small firms have the highest level of SFD. With reference to the mix between long-term and short-term financial debt, while the large and medium firms prefer long-term debt to short-term debt, the small firms prefer short-term debt to long-term debt. Differently in service industry on average the large firms are the most indebted, where the TFD is approximately 55% (of which 42% is LFD and the 13% is SFD) followed by medium firms with approximately 32% (of which 18% is LFD and 14% is SFD) and small firms with approximately 18% (of which 12% is SFD and 6% is LFD). On average large firms have the highest level of TFD and LFD, medium firms have the highest level of SFD. With reference to the mix between long-term and short-term financial debt, while the large and medium firms prefer long-term debt to short-term debt, the small firms prefer short-term debt to long-term debt.
  • 10. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 10 Table 2 – Descriptive Statistics for Large, Medium and Small in Manufacturing and Service Industry ROE ROA ROI TFD SFD LFD SG LargeFirms (SF-L) Mean M:15.2% M:11.0% M:4.0% M:26.6% M:9.4% M:17.2% M:9.5% S:11.2% S:8.2% S:3.5% S:54.6% S:12.7% S:41.9% S:0.4% Median M:12.6% M:9.6% M:2.6% M:26.9% M:7.6% M:16.7% M:3.3% S:12.3% S:7.7% S:2.9% S:52.4% S:10.1% S:43.8% S:0.6% Maximum M:52.2% M:35.2% M:22.9% M:62.9% M:62.9% M:60.0% M:261.7% S:32.0% S:14.5% S:9.5% S:89.6% S:35.0% S:83.6% S:100% Minimum M:-27.9% M:-5.4% M:-13.7% M:0% M:0% M:0% M:-88.7% S:11.2% S:7.7% S:2.9% S:52.4% S:10.1% S:41.9% S:0.4% Variance M:1.9% M:0.4% M:0.4% M:3.3% M:1.1% M:1.9% M:18.9% S:1.0% S:0.1% S:0.1% S:1.3% S:0.9% S:2.0% S:8.9% Std.Dev. M:13.6% M:6.7% M:6.6% M:18.3% M:10.6% M:13.9% M:43.5% S:9.9% S:3.2% S:3.4% S:11.2% S:9.4% S:14.1% S.29.9% Skewness M:0.2 M:1.0 M:0.5 M:0.1 M:2.3 M:0.6 M:3.2 S:-1.1 S:0.1 S:0.1 S:1.2 S: 1.0 S: 0.4 S:1.6 Kurtosis M:1.1 M:2.3 M:0.5 M:-1.0 M:8.6 M:0.1 M:17.1 S:3.1 S:-0.9 S:-1.5 S:2.2 S:0.2 S:1.4 S:6.4 MediumFirms (SF-M) Mean M:11.1% M:5.4% M:3.5% M:28.6% M:13.6% M:15.0% M:2.0% S:5.7% S:3.4% S:1.9% S:32.0% S:13. 6% S:18.4% S:4.7% Median M:7.4% M:4.7% M:1.1% M:29.1% M:12.6% M:12.4% M:1.4% S:7.6% S:4.8% S:3.2% S:32.2% S:12.3% S:15.2% S:1.9% Maximum M:88.1% M:37.2% M:36.5% M:81.7% M:35.7% M:57.0% M:100% S:44.2% S:17.2% S:10.4% S:93.6% S:37.0% S:81.3% S:59.6% Minimum M:-73.5% M:-71.7% M:-5.4% M:0.3% M:0.1% M:0% M:-96.1% S:-51.9% S:-27.8% S:-29.9% S:1.0% S:0.7% S:0.3% S:-27.5% Variance M:3.4% M:1.0% M:0.5% M:3.5% M:0.9% M:1.9% M:13.3% S:2.2% S:0.4% S:0.5% S:2.5% S:0.7% S:2.6% S:3.1% Std.Dev. M: 18.3% M:9.8% M:7.0% M:18.7% M:9.4% M:13.8% M:36.5% S:14.7% S:6.5% S:7.0% S:15.7% S:8.3% S:16.0% S:17.6% Skewness M:0.6 M:-4.4 M:2.10 M:0.3 M:0.4 M:0.9 M:0.1 S:-1.5 S:-2.5 S:-2.3 S:0.8 S:0.7 S:1.3 S:0.9 Kurtosis M:8.2 M:38.3 M:5.1 M:-0.3 M:-0.7 M:0.4 M:2.0 S:4.7 S:9.3 S:7.3 S:2.9 S:-0.5 S:2.7 S:1.2 SmallFirms (SF-S) Mean M:-1.8% M:1.8% M:1.1% M:26.3% M:14.8% M:11.6% M:0% S:2.2% S:0.9% S:0.5% S:18.5% S:12.3% S:6.2% S:7.5% Median