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Normative Framework for Risk Index and Its Empirical Analysis

  • M. V. ShivaaniEmail author
  • P. K. Jain
  • Surendra S. Yadav
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Part of the India Studies in Business and Economics book series (ISBE)

Abstract

This chapter aims to provide a normative framework (primarily based on accounting information) for measurement of corporate risk. The index is based on nine risks and has been developed using expert opinion. In the process two new ratios, namely, modified defensive interval ratio and contingency coverage ratio have been developed. The index has been then empirically computed for sample 429 non-financial companies for each of the 10 years from 2005 to 2015. In addition an aggregative analysis, focussing on phase-wise, age-wise, and industry-wise analysis has been carried out. Further, a dis-aggregative (risk-wise) detailed analysis has been carried out to develop deeper understanding of risks surrounding the Indian corporates.

Keywords

Risk index Market risk Liquidity risk Solvency risk Accounting risk Operating risk 

3.1 Introduction

Risk has long perplexed managers and investors alike. Much of the ambiguity in the domain stems from the use of different definitions of risk for different purposes. The present study considers an all—encompassing definition of risk, recognising both positive and negative connotations. In light of the worldwide corporate debacles like that of Enron and Satyam, focus on risk management has been gaining momentum.

Literature is replete with studies making use of accounting data for bankruptcy predictions. Most of the studies focus on exploring variables that best describe this outcome. But, bankruptcy is just one of the possible outcomes if the vulnerabilities to which the company is exposed materialise. Depending on the severity and area of impact of an event, risks may result in decline in market share, temporary shut-down, management turnover, insolvency, winding-up, to name a few consequences. Further, very few studies provide an explicit interpretation of ratios as a measure of risk.

Thus, recognising the fundamental strength of ratios and need for a risk measurement tool, the present study aims to develop a corporate risk index. The index is proposed to be in the form of a normative framework. Based on nine major risks that corporates face, it is expected to provide all stakeholders an easily comprehensible and applicable tool of corporate risk measurement.

The chapter has been organized into seven sections. Section 3.2 elaborates the methodology employed for index construction. Section 3.3 describes the sample used and sources of data. Section 3.4 examines the findings and presents the analysis of the same. This is followed by a Sect. (3.5) on implications for various stakeholders. Section 3.6 deals with limitation of the study. Last but not the least, Sect. 3.7 contains the concluding observations.

3.2 Methodology

Sometimes risk is viewed as a product of exposure, impact and frequency. However, it is almost impossible to gauge the impact and frequency of a particular threat for a particular company, purely based on publicly available information. The only part of risk that can be estimated with some accuracy is the exposure, using financial information provided in the financial statements.

To this end, financial ratios can be appropriately used. In fact, most of the studies on financial distress and bankruptcy prediction have made use of ratios. The success can be gauged from widespread use of Ohlson’s O score and Altman’s Z score (both of which are exclusively based on ratios). Also, the interpretation of ratios is consistent with them being peculiar representatives of exposure levels.

For example, the most commonly used measure of solvency of a company is debt to equity ratio. If a company has a debt equity ratio of 0.8 (D/E = 0.8/1), it is equivalent to saying that if the value of equity falls by more than 20%, then the investments of debenture-holders may be in danger. In other words company may not be able to pay its debenture-holders in full. Being an external party to firm, one cannot gauge the probability of this event, particularly, when little or no historical data of such event is available. Also, one cannot claim with certainty the severity or impact of this event. In an extreme case it may lead to shut down of company and in more general cases it may lead to loss of reputation following a legal suit and claim for damages. The only part one can say with surety is, exposure, as probability and severity may be conditional and dependent on situations and events beyond public domain.

In view of the above, it is very appropriate to state that ratios are excellent indicators of various risks. Pursuing this stream of thought, and taking cue from work of Tamari (1966), a risk index (more appropriately an ‘exposure index’) has been attempted. This may also be viewed as normative ‘tolerance limits’ for various risks.

