Emerging issues and Challenges in Business ...

Emerging Issues and Challenges in Business & Economics Selected Contributions from the 8th Global Conference Edited by Francesco Ciampi

firenze university press 2009

Emerging Issues and Challenges in Business & Economics: Selected Contributions from the 8th Global Conference/ a cura di Francesco Ciampi. – Firenze : Firenze University Press, 2009. (Atti ; 24) http://digital.casalini.it/9788864530611 ISBN 978-88-6453-059-8 (print) ISBN 978-88-6453-061-1 (online)

Sponsored by Faculty of Economics University of Florence

Progetto grafico di Alberto Pizarro Fernández Immagine di copertina: © Pertusinas | Dreamstime.com © 2009 Firenze University Press Università degli Studi di Firenze Firenze University Press Borgo Albizi, 28, 50122 Firenze, Italy http://www.fupress.com/ Printed in Italy

Contents

Editor’s note IX Francesco Ciampi What Is the Right Governance for Technology Alliances? A Conceptualization Based on the Nature of Technology Arijit Sikdar 11 Enterprise Default Prediction Modeling. Preliminary Findings of a Statistical Study of Italian Small Firms Carlo Vallini, Francesco Ciampi, Niccolò Gordini, Michele Benvenuti 27 Audit Committee Characteristics, Audit Firm Size and Quarterly Earnings Management in Thailand Wiwanya Thoopsamut, Aim-orn Jaikengkit 47 Country Risk of Croatia and the Euro Zone. Is the Pressure Sustainable? Gerhard Fink, Peter Haiss, Ina Paripovic 63 Successful Lebanese Female Leaders: A Reality or a Myth? Renee Ghattas 85 Competition, Technology Innovation and Industrial Structure in the Business Aviation Industry Mario Mustilli, Filomena Izzo 101 The Strategic Role of Innovative Finance and the “Alternative Capital Market” (MAC) for Business Succession in Italian Family Firms Riccardo Passeri, Chiara Mazzi 121 Business Failure Prediction Modeling through Balance Sheet Ratios. Preliminary Findings of a Statistical Study of Italian Small Manufacturing Firms Francesco Ciampi, Niccolò Gordini 143 Pathways to Innovation: Evidence from Competitiveness Clusters in France Sharam Alijani 163

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Hawkers and the Self-Assessment Tax System: Survey Evidence from Malaysia Kwai Fatt Choong, Ming Ling Lai, Kok Thye Ng Multi-Channel Integration Strategies in Retailing: an Exploratory Analysis of the PC and Electronics Industries Daniela Andreini The Teaching Style of the Business Educator: A Corpus-Based Investigation of the Relationship Between Language and Identity Belinda Crawford Camiciottoli Estimating the Monetary Policy Reaction Function for Turkey Bengül Gülümser Kaytanci Determinants of Capital Flight in Developing Economies: A Study of Nigeria Folorunso S. Ayadi End of Semester Student Evaluation of Teaching Effectiveness Questionnaires: An Indicator of Teaching Quality Janine Saba Zakka Economic and Strategic Alliance Between India and the US: The Evolving Role of Outsourcing in a Globalized World Faridul Islam, Ramendra Thakur, Saleheen Khan, Stanley Earl Jenne The Role of Corruption in Emerging Economies: The Case of Romania Daniela Frederick Information Technology and New Business Models in the Tourism Industry Elena Livi Economics of Learning Style: Traditional versus E-Learning (The Case of Jordan) Ahmad M. Mashal, Rusli J. Kaddo, Mofeed A. Abu Musa Measuring Firm Size Using Business Ethics: When a Ethical Code Is Necessary Stefano Guidantoni Strategic Changes in Graduate Business Schools of Lahore, Pakistan: A Complete Make-over Saba Rana Controlling and Evaluation of Human Capital. The Approach by Jac Fitz-enz Daniel Streich

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Emerging Issues and Challenges in Business & Economics

The Influence of Human Factors on Information Security Measures Effectiveness in relation to Ethical Issues Supattra Boonmak Dynamics of the Merger of Emirates Bank International (EBI) and National Bank of Dubai (NBD). Strategic Challenges of Regional Consolidation Dayanand Pandey, Sumit Mitra The Evolution of Financial Accounting Standards in the Philippines Consolacion L. Fajardo An Empirical Investigation of the Antecedents to the Timing of New Brand Introduction Decision Danielle A. Chmielewski Global Perspectives in the Origins, Development & Consequences of Women’s Self Concepts Cultivated through Television Programming and Advertising Michaeline Skiba, Susan Forquer Gupta Competitive Advantage Via Synergy in Processes and Operations: The International Joint Ventures Way Dileep Singh, Geetika Value Creation through Relationship Closeness in a Value Delivery Network: An Exploratory Study Vibhava Srivastava, Tripti Singh Environmental Initiatives under Current Business Tools and Paradigms Saurav Dutta, Raef Lawson, David Marcinko Teaching and Learning Business Innovation by Successive Approximations Eduardo Pol Teamwork Rubric Herbert Rau An Application of Social Network Analysis to Assess Virtual Innovation Team Performance Valerio Alfonsi, Guendalina Capece, Roberta Costa Effect of Exchange Rate, Inflation and Wages on the Purchasing Power of Consumers in Different Economies Jian Zhang

