Factors affecting firms' performance: the case of Greece.
In the era of globalization, competition has become fiercer than ever. Reduced trade barriers, spread of technology and lower costs for communication and transportation have sharpened international competition. The economic changes in Eastern European countries, the completion of the European Union and the appearance of new economic powers in the global market have initiated specific discussion of production structures and the competitiveness of national industries. Intense competition in global and local markets requires firms to improve their financial performance. This is especially true for smaller countries, like Greece where profitability can allow firms to overcome the limitations of their small home markets in order to achieve their maximum potential. This improvement not only benefits the firms themselves, but also has a direct impact on the competitiveness of an economy as a whole. The international business literature is replete with empirical and conceptual works pertaining to competitiveness. However, there is still debate among several disciplines regarding how the performance of these firms should be measured and what factors affect business success.
The purpose of this paper is to empirically implement a comprehensive framework of firm financial performance in the case of Greece. The specific research question is: "What are the factors which affect the financial performance of Greek industrial firms during the period 1997-2004?" The structure of this paper is as follows: Section 2 provides a literature review and presents an analytical framework. Section 3 discusses the data used and describes the methodology. Section 4 provides the estimation and the empirical results, while section 5 offers some conclusions.
Literature Review and Analytical Framework
A large body of previous theoretical and empirical research has tried to investigate various factors affecting firms' financial performance. Theoretical research is based on microeconomic theory and more specifically on the firm's theory of production (see for example Penrose, 1995). Empirical research has been developed around two areas of interest. First a number of researchers investigate how to measure firms' financial performance. There have been various measures of financial performance. For example return on sales reveals how much a company earns in relation to its sales, return on assets determines an organization's ability to make use of its assets and return on equity reveals what return investors take for their investments. The advantages of financial measures are the easiness of calculation and that definitions are agreed worldwide. Traditionally, the success of a manufacturing system or company has been evaluated by the use of financial measures (Tangen, 2003). Table 1 presents an overview of the reviewed measures of financial performance.
A second strand of the empirical literature examines how financial and non-financial factors, such as debt leverage, liquidity, capitalization, investment, size, age, location, export performance and managerial efficiency have an influence on the firms' financial performance and growth.
Debt leverage is measured by the ratio of total debt to equity (debt/equity ratio). It shows the degree to which a business is utilizing borrowed money. Companies that are highly leveraged may be at risk of bankruptcy if they are unable to make payments on their debt; they may also be unable to find new lenders in the future. Leverage is not always bad, however; it can increase the shareholders' return on their investment and make good use of the tax advantages associated with borrowing. The trade-off theory (TO) (Bradley, Jarrell and Kim, 1984; Harris and Raviv, 1991) suggests that every firm has a specific optimal debt-to-equity ratio determined by balancing the present value of expected marginal benefits of leverage (ex. tax savings due to paid interests) against the present value of expected marginal costs of leverage. According to this theory, each company borrows in order to gradually move towards its optimal debt-equity ratio, which in turn maximizes its market value (given by the present value of the sum of the expected costs and benefits of debt). Furthermore Jensen (1986) and Zwiebel (1996) support that increased debt can reduce the probability of a firm's takeover by committing managers to a more efficient business strategy. Thus, there is either a negative or positive influence of leverage on firms' performance.
Liquidity refers to the degree to which debt obligations coming due in the next 12 months can be paid from cash or assets that will be turned into cash. It is usually measured by the current assets to current liabilities (current ratio). It shows the ability to convert an asset to cash quickly and reflects the ability of the firm to manage working capital when kept at normal levels. In financial economics a standard argument to justify the decision of a firm to maintain excess liquidity in its assets relates to both speculative and precautionary motives. A firm can use liquid assets to finance its activities and investments when external finance is not available or it is too costly. On the other hand, higher liquidity would allow a firm to deal with unexpected contingencies and to cope with its obligations during periods of low earnings (Opler et al., 1999, Myers, 1977, Kim et al., 1998). In contrast to the above reasoning, Hvide and These (2007), based on a theoretical model by Evans and Jovanovic (1989), suggest that a moderate amount of liquidity may propel entrepreneurial performance, but that an abundance of liquidity may do more harm than good.