The first step in construction of a risk index is to identify the risks that affect a company. For the purpose, it would be in order to include all possible risks that may affect a company. Since companies are unique in terms of their organizational features, culture, risk appetite, tolerance, management practices; ‘one size fits all’ approach sounds unrealistic. Therefore, based on literature and expert opinion, only those risks have been focused upon which are believed to be pervasive and material. Even COSO propagates management by exception; in operational terms, focus should be on the most important risks only. Accordingly, following risks were identified for the study: accounting risk, attrition risk, competition risk, credit risk, customer satisfaction, exchange rate risk, interest rate risk, liquidity risk, market risk, operational risk, risk of over dependence on a product or division, risk related to innovation, solvency risk and taxation risk. But owing to unavailability of accurate and reliable data on some of the risks (for majority of the companies), only following nine risks could be considered for the construction of the index; market risk, accounting risk, competition risk, contingency risk, credit risk, exchange rate risk, liquidity risk, operating risk, and solvency risk. It is pertinent to note that Muzzy (2008) also advises the use of a filtering process to limit the number of manageable risks to 10 or fewer.

Most finance theories assert that only systematic risk is relevant for investors. But, the present study considers both systematic and unsystematic risk, mainly for two reasons. First, unless the investors are aware of various firm specific risks, they cannot diversify them effectively. Second, the study intends to propose a normative framework that is expected to be of use to all stakeholders from shareholders to prospective investors, regulators, rating agencies, fund managers and most importantly the risk managers in company itself.

It is pertinent to state that every risk has been coded on a scale of 1–5; where 1 represents the least risky proposition and 5, the most risky. Thus, with nine risks, each on a scale of 1–5, the minimum risk index value that a company can have is 9 and the maximum is 45. It may further be noted that every risk score and its corresponding risk scale has been duly verified and validated by experts (both academic and industry). Further, the index values obtained for various companies have been expressed in percentage terms. The maximum possible index value of 45 has been taken as the base to express the computed index values in percentage form. In other words, if a company scores 1 on each of the nine risks, its risk index value will be 9; but while expressing it as a percentage of maximum possible risk index value (i.e. 45), it will be 20% (i.e. 9/45). Similarly, if a company has 80% risk exposure, it will imply that it has a risk index value of 36 (i.e. 80% of 45); the lower is the index value, the lower is the risk and vice versa.

A brief about the nine risks included in the study and the scale construction for each of these risks is described as given below.

Risks:

Modern finance theory is based on the premise that risk surrounding a company can be divided in two parts—systematic risk and unsystematic risk.
  1. 1.

    Market risk:

     

Market risk or systematic risk is that part of company risk which cannot be diversified away. Its sources include inflation risk, political risks, interest rate risk, etc. These risks affect almost all the companies in the economy, but in varying degrees. The most common measure of market risks is, beta. It basically measures the relation between company’s return and returns of market as a whole. A beta equal to 1 represents average level of risk, whereas, a beta more than 1 is usually associated with higher degrees of risk and volatility. Such stock will move with market but to a greater extent.

The following model was used to calculate beta for each company for each year.
$$ R_{t} = \alpha + \beta (R_{m} - R_{f} ) + \varepsilon_{t} $$
(3.1)
where,
\( R_{t} \)

Weekly equity return of a company

\( R_{m} \)

Weekly return of NIFTY 500 index

\( R_{f} \)

Weekly risk-free rate based on 91 day T-bill

\( \beta \)

Sensitivity of company’s returns to market returns

It is noteworthy that Eq. 3.1 has been run for every company for every year separately, subject to availability of data. Thus, close to 4,000 regression equations resulted in the betas required for every firm-year observation. It may be noted that only the companies with stationary returns have been considered for the purpose of regression.

Further, companies having negative betas have not been considered for the purpose of the study. The reason being, they are supposed to be a result of peculiar events that may have happened during the period of estimation. These events may be in form of law suits and takeovers that may have disturbed company’s correlation with the market. As these are rare events and no entire sector has ever recorded negative beta, its underlying factors remain a puzzle (Damodaran 2009).

Therefore, the following risk scales have been developed for various beta measures.

  1. 2.