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Russian Macroeconomic Stability (Sort of) and Consumer Market Proliferation. The Humpbacked Pony Tale in the Era of Petrodollars Nikolai Ostapenko

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Editor’s Note

Francesco Ciampi

The 8th Global Conference on Business & Economics was held at the Faculty of Economics of the University of Florence in the month of October 2008. This international conference was organized by the Association for Business & Economics Research, together with the Department of Business Sciences of the University of Florence, and sponsored by the International Journal of Business & Economics and the Oxford Journal. Business academics and economists from universities and business schools in fifty different countries around the world (representing every continent) presented their most recent research findings, most of which unpublished. The papers had been selected on the basis of a double blind peer review process carried out by the scientific committee of the conference. During the thirty-six conference sessions, 140 papers were presented and discussed. They dealt with various areas of business and economics (strategic management, finance, marketing, accounting, business ethics, business law and others), and focused on a range of industrial sectors and services (from the banking sector to the oil industry, from textile production to automobile manufacturing). This monograph, edited by myself, consists of a selection of the papers presented at the conference. It was not easy to choose among all the contributions. I selected papers on the basis of two fundamental criteria: disciplinary representativeness and content originality. More specifically, I aimed, on the one hand, to offer a selection that was as representative as possible of the numerous disciplinary areas covered during the conference and, on the other hand, to include contributions that I considered to be particularly interesting and/or innovative with respect to the state of the art of their research topics. I would like to thank, first and foremost, Prof. Atul Gupta of Lynchburg College (Virginia, USA) for granting me the honor of co-chairing with him such a prestigious

X

Editor’s note

conference and for having chosen the Faculty of Economics of the University of Florence as the conference venue. Special thanks also go to Prof. Giampiero Nigro (Dean of the Faculty of Economics of the University of Florence) and to Prof. Francesco Giunta (Chair of the Department of Business Sciences of the University of Florence) for their personal encouragement (in addition to their logistic and organizational support), which proved crucial to the successful outcome of the conference. Finally, I thank Prof. Carlo Vallini who, also in this occasion, has been an irreplaceable point of reference.

Francesco Ciampi, University of Florence Florence, Italy, September 2009

Business Failure Prediction Modeling through Balance Sheet Ratios. Preliminary Findings of a Statistical Study of Italian Small Manufacturing Firms Francesco Ciampi, Niccolò Gordini1

Abstract Previous empirical research shows the effectiveness of using sets of economicfinancial ratios for company default prediction statistical modeling. However, such research rarely focuses on small enterprises (SEs) as specific units of analysis. In Italy, SEs account for more than 98% of all firms and employ over 70% of the total workforce. The results of our statistical analyses, conducted on a sample of small manufacturing firms in Northern and Central Italy, show that both discriminant analysis and logistic regression are effective tools for designing SEs’ default prediction models based on economic-financial ratios. In addition, the proposed models gain in prediction accuracy when they are specifically constructed for separate business sectors and separate company size groups. Without denying the value of jointly using quantitative and qualitative variables to measure a firm’s rating, this paper confirms that: i) SEs’ credit rating models must adequately weight sets of appropriately selected financial and economic ratios; ii) SEs’ credit rating should be modeled separately from that of large and medium-sized firms; and iii) SEs’ credit rating models need to be specifically designed so as to take into account the diverse economic and financial profiles of firms in different industries and at different stages of growth. Keywords: Small Enterprises, Default Prediction Models, Rating, Basel II

1

Francesco Ciampi, University of Florence, Italy – [email protected] Niccolo Gordini, University of Florence, Italy –[email protected]

