The Textile Sector has played an important role in the Economic Growth and Industrial Development of India over the last sixty years. The Indian textiles industry is extremely varied in nature, with the hand-spun and hand-woven sector, at one end, and the capital intensive, sophisticated mill sector, at the other. The decentralised power-looms/hosiery and knitting sector forms the largest section of the Textiles Industry. This also provides the industry with the capacity to produce a variety of products, suitable to different market segments, both within and outside the country. In spite of its contribution to Indian Economic and Industrial Development, the Textile Sector is beleaguered with problems, such as technology obsolescence, low productivity and low bargaining power, in a customer centric market. All such factors affect the financial performance of the Textile Manufacturing Units. This paper analyses the performance of some of the textile companies in the sector, using financial ratio analysis. Such analysis will help in detecting early warning signals of financial deterioration, in the Textile Sector, and by studying such warning signals, suitable financial performance enhancing strategies could be adopted in the future.

KeyWords

Textile Sector, Financial Ratios, Z’ score

1. Introduction

The Indian Textile Sector has an overwhelming presence, in the economic life of the country. The Textile Sector, apart from providing one of the basic necessities of life, also plays a pivotal role, through its contribution to industrial output, employment generation and export earnings of the country. This sector is one of the earliest to come into existence in India. Prior to Independence, the British rule in India resulted in colonisation and systematic exploitation of the Indian Economy. The British Rule, in its effort to convert India into a market for its manufacturing industry, further destroyed India’s traditional manufacturing base. (Prasad, 2010). The British government in India followed the policy of offering protection to some selected Industries, since 1923. (Kapila Uma, 2007). Despite these adverse conditions, put in place by Britain, some industries made progress in India and textile was one of them.

Today the Textile Sector contributes about 14% to the industrial production, 4% to the GDP and 11% to the country’s export earnings. The textile sector is the second largest provider of employment, after agriculture, with around 35 million people, currently associated with it. (Ministry of Textiles, Annual Report 2012-13).

The Indian Textile Industry has a share of 5 percent and 4 percent in global exports of textile and clothing, as against China’s 18 percent and 35 percent respectively. (Prasad and Shekhar, 2013). The Indian textiles industry is extremely varied in nature, with the hand-spun and hand-woven sector at one end, and the capital intensive, sophisticated mill sector at the other. The decentralised powerlooms/hosiery and knitting sector forms the largest section of the Textiles Industry.

The government has taken a number of steps, to prepare the Textile Sector, for the global competition, but the opportunities unleashed have not materialised in full because of reservation of certain textile items for the SSI sector, until recently, the lack of labour market flexibility and the exit policy. Such constraints prevent the development of scale economies in the sector. It has also resulted in a longer lead time and infrastructural and administrative bottlenecks, which further hamper the growth prospects of the sector. Apart from the above constraints, the sector is exposed to stiff competition, in the global market, from China, Sri Lanka, Bangladesh and Pakistan.

Some of the important problems faced by the Textile Sector can be listed as:

• Presence of decentralised power looms, with 60 percent of the production capacity (Unorganised sector)
• Obsolete Technology
• Infrastructural

All the above problems and factors affect the financial performance of the companies, operating in this sector. Such adverse financial performance poses a serious threat as regards the existence of some of the companies in the sector. This paper analyses the financial performance of selected companies, from the Textile Sector, using ratio analysis as a tool. The present document evaluates the financial performance of 15 textile companies, registered with the Board of Industrial and Financial Reconstruction (BIFR) and 15 non-BIFR textile companies that are randomly chosen.

1. Conceptual Framework and Research Methodology

• Conceptual Framework

The ‘financial distress’ state of an Industrial unit, affects all stakeholders of the business, such as shareholders, the work-force, vendors, suppliers and the creditors. There are different reasons for such distressed conditions. All such reasons can be classified as Internal or External reasons. In some cases both reasons, Internal and External, are responsible for the ‘financial distress’ condition of a unit.

