This chapter introduces the research methodology which will be used to empirically examine whether macroeconomic variables are prominent determinants of financial distress in Mauritius. In other words, the study seeks to determine the relationship macroeconomic variables with financial distress. In addition, the hypothesis, the research design, the development of the model to be used as well as the data sources and methodology are also discussed in this section.
Several problems may be encountered while pursuing the aim of improving the power of the failure predictive model by taking the macroeconomics indicators into account. Sometimes, small samples are used or data collected are for a too short period of time. However, this analysis will try to evade these problems by:
Data Description and Collection
The data and information required in this study is gathered from secondary sources only. Secondary data which is published data, already released by the company's perfectly suit the particular purpose of this study. Thus the data for the selected companies were gathered mostly from the Annual report of listed companies on the Stock Exchange of Mauritius (SEM), the online records of the Mauritius Registrar of Companies, while the macroeconomic data were collected from the Central Statistics Office (CSO) of Mauritius, the Bank of Mauritius and the World Bank website.
3.1.1 Sample Design
The sample used in this survey is taken from the companies listed on the Stock Exchange of Mauritius (SEM) based on a sample period of over twenty years (1992-2011). The sample consists merely of firms listed on the Official market. The reasons for such choice are:
It is easier to obtain data for the listed companies as they are readily available from the Stock Exchange of Mauritius Ltd Fact books. And in case more information is needed, they could be obtained at the Registrar of Companies database in Mauritius.
The firms listed on the Official Market have to abide to the listing requirements imposed on them. In fact, these requirements are based on the firm's trading history, size and the extent of public disclosures amongst others. These requirements ensure a reasonable liquid and well regulated market of securities. Hence, there is no issue for being bias as all firms are treated equally.
Selection of the Sample
The population of 41 companies listed on the Official Market of the Stock Exchange of Mauritius has been considered. The sample used consists of listed firms in different sectors of the economy namely leisure and hotel, commerce, investments, sugar, industry, bank and insurance and transport. The sample considered was however selected base on several conditions. Firstly, firms from foreign sector are excluded because there is a possibility that their capital structures might be influenced by other factors when compared with their Mauritian counterparts. Moreover, the companies selected should have been listed on the SEM prior to the required time frame which in our study is year 1992 as well as the companies must have a complete set of financial data available in its annual report since 1992. Consequently, sixteen companies not meeting the criteria were eliminated and, our final sample consists of only 25 listed firms which is also a representative sample of the market.
Variable Selection
3.2.1 The Explanatory Macroeconomic Variables
1. Money market and credit conditions (Money supply and interest rates)
Market interest rate which depends on the general economic climate does influence the performance of businesses. An increased in cost of borrowing especially if the borrowing costs of a company exceed its profits margins may be a reason for increasing difficulty for a company. It can hence be obviously deduced that, companies with relatively little debt will be least likely to fail in time of high interest. Undeniably, this fact can be proved in line with the findings of Liu & Wilson (2002) who conclude that those firms, which are unable to manage their assets efficiently during period of high interest rate, are the one having a greater probability of default, thus, increasing the occurrence of corporate financial distress. Being a crucial element, interest rate is included in the study.
Moreover, in this study, we expect firm's propensity to fail to increase during periods of relatively tight credit conditions. Vulnerable firms often declare their bankruptcy when the access to credits becomes difficult or impossible. It is argued that small firms are more prone to bankruptcy because their access to the credit markets is more limited than that of large firms (Bernanke and Gertler 1995). Thus, the two series chosen to reflect the money market and credit conditions are money supply and interest rate.
2. Uncertainty measured by Inflation; GDP deflator
To mirror the cost of living in Mauritius, and hence, the availability of a goods sold, inflation is measured as a ratio of the annual growth rate of the GDP implicit deflator was also considered in the analysis. It should be noted that the annual Inflation; GDP deflator shows the rate of change in the price level in the economy as a whole. Liu and Wilson (2000) and Graves and Smith (2002) find that increases in the price level, leading to increases in the costs of inputs, were linked to corporate financial distress. In fact, inflation hurts companies through the presence of sticky wages and prices environment which impose real costs on firms and their workers. In this context as Inflation has a negative impact on most business, a positive relationship between the inflation; GDP deflator and financial distress is expected.
3. Economic health measured by the growth rate of GDP
The survival of the firm depends, to a certain extent, on the economic health of the country such that weak companies disappear in recessionary periods. In addition, Economic analysts considered the growth rate of GDP as a major and accurate indicator of a country's economic health in various researches such as in the study of Sudarsanam & Lai, 2001.In this study, we expect a negative correlation between an increase in GDP growth and the health of firms.
4. Economic conditions and confidence measured by the total value of Stock traded as a percentage of GDP
A variety of studies such as El Hennawy & Morris, 1983 and Sudarsanam & Lai, 2001 have confirmed that different companies and sectors are more or less at risk according to the different stages of the economic cycle. Thus, in this study the total value of shares traded on the Stock Exchange of Mauritius during the time frame selected as a ratio to GDP is to be used as a tracking stock market trends indicator of the economic conditions and confidence level of investors in Mauritius.