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F. Ciampi, N. Gordini

1. Introduction A large number of empirical studies have used univariate and multivariate statistical techniques to show how effective financial ratio sets can be when constructing company default prediction models (e.g., Altman, 1968, 1993; Altman, Brady, Resti, & Sironi, 2005; Beaver, 1967; and Blum, 1974a). Several authors have recently discussed the widespread adoption by banks, and other credit institutions, of formal scoring systems to determine credit ratings, and have examined its effects on the ability of small and medium-sized enterprises (SMEs) to obtain credit (e.g., Altman, 2004; Altman, & Sabato, 2005; Altman, & Saunders, 2001; Basel Committee on Banking Supervision, 2006; Berger, 2006; Berger, & Scott, 2007; Berger, & Udell, 2006; Heitfield, 2004; Saurina, & Trucharte, 2004; Schwaiger, 2002; Scott, Srinivasan, & Woosle, 2001; Udell, 2004). These changes have assumed major operational significance since the New Basel Capital Accord known as Basel II has come into force. This study defines small firms (SEs) as those having a turnover of less than 15 million euros. In Italy, SEs account for more than 98% of all firms and employ over 70% of the workforce. SEs are known to be able to react quickly and find creative solutions when faced with the increasing turbulence in the business world, and with the wide range of ensuing problems regarding competitiveness, social and cultural issues and technological innovation. SEs are able to effectively provide products and services for market segments which may be too difficult for large firms to reach, or not sufficiently profitable. SEs also make a valid contribution to innovation, although mainly through informal channels. Their real strength is their essentially “personal” character in all aspects of basic company structure, namely ownership, management and operating systems. This means that they can be more flexible, quicker, and can streamline company decision-taking. The downside is that small entrepreneurs find it difficult to keep on top of the huge amount of information available (and necessary) in order to consciously take the many complex decisions involved in business activities. Often small entrepreneurs are endowed with highly specialized production skills, but one of the hardest tasks they face is how to cope with all the multifaceted skills they have to acquire that are not directly related to production. Frequently, they are not sufficiently versed in tasks of company management, such as marketing, accountancy systems and (of most relevance to this paper) financing. It is no secret that a SE’s financial structure is rarely sufficiently robust. The company is undercapitalized, and medium and longterm credit access may be problematic. As a consequence, SEs often highly depend on short-term bank loans. These loans should meet temporary requirements, but many SEs find that they have to rely on them for financial commitments of much longer duration (Ciampi, 1994).

Business Failure Prediction Modeling

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The particular characteristics of SEs justify attempts to construct and test default prediction models that can incorporate the specific SE credit risk profiles. Very few studies have focused on credit risk models especially designed for SEs. Altman and Sabato (2006) showed the effectiveness of default prediction models developed for SMEs, working on the wider category of firms with sales less then 50 million euros. We need to go as far back as 1972 (Edmister) to find research targeting credit risk models for SEs. Our study aims to develop and test SE default prediction models based on an appropriately selected set of financial-economic ratios. To do so, we use discriminant analysis and logistic regression analysis.

2. Literature review The literature dealing with company default prediction methodologies is extensive and many possible alternatives to predict business failure have been examined over the years. Beaver (1967; 1968) and Altman’s (e.g., 1968) studies represent a landmark for future research on credit risk models. Using univariate discriminant analysis, Beaver (1967) focused on large firms which failed in the period 1954-1964 and compared them with a number of firms which did not. He used a sample consisting of 158 firms (79 which failed and 79 which did not) and 14 financial ratios. He found that the ratio of annual cash flow to total debt would misclassify only 13 percent of the firms one year before bankruptcy and 22 percent of the firms five years before bankruptcy (see Edmister, 1972). In a later study, Beaver (1968) stated: “financial ratios can be useful in the prediction of failure for at least five years prior to event”. Altman (1968) used a multiple discriminant analysis technique to assess the effectiveness of ratio analysis in predicting enterprise bankruptcy. He used a sample of 33 manufacturing firms which were declared bankrupt under Chapter X during the period 1946-1965, and a stratified sample of 33 non-failing manufacturing firms. From an initial list of 22 potentially helpful financial ratios, he selected the following five ratios: working capital/total assets, retained earnings before interest and taxes/total assets, equity market value/book value of total debts and sales/total assets. These variables were classified into five ratio categories as follows: liquidity, profitability, leverage, solvency and activity ratios. The predictive ability of Altman’s function on a 66 firm holdout sample was 79% one year before failure. Blum (1974a) examined a sample of 115 industrial firms which failed in the years 1954-1968 with liabilities greater than $1 million and a stratified sample of 115 nonfailing firms which were similar with respect to industry, annual sales, and number of employees. The predictive ability of this model was 93-95% for bankruptcy occurring within one year. The accuracy declined to 80% for predictions related to failures oc-

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curring two years later and to 70% for predictions concerning failures three years later. Edmister (1972) examined 19 financial ratios and five methods of analysis. Multiple discriminant analysis was employed to select a set of ratios and analytical methods which would best discriminate between future failure and non-failure of SEs. The results of this study confirmed the value of ratio analysis. Among the more recent studies, Altman and Sabato (2006) considered the fundamental role played by small and medium sized enterprises (SMEs) in the economic development of many countries and developed a distress prediction model specifically for these firms. Berger and Scott (2007) analyzed the potential effects of small business credit scoring on credit availability. They found that banks that implement scoring systems increase small business credit availability. Although discriminant analysis has been used for default prediction modeling by many other authors (e.g., Altman, Haldeman, & Narayanan, 1977; Deakin, 1972; Lussier, 1995; Micha, 1984) and the New Basel Capital Accord has recently led many analysts to focus on SEs, the subject of credit risk modeling specifically for small enterprises still remains a largely unexplored area.

