ASIAN JOURNAL OF MANAGEMENT RESEARCH Online Open Access publishing platform for Management Research Copyright 2010 All rights reserved Integrated Publishing association Review Article ISSN 2229 3795 A study on measuring the financial soundness of select firms with special reference to Indian steel industry An empirical view with Z score M.S.Ramaratnam, R.Jayaraman Assistant Professor (Senior Grade), Faculty of Management Studies, SCSVMV University, Enathur, Kanchipuram, Tamil Nadu hellomsraman@gmail.com ABSTRACT Measuring financial soundness of a firm has become an imperative and imminent need in the context of emerging hyper competition at almost every sector of the business. Financial soundness of a firm is reflected through various financial parameters which are closely associated with each other. A general belief is that a firm s operating performance depends on certain key financial factors viz., turnover, profit, asset utilization etc and the variables which are found in profit and loss account and balance sheet of a firm have a direct or indirect relation with each other. By establishing a close relationship between the variables, a firm can analyze its financial performance in terms of liquidity, profitability, viability and sustainability. In order to measure the performance, ratios, the indicators, are normally used to identify the financial health of the firm. As far as ratios are concerned, there are more than 40 different types of ratios are available to analyze and predict the financial soundness of a firm. Since single ratio does not convey much of the sense, Altman combined a number of accounting ratios to form an index of profitability, which is regarded as an effective indication of corporate performance in predicting financial soundness of a firm. By keeping this in view, this paper has made an attempt to analyze and predict the financial health by way of applying Altman s Z Score in the select companies of Indian steel industry. Key words: Altman s Z Score, liquidity, profitability, viability and sustainability 1. Introduction Indian steel industry is witnessed as one of the most crucial sectors of our economic growth and the industry occupies a significant proportion in terms of industrial output of our country. In the light of LPG, the steel industry turned in to competitive in nature as the private sectors too are in the fray but at the same time India, being a largest market with potential having more than one billion people, the industry has opportunity to enlarge its market share by adopting a series of readjusting and restructuring measures including up gradation of technology. In order to manage stiff competition, steps to be taken to reduce the cost of production. As of now the steel industry is going through a tough time globally. Over capacity and demand slowdown has resulted in lowering the prices and this coupled with the poor demand outlook for the domestic market. Further anti dumping duties imposed by US and many European countries, the exports are ASIAN JOURNAL OF MANAGEMENT RESEARCH 724
severely affected and it leads to worsen the domestic demand supply imbalances. These factors have made rigorous impact on financial performance of the sector and thereby the companies have witnessed a downward trend in terms of their financial performance. Further power consumption of the industry is at larger side i.e. 60% of direct cost is attributed to power consumption, the companies are struggling to recover the cost and normally gestation period is also high, for the sector, the companies operating in the sector have to wait to reap the benefit. A recent study shows that Indian banks and financial institutions have an exposure of about Rs 5000 crore to the sector, which also accounts for about 20% of their NPAs. In spite of their fairly strong operation, most of the Indian companies are unable to service their debt, because the obligation of debt is extremely large in relation to their earning capacity. Though the companies have taken measures to improve their financial performance by the way of restructuring their financial operations, they are not in a position to improve their debt servicing ability by reducing debt levels. The major factors have been identified as reasons for pulling back of steel industry and they are as follows: Long gestation period Power supply Input cost Lack of effective logistics and Impact of global economic slowdown Apart from the