The internal reasons could be attributed to mismanagement of basic functional areas of business, such as Finance, Marketing, Human Resource, Operations and Systems. These are avoidable reasons and can be controlled. The external reasons are more exogenous in nature and generally non-controllable. They are attributed to business environment factors,, such as socio-economic issues, lack of credit and infrastructure and global business scenarios.

All such reasons, put together, affect the financial position of an Industrial Unit and if proper measures are not taken, then such an entity might fall into a ‘financial distress’ state.

The ‘financial distress’ state or Industrial Sickness is a qualitative phrase and is difficult to measure or quantify. In spite of this, there are certain criteria, which define the ‘financial distress’ state of a unit. In the Indian context, Government Agencies, Banks and Trade Organisations have defined the distressed state of a unit. In the wake of sickness, in the country’s industrial climate, in the eighties, the Government of India set up a committee, under the chairmanship of Shri. T. Tiwari, in 1981, to examine the matter and recommend suitable remedies.

Based upon the recommendations of the Committee, the Government of India enacted a special legislation, namely the Sick Industrial Companies (Special Provisions) Act, 1985 also known as SICA. A Board of Experts, named the Board for Industrial and Financial Reconstruction (BIFR), was set up in January 1987. The criteria to determine sickness in an industrial company are (A):

• The accumulated losses of the company ought to be equal to or more than its net worth e. its paid up capital plus its free reserves
• The company should have completed five years, after incorporation, under the Companies Act, 1956
• It should have 50 or more workers on any day of the 12 months preceding the end of the financial year, by reference to which, sickness is
• It should have a factory

Apart from the SICA definition, in 1975, the State Bank of India has appointed a study team - the Varshney committee, which defined a sick unit as “One that fails to generate internal surpluses on a continuous basis and depends for its survival on frequent infusion of external funds” (B).

One criticism of these definitions is that they are highly skewed towards abnormal financial parameters and economic criteria. These definitions do not articulate operational parameters, such as marketing, production and human and industrial relations. The distress state/sickness should be viewed from a social point of view, as, when a unit becomes distressed/sick; it fails to meet its social obligations to society, as a creator of employment.

The financial distress condition or sickness in the Textile Sector must be looked at from the point of view of its social context, as it employs the maximum number of people, after the agricultural sector. Hence, it is important to evaluate the financial performance of the industrial Units in the Textile Sector, so that forewarning indicator ratios could be identified and the ‘financial distress’ state could be predicted and arrested in advance.

Many studies are done in the Indian Context, in order to evaluate the financial performance of an Industrial Unit and to predict the ‘financial distress’ state or sickness, using ratio analysis. One of the first attempts was made by the National Council of Applied Economic Research (NCAER), in 1979, which was sponsored by the Punjab National Bank. The sample size for study was 162 industrial units: 81 sick and 81 healthy units, taken from different industries, such as Cement, Cotton Textile, Jute, Light Engineering and Sugar. The study used 25 ratios and developed Multiple Discriminant models for each type industry.

In his study, Paranjape (1980) tested 16 ratios in the textile firms. The study suggested the four ratios that discriminate better between sick and non-sick firms. The four ratios were Raw Material Consumed/Sales, Inventory / Current Assets, Retained Earnings / Total Assets and EBIT plus Depreciation / Total Liability. The study claimed that the model predicts sick and non-sick firms, with 90 per cent accuracy, one year prior to sickness.

Misra Banarasi (1990) made an attempt to analyse the effectiveness of the various financial ratios, in segregating sick and non sick industrial enterprises, through Univariate and Multivariate analysis. The study developed a Multiple Discriminant model for monitoring the Industrial Sickness of Textile Units.

In his study, Kortikar (1997) classified firms into three distinct groups, i.e. Sick, Distressed and Healthy. He derived Discriminant score Z. The accuracy of correct classification of Kortikar’s model has been 77.7 per cent, one year in advance, while it has been 73 per cent, 69.9 per cent, 61.5per cent and 50.5 per cent each for two, three, four and five years in advance, respectively. Kortikar’s study also suggested a Discriminate Model for the Textile Industry. The study of the Indian cotton industry by Kaur and Rao (2009) revealed that profitability, growth offering, liquidity and business risk were the most important determinants of debt equity choice, followed by uniqueness.