3. The data set Our survey population (hereafter, reference population) is made up of all the manufacturing firms included in AIDA and/or CERVED Databases2 (hereafter, the Databases) that are situated in Central-Northern Italy3, that operate in one of the nine manufacturing sectors listed in Table 1, that were set up prior to 1998 and whose turnover in 2001 was below 15 million euros.

2

The AIDA Database contains the historical time series of complete balance sheets for approximately 540,000 companies operating in Italy, while the CERVED Database contains full financial records of over 800,000 Italian companies. 3 Central-Northern Italy is made up of the following Regions: Abruzzo, Emilia Romagna, Friuli Venezia Giulia, Lazio, Liguria, Lombardia, Marche, Piemonte, Trentino Alto Adige, Toscana, Umbria, Valle d’Aosta, and Veneto.


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Table 1. Manufacturing categories Categories

Sectors

1

Food and Drink

2

Textiles and Clothing

3

Tanneries, Products in Leather and Other Skins

4

Wood products, paper products; printing and publishing

5

Chemical products, synthetic and artificial fibers, rubber-goods and plastics

6

Metallurgy and metal products

7

Mechanical machines and tools

8

Electric, electronic and optical machines and tools

9

Other manufacturing industries

Our Training Set, i.e., the sample used for prediction modeling, was made up of 1,000 small manufacturing companies, of which half (500) were defaulting during the year 2005 and half (500) non-defaulting4. Our Training Set selection was based on the case-control method. This method is most frequently adopted for medical observation, when rare diseases are being studied or new medications are being tested. It compares two groups of subjects: those sick or in need of treatment (the cases) and a very similar group of subjects who are healthy (the controls). Researchers study the medical and lifestyle histories of the people in each group to learn what factors may be associated with the disease. Casecontrol studies can yield important scientific findings with relatively little money, time, and effort compared with other study designs. Our two groups of companies (cases/controls) were selected separately. The “cases” presented the outcome which was to be researched (financial insolvency) while the “controls” were companies with similar characteristics (geographical location, size and business sector) which did not present this outcome. The “cases” were collected from the Databases by way of proportional stratified random sampling. This method enabled us to maximize the representativeness of the sample with regard to three stratification criteria: i) business sector (the type of manufacturing business in which a firm operated);

4

In this study, the terms defaulting/failed/insolvent are used to refer to enterprises that were undergoing formal legal proceedings for debt in 2005 (bankruptcy procedures, forced liquidation, etc.), i.e., enterprises that had defaulted on their financial commitments.

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ii) size (turnovers5 of less than 2.5m., 2.5m-5.0m, 5.0m-10.0m, 10.0m-15.0m euros); iii) geographical location (Central Italy or Northern Italy). To be able to judge the degree to which certain “exposition factors” might affect the outcome to be investigated (i.e. whether a company would fail or not), a control group was selected randomly from the Databases. These control firms (the “controls”) were not insolvent in 2005, were the same in number as the “cases” and were selected randomly from the Databases with the same three stratification criteria regarding business, size and location. The “exposition factors” we studied were a set of financial and economic ratios calculated on company balance sheet data for the 2001 financial year. 2001 was sufficiently prior to 2005 for one to be able to reasonably presume that the data had not yet been greatly conditioned by the pathological symptoms that would later bring the “cases” to a state of insolvency. Figure 1 and Figure 2 show the case-group (the defaulting companies) divided according to manufacturing sector and to annual turnover. Manufacturing Categories 25 20 15 10 5 0 1

2

3

4

5

6

7

8

9

Figure 1: Percentage distribution of insolvent firms by manufacturing category (as listed in Table 1)

As can be seen in Figure 1, the highest percentages are in textiles/clothing (22% of all “cases”) and metallurgy/metal products (15%). In Figure 2, the average turnover

5

Company size is determined on the basis of turnover in 2001.