above factors, inadequate infrastructure, dependence on coal and low R&D investment have also emerged as undisputable reason for further weakening of Indian steel industry. Therefore application of financial management technique is absolutely necessary which in turn would help the steel companies in increasing their productivity and profitability 2. Statement of the problem Steel industry represents an integral part of Indian economy. Since the industry faces ups and downs over the period of time, the companies in the industry have reported reduction in profit and in some rare cases even loss. As and when the industry is caught in a vicious down cycle, the firms have rendered operations unviable and they face threats to their viability and sustainability so that the study is taken to establish a relevance to the present day problem. 2.1 Review of literature Out of the innumerable studies available on the subject some of the most appropriate studies have been revived. Altman used multiple discriminate analyses (MDS) in his effort to find out a bankruptcy prediction model. He selected 33 publicly traded manufacturing bankrupt companies between 1946 and 1965 and matched them to 33 firms on a random basis. The result of MDS exercise yielded equations called Z Score that correctly classified 94% of the bankrupt companies and 97% of the non bankrupt companies a year prior to bankruptcy. This percentage dropped when trying to predict bankruptcy two or more years before it occurred. The ratios used in Altman model are working capital over total assets, retained earnings over total assets, earnings before interest and taxes over total assets market, value of the equity over book value of total liabilities and sales over total assets. ASIAN JOURNAL OF MANAGEMENT RESEARCH 725
Altman in this paper discussed two of the primary motivating influences on the recent developments of credit scoring models, the important implication of base, its proposed capital requirement on credit requirement on credit assets and enormous amount and rate of defaults and bankruptcies in USA in 2001 02. Two of the more prominent credit scoring techniques of Z score and KMV s EDF models are reviewed. Both models are assessed with respect to default probability in general. Aziz emphasized in his article that accrual accounting ratios were shown to predict bankruptcy accurately for manufacturing industries. Such financial ratios usually lack theoretical justification. Since bankruptcy is cash oriented phenomenon, the use of variable based on cash flows is theoretically appealing. Statistics shows that more than 300 companies go out of business every week. The high rate of bankruptcy is attributed to the combined effect fierce competition in the market place and heavier debt burdens carried by the companies. While few firms were affected by the challenges, a large numbers of firms were affected by the competition. Gupta attempted a refinement of Beavers method with the objective of building a forewarning system of corporate sickness. A sample non parametric test for measuring the relative differentiating power of various financial ratios was used. The study, among 728 textile and non textile group of industries, revealed that earnings before depreciation, interest and taxes to sales and operating cash flows to sales had higher degree of sickness. The analysis is based on logistic regression, where the bankrupt event is explained by accounting and market based variables. In accordance with the literature, the liquidity and profitability ratios turned out to be the most important variable in forecasting default followed by the company size and its activity. Melody Y. King et al in their study attempted to provide an empirically support rationale for classifying the firms in to two groups, those declaring bankruptcy within two years and those remaining solvent. The apparent rationale for engaging in reverse splits differs between two groups. I.e. weak forms attempting to increase their stock price while solid firms seeking to reposition their stocks in the market. This generated an understanding of corporate rationale for engaging in reverse splits and relative success of Z score and artificial neural networks in forecasting the two groups. Praveen kataria in his study attempted to predict corporate sickness of the companies. Financial information about all the sick companies was collected for five years before sickness. Healthy