M:2.6% M:2.9% M:1.2% M:24.7% M:11.8% M:9.3% M:0% S:1.7% S:1.4% S:0.9% S:13.1% S:9.0% S:1.0% S:-0.6% Maximum M:58.4% M:27.3% M:40.1% M:75.2% M:68.2% M:65.2% M:148.9% S:146.8% S:24.4% S:26.7% S:102.4% S:75.8% S:63.5% S:221.1% Minimum M:-209.3% M:-57.5 M:-58.7% M:0% M:0% M:0% M:-100% S:-56.4% S:-21.5% S:-26.4% S:0% S:0% S:0% S:-77.4% Variance M:7.2% M:1.1% M:1.4% M:2.6% M:1.7% M:1.3% M:11.7% S:4.6% S:0.5% S:0.6% S:3.5% S:1.8% S:1.3% S:17.4% Std.Dev M:26.8% M:10.5% M:11.8% M:16.2% M:13.1% M:11.6% M:34.2% S:21.6% S:7.3% S:8.0% S:18. 8% S:13.6% S:11.2% S:41.8% Skewness M:-3.1 M:-1.8 M:-1.0 M:0.51 M:1.5 M:1.6 M:0.7 S:2.6 S:-0.5 S:-0.5 S:1.5 S:1.8 S:3.3 S:2.5 Kurtosis M:18.3 M:8.0 M:5.7 M:-0.2 M:2.6 M:3.5 M.3.9 S:19.0 S:1.8 S:2.3 S:3.3 S:4.3 S:13.2 S:9.3 Note: M = Manufacturing industry (IF(1)); S = Service Industry (IF(0)) Correlation analysis, as reported in Table 3, was used to find the existence of linear relationship between economic performance of the firm and its financial debt. Note that even in the presence of correlation between the variables it is reasonable to expect low level of correlation. The investment policy explains most of the firm economic performance. In manufacturing industry the analysis found a correlation between economic performance of the firm and its financial debt but with non-unique direction. Particularly in large firms, ROE, ROA, and ROI are negatively correlated with TFD, LFD and SFD. In medium firms, ROE and ROA are
  • 11. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 11 positively correlated with TFD, LFD and SFD while ROI is positively correlated with TFD and SFD and negatively correlated with LFD. In small firms, ROE is negatively correlated with TFD, LFD and SFD while ROA and ROI are negatively correlated with TFD and SFD and positively correlated with LFD. Also in service industry the analysis found a correlation between economic performance of the firm and its financial debt but with non-unique direction. In large firms, ROE is negatively correlated with TFD and LFD while positively correlated with SFD; ROA is positively correlated with TFD, LFD and SFD; ROI is negatively correlated with TFD and SFD and positively correlated with LFD. In medium firms, ROE is negatively correlated with TFD, LFD and SFD; ROA is positively correlated with TFD and LFD and negatively correlated with SFD; ROI is negatively correlated with TFD and LFD and positively correlated with SFD. In small firms, ROE is positively correlated with TFD, LFD, SFD while ROA and ROI are negatively correlated with TFD and SFD and positively correlated with LFD. Table 3 – Correlation matrix for Large, Medium and Small firms in Manufacturing and Service Industry ROE ROA ROI TFD SFD LFD SG LargeFirm (SF-L) ROE M: 1 S: 1 ROA M: 0.70 M: 1 S: 0.50 M: 1 ROI M: 0.28 M: 0.15 M: 1 S: 0.29 S: -0.22 S: 1 TFD M: -0.11 M: -0.14 M: -0.33 M: 1 S: -0.02 S: 0.47 S: -0.08 M: 1 SFD M: -0.19 M: -0.19 M: -0.18 M: 0.65 M: 1 S: 0.08 S: 0.35 S: -0.53 S: -0.06 M: 1 LFD M: -0.01 M: -0.04 M: -0.29 M: 0.81 M: 0.09 M: 1 S: -0.06 S: 0.14 S: 0.30 S: 0.75 