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of the “cases” is shown to be quite small: over 65% of companies that defaulted in 2005 had a turnover of less than 5 million euros in 2001. Turnover in millions of Euros 50 40 30 20 10 0 0-2,5

2,5-5

5-10

10-15

Figure 2: Percentage distribution of insolvent firms by turnover

In addition to the Training Set (500 “cases” + 500 “controls”), a holdout sample (Testing Set) was also used. The Testing Set, which is designed to check the reliability of our prediction models, comprises 1,000 firms, of which half (500) were defaulting and half (500) non-defaulting in 2005. These firms were also taken from the reference population through stratified random sampling.

4. Selection of variables The survey’s initial “indicators” (possible risk predictors) were a set of 22 financialeconomic ratios (hereafter, financial ratios; see Table 2). We used two basic criteria to select these ratios: i) their frequency in the research literature on company default prediction (e.g., Altman, 1968, 1993; Altman, Brady, Resti, & Sironi, 2005; Altman, Haldeman, & Narayanan, 1977; Altman, & Sabato, 2005, 2006; Beaver, 1967; Blum, 1974b; Crouhy, Mark, & Galai, 2001; Edmister, 1972); ii) their ability to describe essential aspects of three areas of a company’s economic and financial profile: profitability, leverage and liquidity.

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Table 2: Financial ratios examined in the first stage of analysis X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22

ROE = Net Profit/E ROI = Ebit/Net Operative Assets ROS = Ebit/Turnover VA/Turnover Ebitda/Turnover Labour Costs/VA Ebitda/(Ebitda + FC) IE/Turnover IE/Ebitda Turnover/Number of Employees VA/Number of Employees Cash Flow/Turnover Cash Flow/ TD ROD = IE/TD IE/BL BL/Turnover NFP/Turnover TD/E Financial Debts/E TD/Ebitda E/Long-Term Assets ATR = (Current Assets - Inventories)/Current Liabilities

VA = Value Added IE = Interest Expense FC = Fixed Costs TD = Total Debts E = Equity NFP = Net Financial Position BL = Bank Loans EBIT = Operating Revenue – Operating Expenses + Non Operating Income EBITDA = Ebit + Depreciation + Amortization ATR: Acid Test Ratio

The number of these variables was progressively reduced as variables were further selected in terms of their ability to predict default (see Figure 3). Univariate analysis was applied to each indicator and the Variance Inflation Factor (VIF) method was adopted for multicollinearity analysis. Next, we conducted a Principal Components Analysis (PCA). This was followed by the statistical selection process known as the Stepwise Method.


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Selection of financial ratios based on: -frequency in the literature; -ability to describe essential features of a company’s economic and financial profile ↓ Univariate Analysis ↓ Multicollinearity Analysis (VIF Method) ↓ Elimination of Predictors with VIF > 6 ↓ Application of PCA and Stepwise Methods Figure 3: Selection process of financial ratios (“risk factors”)

4.1 Univariate analysis The distribution of values of each financial ratio was an initial indication of the relationships between the possible risk predictors and SE state (defaulting/nondefaulting). Our analysis of the mean and median values for each ratio in the two Training Set groups (Table 3) led to the following considerations : − regarding leverage variables, both defaulting and non-defaulting firms had a relatively high recourse to debts, as is frequently the case in Italian SEs (e.g., Ciampi, 1994). However, the defaulting companies had higher debt levels. To exemplify this, the average Total Debts/Equity ratio (X18) was over double in the defaulting firms; and the average Financial Debt/Equity ratio (X19) was 3.6 times higher. Hence the companies that failed in 2005 presented a high level of structural financial imbalance (i.e. marked undercapitalization and a high level of debt) even in 2001, when compared to the averages in the 1,000 companies examined; − regarding profitability, the “controls” performed on average better than the “cases”. For example, the controls’ average ROE (X1) was positive (+0.138%), whereas in the “cases” it was negative (-0.163%); average ROI (X2) in the “controls” was 0.55%, almost twice that of the defaulting firms (0.30%); − the situation is similar regarding liquidity: in the “controls”, average Acid Test ratio (X22) is over 77% higher than in the companies that would later fail.

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Table 3: Mean and median financial ratio values

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22

GROUP 0: defaulting firms Mean Median -0.163 -0.020 0.300 0.216 0.065 0.065 0.285 0.276 0.091 0.085 0.718 0.618 0.303 0.310 0.047 0.033 0.416 0.345 3.549 2.344 0.768 0.630 0.036 0.034 0.047 0.042 0.071 0.060 0.085 0.070 0.224 0.205 -0.222 -0.168 11.090 9.532 2.822 1.720 7.697 8.643 1.240 0.519 0.616 0.604

GROUP 1: non-defaulting firms Mean Median 0.138 0.071 0.550 0.361 0.108 0.092 0.316 0.297 0.141 0.119 0.559 0.559 0.397 0.393 0.019 0.017 0.175 0.117 6.699 4.991 0.814 0.505 0.079 0.062 0.215 0.122 0.052 0.045 0.065 0.054 0.102 0.992 -0.097 -0.083 5.120 4.398 0.786 0.665 6.012 4.746 2.242 1.538 1.093 0.854