In their study of the Indian Cotton Textile Industry, Rao and Azhagaiah (2010) observed that the linear growth rate of the Performance Index, Utilisation Index and Efficiency Index, in respect of Working Capital Efficiency, for small size firms, was significant, while for a medium size firm, the trend of Utilisation index alone was significant. The trend of Performance Index, Utilisation Index and Efficiency Index for large size firms was insignificant.

The study results of 142 textile companies by Singala H.K. (2011) shows the firm’s size as an important factor affecting profitability

i.e. ratios such as PAT/ Net Sales, PAT/ Total Assets and ROCE.

The literature reviewed suggests that most followed the method of financial performance analysis, by using Discriminant analysis. It also indicates that size and financial performance go hand in hand, i.e. the bigger the firm, the higher is the profitability. In light of the literature surveyed, it is important to study the financial performance of the textile industry, in today’s context, in terms of financial ratios of two samples i.e. BIFR and non BIFR companies. It is also important to validate the performance of the model(s) suggested by Altman (1968, 1993)

• Research Methodology

The present study analyses the financial performance of 30 Indian textile companies, using a ratio analysis, from their past 5 year financial data (2008-12). The study draws a random sample of 15 units each, from two groups, for which the financial data of at least three years is available for analysis. One group is defined as BIFR registered Textile Companies and the other as Non BIFR Textile Companies. The units in both the samples are identical, in terms of Sales and Total Asset size. The type of study primarily is explanatory in nature and based solely on secondary data. The financial data of 30 companies has been taken from the online web database moneycontrol.com. The Data has been analysed using financial ratio analysis and descriptive and inferential statistics. The Study has tested a null hypothesis.

Ho: There is no difference between the five year average of financial ratios for BIFR and Non BIFR Groups

One of the limitations of current study is that it is based upon a relatively small sample of 30 textile companies; sufficient for exact statistical tests, but the result might be sample specific.

1. Data Analysis and Interpretation

• Overview

A financial ratio is the relative magnitude of two selected numerical values taken from an entity’s financial statements. It is generally used to evaluate the financial performance of an entity. There are many standard ratios used to try to evaluate the overall financial condition of an organisation. Financial ratios may be used by managers within a firm, by current and potential shareholders (owners) of a firm and by a firm's creditors.

The present study has selected eleven financial ratios, looking at their popularity in terms of the frequency of use, in business literature. The ratios used in the study are listed in Table 1. The ratios are basically drawn from a broad category, such as Liquidity, Leverage, Profitability, Activity and Turnover. The ratios numbers from 7 to 11 are suggested by Altman (1968), in his celebrated study related to the bankruptcy prediction model, using discriminate analysis. Prof. Altman has used five ratios, which were the best discriminator between the two groups viz. Bankrupt Companies and Non-Bankrupt companies. After assigning weights to these ratios, he derives a Score, which is known as Z-Score.

The Z Score can be computed by using

Z= 0.012 X1 + 0.014 X2 + 0.033 X3 + 0.006 X4 + 0.999 X5

Where

X1 –Working Capital/Total Assets X2 – Retained Earnings/Total Assets

X3 – Earning Before Interest and Taxes/Total Assets X4 – Market Value of Equity/Book Value of Debt

X5 – Sales / Total Assets

The pass mark for Altman’s Z Score was 2.675. The companies with a Z Score below 1.80 would be classified as potential failures, while the scores between 1.80 and 2.675 were considered the zone of ignorance. This model was primarily used to know the probability of default, among public traded companies. As it is somewhat difficult to estimate the market value of the equity for private companies, Altman (1993) further revised the equation for the privately traded companies, whose market value of equity is difficult to obtain or estimate. The Revised Z’ Score has been given as

Z’= 0.717 X1 + 0.847 X2 + 3.107 X3 + 0.420 X4 + 0.998 X5

The pass mark for Altman’s Z Score was 2.90. The companies with a Z Score below 1.23 would be classified as potential failures, while the scores between 1.23 and 2.90 were considered the zone of ignorance. In this paper Altman’s Z’ score is used, due to non availability of data about the market value of equity relating to the BIFR group, as they are either not traded or thinly traded.