S: -0.61 S: 1 SG M: 0.29 M: 0.11 M: 0.09 M: 0.08 M: 0.09 M: 0.03 M: 1 S: -0.05 S: -0.29 S: 0.18 S: -0.22 S: -0.32 S: 0.04 S: 1 MediumFirm (SF-M) ROE M: 1 S: 1 ROA M: 0.86 M: 1 S: 0.94 S: 1 ROI M: 0.65 0.52 M: 1 S: 0.85 S: 0.88 S: 1 TFD M: 0.38 M: 0.28 M: 0.06 M: 1 S: -0.11 S: 0.03 S: -0.08 S: 1 SFD M: 0.42 M: 0.33 M: 0.20 M: 0.71 M: 1 S: -0.04 S: -0.03 S: 0.02 S: 0.22 S: 1 LFD M: 0.23 M: 0.16 M: -0.05 M: 0.88 M: 0.28 M: 1 S: -0.08 S: 0.05 S: -0.09 S: 0.86 S: -0.30 S: 1 SG M: 0.05 M: 0.02 M: 0.01 M: 0.01 M: 0.03 M: -0.01 M: 1 S: 0.08 S: 0.07 S: 0.14 S: -0.44 S: -0.09 S: -0.39 S: 1 SmallFirm (SF-S) ROE M: 1 S: 1 ROA M: 0.78 M: 1 S: 0.45 S: 1 ROI M: 0.67 M: 0.84 M: 1 S: 0.43 S: 0.76 S: 1 TFD M: -0.15 M: -0.01 M: -0.05 M: 1 S: 0.19 S: -0.09 S: -0.14 S: 1 SFD M: -0.17 M: -0.05 M: -0.09 M: 0.71 M: 1 S: 0.25 S: -0.15 S: -0.25 S: 0.81 S: 1 LFD M: -0.01 M: 0.06 M: 0.04 M: 0.60 M: -0.13 M: 1 S: 0.01 S: 0.02 S: 0.07 S: 0.70 S: 0.15 S: 1 SG M: 0.11 M: 0.14 M: 0.21 M: -0.19 M: -0.16 M: -0.09 M: 1 S: 0.16 S: 0.19 S: 0.29 S: -0.04 S: -0.02 S: -0.05 S: 1 Note: M = Manufacturing industry (IF(1)); S = Service Industry (IF(0))
  • 12. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 12 4.2. Regression analysis Regression analysis was used to investigate the relationship between economic performance of the firm and its financial debt for large, medium and small firms in manufacturing and service industry. In Table 4 were reported the correlations between dependent variable (measured by ROE, ROA ROI) and independent variable (measured by TFD, LFD, SFD) based on regression equations while in Table 5 were reported only the statistically significant regressions with coefficients at the significance level of the 0.05. Table 4 – Pearson’s correlation analysis for Large, Medium, Small firms in Manufacturing and Service Industry MANUFACTURING FIRMS (IF(1)) SERVICE FIRMS (IF(0)) TFD LFD SFD TFD LFD SFD Large Firms (SF-L) ROE -0.078 -0.006 -0.240 -0.015 -0.040 0.082 ROA -0.051 -0.019 -0.124 0.138** 0.032 0.123* ROI -0.118** -0.139* -0.113 -0.023 0.071. -0.192*** Medium Firm (SF-M) ROE 0.371*** 0.309* 0.821*** -0.103 -0.072 -0.075 ROA 0.149** 0.113 0.352*** 0.012 0.019 -0.021 ROI 0.024 -0.025 0.149* -0.037 -0.039 0.016 Small Firm (SF-S) ROE -0.243* -0.017 -0.346* 0.214. 0.023 0.406** ROA -0.006 0.051 -0.043 -0.037 0.013 -0.082 ROI -0.037 0.043 -0.083 -0.058 0.051 -0.148* Signif. Codes: 0 ‘***’; 0.001 ‘**’; 0.01 ‘*’; 0.05 ‘.’; 0.1 ‘’; 1 Table 5 – Significant Regressions for Large, Medium, Small Firms in Manufacturing and Service Industry DEPENDENT VARIABLE INTERCEPT TFD SFD LFD SG ESTIMATE MODEL EVALUATION MANUFACTURING LARGEFIRMS (IF(1)-SF-L) ROA Estimate: 0.12052*** Std.Error: 0.01105 Estimate: - 0.13142 . Std.Error: 0.07759 Estimate: 0.01962 Std.Error: 0.01899 Residual Std.Error: 0.06638 (on 62 DF) Multiple R-Squared: 0.05526 Adjusted R-Squared: 0.02478 F-statistic: 1.813 (on 2 and 62 DF) p-value: 0.1717 ROI Estimate: 0.07084*** Std.Error: 0.01389 Estimate: - 0.12182** Std.Error: 0.04312 Estimate: 