Empirical Results 1 Preliminary Analysis

As illustrated in the previous session, the analysis was carried out on a balanced sample of 40 companies, 20 failed and 20 healthy, 70% of which were included in the training data set used for estimating the forecasting models, while the remaining 30% were selected for the test set used for evaluating the predictive power of those models.

In order to investigate the main characteristics of failed and healthy firms, a preliminary explorative analysis was carried out on the set of 37 prediction variables (see table 2) for the three years of interest: First year before failure (2003), Second year before failure (2002) and Third year before failure (2001).

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(³⁰) BELLOVARI J.L.,et al. (2007), op.cit.

(³¹) ALTMAN E.I. (2000), Predicting financial distress of companies: revisiting the Z-score and Z^TM model, New York University, Working Paper; DIMITRAS A.et al. (1996), op. cit.

Table 2: Definition of financial ratios

V Financial Ratios Type

V1 Sales Profitability

V2 Net income Profitability

V3 Total Assets Size

V4 Shareholders’ Equity Size and Capitalization

V5 Value Added Profitability

V6 Total debts/Total assets Leverage

V7 Operating income/Sales Operating structure V8 Net Worth/Fixed Assets Size and Capitalization V9 Net Worth - Fixed Assets Size and Capitalization

V10 Net Worth/Total Assets Leverage

V11 Net Worth/Capital Stock Size and Capitalization V12 Net Worth/Intangible assets Size and Capitalization V13 Current Assets/Total Assets Liquidity

V14 ROE Profitability

V15 ROA Profitability

V16 ROS Profitability

V17 Net income/Total Assets Profitability

V18 EBITDA Profitability

V19 Ebitda/Sales Profitability

V20 Net Income/Operating income Profitability

V21 Capital Turnover Turnover ratios

V22 Long-term Assets Turnover Turnover ratios V23 Current Assets Turnover Turnover ratios

V24 Cost of debts Leverage

V25 Total debts/Sales Leverage

V26 Interest Expenses/Sales Operating structure

V27 Borrowing cost Leverage

V28 Quick Ratio Liquidity

V29 Quick Assets – Current Liabilities Liquidity

V30 Net quick assets Liquidity

V31 Current ratio Liquidity

V32 Working Capital Liquidity

V33 Accounts receivable turnover Turnover ratios

V34 Inventory turnover Turnover ratios

V35 Accounts payable turnover Turnover ratios

V36 Cash Flow Liquidity

V37 Cash flow/Sales Profitability

Some descriptive statistics, such as mean, median and standard deviation, have been calculated for the two groups of firms. As it can be expected, for the healthy firms, the mean and median values of most of the variables are higher, expect for V6, V9, V11, V13, V23, V25, V27, V28, V31, V33, V34, V35, for which the values are lower. It could depend on the influence and impact of these variables on the firm’s status (healthy/not healthy).

Then, looking at the failed firms, for most of the variables (such as V2, V4, V7, V8, V9, V10, etc.) the values become negative.

Moreover, when comparing the mean values with the median ones, it has been noted that some variables show an asymmetric behaviour. In particular, a positive symmetry can be observed for most of the variables, whereas for few ones (i.e. V9, V33) there are negative

asymmetry with a mean value less than the median(³²).

The correlation matrix for the 37 indicators was also carried out in order to investigate the bivariate linear relations among the variables of interest. As expected, there is evidence of a strong correlation structure among some of the variables that slightly change over the years. We considered those correlation coefficients greater or equal to |0.80| to be significant, highlighting that any correlations below that value are not harmful for an appropriate variable selection. A synthesis of the results is reported in Table 3, where the indicators with a significant linear relation are listed.

5.2. Classification Analysis

The classification analysis aimed at investigating the discriminant power of each of the indicators as well as selecting those variables that can be included in the forecasting models.

The predictive performance of the estimated models one, two and three years prior to failure, is then investigated.

In the first step, following an univariate approach, a t-test for the means differences was performed in order to evaluate the capability of each variable to discriminate between the two groups, failure and healthy companies in the tree years of interest. The results suggest selecting as potential predictors of the failure status, the indicators for which the t-test rejects the null hypothesis of equal means. From the test’ results for each years, it can be noted that the most significant variables for which the null hypothesis is rejected, are: V1, V5, V10, V14, V15, V16, V18, V19, V26, V36, for 2001; V1, V2, V4, V5, V10, V12, V15, V16, V18, V19, V26, V36, for 2002; V1, V4, V5, V6,V10, V12, V14, V15, V16, V18, V19, V25, V26, V35 for 2003.

However, while univariate analysis suggests specific variables as candidates for model building, in a multivariate setting it may be the case that a collective set of variables might achieve a better degree of discrimination between the two groups of firms. To avoid potential biases, therefore, we supplement our model development process with other methods of selection of the variables. In particular, we use the stepwise method to determine the final set of variables to be included in the discrimination model. The step-by-step forward selection begins with no variables in the model and at each step-by-step all variables are evaluated to determine which one will contribute most to the discriminatory power of the model, measured by Wilks’ λ statistics, and have to be included in the models. In the same way, that variable which fails to meet the criterion to stay will be removed. When all the variables in the model meet the criterion to stay and none of the other variables meet the criterion to enter, the stepwise selection process stops. As a tolerance level, we chose a λ statistics greater than 0.80 to stop the iterative process (³³).