Each ratio of the past five years, including Altman’s, has been analysed for each company and average value of it has been calculated. The five years average value of a particular ratio has been tested between the two group companies i.e. BIFR and Non BIFR, using a T-test for significance. The paper also tests the validity of Altman’s Z’ score, by computing and classifying the companies, belonging to two groups, namely BIFR and Non BIFR.

Table 1: Ratios Used in the present research study

Sr. Nos.	Name of the Ratio
1	Current Ratio
2	Inventory to Gross Current Assets
3	Debt to Equity Ratio
4	Inventory in Number of Days
5	Debtors Turnover in Number of Days
6	Gross Profit Margin –PBDIT to Sales
7	X-1 Working Capital to Total Assets
8	X-2 Retained Earnings to Total Assets
9	X-3 EBIT to Total Assets
10	X-4 Book Value of Equity to Book Value of Debt
11	X-5 Sales to Total Assets

• Interpretation of the Results

After calculating the eleven ratios for the last five years i.e. from 2008 to 2012, for 15 companies each, from two groups - BIFR and non BIFR, the following frequency distribution has been tabulated for each of the ratios:

Table 2: Distribution of Average Current Ratio (Five Years) for the Two Groups

 

Range	BIFR	Non-BIFR	Grand Total
0.00 - 0.50	2	1	3
0.51 - 1.00	2	1	3
1.01 - 1.50	3	4	7
1.51 - 2.00	1	2	3
2.01 - 2.50	3	3	6
2.51 - 3.00	1	1	2
Above 3.01	3	3	6
Grand Total	15	15	30

Table 2 indicates that out of 30 companies, 16 have an average current ratio of less than 2. Of these 16 companies, 8 are from the BIFR group and the remaining 8 from the non BIFR group. 14 companies have an average current ratio of more than 2 and again the division is equal - 7 companies from each group. This shows that both groups have a similar current ratio.

Table 3: Distribution of Average Inventory to Gross Current Asset Ratio (Five Years) for the Two Groups

Range	BIFR	Non-BIFR	Grand Total
0.00 - 0.20	2	1	3
0.21 - 0.40	2	4	6
0.41 - 0.609	5	1	4
0.61 - 0.80	2	5	7
0.81 - 1.00	0	0	0
Grand Total	15	15	30

Table 3 shows that these 30 sample companies, from the textile sector, have a somewhat similar proportion of inventory, in their gross current assets. In order to prevent a stock out situation, these companies have to keep a good amount of inventory. As the inventory is piled up, as a major portion of their current assets, the overall working capital management may face some issues, in all such companies.

Table 4: Distribution of Average Debt to Equity Ratio (Five Years) for the Two Groups

Range	BIFR	Non-BIFR	Grand Total
Negative	6	1	7
0.01 - 1	1	6	7
1.01 - 2	1	2	3
2.01 - 3	1	1	2
3.01 - 4	0	3	3
4.01 - 5	0	0	0
Above 5.01	6	2	8
Grand Total	15	15	30

Table 4 indicates that most of the BIFR group companies have a debt to equity ratio, which is either negative or higher than 5. This clearly reflects their poor performance, due to high burden of debt, causing higher interest flow as expenses. The negative number is due to the fact that 6 companies have had their net worth completely eroded, due to continuing losses. The non BIFR group is relatively better off, as 9 companies from the group have debt to equity ratio in the range of 0 to 3, while 2 companies have the same ratio

– of more than 5. One company from the non BIFR group has a negative ratio, indicating that it is not doing well. This ratio sends an alert to non-BIFR companies that they should be careful, as a high debt burden may take a toll on their financial performance.

Table 5: Distribution of Average Inventory in Number of Days (Five Years) for the Two Groups

Range in Days	BIFR	Non-BIFR	Grand Total
10 - 20	0	2	2
21 - 30	1	2	3
31 - 40	1	0	1
41 - 50	1	0	1
51 - 60	1	1	2
Above 61	11	10	21
Grand Total	15	15	30

Table 5 explains that both group companies have to keep a large number of days inventory. Out of 30, 12 BIFR companies and 11 non BIFR companies have a ratio of more than 50 days, which means that inventory can be turned maximum 7 times in a year, assuming 350 days in a year.