Therefore, the effect of liquidity on firms' financial performance is ambiguous.
The capitalization rate or the ratio of fixed assets to total assets, measures the extent to which fixed assets are financed with owners' equity capital. A high ratio indicates an inefficient use of working capital which reduces the firm's ability to carry accounts receivable and maintain inventory and usually means a low cash reserve. This may often limit the ability of the firm to respond to increased demand for products or services. The fixed assets to total assets ratio affects firm's profitability negatively (Notta O. and Vlachvei A., 2007; Agiomirgiannakis et al., 2006). This can be attributed to the reduced level of current assets which could lead to a lower level of sales, since the firm will be short of the necessary materials, stocks, etc., with a reduced level of activity overall.
Net investment (ratio of the net investment to the total assets) refers to an activity of spending, which increases the availability of fixed capital goods or means of production. Net investment is the total spending on new fixed investment minus replacement investment, which simply replaces depreciated capital goods. This ratio helps to give a sense of how much money a company is spending on capital items used for operations (such as property, plants and equipment). Continued investment in the capital of a firm is crucial because the useful life of existing capital diminishes over time. The amount of net investment compared to such things as revenue will differ between industries and between businesses depending on how capital intensive the business is. This ratio is positively related to firm performance since new investments expand the production and the cash flow generating capacity of the firm.
The size of the firm affects its financial performance in many ways. Large firms can exploit economies of scale and scope and thus being more efficient compared to small firms. In addition, small firms may have less power than large firms; hence they may find it difficult to compete with the large firms particularly in highly competitive markets. On the other hand, as firms become larger, they might suffer from x-inefficiencies, leading to inferior financial performance. Theory, therefore, is equivocal on the precise relationship between size and performance (Majumdar, 1997, p.233)
Regarding firm age, older firms are more experienced, have enjoyed the benefits of learning, are not prone to the liabilities of newness (Stinchcombe, 1965), and can, therefore, enjoy superior performance. Older firms may also benefit from reputation effects, which allow them to earn a higher margin on sales. On the other hand, older firms are prone to inertia, and the bureaucratic ossification that goes along with age; they might have developed routines, which are out of touch with changes in market conditions, in which case an inverse relationship between age and profitability or growth could be observed. Older firms are unlikely to have the flexibility to make rapid adjustments to changing circumstances and are likely to lose out in the performance stakes to younger, and more agile, firms (Marshall, 1920). Thus the results may reveal bidirectional interactions between age and firm performance.
With the rapid advancement in transportation and communications, the role of location in determining firm performance may decrease, with respect to factors that can be easily sourced across regions in free-market economies, such as capital, goods, and technology (Li, 2004). However, the enduring competitive advantages in a global economy lie increasingly in local things--knowledge, relationships, and motivation that distant rivals cannot match (Porter, 1998). Such advantages are specific to a particular location and thus immobile. In order to access such advantages, a firm must locate in their proximity.
Export performance is the relative success or failure of the efforts of a firm or nation to sell domestically-produced goods and services in other nations. There are two views concerning international exchange. The first, (classical theory) recognizes the benefits of trade. The second concerns itself with the possibility that some industries can be harmed and others can be benefited by foreign competition (new trade theories).
The adoption of best management practices is a source of competitive advantage, positively related to firms' performance, growth and survival. According to Timmons (1994) entrepreneurs who succeed possess not only an innovative behavior but also solid general management skills. Bird (1995) and Ronstadt (1984) conclude that entrepreneur's management skills were conducive to business performance and growth. Successful firms will be those that have developed a core competence in entrepreneurship where a core competence refers to 'a combination of complementary skills and knowledge bases embedded in a group or team that results in the ability to execute one or more critical processes to a world-class standard (Cayne, Hall and Clifford, 1997).
From the above variables, the first four could be categorized as financial drivers, the next four as non-financial drivers and the last one as a combination of financial and non-financial drivers. In summary, all the previous theoretical and empirical investigation can be incorporated in an analytical framework that includes a comprehensive set of links between financial performance indicators and drivers, shown in Figure 1.