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(³²) Detailed results are available upon requests from the authors.

(³³) WILKINSON L., AND DALLAL G.E. (1981), Tests of significance in forward selection regression with an F-to enter stopping rule, Technometrics, 23. 377-380; BURNHAM K.P.,ANDERSON D.R.(2002), Model Selection and Multi-Model Inference. A Practical Information-Theoretic Approach 2nd ed., Springer, New York.

Table 3: Financial Ratios Correlation Analysis

V Financial Ratios 2001 2002 2003

V1 Sales V5

V2 Net income V12, V36 V36 V18,V29,V30,V34,V36

V3 Total Assets V25, V29, V33, V34 V29, V33, V34

V4 Shareholders’ Equity V10, V12, V36 V12, V32, V36 V12

V5 Value Added V1 V18,V36

V7 Operating income/Sales V21, V23 V28, V31, V37 V28,V31

V8 Net Worth/Fixed Assets V22

V9 Net Worth - Fixed Assets V32 V32 V32

V10 Net Worth/Total Assets V4

V11 Net Worth/Capital Stock

V12 Net Worth/Intangible assets V2, V4, V36 V4, V32, V36 V4

V15 ROA V15, V16, V19 V19

V16 ROS V15, V19 V19 V19

V17 Net income/Total Assets V37

V18 EBITDA V2, V5, V29,V36

V19 Ebitda/Sales V16 V15, V16 V16

V20 Net Income/Operating

income V33

V21 Capital Turnover V7, V23

V22 Long-term Assets Turnover V8

V23 Current Assets Turnover V7, V21

V25 Total debts/Sales V3, V29, V33, V37 V29, V33, V34 V33,V35

V28 Quick Ratio V31 V7, V31, V37 V31,V7

V29 Quick Assets – Current

Liabilities V3, V25, V33, V34,

V37 V3, V25, V30, V33,

V34 V36,V34,V2, V18,

V30

V30 Net quick assets V29 V36,V2, V29

V31 Current ratio V28 V7, V28, V37 V7,V28

V32 Working Capital V9 V4, V9, V12 V9

V33 Accounts receivable turnover V3, V25, V29, V34, V37 V3, V25, V29, V34 V35,V20,V25 V34 Inventory turnover V3, V29, V33, V37 V3, V25, V29, V33 V29,V36, V2

V35 Accounts payable turnover V33,V25

V36 Cash Flow V2, V4, V12, V29 V2, V4, V12 V2, V5, V18, V29,V30,

V34

V37 Cash flow/Sales V25, V33, V34 V7, V28, V31 V17

* bivariate linear relation with ρ^≥0.80 have been reported for t-1, t-2 and t-3.

As classification technique we applied the classical methods of Multivariate Discriminant Analysis (MDA) in order to derive forecasting models for default risk. The MDA is used to

determine those variables that discriminate between two or more pre-defined groups, which in our case were failed and non-failed companies. This is achieved by the statistical decision rule of maximizing the between-group variance relative to the within-group variance (³⁴).

Each firm receives a single composite discriminant score which is then compared to an optimal cut-off value, which determines to which group the company belongs to. If their discriminant score is less than the cut-off point, they are classified as failing, whereas if their score exceeds or equals the cut-off point, they are classified as non-failing. Two types of misclassifications can be made: a type I error is made when a failing firm is misclassified as a non-failing firm, whereas a type II error is made when a non-failing firm is wrongly assigned to the failing group.

In order to investigate the predictor capability one, two and three years prior to failure, we construct three Linear Discriminant Functions (LDF) at t-1, t-2 and t-3.

From the results of the stepwise procedures along with the statistical screening, we selected as a predictor for the first year before failure model, 2003, the following five variables: Total debts/Total assets, Net Worth/Total Assets, ROA, ROS, and Interest Expenses/Sales.

The second model refers to the prediction of failure two years in advance. The variable selection has been redefined on the 2002 data-base and the final predictors are: Net-Worth/Total Assets, ROA, ROS and Interest-expenses/Sales.

The same process was carried out for the third year before the failure. In this case, the ROE was included in the model instead of the ROA which was included in models 1 and 2(³⁵).

The discriminant coefficients of the three LDF estimated from the information related to the year 2003, 2002 and 2001 are reported in Table 4.

Table 4: Coefficients of Discriminant Function

First year before failure - 2003

Variables Coefficients V6 Total-debts/Total assets 0,0003129

V10 Net-Worth/Total Assets 0,0901252

V15 ROA 0,2852788

V16 ROS 0,0143223

V26 Interest-expenses/Sales 0,3248475 Second year before failure - 2002

V10 Net-Worth/Total Assets 0,0788659

V15 ROA 0,1467619

V16 ROS 0,0629249

V26 Interest-expenses/Sales 0,3655981 Third year before failure - 2001

V10 Net-Worth/Total Assets 0,1376484

V14 ROE 0,0131991

V16 ROS 0,4535639

V26 Interest-expenses/Sales -0,2026336 ____________

(³⁴) ANDERSON T. (2003), An Introduction to Multivariate Statistical Analysis, 3rd Edition, Wiley, New York.