0.01760 Std.Error: 0.01812 Residual Std.Error: 0.06344 (on 62 DF) Multiple R-Squared: 0.121 Adjusted R-Squared: 0.09264 F-statistic: 4.267 (on 2 and 62 DF) p-value: 0.01836 ROI Estimate: 0.06287*** Std.Error: 0.01276 Estimate: - 0.14002* Std.Error: 0.05717 Estimate: 0.01481 Std.Error: 0.01833 Residual Std.Error: 0.06436 (on 62 DF) Multiple R-Squared: 0.09535 Adjusted R-Squared: 0.06617 F-statistic: 3.267 (on 2 and 62 DF) p-value: 0.04476 ROE Estimate: 0.16930*** Std.Error: 0.02167 Estimate: - 0.27962 . Std.Error: 0.15220 Estimate: 0.09813* Std.Error: 0.03726 Residual Std.Error: 0.1302 (on 62 DF) Multiple R-Squared: 0.1323 Adjusted R-Squared: 0.1043
  • 13. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 13 F-statistic: 4.725 (on 2 and 62 DF) p-value: 0.01231 MANUFACTURING MEDIUMFIRMS (IF(1)-SF-L) ROA Estimate: 0.011529 Std.Error: 0.017493 Estimate: 0.148820** Std.Error: 0.051258 Estimate: 0.006501 Std.Error: 0.026349 Residual Std.Error: 0.09599 (on 97 DF) Multiple R-Squared: 0.08056 Adjusted R-Squared: 0.06161 F-statistic: 4.25 (on 2 and 97 DF) p-value: 0.01701 ROA Estimate: 0.006092 Std.Error: 0.016651 Estimate: 0.351860*** Std.Error: 0.100787 Estimate: 0.004300 Std.Error: 0.025903 Residual Std.Error: 0.09432 (on 97 DF) Multiple R-Squared: 0.1122 Adjusted R-Squared: 0.09391 F-statistic: 6.13 (on 2 and 97 DF) p-value: 0.003112 ROI Estimate: 0.015047 Std.Error: 0.012347 Estimate: 0.149222* Std.Error: 0.074736 Estimate: 0.001494 Std.Error: 0.019207 Residual Std.Error: 0.06994 (on 97 DF) Multiple R-Squared: 0.03966 Adjusted R-Squared: 0.01985 F-statistic: 2.003 (on 2 and 97 DF) p-value: 0.1405 ROE Estimate: 0.004613 Std.Error: 0.031365 Estimate: 0.371238*** Std.Error: 0.091904 Estimate: 0.025902 Std.Error: 0.047244 Residual Std.Error: 0.1721 (on 97 DF) Multiple R-Squared: 0.1465 Adjusted R-Squared: 0.1289 F-statistic: 8.323 (on 2 and 97 DF) p-value: 0.0004615 ROE Estimate: - 0.0008932 Std.Error: 0.0298299 Estimate: 0.8185188*** Std.Error: 0.1805609 Estimate: 0.0208600 Std.Error: 0.0464045 Residual Std.Error: 0.169 (on 97 DF) Multiple R-Squared: 0.1772 Adjusted R-Squared: 0.1602 F-statistic: 10.45 (on 2 and 97 DF) p-value: 7.793e-0.5 ROE Estimate: 0.06422* Std.Error: 0.02669 Estimate: 0.30957* Std.Error: 0.13119 Estimate: 0.02856 Std.Error: 0.04966 Residual Std.Error: 0.1809 (on 97 DF) Multiple R-Squared: 0.057026 Adjusted R-Squared: 0.03758 F-statistic: 2.933 (on 2 and 97 DF) p-value: 0.05799 MANUFACTURING SMALLFIRMS (IF(1)-SF-L) ROE Estimate: 0.03873 Std.Error: 0.03497 Estimate: - 0.21428 . Std.Error: 0.11369 Estimate: 0.06965 Std.Error: 0.05399 Residual Std.Error: 0.2663 (on 213 DF) Multiple R-Squared: 0.02919 Adjusted R-Squared: 0.02008 F-statistic: 3.202 (on 2 and 213 DF) p-value: 0.04263 ROE Estimate: 0.02902 Std.Error: 0.02746 Estimate: - 0.31606* Std.Error: 0.14009 Estimate: 0.06952 Std.Error: 0.05350 Residual Std.Error: 0.2654 (on 213 DF) Multiple R-Squared: 0.03604 Adjusted R-Squared: 0.02699 F-statistic: 3.981 (on 2 and 213 DF) p-value: 0.02006 SE RVI CE LA RG ROA Estimate: 0.01357 Std.Error: Estimate: 0.12485* Std.Error: Estimate: - 0.02238 Std.Error: Residual Std.Error: 0.02972 (on 32 DF) Multiple R-Squared:
  • 14. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 14 0.02562 0.04594 0.01724 0.2579 Adjusted R-Squared: 0.2115 F-statistic: 5.56 (on 2 and 32 DF) p-value: 0.008461 ROI Estimate: 0.058905*** Std.Error: 0.008812 Estimate: - 0.190478** Std.Error: 0.056868 Estimate: 0.001585 Std.Error: 0.017947 Residual Std.Error: 0.03 (on 32 DF) Multiple R-Squared: 0.2848 Adjusted R-Squared: 0.2401 F-statistic: 6.371 (on 2 and 32 DF) p-value: 0.004689 ROI Estimate: 0.00558 Std.Error: 0.017562 Estimate: 0.069704 . Std.Error: 0.039698 Estimate: 0.019470 Std.Error: 0.018876 Residual Std.Error: 0.0333 (on 32 DF) Multiple R-Squared: 0.1189 Adjusted R-Squared: 0.06384 F-statistic: 2.159 (on 2 and 32 DF) p-value: 0.1319 SERVICE SMALLFIRMS (IF(1)-SF-L) ROI Estimate: 0.01829 . Std.Error: 0.01004 Estimate: - 0.14556** Std.Error: 0.05428 Estimate: 0.05505** Std.Error: 0.01758 Residual Std.Error: 0.07522 (on 102 DF) Multiple R-Squared: 0.1446 Adjusted R-Squared: 0.1278 F-statistic: 8.622 (on 2 and 102 DF) p-value: 0.0003472 ROE Estimate: - 0.02492 Std.Error: 0.02931 Estimate: 0.22147* Std.Error: 0.10999 Estimate: 0.08594 . Std.Error: 0.04950 Residual Std.Error: 0.2116 (on 102DF) Multiple R-Squared: 0.06268 Adjusted R-Squared: 0.0443 F-statistic: 3.411 (on 2 and 102 DF) p-value: 0.03683 ROE Estimate: - 0.03436 Std.Error: 0.02780 Estimate: 0.40952** Std.Error: 0.15036 Estimate: 0.08424 . Std.Error: 0.04871 Residual Std.Error: 0.2083 (on 102 DF) Multiple R-Squared: 0.0915 Adjusted R-Squared: 0.07368 F-statistic: 5.136 (on 2 and 102 DF) p-value: 0.007493 Signif. Codes: 0 ‘***’; 0.001 ‘**’; 0.01 ‘*’; 0.05 ‘.’; 0.1‘’; 1 In manufacturing industry the relationship between economic performance of the firm and its financial debt are more evident than in service industry. In manufacturing industry, the analysis found discordant results. While negative relationship between firm economic performance and financial debt in the large and small firms was found, in medium firms a positive relationship between them was found. Particularly in large manufacturing firms were found negative relationships between ROE, ROA and SFD and between ROI and TFD, LFD. Therefore the increase of financial debt has negative effect on firm performance. While the increase of TFD and LFD has a negative effect on ROI with its decrease, the increase of SFD has a negative effect on ROE and ROA with their decrease. Between ROE, ROA and TFD, LFD and between ROI and SFD were not found statistically significant relationships. In medium manufacturing firms were found positive relationships between ROE and TFD, LFD, SFD, between ROI and SFD and between ROA and TFD, SFD. Therefore the increase of TFD has a positive effect on ROE and ROA with their increase, the increase of LFD has a positive effect on ROE with its increase and the increase of SFD has a positive effect on ROE, ROA and ROI with their increase.