5. Unemployment
Besides being a serious issue and prominent indicator, unemployment can have a negative impact both on businesses and the economic health of the country. Indeed, unemployment imposes significant costs on both the firms and country in general. A high rate of unemployment implies that the population will cut back on spending. As a result of which some business may have to reduce their prices in order to increase sales which sometimes may results into negative profits or worse still, some business might go bankrupt. Due to the financial hardship produced, unemployment should thus be included in the analysis.
The dependent Variable
For testing the financial health of companies listed on SEM, the study applied the Altman's Zeta Model (1968) which is based on the Multiple Discriminant Analysis (MDA) which was chosen as the appropriate statistical technique meeting the purpose of this analysis.
MDA is statistical technique which is used to classify an observation into one of several groupings depending on the observation's individual characteristics. After setting the different groups, data are then collected for the objects in the group. In its most simple form, MDA attempts to derive a linear combination of the characteristics which 'best' discriminates between the groups. The discriminant function used is as shown below:
Z= V1X1 + V2X2 + V3X3 + . . .+ VnXn
Where V1, V2 , V3, . . . , Vn = discriminant coefficients, and
X1, X2, X3, . . ., Xn = independent variables
The individual variable values will be transformed into a single discriminant score to be known as the z value, which in turn will be used to classify the object. In this analysis, there will only be two groups, consisting of distressed and non distressed companies. According to Altman (1968), the discriminant function is as specified below:
Z = 1.2 X1 + 1.4X2 + 3.3 X3 + 0.6X4 + 1.0X5
Where:
Z = Score
X1 = Working Capital/Total Assets
X2 = Retained Earnings/Total Assets
X3 = Earnings Before Interest and Tax/Total Assets
X4 = Market Value of Equity/ Total Liabilities at book value
X5 = Sales/Total Assets
When using this model, Altman concluded that a :
Z score < 1.81 = High probability of bankruptcy meaning there is a high probability of default.
Z score > 3 = Low probability of bankruptcy, that is, the firm is financially sound.
Z score = in between 1.81 and 3.0 = Indeterminate
Financial Ratios
X1 , Working capital/ Total assets
Being a liquidity ratio, working capital/ total assets ratio measures the liquid assets of a company in respect to the total capitalisation which measures the firm's ability to meet its maturing short-term obligations. Working capital, which is the difference between current assets and current liabilities, considers liquidity and the size characteristics of a firm. Normally, a firm with operating losses will experience shrinked current assets in relation to total assets.
X2, Retained earnings/ Total assets
This ratio is an indicator of the 'cumulative profitability' of the firm over time. Indeed, retained earnings refer to the total amount of reinvested earnings or losses that a firm had accumulated over its lifetime. Thus, the age of the firm is inherently accounted for in this ratio. For instance, a new firm will have a low RE/TA ratio due to the fact that it had no time to build up its cumulative profits. Moreover, this ratio also considers the leverage of a firm as firm with high RE, as compared to TA, tend to finance their assets through the help of profits retained and not through the use of debt. It thus, indicates the efficiency of the management in manufacturing, sales, administration amongst the other activities of the firm.
X3, Earnings before interest and Taxes/ Total assets
This ratio is a measure of the potential productivity of the firm's assets which is critical for the long term survival of the company. Relating to the earning power of its assets which is indirectly the ultimate existence of an enterprise, EBIT/TA is a prominent element of determining the efficiency of firms in utilizing its resources. It accounts for the overall effectiveness of the management by considering the returns generated on sales and investment.
X4, Market Value of Equity/ Total Liabilities
X4 shows how the market views the company. The assumption is that with information being transmitted to the market on a constant basis, the market is able to determine the worth of the company. This is then compared to firm's debt. Equity is considered by the combined market value of all shares, while debt combines both current and long term liabilities. The aim of this ratio is to determine the extent to which the assets of a company can decline in value before the liabilities exceed the asset and the latter becomes insolvent.
X5, Turnover/Total Asset
Known as a 'measure of the management ability to compete', the sales/total asset ratio shows the sales generating capacity of the firm's assets. Thus, it is normally used by management to deal with competitive conditions.
Methodology
Research Model
In this thesis, a linear function model is estimated by employing the explanatory variables to determine the linkages between financial distress and hypothesized macroeconomic determinants of financial distress. Thus
Financial distress =Æ’ (Rate of Interest, Inflation, Economic growth (GDP) Stock market index, Money Supply).
This is expressed in the following regression equation:
Zt = α0 + β1 Ln (ROI) + β2 Ln (CPI) + β3 Ln (GDP) + β4 Ln (SEMDEX) + β5 Ln (Ms) +et
Where Z represents a firm's degree of financial distress, measured by Altman's Z-scores while the parameter of α0 is the intercept and β1, β2, β3, β4, β5, are coefficients of the independent variables and et is the error term.
3.4 Hypothesis
Thus the study yields the following hypothesis for testing:
Null hypothesis
H0 : b = 0 (there is no significant relationship between macroeconomic variables and financial distress among firms in Mauritius)
Alternate hypothesis
H1: b ≠0 (there is a significant relationship between macroeconomic variables and financial distress among firms in Mauritius)