Such a high inventory level shows that little effort has been taken by these companies, on the concept of Just in Time, which may not be a good sign.

Table 6 : Distribution of Average Debtors in Number of Days (Five Years) for the Two Groups

Range in Days	BIFR	Non-BIFR	Grand Total
10 - 20	0	4	4
21 - 30	1	1	2
31 - 40	1	2	3
41 - 50	2	0	2
51 - 60	2	1	3
Above 61	9	7	16
Grand Total	15	15	30

Table 6 indicates that BIFR group companies are not in a position to collect the receivable from debtors in time and this is affecting their liquidity position. 9 out of 15 BIFR companies have outstanding debtors of more than 60 days, as against 7 out of 15 non BIFR companies. As the number of days the sale gets stuck with debtors becomes more, the chances of it becoming doubtful increases. This ratio, when coupled with inventory turnover, explains the liquidity issue of BIFR companies, as the higher the capital locked in the inventory, the more the number of days the proceeds from sales are yet to be collected. The non BIFR companies are a little better in this regard, vis-à-vis with the BIFR group.

Table 7: Distribution of Average Profit Before Depreciation, Interest and Taxes to the Sales Ratio (Five Years) for the Two Groups

Range in Percentage	BIFR	Non-BIFR	Grand Total
Less than 0	14	0	14
0.00 - 1	1	14	15
1.01 - 2	0	1	1
2.01 - 3	0	0	0
3.01 - 4	0	0	0
4.01 - 5	0	0	0
Above 5.01	0	0	0
Grand Total	15	15	30

This ratio is an important one, as it reflects the ability of an entity to manage its costs efficiently. This ratio is calculated by subtracting all the costs, barring Interest Depreciation and Taxes, from the sales and then dividing the result by the sales. As it excludes the Interest and Depreciation, which are proxies to the Capital Structure, and the Asset size, one can compare two entities for their cost efficiencies. Table 7 indicates all but one BIFR company as having this ratio in the negative territory, which indicates their inability to manage and control costs. The Table also explains that the non BIFR companies are also not in a good position, as for 14 out of the 15 companies the ratio is one or less than one. This indicates that in the future some of the non-BIFR companies may become loss making entities, unless they make a conscious effort in cost reduction and control.

Table 8: Distribution of Average X-1 Ratio (Five Years) for the Two Groups

 

Range	BIFR	Non-BIFR	Grand Total
Negative to Zero	1	1	2
0.01 to 0.20	5	4	9
0.21 to 0.40	6	8	14
0.41 to 0.60	3	1	4
0.61 and Above	-	1	1
Grand Total	15	15	30

• is the ratio of the working capital to total It can be observed from Table 8 that X-1 for most of the companies from the BIFR group is less than 0.40 i.e. 40 percent as against 12 companies from the non BIFR group. Each of the group has one company with a negative ratio. The working capital composition in the total asset, for the both groups, is more or less similar in nature.

Table 9: Distribution of the Average X-2 Ratio (Five Years) for the Two Groups

Range	BIFR	Non-BIFR	Grand Total
Negative to Zero	11	5	16
0.01 to 0.20	4	4	8
0.21 to 0.40	-	3	3
0.41 to 0.60	-	3	3
0.61 and Above	-	-	-
Grand Total	15	15	30

• is the ratio of retained earnings to total assets. It can be observed from Table 9 that X-2 for most of the companies from the BIFR group is either zero or negative, as against 5 companies from the non BIFR group. This indicates the ‘financial distress’ condition of the companies from the BIFR It also reflects the poor picture of some of the companies from non BIFR groups, as 5 of them are either zero or negative X-2, while 4 companies from each group have this ratio between 0.01 and 0.20. Only 6 companies out of 15 from the non BIFR group are in a better financial position than the rest.