Management competence index, a combination of financial and non financial drivers, is connected with indicators and co-drivers by a specific way in order to underline the unique relation between effective management and firm financial performance factors.
[FIGURE 1 OMITTED]
Data and Methodology
The purpose of this paper is to identify the factors which affect firm performance in Greece. We used ICAP  database for 102 firms listed on the Athens Stock Exchange during the period 1997-2004. Firms are assigned to an industry group if more than 60% of their annual sales are from activities within that industry. Our initial sample consisted of 150 firms. The following firms were excluded from the sample:
* Firms belonging to industries with too few firms listed at the stock market (less than four firms).
* Firms involved in different activities as they could not be assigned to a particular industry.
* Banks, other financial institutions, and insurance companies, because of their special financial structure.
* Investment companies, because their incomes mainly results from the value of their holding portfolios. This value depends on the financial structure and business conditions of the firms whose stocks are included in the portfolio rather than the financial structure of the investment companies.
The resulting sample for the eight year period 1997-2004 consisted of 102 firms in 15 industries.
We collected data for each firm from two sources; First, from the ICAP Hellas database and second on the basis of a questionnaire. Furthermore, we validated questionnaires' financial data and export activity of firms from the ICAP data base and the "Greek Export directory 2004-2005" respectively.
Information was compiled on the following areas:
* Financial data of the firm
* Level of education of the management team members
* Shareholding percentage of the management team members
* Existence of innovation in the firm
* Average years of experience of the management team members
* Average age of the management team members
* Number of employees
* Number of employees having tertiary education
* Location of the firm
* Age of firm
* Export activity of the firm
It appeared that 102 of these 150 firms have management teams fulfilling at least three out of five criteria that are described below:
1) The average number of experience of the members of the management team is twenty (20) years.
2) The management team holds on average 34% of the company's shares.
3) Most of the management team's members hold a university degree in finance or in engineering.
4) The average age bracket of the management team is 50-60 years old.
5) All management teams implement innovation practices. Innovation, according to Schumpeter (1934) and other more recent researchers (Lumpkin and Dess, 1996; West & Farr, 1990), refers to the introduction of a new product or a new technique in production or a new market or a new organization structure in the firm. If any of the above has taken place within the last four years the management team is an innovator.
We use three measures to evaluate the financial performance:  (a) Return on sales (ROS) or profit margin: ROS reveals how much a company earns in relation to its sales. These measures determine the company's ability to withstand competition and adverse rising costs, falling prices or declining sales in the future. (b) Return on assets (ROA): ROA is one of the most widely used financial models for performance measurements and it was developed by Dupont in 1919. ROA determines a firm's ability to make use of its assets. (c) Return on equity (ROE): ROE measures what return investors (i.e. stockholders) are getting for their investments in the firm. In other words it tells how well the company is doing for the investor (Tangen, 2003). We use three empirical models, one for each depended variable of the firm's performance.
Based on the analytical framework presented in the previous section, we make the hypothesis that the following independent variables might affect the firms' performance:
1. Leverage (lev) as measured by the ratio of total debt to equity (debt/equity ratio)
2. Liquidity (liquid), as measured by the ratio of current assets to current liabilities
3. Capitalization ratio (capital) as measured by the ratio of fixed assets to total assets
4. Investment (net_inv), as measured by the ratio of net investment to the total assets
5. Size (size), as measured by the total number of the firm's employees
6. Age (age), as measured by the number of years since establishment
7. Location (loc). We test if the location of firms established in the two biggest Greek cities (Athens and Thessalonica) affects their competitiveness. Location is a dummy variable with two values, 1 for Athens and Thessalonica and 0 otherwise. We expect that firms located in Athens or Thessalonica could be better positioned (i.e. closer to their markets) to take advantage of changes in market conditions.