companies were matched with the sick companies on the basis of industry composition size. 54 financial ratios and 8 macro economic variables were taken to study their effect along with financial ratios. Two group linear discriminate analyses were applied in two parts. In the first part, only financial ratio was taken in discriminate analysis, while the macroeconomic variable was included along with the financial ratios in the second part. The result showed that macroeconomic variable had very little impact on discriminant function. Rekha Pai dealt with the prediction of industrial sickness using multiple discriminant analysis. The data set constitutes 21 financial ratios of 34 Indian sick companies in 2000 01 and 38 contemporary non sick companies, both selected irrespective of size and industry category 3 years prior to sickness. The multiple discriminant analysis (MDS) showed greater accuracy in predicting industrial sickness up to three years in advance. The model was validated further using a test model, while exhibited very high predictive accuracy of the proposed model. Ramakrishna in his paper examined two well known financial distress model namely multiple discriminate analysis and logistic regression analysis by using a sample of 298 firms. The study found that cash flow and working capital are important predictive variables, irrespective of when compared to any other models. The selected models were also found to be capable of predicting ASIAN JOURNAL OF MANAGEMENT RESEARCH 726
with minimum errors, one year in advance, which is vital for the bankers, restructuring agencies and the management to initiate revival process before the company actually gets in to financial distress. Wayne in his study took the case of CLECS, telecom department in the US companies. The high rate of bankruptcy was attributed to combined effect of fierce competition in the market places and heavier debt burdens carried by companies. The study revealed that 176 publicly held US Companies filed for bankruptcy which has further increased to 279. John R. Grabski has written an article on the dynamic Z score in April 2008. In his paper, he suggests that the time tested Altman Z score, Originally designed to predict corporate default represents considerable value when used as a corporate performance metric if measured continuously as opposed to one moment in time. Indeed, one could reason that if the measure has merit as a predictor of default, then it only make sense to manage the underlying drivers in order to optimize the ongoing viability of the firm. 2.2 Objectives To examine the overall financial performance of selected steel companies To predict the financial health and viability of the selected sample companies 2.3 Methodology of the study The study is analytical in nature. It is related with the analysis of financial health of selected steel companies viz., JSW Steel, SAIL, Steel exchange of India, Tata steel and Visa steel. The study is mainly based on secondary data. The required accounting information was drawn from PROWESS Data Base. 2.4 Hypotheses for the study H0: Networking capital to total asset ratio is equal in the sample units H0: Retained earnings to total assets ratio is uniform in the sample units H0: EBIT to total assets is uniform in the sample units H0: Equity debt ratio is uniform in the sample units H0: Total asset turnover ratio is uniform in the sample units H0: Z Score value is uniform in the sample units. 2.5 Tools used for the analysis For the purpose of analysis, the authors have used Altman s Z score to predict, analyze and compare the financial health of sample units. In order to study the financial soundness of sample units, different ratios are calculated and the simple statistical techniques such as mean and ANOVA test are applied to analyze the consistency, stability and overall trends in the different ratio used in Altman Z Score. ASIAN JOURNAL OF MANAGEMENT RESEARCH 727