Table 10: Distribution of Average X-3 Ratio (Five Years) for the Two Groups

Range	BIFR	Non-BIFR	Grand Total
Negative to Zero	11	1	12
0.01 to 0.20	4	14	18
0.21 to 0.40	-	-	-
0.41 to 0.60	-	-	-
0.61 and Above	-	-	-
Grand Total	15	15	30

• is the ratio of Earning Before Interest and Taxes (EBIT) to total assets. It can be observed from Table 10, that X-3 for most of the companies from the BIFR group is either zero or negative, as against 1 company from the non BIFR group. A company having low or negative value of X-3, will also have a low Z’ Score, due to the weight of X-3 in composition of the Z’ Score. This ratio also explains the relatively better performance of non BIFR group companies, due to higher X-3. It also signals that if non BIFR companies do not manage their expenses properly, their financial performance will get affected severely.

Table 11: Distribution of Average X-4 Ratio (Five Years) for the Two Groups

Range	BIFR	Non-BIFR	Grand Total
Negative to Zero	8	2	10
0.01 to 0.25	6	1	7
0.26 to 0.50	1	3	4
0.51 to 0.75	-	1	1
0.76 and Above	-	8	8
Grand Total	15	15	30

• is the ratio of book value of equity to the total value of liability (short and long term). It can be observed from Table 11 that the X-4 for most of the companies from the BIFR group is either zero or negative, as against 2 companies from the non BIFR A company, having a low or negative value of X-4, indicates that the net worth is completely eroded and there is a debt burden. This ratio also explains the relatively better performance of non BIFR group companies as 8 of them have this ratio above 0.76 and the higher this number better it is financially.

Table 12: Distribution of Average X-5 Ratio (Five Years) for the Two Groups

Range	BIFR	Non-BIFR	Grand Total
Less than or equal to 0.25	3	1	4
0.26 to 0.50	3	1	4
0.51 to 0.75	2	1	3
0.76 to 1.00	3	6	9
1.01 and Above	4	6	10
Grand Total	15	15	30

• is the ratio of sales to total It explains how a company is utilising its assets in getting sales. The higher this ratio, the better it is. It can be observed from Table 12 that X-5 for majority of the companies from the BIFR group is less than 0.75, as against only 3 companies from the non BIFR group. This ratio explains the relatively better performance of non BIFR group companies, due to the ability of turning their assets efficiently.

Table 13: Distribution of Average Altman’s Z’ Score (Five Years) for the Two Groups

Range	BIFR	Non-BIFR	Grand Total
Z' Less than 1.23	12	4	16
Z' greater than 1.23 and lesser than 2.90	3	7	10
Z' greater than 2.90	0	4	04
G rand Total	15	15	30

Table 13 indicates the Altman’s Z’ score distribution of 30 companies, from the two groups. The Z’ score correctly classifies 12 out of the 15 BIFR companies,, as they are in financial distress and it also shows the financial distress of 4 non BIFR companies. The Z’ score for 7 non BIFR companies lies between 1.23 and 2.90, which is a forewarning for these companies. If they do not take proper measures in terms of cost control and asset utilisation, they may fall into financial distress soon. The overall predictive ability of the Z’ Score is fairly reasonable.

3.2 Testing of the Hypothesis

The study has tested the following hypothesis for the ratios 1 to 11 and the Z’ score, which is a product of ratios 7 to 11, using a student’s t-test.

Ho: There is no difference between the Five year Average of financial ratio for BIFR and Non BIFR groups

Table 14: Student ‘t’ test results summary

Sr. No.	Ratio Name	Mean Value		Std. Deviation		t – statistics at 5 percent level of significance	p value
		Non BIFR	BIFR	Non BIFR	BIFR
1	Current Ratio	1.9792	1.9862	1.113	1.2786	0.02	0.987
2	Inventory to Gross C.A.	0.5063	0.4494	0.1898	0.1825	0.84	0.41
3	D/ E	6.7885	3.7764	31.2274	13.7012	0.34	0.735
4	Inventory Turnover in number of days	89.42	175.91	77	185.6	-1.67	0.106
5	Debtors Turnover in number of days	61.93	112.4	41.63	297.05	-1.56	0.131
6	PBDIT / Sales	0.19	-0.9402	0.2984	3.5949	1.22	0.233
7	X -1	0.25	0.20	0.18	0.23	0.62	0.54
8	X -2	0.14	-0.69	0.25	1.73	1.82	0.079
9	X -3	0.05	-0.05	0.042	0.12	3.18	0.0036*
10	X -4	1.67	-0.05	2.53	0.31	2.63	0.0138
11	X -5	1.00	0.62	0.49	0.41	2.28	0.0305
12	Z' Score	2.18	0.00	1.45	2.12	3.27	0.0029*