8. Export (export), which is a dummy variable taking the value 1, if the firm is an exporter and 0 otherwise.
9. Management efficiency (mc_index), as measured by the following management competence index used by Skandalis et al. (2008):
management competence index = profit/number of professionals
Profits are calculated before taxes for each consecutive year, between 1997-2004. As number of professionals, we keep the same number for all years (even though it is the actual figure of 2003) because we consider that there are small changes of this number over the years. If there are any changes, then these changes will have little effect to the final result of the index. As "professionals" we consider the personnel which fulfil two criteria: (a) It processes a university degree (tertiary education) and (b) It is under the direct control or part of the management team.
Estimation and Empirical Results
The relationship between competitive sources and performance were tested using panel regression analysis for the following reasons: First, because panel data suggests that firms are heterogenous and therefore do not run the risk of obtaining biased results. Second, because panel data gives more informative data, more variability, less collinearity among the variables, more degrees of freedom and more efficiency. Finally, panel data are able to identify and measure effects that are simply not detectable in pure cross section or pure time-series data. The panel regression model consists of three separate regressions on the same set of explanatory variables. The regression model is:
Financial performance = [b.sub.0] + [b.sub.1](lev(-1)) + [b.sub.2] (liquid) + [b.sub.3] (capital) + [b.sub.4] (netinv) + [b.sub.5] (lnsize) + [b.sub.6] (lnage) + [b.sub.7] (loc) + [b.sub.8] (export) + [b.sub.9] (lnmc_index) + [u.sub.t] (1)
Financial performance is measured by either ROA or ROE or ROS and "u" denotes a random disturbance term. The regression coefficient (bj) represents the expected change in the performance indicator associated with one-unit change in the z'th independent variable, i.e. competitive sources. Lev(-1), Liquid, Capital, Net_inv, Lnsize, Lnage, Loc, Export, Lnmc_index, represent leverage, liquidity, rate of fixed to total assets, net investment ratio, size, age, location, export activity and management competence index respectively.
We run three panel least squares regression, one for each dependent variable, with a time series component of 8 (eight) years, 1997-2004. The cross sectional observations were 102. The method of estimation was panel least squares and the effects specification was random , while for the covariance matrix cross section weights (PCSE) and White cross section weights were used with no d.f correction. In all three regressions, we used the lagged value of leverage (lev(-1)), the natural logarithm of age (lnage), size (lnsize) and management index (lnmc_index). All three dependent variables were expressed in their natural logarithm form (lnroa, lnroe, lnros), so the final estimation involved unbalanced panel data. Tables 3, 4 and 5 show the estimated coefficients with their t-ratios.
According to the results obtained, the panel regression models with dependent variables leverage, liquidity, fixed to total assets, net investment ratio, firm size, firm age, firm location, firm export activity and management competence index are all significant at p<0.01.
An interesting result is the positive and significant impact that an increase in the leverage of firms has on their competitiveness when it is measured by Return on Assets (ROA), Return on Equity (ROE) and Return on Sales (ROS). This result is in line with the trade-off theory presented in the previous section. It is also similar to the results obtained by Majumdar (1997) for the case of India, but it is different to the ones obtained by Agiomirgiannakis et. al (2006) for the case of Greece during the period 1995-99. It seems that in the more recent period, greater reliance on debt makes Greek managers more stringent regarding the monitoring of their firms which then become more competitive.
As we can see from Tables 3-5, liquidity negatively influences performance. More specifically liquidity is negative and significant when performance is measured by ROA and ROE. In other words, when liquidity is excessive the effect on profitability is negative. Our result is in line with the theoretical work of Evans & Jovanovic (1989) and with the empirical result of Majumdar (1997).
Ratio of Fixed Assets to Total Assets (capitalization) is negatively related to financial performance in two out of three cases.  This can be attributed to the reduced level of current assets which could lead to a lower level of sales, since the firm will be short of the necessary materials, stocks, etc., with a reduced level of activity overall. In other words this relationship indicates that when this ratio is high there is an inefficient use of working capital which limits Greek firms' ability to carry accounts, to maintain inventory, and to respond to an increased demand.
Net investment ratio has positive influence in all three measures of competitiveness. It is very significant (sig. at the 1% level) for ROA and ROE but not so for ROS (sig. at the 10% level). This positive influence means that the amount of money a Greek firm spends on capital items used for operations is vital because the useful life of existing capital diminishes over time. In other terms new investments influence positively Greek firms' performance.