Z score ingredients Based on Multiple discriminate analyses (MDA), the following model is developed by Altman Z = (T1 x 0.012) + (T2 x 0.014) + (T3 x 0.033) + (T4 x 0.006) + (T5 x 0.0999) T1 It is the ratio of working capital to total assets ((WC/TA) x 100). It is the measure of the net liquid assets of a concern to the total capitalization. T2 T3 T4 T5 It is the ratio of retained earnings to total assets. It indicates the efficiency of the management in manufacturing, sales, administration and other activities. It is the ratio of EBIT to total assets (EBIT/TA) X 100. It is a measure of productivity of assets employed in an enterprise. The ultimate existence of an enterprise is based on the earning power (profitability) It is the ratio of value of equity to book value of debt (VE/BVD) X 100. It is reciprocal of the familiar debt equity ratio. This measure shows how much assets of an enterprise can decline in value before the liabilities exceed the assets and the concern becomes insolvent. It is the ratio of sales to total assets (S/TA). The capital turnover ratio is a standard financial measure for illustrating the sales generating capacity of the assets. Financial Ratio co efficient of the ratio (recommendation by Altman) Networking capital to total assets (T1) 0.012 Retained earnings to total assets (T2) 0.014 EBIT to total assets (T3) 0.033 Market value of equity to 0.006 total liabilities (T4) Net sales to total assets (T5) 0.0999 Measurement of Financial Health Altman established the following guidelines to be used to classify firms as either financially sound or bankrupt. SCORE INTERPRETATION Above 3.00 The company is financially safe 2.77 2.99 The company is on alert to exercise the caution 1.8 2.00 There are chances that the company could go bankrupt in the next two years Below 1.8 The company s financial position is embarrassing Below Z score of 1.8, the unit is considered to be in bankruptcy zone. Its failure is certain and extremely likely and would occur probably within a period of two years. ASIAN JOURNAL OF MANAGEMENT RESEARCH 728
If a unit has a Z score between 1.8 and 3, its financial viability is considered to be healthy. The failure in this situation is uncertain to predict. Above Z score of 3, the unit is in too healthy zone. Its financial health is very viable and not to fall. 3 Empirical analyses Table 1: Showing the Net working capital to total assets ratio of select companies from 2006 to 2010 YEAR JSW SAIL EXCHANGE OF INDIA TATA VISA MEAN SD 2006 0.04 0.21 0.62 0.16 0.45 0.22 0.32 2007 0.08 0.37 0.63 0.27 0.22 0.28 0.26 2008 0.15 0.44 0.40 0.63 0.04 0.27 0.32 2009 0.23 0.45 0.37 0.01 0.15 0.09 0.31 2010 0.19 0.41 0.58 0.02 0.13 0.14 0.34 MEAN 0.14 0.38 0.52 0.15 0.09 0.20 0.26 SD 0.08 0.10 0.13 0.31 0.25 0.17 0.10 The above ratio indicates the level of liquid asset to the total capitalization of the company. From the table it is observed that in the company SAIL, the ratio percentage is in increasing trend until the year 2009 showing that the company has greater ability to meet the current obligations. In JSW Steel, the ratio percentage is negative indicating the firm s inability to meet the current obligations. The company steel exchange of India has shown the highest percentage of 63.1 in the year 2007 and the rest of the years the company could manage the current obligation in a better way by having the ratio at higher side. Table 2: Showing the ANOVA (single factor) of net working capital to total assets ratio of select companies Source of Variation SS df MS F P value F crit Between Groups 1.240115 4 0.310029 11.16838 0.00021 3.055568 Within Groups 0.416393 15 0.02776 Total 1.656508 19 ASIAN JOURNAL OF MANAGEMENT RESEARCH 729
Since the calculated value (11.16838) is greater than the table value (3.055568) the null hypothesis is rejected and it is proved that the Networking capital to total asset ratio is not equal in the sample units. Table 3: Showing the Retained earnings to total assets ratio of select companies from 2006 to 2010 YEAR JSW SAIL EXCHANGE OF INDIA TATA VISA MEAN SD 2006 0.46 0.50 0.11 0.75 0.39 0.44 0.23 2007 0.52 0.61 0.17 0.56 0.25 0.42 0.20 2008 0.47 0.73 0.29 0.47 0.23 0.44 0.19 2009 0.39 0.67 0.26 0.41 0.15 0.38 0.20 2010 0.43 0.59 0.25 0.58 0.14 0.40 0.20 MEAN 0.45 0.62 0.22 0.55 0.23 0.41 0.18 SD 0.05 0.09 0.07 0.13 0.10 0.09 0.03 The ratio indicates the ability of the firm to earn profit and thereby securing retained earnings. Normally a firm has higher retained earnings, the firm will not starve for liquidity crunch and also the firm can reinvest in the appropriate venture at cheaper cost. In this aspect, all the selected sample units have sufficient amount of retained earnings related to their total assets. As far as the JSW steel is concerned, the company maintains on an average of 45% of retained earnings to total assets throughout the study period. The company SAIL maintains on an average of 62% of retained earnings to total assets indicating the company is in good position in terms of liquidity aspect as well as the exploitation of near opportunity for investment if any. Tata steel has maintained on an average of 55% of retained earnings next to the SAIL. Table 4: Showing the ANOVA (single factor) of Retained earnings to total assets ratio of select companies Source of Variation SS df MS F P value F crit Between Groups 0.679982 4 0.169996 20.34124 8.07E 07 2.866081 Within Groups 0.167144 20 0.008357 Total 0.847126 24 ASIAN JOURNAL OF MANAGEMENT RESEARCH 730