4.2 Multicollinearity and stepwise analysis The multicollinearity analysis of the variables examined was carried out using the VIF Method, and enabled us to exclude those ratios: Ebitda/Turnover, ROS, and Net Financial Position/Turnover (see Table 4). In order to further reduce the number of variables to those most valuable as possible default predictors, we applied the Stepwise Method6. We did this after we had as-

6

This is a well-known heuristic method with low computational complexity. It often gives satisfactory results. In this method, each of the n variables is tried, one at a time, and one-variable n linear regression models are constructed. The variable (X(1)) which gives the “best” model (Y = a + b1X(1)) is the first to be selected. Each of the other Xi variables is examined (excluding X(1), which has already been selected), and the X(2) variable is selected. This is the variable presenting the “best behaviour” when placed in a regression model with two independent variables, one being X(1). Hence the model: Y = a + b1X(1) + b2X(2) is the best two


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certained that the Principal Components Analysis was of limited usefulness in that the “First Principal Component” could explain only 23% of total variance, and the “First Two” explained only 32% of total variance. At each step of the Stepwise process, we added (from the remaining variables) the one with the highest F value above the standard of 3.84. This enabled us to reduce the significant variables to five (see Tables 5 and 6). Table 4: Multicollinearity test CASH FLOW/TURNOVER CASH FLOW/TOTAL DEBTS EBITDA/TURNOVER VALUE ADDED/TURNOVER LABOUR COSTS/VALUE ADDED INTEREST EXPENSE/TURNOVER INTEREST EXPENSE /EBITDA ROS EBITDA/(EBITDA + FIXED COSTS) ROI ROE ROD INTEREST EXPENSE/BANK LOANS BANK LOANS/TURNOVER NET FINANCIAL POSITION/TURNOVER TOTAL DEBTS/EQUITY TOTAL DEBTS /EBITDA FINANCIAL DEBTS /EQUITY EQUITY/LONG TERM ASSETS ACID TEST RATIO TURNOVER/NUMBER OF EMPLOYEES VALUE ADDED/NUMBER OF EMPLOYEES VALUE ADDED/NUMBER OF EMPLOYEES

VIF 3.604 4.518 10.882 2.867 1.702 1.449 1.182 9.666 2.589 2.164 1.441 1.269 1.287 5.459 6.095 2.818 1.315 2.497 1.747 3.529 4.617 4.687 4.687

variable model when one variable is X(1). The third variable is selected using the same criteria; and the process is repeated until no new variable makes any significant contribution to the model, or until the selection of a predetermined number of variables has been achieved.

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Table 5: Selection of variables with stepwise method Steps 1 2 3 4 5

Entered Variables TD/E ACID TEST RATIO (ATR) ROI BL/TURNOVER ROE

P-Value 0.001 0.000 0.000 0.000 0.000

Table 6: Categories of selected variables Selected Variables TD/E BL/TURNOVER ROE ROI ACID TEST RATIO (ATR)

Categories Leverage Leverage Profitability Profitability Liquidity

5. Construction and testing of prediction models There is a vast literature on statistical methodologies for prediction modeling based on the relationships between financial ratios and company insolvency. Drawing on prevailing literature, we adopted two of the most frequently chosen methodologies, namely discriminant analysis and logistic regression. 5.1 Discriminant analysis For many years this has been the method that researchers have most frequently implemented when constructing company default prediction models (e.g., Altman, 1968; Altman, Haldeman, & Narayanan, 1977; Blum, 1974b; Edmister, 1972; Rosenberg, & Gleit, 1994). After applying discriminant analysis to our Training Set, we established the goodness of the classification rule obtained by testing the rule on both the Training Set and the Testing Set. Table 7: Validity test of discriminant function on Training Set and on Testing Set OBSERVED STATE Training Set Testing Set

0 1 0 1

PREDICTED STATE 0 1 77% 23% 24% 76% 74% 26% 23% 77%

PERCENTAGE OF CORRECTLY CLASSIFIED FIRMS 76.5% 75.5%


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The Observed State 0 lines show the percentage of correctly classified insolvent firms and the percentage of misclassified insolvent firms (Type 1 error). The Observed State 1 lines give the percentage of misclassified non-insolvent firms (Type 2 error) and the percentage of non-insolvent firms that were correctly classified. The last column (percentage of correctly classified firms) shows the model’s overall prediction accuracy (overall percentage of correctly classed firms). The validity test on the Training Set shows a global accuracy level of 76.5% with 23% of Type 1 errors and 24% Type 2 errors. The validity test on the Testing Set was found to give 26% Type 1 errors, 23% Type 2 errors and an overall prediction accuracy level of 75.5%. 5.2 Discriminant analysis by size and business categories When discriminant analysis was applied separately to each of the 4 size groups shown in Table 8, the model’s mean prediction accuracy (checked on the Testing Set) was found to be higher (80.5%) than when the discriminant function was calculated on the aggregate of the Training Set (75.5%), as can be seen in Table 9. Table 8: Size groups Size Groups Class 1 Class 2 Class 3 Class 4