The variable of size is positive and very significant for ROE and insignificant in the other two cases. The positive relationship between size and performance indicates that larger firms are more profitable, according to theory and other empirical findings (e.g. Voulgaris et al., 2005). The age of firm is negatively related to all three measures of financial performance. This relation agrees with the findings of Agiomirgiannakis et al. (2006).
As we can see from the tables, variables for location are positive and significant in all three regressions. Companies located in Athens or Thessalonica are benefited from their position (i.e. they are closer to their markets). This result is in line with the result found in Fotopoulos and Louri (2004), according to which location in greater Athens vs the rest of Greece affects survival of firms positively.
The results from the panel regression analysis show that exports positively relate to Return on Assets. This result is in the opposite direction to the findings of Agiomirgiannakis et al. (2006) where exports relate negatively to firm profitability. The positive influence can be explained by the fact that in the more recent period Greek firms have found an easy outlet of their activities to South Eastern European markets (Balkans). Within the Balkans, the Greek export share has a significant increase in the last fifteen years and has reached one of the first places in the ranking of region's leading exporters. Thus firms involved in such export activities are more profitable compared to non-exporting firms.
It also appears that management competence index is significant (sig. at the 1% level) in all three regressions and has the correct sign. More specifically it is shown that professionals who are managed by a team which carries all the attributes we specified influences positively Greek firms' financial performance. It should be noted that management competence is very important for Greek firms since the management team decides for the location of the firm, for its size and its net investments.
In our study we examined the factors affecting financial performance of Greek industrial firms, using a sample of 102 listed firms in the Athens Stock Exchange over the period 1997-2004. Financial performance of firms was measured with the use of three indicators; return on assets, return on equity and return on sales. An econometric approach allows the data to determine the functional relationship and the impact of leverage, size, age, location, export activity, net investment and management effectiveness on economic performance, while taking into account the heterogeneity among firms. Summarizing the results, it is found that debt leverage, export activity, location, size and the index for management competence are significantly correlated, as expected, with the economic performance of firms. Our results indicate that profitable firms in Greece are large, young, exporting firms with a competitive management team, which have an optimal debt-equity ratio and use their liquidity to finance their investments. Finally, our approach can be used as a useful tool to understand practical problems that arise when managers consider strategies to improve firm performance.
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 ICAP Hellas is a Greek private research company.
 We have chosen firms which are listed in the Athens Stock Exchange because these firms are large firms and they have more accurate data.
 As it was discussed before, the suggested variables are very common and have been used by many other researchers. See among others Hart and Ahuja (1996); Konar and Cohen (1997); Agiomirgiannakis et al. (2006).
 Panel EGLS (Cross-section random effects)
 Agiomirgiannakis et al. (2006) also found a negative effect, verifying the assumption that capital intensive Greek firms do not operate on an efficient scale
Panagiotis G. Liargovas can be contacted at: firstname.lastname@example.org
Panagiotis G. Liargovas (1) and Konstantinos S. Skandalis (2)
(1) Department of Economics, University of Peloponnese, Greece