Since the calculated value (20.34124) is greater than the table value (2.866081) the null hypothesis is rejected and it is proved that the retained earnings to total asset ratio is not uniform in the sample units. Table 5: Showing the Return on total assets of select companies from 2006 to 2010 YEAR JSW SAIL EXCHANGE OF INDIA TATA VISA MEAN SD 2006 0.08 0.12 0.16 0.18 0.02 0.11 0.07 2007 3.11 0.42 0.22 2.37 0.28 1.28 1.36 2008 3.95 0.56 0.31 2.97 0.31 1.62 1.72 2009 4.10 0.68 0.32 3.30 0.25 1.73 1.83 2010 5.04 0.81 0.33 4.19 0.28 2.13 2.30 MEAN 3.25 0.52 0.27 2.60 0.23 1.37 1.44 SD 1.91 0.26 0.08 1.50 0.12 0.77 0.86 Return on total assets indicates the ability of the firm to ensure earning capacity against its total assets. A firm s ability to earn is measured by the operating profit with which the firm enjoys over the period and such case SAIL, steel exchange of India and visa steel reap 52%, 27% and 23% on its investments respectively. Whereas JSW steel and Tata steel show that their return on investment is more than 100% from the year 2007 onwards. Table 6: Showing the ANOVA (single factor) of Return on total assets of select companies Source of Variation SS df MS F P value F crit Between Groups 41.5843 4 10.39607 8.691431 0.00031 2.866081 Within Groups 23.92258 20 1.196129 Total 65.50688 24 Since the calculated value (8.691431) is greater than the table value (2.866081) the null hypothesis is rejected and it is proved that the return on total asset ratio is not uniform in the sample units. ASIAN JOURNAL OF MANAGEMENT RESEARCH 731
Table 7: showing the equity to debt ratio of select companies from 2006 to 2010 YEAR JSW SAIL EXCHANGE OF INDIA TATA VISA MEAN SD 2006 1.03 3.23 0.88 4.00 1.92 2.21 1.37 2007 1.27 4.55 0.83 1.47 0.68 1.76 1.59 2008 0.99 8.33 1.61 0.93 0.51 2.48 3.30 2009 0.75 4.76 1.89 0.76 0.33 1.70 1.81 2010 0.83 2.56 1.28 1.47 0.29 1.29 0.84 MEAN 0.97 4.69 1.30 1.73 0.75 1.89 1.61 SD 0.20 2.23 0.46 1.31 0.67 0.98 0.81 Equity to debt ratio indicates the proportion of owner s fund to the long term debt. The idle ratio is 1:1. Where debt is more, the company has an obligation to pay interest to the creditors and thereby the shareholders risk may be increased. As far as the sample units are concerned, the companies have more equity capital rather than debt. Since the investment is in higher side, the companies have to rely on debt, so that the burden of debt will be more. In the initial stage almost all the sample units have ample percentage of equity than debt but in due course the ratio is eroded by way of adding more debt to its capital structure to meet their required investment. Among the companies SAIL is not much dependent on debt so that the average ratios stands at 47% whereas other companies have relied on debt as a source of their investment, the ratio significantly comes down during the study period. Table 8: Showing the ANOVA (single factor) of equity to debt ratio of select companies Source of Variation SS df MS F P value F crit Between Groups 54.6368 4 13.6592 11.03117 0.000225 3.055568 Within Groups 18.57355 15 1.238237 Total 73.21035 19 Since the calculated value (11.03117) is greater than the table value (3.055568) the null hypothesis is rejected and it is proved that the equity debt ratio is not uniform in the sample units. ASIAN JOURNAL OF MANAGEMENT RESEARCH 732