Turnover (Millions of Euros) 0-2,5 2,5-5 5-10 10-15

Table 9: Validity test on Testing Set of discriminant function calculated separately for each size group Size Groups

Percentage of Testing Set

Percentage of correctly classified firms

Class 1 (turnover 0-2,5)

38%

82%

Class 2 (turnover 2,5-5) Class 3 (turnover 5-10)

28% 15%

75% 83%

Class 4 (turnover 10-15) Mean

19%

82% 80.5%

Class 1 has the largest number of companies (382, i.e. 38% of sample). As shown in Table 10, in the Testing Set, 82% of firms proved to be correctly classified: 151 out of 191 (79%) for firms in Group 0 (insolvent firms), and 160 out of 191 (84%) for firms in Group 1 (non-insolvent firms).

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Table 10: Validity test on Training Set and on Testing Set of discriminant function calculated separately for Class 1 size group OBSERVED STATE Class 1 Observations

Testing Set

0 1 0 1

Training Set

PREDICTED STATE 0 1 79% 21% 16% 84% 82% 18% 16% 84%

PERCENTAGE OF CORRECTLY CLASSIFIED FIRMS 82% 83%

The prediction accuracy rate is lower for Class 2 (75%): 99 out of 139 firms (72%) are correctly classified in Group 0 and 110 out of 139 (79%) in Group 1 (Table 11). Table 11: Validity test on Training Set and on Testing Set of discriminant function calculated separately for Class 2 size group OBSERVED STATE Class 2 Observations

Testing Set

0 1 0 1

Training Set

PREDICTED STATE 0 1 72% 28% 21% 79% 77% 33% 21% 79%


Class 3 size group (149 firms, i.e. 15% of sample) has the highest prediction accuracy rate (83%). There are 20% Type 1 errors and 13% Type 2 errors (Table12). Table 12: Validity test on Training Set and on Testing Set of discriminant function calculated separately for Class 3 size group OBSERVED STATE Class 3 Observations

Testing Set Training Set

0 1 0 1

PREDICTED STATE 0 1 80% 20% 13% 87% 83% 17% 15% 85%


For Class 4 size group, the percentage of accurately predicted Testing Set firms is the same as that in Class 1 (82%). The percentage of correct classification is 84% for


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Group 0 firms and 80% for Group 1 firms. We note that only in the Class 4 size group was the Type 2 error rate higher than the Type 1 error rate (Table 13). Table 13: Validity test on Training Set and on Testing Set of discriminant function calculated separately for Class 4 size group OBSERVED STATE Class 4 Observations

Testing Set 0 1 Training Set

0 1

PREDICTED 0 84%

1 16%

20% 86% 18%

80% 14% 82%


The prediction accuracy rate (checked on the Testing Set) is higher still when discriminant analysis is applied to separate manufacturing categories (Table 14). The mean value is now 84.5%, and for some categories it is even higher (mechanical machines: 91%; leather: 88%; wood and by-products: 87%; and chemical products: 86%). The improvement in prediction accuracy when discriminant analysis is applied to separate size groups and to separate manufacturing categories (80.5% and 84.5% respectively, compared to 75.5% when discriminant function is calculated on the aggregate of the Training Set) indicates that Italian SEs have quite diverse economic/financial profiles in different business sectors and at different stages of growth. Table 14: Validity test on Testing Set of discriminant function calculated separately for each manufacturing category Manufacturing categories

Percentage of Testing Set

Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7 Class 8 Class 9 Mean

6% 22% 8% 8% 6% 15% 12% 9% 14%

Percentage of correctly classified defaulting (nondefaulting) firms 83% (83%) 77% (82%) 87% (89%) 88% (87%) 87% (85%) 87% (80%) 92% (90%) 78% (89%) 78% (82%)

Percentage of correctly classified firms 83% 80% 88% 87% 86% 83% 91% 83% 80% 84.5%