(³⁵) It can be noticed from the correlation analysis, Table 2, that the ROA for the year 2001 shows strong linear relation in terms of correlation coefficient with other three indicators while the ROE does not.

Using the estimated values, we obtained the discriminant score that allowed each firm to be assigned to either the first or second group (failed or healthy). Repeating the process for the training and test data sets, the classification errors for each firm was obtained. The Confusion Matrices of table 5 summarize the prediction results.

Table 5: Confusion Matrices

First year before failure - 2003 Predicted Class

Training set Test set

Failed Healthy Failed Healthy Failed 0,50% 0,00% 0,50% 0,00%

Actual Class

Healthy 0,038% 0,46% 0,00% 0,50%

Second year before failure - 2002 Predicted Class

Training set Test set

Failed Healthy Failed Healthy Failed 0.50% 0,00% 0,43% 0,07%

Actual Class

Healthy 0.038% 0,46% 0,00% 0,50%

Third year before failure - 2001 Predicted Class

Training set Test set

Failed Healthy Failed Healthy Failed 0.50% 0,00% 0,50% 0,00%

Actual Class

Healthy 0,038% 0,46% 0,07% 0,43%

The predictive performance of the LDF seems to be influenced by the forecast horizon as highlighted by the predictor errors, εt, that slightly increases from t-1 to t-3. The results are very similar as would be expected, given the likeness in the estimated variables.

However, model 1 shows better prediction accuracy in the test set confirming that the time period prior to failure is a relevant issue.

The indicators selected as predictors for the three estimated models are included, at different levels, in many other empirical studies. In particular, ROS, Net-Worth/Total Assets and ROA have been selected by Altman (³⁶) in the list of those variables which can be considered as effective indicators and predictors of corporate distress. Total debts/Total assets is one of the most often selected predictors in forecasting distress models, it has been included by Alberici (³⁷) and Laitinen T., Kankaanpää M. (³⁸) among the others (³⁹).

The empirical results highlight how there are clear indications of unbalance since three years prior to bankruptcy. In fact, the failed firms manifest evident signs of inefficiency, low competiveness as well as high undercapitalization with the subsequent need to resort to debt, as highlighted by the presence of ROS, Net-Worth/Total assets among the indicators.

____________

(³⁶) ALTMAN E.I. (2000), op. cit.

(³⁷) ALBERICI A. (1975), Analisi dei Bilanci e Previsione delle Insolvenze, Isedi, Milano.

(³⁸) LAITINEN T.,KANKAANPÄÄ M. (1999), op.cit.

(³⁹) For a review on the variable selection in business failure, see DIMITRAS A.et al. (1996), op. cit.

6. Conclusion

In this paper, we have investigated the determinants of bankruptcy of the building industry in the Campania region. A data-set of financial statements of a balanced sample of companies for a given time period were analyzed by means of classification techniques.

In order to select the two balanced classes of healthy and failed firms, we used the concept of legal failure to include those firms which had gone bankrupt during the year 2004. This in order to have at least four future reports to evaluate the real status of the selected firms.

The predictive power of opportunely chosen financial indicators was assessed within a univariate and multivariate frameworks. In the study, we started with the application of the traditional discriminant analysis. In particular, three LDF were estimated for the diagnosis and prediction of the risk of bankruptcy one, two and three years prior to failure. The performance of the forecasting methods leads to high quality results with a low percentage of predictor errors. Moreover, as expected, the predictor’s capability decreases as the time horizon increases.

The results obtained with the LDF should be compared with some other forecasting methods in order to better evaluate the predictor power of each model. However, since our interest was focused on a specific sector of activity in the Campania region in a given period of time, the number of population units for the failed companies and, consequently, the size of the selected balanced sample does not allow to successfully apply more complex techniques such as the Artificial Neural Networks. As it is well know, the performance of such techniques should lead to better results, compared to the classic MDA, only if the sample is of a reasonable size. Therefore, a comparative predictive performance evaluation with different statistical approaches is left to further investigation.

Overall, while the results obtained show that the models achieve classification accuracy, empirical evidence of the screening analysis of the financial information suggests more than one issue that requires further investigation. The accuracy of the prediction models may be enhanced to include non-financial predictors as well as consider time as a relevant factor, switching from a static to dynamic model. These issues along with the aforementioned comparison analysis will be the purpose of a future study.

A^LESSANDRA A^MENDOLA

Straordinario di Statistica

MARCO BISOGNO Associato di Economia aziendale

MARIALUSIA RESTAINO Ricercatore di Statistica

L^UCA S^ENSINI

Ricercatore di Economia aziendale Università degli Studi di Salerno

Dipartimento di Scienze Economiche e Statistiche Dipartimento di Studi e Ricerche Aziendali

Nel documento Rivista Italiana di Ragioneria e di Economia Aziendale fondata nel 1901 (pagine 115-122)