  • 15. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 15 Between ROI and TFD, LFD and between ROA and LFD were not found statistically significant relationships. Finally in small manufacturing firms were found negative relationships between ROE and TFD, SFD. Thus the increase of TFD and SFD has a negative effect on ROE with its decrease. Between ROA, ROI and TFD, LFD, SFD and between ROE and LFD were not found statistically significant relationships. Also in service industry the analysis found discordant results. In large service firms were found positive relationships between ROA and TFD and between ROI and LFD while a negative relationship was found between ROI and SFD. Therefore the increase of TFD and LFD has a positive effect on ROA and ROI respectively with their increase while the increase of SFD has a negative effects on ROI with its decrease. Between ROE and TFD, LFD, SFD, between ROA and LFD, SFD, and between ROI and TFD, SFD, were not found statistically significant relationships. In small service firms were found positive relationships between ROE and TFD, SFD while a negative relationship was found between ROI and SFD. Therefore the increase of TFD and SFD has positive effect on ROE while the increase in SFD has negative effect on ROI. Between ROE and LFD, between ROA and TFD, LFD, SFD, and between ROI and TFD, LFD, were not found statistically significant relationships. For all medium service firms were not found a statistically significant relationship between dependent and independent variables. The regression analysis results can be summarize in order to the sign of the relationship (positive or negative) between economic performance and financial debt of the large, medium and small firms in manufacturing and service industry as reported in Tables 6. Table 6 – Correlation sign between economic performance and financial debt of the Large, Medium and Small Firms in Manufacturing and Service Industry POSITIVE CORRELATION NEGATIVE CORRELATION TFD LFD SFD TFD LFD SFD ROE Medium Manufacturing Small Service Medium Manufacturing Medium Manufacturing Small Service Small Manufacturing Large Manufacturing Small Manufacturing ROA Large Service Medium Manufacturing Medium Manufacturing Large Manufacturing ROI Large Service Medium Manufacturing Large Manufacturing Large Manufacturing Large Service Small Service Therefore in manufacturing industry, the analysis results confirmed the hypothesis 1. They were found relationship between economic performance of the firm and its financial debt for large, medium and small firms. Differently in the service industry, the analysis results confirmed the hypothesis 2 partially only. They were found relationship between economic performance of the firm and its financial debt for large and small firms but not for medium firms.
  • 16. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 16 5. CONCLUSIONS This paper is a moderate attempt to understand the relationship between capital structure and economic performance of the Italian large, medium and small firms in manufacturing and service industry listed in Italian Stock Exchange. The basic hypothesis is that the financial debt affects (negatively or positively) the economic performance of the large, medium and small firms in manufacturing and service industry. The analysis found a significant relationship between economic performance of the firm and its financial debt but with discordant results. Also the relationship is more relevant in manufacturing industry than in service industry. The results of the analysis can be summarize as following: 1. The economic performances of the firms, on average, are higher in manufacturing industry than in service industry. On average, both in manufacturing and service industry, the large firm have the best economic performance followed by medium firm and small firm. 2. In manufacturing industry financial debt is lower than service industry for large and medium firms. It is relevant to note that Italian firms usually tend to use commercial debt (trade payables) as a substitute of financial debts. Looking at financial debt duration, both in the manufacturing and service industry, large and medium firms prefers long-term debt to short-term debt while small firms prefer short-term debt to long-term debt. It is mainly due to the possibility for the large and medium firms obtain debt at better condition than small firms. 3. For the large firms the analysis found discordant results between manufacturing and service industry. In large manufacturing firms, compared to positive economic performances (on average ROE is 15%, ROA is 11% and ROI is 4%), TFD is 27% (of which LFD is 17% and SFD is 9%). For these firms, the analysis found a negative correlation between ROI and TFD, LFD, and between ROA, ROE and SFD. In large service firms, compared to positive economic performances lower than large manufacturing firms (on average ROE is 11%, ROA is 8% and ROI is 3%), TFD is higher than large manufacturing firms (on average 55% of which the LFD is 42% and SFD is 13%). For these firms, the analysis found a positive correlation between ROA and TFD and between ROI and LFD and negative correlation between ROI and SFD. 4. Also for the medium firms the analysis found discordant results between manufacturing and service industry. In medium manufacturing firms, compared to positive economic performances (on average ROE is 11%, ROA is 5% and ROI is 3%), TFD is 29% (of which LFD is 15% and SFD is 14%). For these firms, the analysis found a positive correlation between ROE and TFD, LFD, SFD, between ROA and TFD, SFD, and between ROI and SFD. In medium service firms, compared to positive economic performances lower than medium manufacturing firms (on average ROE is 6%, ROA is 3% and ROI is 2%), TFD is higher than medium manufacturing firms (on average 32% of which the LFD is 18% and SFD is 14%). For these firms, the analysis did not found a correlation between ROE, ROA, ROI and TFD, LFD, SFD. 5. For the small firms the analysis found a concordant results in manufacturing and service industry. In small manufacturing firms, compared to negative ROE (-2%) and positive ROA (2%) and ROI (1%), TFD is 26% (of which SFD is 15% and LFD is 12%). For these firms, the analysis found a negative correlation between ROE and TFD, SFD.