*Significant at 1 percent level of significance

The results of the t-test are shown in Table 14, which clearly indicates that the Hypothesis cannot be refuted for the ratios 1 to 8 for the study sample and for the ratios 9 to 11 i.e. X-3, X-4 , X-5, the Z’ score study has enough evidence to refute the Hypothesis.

1. Conclusion

The present study observes that the 30 companies, from the textile sector, which are drawn from two groups - BIFR and Non BIFR are similar in their financial performance. It shows that the financial performance of all these 30 companies is not satisfactory. The study sample companies are burdened with debt, high levels of inventory, stick debtors and poor asset utilisation. Cost efficiency is clearly lacking in them and in order to improve the margins, all round efforts are required in terms of cost control, reduction and asset utilisation. The absence of such efforts will result in poor financial performance of these companies, irrespective of the group they belong and will affect all stakeholders associated with them in the long run.

Notes

2014. BIFR web site – bifr.nic.in, accessed 10th January, 2014.
2015. For Varshney Committee (1975) see Kaveri S. (1983) “How to Diagnose, Prevent and Cure Industrial Sickness (A practical Approach)”. New Delhi: Sultan Chand & Sons, pp. 21-2.

References

• Altman, Edward I. 1968. ”Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy”, The Journal of Finance, 23 No.4, pp. 589-609.
• Altman Edward I. (1993) see Altman Edward and Hotchkiss. (2006) “Corporate Financial Distress and Bankruptcy- Third Edition”. Hoboken, New Jersey: John Wiley and Sons, pp 240-6.
• Kapila Uma (ed.). “India’s Economic Development Since 1947”. New Delhi: Academic Foundation, p. 570.
• Kaur R. and Rao N.K. 2009. “Determinants of Capital Structure: experience of Indian Cotton Textile Industry”, Vilakshan, XIMB Journal of Management, September, pp. 97-112.
• Kortikar N. 1997. “Corporate sickness predicting at Incipient Stage”. Mumbai: Himalaya Publishing House.
• Ministry of 2013. “ Annual report 2012-13” New Delhi: Government of India.
• Misra, 1990. “Monitoring of Industrial Sickness”. New Delhi: Deep & Deep Publications.
• 1979. “A Study of Industrial Sickness”. New Delhi: National Council for Applied Economic Research. A Punjab National Bank sponsored Study.
• Paranjape 1980. “ Predicting Corporate Sickness :The Case Of Indian Textile Industry, unpublished dissertation for the fellow programme in management, IIM-A as mentioned in Kortikar R.N. (1997), op. cit., p. 25
• Prasad S. 2010. “ Indian Economy- A performance Review 1947-48 to 2010-11”, New Delhi: New Century Publication, p.37
• Prasad S. and Shekhar Himanshu. 2013. “Sixty Years of the Indian Economy 1947-48 to 2012-13”. New Delhi: New Century Publication, p. 419.
• Rao V. and Azhagaiah R. 2010. “Financial Management Focus on Working Capital Utilisation in the Indian Cotton Textile Industry: Methodological Analysis”, Journal of Financial Management and Analysis , 23(2), pp. 63-84.
• Singla H.K. 2011. “ Does firm size affect profitability? An empirical investigation of Indian Textile Industry”, Paradigm, XV(1 & 2), pp. 18-25.

Authored by

Dr. Nitin Kulkarni

nitink_iom@met.edu

 

Prof. Sandeep Chopde

sandeepc_iom@met.edu

 

Mrs. Yashashree Gurao Kokate

yashashreeg_amdc@met.edu

Tags: MET Institute of Management