(2) Department of Economics, University of Athens, Greece
Table 1: Measures of Financial Performance Study Financial performance measures Cochran, Wood and Jones, 1985 Return on assets (ROA), Return on Equity (ROE), Net profit margin, Firm's assets Kesner, 1987 ROA, ROE and lagged total returns to investors Mallette and Fowler, 1992 ROE Opler and Titman, 1994 Growth in sales, Growth in profitability and stock returns Klassen and McLaughlin, 1996 Stock market returns Hart and Ahuja, 1996 Return on sales (ROS), ROA, ROE Konar and Cohen, 1997 ROA, ROE Majumdar, 1997 ROS Thomas and Tonks, 1999 Monthly excess stock market returns over the risk free rate. Becker-Blease et al., 2005 EBITDA margin, EBIT margin, EBITDA as a percent of total assets, EBIT to total assets. Skandalis et al., 2008 Sales growth, Growth in profitability, Stock returns annual percentage change Agiomirgiannakis et al., 2006 ROA Bobillo et al., 2006 Sales, Net profit margin Table 2: Firms by Industry INDUSTRY No. OF FIRMS Construction 13 Printing-publishing 6 Computers 7 Transport 3 Retailing 6 Food and drink 16 Basic metals 10 Elastics & plastics 5 Non-metallic ore & cement 5 Clothing 2 Machines-equipment 3 Metallic products 2 Refineries 1 Private hospitals 1 Wholesaling 22 Total number of Firms 102 Table 3: Determinants of Return on Assets LnROA-random Coefficient t-Statistic Lev(-1) 0.0713 *** 3.8018 Lnsize 0.0337 0.6340 Lnage -0.0803 -0.5087 Loc 0.3135 *** 3.1855 Liquid -0.0686 ** -2.4969 Capital -1.8373 *** -5.1722 Export 0.3674 *** 3.8265 R-squared: 0.296 Net_inv 1.5694 *** 4.1038 F-statistic : 32.298 Lnmcindex 0.1453 *** 15.7439 Prob(F-statistic): 0.000 * Significant at the 10% level, ** Significant at the 5% level, *** Significant at the 1% level Table 4: Determinants of Return on Equity LnROE-random Coefficient t-Statistic Lev(-1) 0.1652 *** 5.2380 Lnsize 0.2208 *** 4.9347 Lnage -0.2102 * -1.6869 Loc 0.2022 * 1.9507 Liquid -0.1140 *** -3.5793 Capital -2.4808 *** -5.5286 Export -0.0957 ** -2.2658 R-squared: 0.593 Net_inv 1.2038 *** 2.9577 F-statistic : 112.54 Lnmcindex 0.6041 *** 7.6801 Prob(F-statistic): 0.000 * Significant at the 10% level, ** Significant at the 5% level, *** Significant at the 1% level Table 5: Determinants of Return on Sales LnROS Coefficient t-Statistic Lev(-1) 0.0408 *** 3.2900 Lnsize -0.0088 -0.1811 Lnage -0.2332 ** -2.3486 Loc 0.2171 *** 2.9416 Liquid 0.0125 0.9803 Capital 0.2996 ** 2.5206 Export 0.1251 0.9575 R-squared: 0.067 net_inv 0.2662 ** 2.0391 F-statistic : 5.536 Lnmcindex 0.0783 *** 8.5990 Prob(F-statistic): 0.000 * Significant at the 10% level, ** Significant at the 5% level, *** Significant at the 1% level Figure 2: Average Years of the Management Team in the Company Series: YEARS Sample 1 102 Observations 102 Mean 20.00930 Median 19.50000 Maximum 47.00000 Minimum 2.000000 Std. Dev. 10.45073 Skewness 0.093021 Kurtosis 2.104592 Jarque-Bera 3.570796 Probability 0.167730 Figure 3: Stock Ownership of Management Team Series: STOCK Sample 1 102 Observations 102 Mean 33.93701 Median 31.10000 Maximum 87.08000 Minimum 0.000000 Std. Dev. 30.08760 Skewness 0.208715 Kurtosis 1.495240 Jarque-Bera 10.36384 Probability 0.005617 Figure 4: Higher Education of Management Team (dummy) (1=tertiary, 0=secondary) Series: EDU Sample 1 102 Observations 102 Mean 0.784314 Median 1.000000 Maximum 1.000000 Minimum 0.000000 Std. Dev. 0.413329 Skewness -1.382521 Kurtosis 2.911364 Jarque-Bera 32.52657 Probability 0.000000 Figure 5: Average Age of the Management Team (dummy) (1=30-50 years old, 2=50-60 years old, 3=60+ years old) Series: AGE Sample 1 102 Observations 102 Mean 2.029412 Median 2.000000 Maximum 3.000000 Minimum 1.000000 Std. Dev. 0.681522 Skewness -0.035892 Kurtosis 2.173322 Jarque-Bera 2.926336 Probability 0.231502
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|Author:||Liargovas, Panagiotis G.; Skandalis, Konstantinos S.|
|Publication:||Global Business and Management Research: An International Journal|
|Date:||Apr 1, 2010|
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