  • 17. International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 5, Issue 3, March (2014), pp. 01-20 © IAEME 17 In small service firms, compared to positive economic performances (on average ROE is 2%, ROA is 1% and ROI is 0.5%), TFD is lower than small manufacturing firms (on average 18% of which the SFD is 12% and LFD is 6%). For these firms, the analysis found a negative correlation between ROI and SFD and positive correlation between ROE and TFD, SFD. Therefore in manufacturing industry, the analysis results confirmed the hypothesis 1 while in service industry the hypothesis 2 was confirmed only partially. REFERENCES 1. Abor J., 2005. The effect of capital structure on profitability: empirical analysis of listed firms in Ghana. Journal of Risk Finance, 6(5), pp. 438-445. 2. Amihud Y., Lev B., 1981. Risk reduction as a managerial motive for conglomerate mergers. Bell Journal of Economics, 12(2), pp. 605-6017. 3. Balakrishnan S., Fox I., 1993. Asset specificity, firm heterogeneity and capital structure. Strategic Management Journal, 14 (1), pp.3-16. 4. Barclay M.J., Smith C.W., 1995. The maturity structure of Corporate Debt. Journal of Finance, 50, pp.609-632. 5. Barton S.L., Gordon P.J., 1987. Corporate strategy: useful perspective for the study of capital structure?. Academy of Management Review, 12, pp.67-75. 6. Barton S.L., Gordon P.J., 1988. Corporate strategy and capital structure. Strategic Management Journal, 9(6), pp.623-632. 7. Bergh D.D., 1995. Size and relatedness of units sold. Strategic Management Journal, 16(3), pp. 221-240. 8. Bethel J.E., Liebeskind J., 1993. The effects of ownership structure on corporate restructuring. Strategic Management Journal (summer special issue), 14, pp. 15-31. 9. Bettis R., 1983. Modern Financial Theory, Corporate Strategy and Public Policy: Three Conundrums. Academy of Management Review, 8, 3, pp. 406-415. 10. Bistrova J., Lace N., Peleckiene V., 2011. The Influence of Capital Structure on Baltic Corporate Performance. Journal of Business Economics and Management, 12(4), pp. 655-669. 11. Booth L., Aivazian V., Demirguc-Kunt, Maksimovic V., 2001. Capital structure in developing countries. Journal of Finance, 55(1), pp.87-130. 12. Bradley M., Jarrel G.A, Han Kim E., 1984. On the existence of an optimal capital structure: Theory and evidence. Journal of Finance, 39, pp. 857-880. 13. Brennan M., Kraus A., 1987. Efficient financing under asymmetric information. Journal of Finance, 42, pp. 1225-1243. 14. Chittenden F., Hall G., Hutchinson P., 1996. Small firm growth, access to capital markets and financial structure: review of issue and an empirical investigation. Small Business Economics, 8 (1), pp. 59-67. 15. Constantinides G.M., Grundy B.D., 1989. Optimal investment with stock repurchase and financing as signals. The review of Financial Studies, 2, pp. 445-466. 16. Diamond D.W., 1989. Reputation acquisition in debt markets. Journal of Political Economy, 97, pp. 828-862. 17. Esperanca J.P., Ana P.M.G., Mohamed A.G., 2003. Corporate debt policy of small firms: An empirical (re)examination. Journal of Small Business and Enterprise Development, 10(1), pp. 62-80. 18. Fama E.F., French K.R., 1998. Taxes, financing decisions, and firm value. Journal of Finance, vol.53, pp.819-843. 19. Fama E.F., French K.R., 2002. Testing trade-off and pecking order predictions about dividends and debt. Review of Financial Studies, 15, pp.1-33.
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