Table 9: Showing the Total assets turnover ratio of select companies from 2006 to 2010 YEAR JSW SAIL EXCHANGE OF INDIA TATA VISA MEAN SD 2006 0.72 1.69 7.05 1.24 2.42 2.62 2.55 2007 0.88 1.61 4.4 0.74 2.01 1.93 1.48 2008 0.75 1.55 4.2 0.43 1.58 1.70 1.48 2009 0.73 1.25 2.49 0.43 1.23 1.23 0.79 2010 0.85 0.84 1.92 0.4 1.25 1.05 0.57 MEAN 0.79 1.39 4.01 0.65 1.70 1.71 1.36 SD 0.07 0.35 2.01 0.36 0.51 0.66 0.77 Total assets turnover ratio reveals the efficiency of the firm in utilizing its assets to convert into sales. Since the demand for the steel increases over the period of time, the ratio of this kind shows healthy trend during the study period. From the table it is inferred that SAIL, Steel exchange of India and Visa steel have effectively utilized their assets in converting into sales and the same will be revealed by way of analyzing the percentage of conversion towards sales for the above companies. The percentage of conversion stands at 139%, 400% and 170% with respect to SAIL, Steel Exchange of India and Visa steel. Table 10: Showing the ANOVA (single factor) of Total assets turnover ratio of select companies Source of Variation SS df MS F P value F crit Between Groups 54.6368 4 13.6592 11.03117 0.000225 3.055568 Within Groups 18.57355 15 1.238237 Total 73.21035 19 Since the calculated value (11.03117) is greater than the table value (3.055568) the null hypothesis is rejected and it is proved that the total assets turnover ratio is not uniform in the sample units. ASIAN JOURNAL OF MANAGEMENT RESEARCH 733
Table 11: Showing the Z score points of select companies from 2006 to 2010 YEAR JSW SAIL EXCHANGE OF INDIA TATA VISA MEAN SD 2006 2.18 4.98 9.00 5.10 4.71 5.19 2.44 2007 12.53 7.01 6.62 10.55 3.96 8.13 3.40 2008 14.86 9.93 7.09 12.18 3.26 9.47 4.49 2009 14.98 7.82 5.48 12.36 2.27 8.58 5.13 2010 18.36 6.35 4.83 15.95 2.40 9.58 7.11 MEAN 12.58 7.22 6.60 11.23 3.32 8.19 3.73 SD 6.64 2.79 2.78 6.66 1.76 4.13 2.34 The Z score value of sample companies during the period under review have been displayed in the table 1.11.The selected sample units have registered the score much above the suggested value of financial health over the period of time during our study period. JSW Steel and Tata steel have registered the score point to the extent of 12.58 and 11.23 respectively. Whereas SAIL and Steel exchange of India have scored 7.22 and 6.60 respectively, Visa steel has registered the score of 3.32 which is the lowest among the sample units. Table 12: Showing the ANOVA (single factor) of Z score points of select companies from 2006 to 2010 Source of Variation SS df MS F P value F crit Between Groups 278.3363 4 69.58408 5.72477 0.003074 2.866081 Within Groups 243.0983 20 12.15491 Total 521.4346 24 Since the calculated value (5.72477) is greater than the table value (2.866081) the null hypothesis is rejected and it is proved that the Z score points are not uniform in the sample units. 4. Major findings On an aggregate basis, all the selected sample units are financially healthy during the study period ASIAN JOURNAL OF MANAGEMENT RESEARCH 734
Shortage of working capital results the companies to go for debt raising which in turn cause high earning for share and it is favorable for profitability of the company. Since the debt position is lower than the equity value, it helps the company to maintain a reasonable leverage position. Operating efficiency of the firm is good for JSW Steel and Tata steel. The retained earnings ratio of the sample companies is quite satisfactory which strengthens the viability over the period of time. 4.1 Conclusion Financial health of a firm is a centre theme for share holders. Any decision of a firm is taken on the basis of financial soundness of a firm. In this context, Altman s Z score plays a vital role in deciding the financial bankruptcy of a firm and there by a firm can judge its financial position. The present study was conducted to analyze, predict and compare the financial performance of sample firms drawn from Indian Steel industry. The study revealed that all the selected companies are financially sound during the study period. 5. References 1. Altman, (1968). Financial ratios discriminate analysis and prediction of corporate bankruptcy, Journal of finance, sep. 598. 2. Altman, (2002). Corporate distress prediction models in turbulent economic and base environment, Journal of Finance, 5. 3. Abdul Aziz, (1984). Bankruptcy prediction and investigation of cash flow based models, PhD. Thesis at Dallas, 3 9. 4. Beaver, W.H., (1966). Financial ratios and predictions of failure: Empirical research in according selected studies, Journal of accounting research, 77 111. 5. Eidleman Gregous, (1995). Z Scores guidance to failure prediction by CPA Journal online, Feb. 4. 6. Gupta, L.C. (1999). Financial Ratios as forewarning indicators of corporative sickness, Bombay 1C1C1, XIX (4) 37. 7. Gupta, R.L. & Radhasway M., (1995). Financial management analysis, 5 th edition, Sultan Chand & Sons, New Delhi, 45. ASIAN JOURNAL OF MANAGEMENT RESEARCH 735