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The credit rating models for Italian SEs which banks and other lending institutions have adopted, after the New Basel Capital Accord came into force, must take into account this diversity if these models are to be valid and reliable. 5.3 Logistic regression Though discriminant analysis has for many years been the most frequently adopted method for default prediction modeling, recent studies have shown that where the dependent variable is binary (defaulting/non-defaulting), logistic analysis is more effective (e.g., Altman, & Sabato, 2006; Becchetti, & Sierra, 2002; Charitou, & Trigeorgis, 2002; Mossman, Bell, Swartz, & Turtle, 1998). Logistic regression analysis was applied to the same sample of firms and to the same predictors (financial ratios) as those used in discriminant analysis. Our analysis (Table 15) led to the following logistic regression equation: Log (PND/(1-PND)) =-1.201-0.137*TD/E+3.160*ATR2.447*BL/TURNOVER+0.471*ROI+3.272*ROE where PND stands for the probability of non-default, i.e. that a company will not fail. Table 15: Logistic equation coefficients ROI ROE BL/TURNOVER TD/E ACID TEST RATIO (ATR) CONSTANT

B 0.471 3.272 -2.447 -0.137 3.160 -1.201

Wald 1.376 10.695 5.364 13.117 14.497 2.835

P-Value 0.002 0.001 0.002 0.000 0.000 0.092

In Table 16 we give the synthesis results for the classification rule generated by the above logistic equation when it was tested on the Testing Set (with a cut-off value of p=0.50). Table 16: Validity test of logistic equation on Testing Set OBSERVED STATE 0 1 Global percentage

PREDICTED 0 1 824 176 223 777

PERCENTAGE OF CORRECTLY 82% 78% 80%

The overall prediction accuracy rate is 80%. This is higher than that obtained with discriminant analysis (75.5%), and all the validity tests on the model’s goodness-of-fit,


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adaptability and reliability confirm the predictive power of our logistic equation. The P value from Hosmer-Lemeshow’s test is equal to 0.917; the Log-likelihood test is 166.526; and Wald’s test is significant for all the variables as it is below 0.05. The prediction accuracy rates (checked on the Testing Set) of the logistic regression analysis become higher when the analysis is applied to separate categories (separate size groups and separate manufacturing categories) than those obtained when the logistic classification rule is calculated on the aggregate of the Training Set (see Table 17). Table 17: Validity test on Testing Set of logistic regression equation calculated for each separate category and group Size Groups and Manufacturing Categories

Class1 Class 2 Class 3 Class 4 Mean Class 1-4 Manufacturing Cat. 1 Manufacturing Cat. 2 Manufacturing Cat. 3 Manufacturing Cat. 4 Manufacturing Cat. 5 Manufacturing Cat. 6 Manufacturing Cat. 7 Manufacturing Cat. 8 Manufacturing Cat. 9 Mean Categories 1-9

Percentage of total

38% 28% 15% 19%

Percentage of correctly classified defaulting (nondefaulting) firms 80% (86%) 86% (86%) 82% (86%) 90% (84%)

6% 22% 8% 8% 6% 15% 12% 9% 14%

93% (90%) 82% (82%) 94% (92%) 92% (90%) 92% (90%) 88% (90%) 89% (90%) 84% (86%) 81% (82%)

Overall percentage of correctly classified Testing Set firms 83% 86% 84% 87% 85% 91% 82% 93% 91% 91% 89% 89% 85% 81% 88%

Overall percentage of correctly classified Testing Set firms with discriminant analysis 82% 75% 83% 82% 80.5% 83% 80% 88% 87% 86% 83% 91% 83% 80% 84.5%

The logistic equation calculated separately for each size group has a mean prediction accuracy rate of 85%. When calculated for each separate manufacturing category, the mean prediction accuracy is 88%. Over 80% of companies in all of the manufacturing categories and size groups are correctly classified. In four manufacturing categories (food and drink, leather, wood and by-products, and chemical products), there is over 90% correct classification.

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6. Conclusions We carried out statistical analysis on a representative sample of manufacturing firms based in Central and Northern Italy with the aim to construct prediction models for SE default. Two models were constructed, one implementing discriminant analysis and the other with logistic regression. Our results show that economic-financial ratios can be effective tools in SE default prediction modeling. The overall prediction accuracy rate of our two models is highly satisfactory (75.5% for discriminant analysis and 80% for logistic regression). It is also particularly significant that there is a marked improvement in effectiveness for both models when they were applied separately to different enterprise size groups and (especially) to different manufacturing categories. While recognizing the benefits of using both qualitative (cfr. Lehmann, 2003) and quantitative variables to measure a firm’s rating, our study confirms: 1) the need to rate SE credit risk separately from that of large and medium-sized firms; 2) the need for SE default prediction models to appropriately weight an adequately selected set of financial/economic ratios; 3) the need for these models to take into account the diverse (economic and financial) physiological profiles of firms in different business sectors and at different stages of growth.

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