    Accounting risk:

     

Accounting risk relates to an error in accounting practice or policy which will necessitate a restatement of earnings, leading to, inter-alia, legal issues. Since most of the stakeholders (particularly investors) of the company, receive substantial and significant information about the company from its financial statements, Section 129 of Companies Act 2013 mandates that preparation and presentation of financial statements should be as per the Accounting Standards prescribed by Institute of Chartered Accountants of India (ICAI) and notified under Section 133 of Companies Act 2013. Further, an audit of the same by qualified personnel provides confidence that the affairs of the company are being conducted as required. It ensures appropriate ‘provisioning’ and reliable ‘estimation’ (wherever they are required). Also, it confirms that financial statements provide a true and fair view of the affairs of the company.

Thorley et al. (2011) state that financially distressed as well as bankrupt companies have ‘statistically significant’ qualified opinions. It is further suggested that, considering the bases of auditor’s opinion, it may be a reasonable proxy for operational risk also.

Thus, based on possible forms of auditor’s opinion, accounting risk is proposed to be measured as per Exhibit 3.2:
Exhibit 3.1

Scoring in relation to market risk

Beta range

Risk score

0 < β ≤ 0.95

1

0.95 < β ≤ 1.05

2

1.05 < β ≤ 2

3

2 < β ≤ 4

4

4 < β

5

Exhibit 3.2

Scoring in relation to accounting risk

Auditor opinion

Risk score

Unqualified

1

Emphasis of matter

2

Qualified

3

Adverse

4

Disclaimer

5

Exhibit 3.3

Scoring in relation to competition risk

Growth/decline in market share

Risk score

Growth

1

0% < Decline ≤ 20%

2

20% < Decline ≤ 35%

3

35% < Decline ≤ 50%

4

50% < Decline

5

Exhibit 3.4

Scoring in relation to contingency risk

Inverse of contingency coverage ratio (ICCR)

Risk score

0 < ICCR ≤ 0.1

1

0.1 < ICCR ≤ 0.25

2

0.25 < ICCR ≤ 0.5

3

0.5 < ICCR ≤ 1

4

1 < ICCR

5

Exhibit 3.5

Scoring in relation to credit risk

Credit risk (CR)

Risk score

0 < CR ≤ 0.3

1

0.3 < CR ≤ 0.55

2

0.55 < CR ≤ 0.75

3

0.75 < CR ≤ 0.95

4

0.95 < CR

5

Exhibit 3.6

Scoring in relation to exchange rate risk

Foreign exchange gain/loss

Profit before tax/loss

Risk score

No gain or loss

Profit/loss

1

Gain

Profit

2

Gain

Loss

3

Loss

Profit

4

Loss

Loss

5

Exhibit 3.7

Scoring in relation to liquidity risk 1

Inverse of acid-test ratio (IATR)

Risk score

0.667 ≤ IATR ≤ 0.8

1.25

0 < IATR < 0.4

2.5

0.4 < IATR ≤ 0.667

3.75

0.8 < IATR ≤ 1

3.75

IATR > 1

5

Exhibit 3.8

Scoring in relation liquidity risk 2

Modified defensive interval (MDI) (in days)

Risk score

180 < MDI

1

90 < MDI ≤ 180

2

30 < MDI ≤ 90

3

15 < MDI ≤ 30

4

0 < MDI ≤ 15

5

Exhibit 3.9

Scoring in relation to operating risk

Degree of operating leverage (DOL)

Risk score

DOL ≤ 0

1

0 < DOL ≤ 1.5

2

1.5 < DOL ≤ 3

3

3 < DOL ≤ 5

4

5 < DOL

5

Exhibit 3.10

Scoring in relation to solvency risk 1

Total debt to shareholders’ funds (TD/E)

Risk score

0 < TD/E ≤ 0.33

1

0.33 < TD/E ≤ 0.5

2

0.5 < TD/E ≤ 0.6

3

0.6 < TD/E ≤ 0.75

4

0.75 < TD/E

5

Exhibit 3.11

Scoring in relation to solvency risk 2

(Inverse of interest coverage ratio) (IICR)

Risk score

0 < IICR < 0.33

1.25

0.33 ≤ IICR < 0.5

2.5

0.5 ≤ IICR ≤ 1

3.75

1 < IICR

5

  1. 3.

    Competition risk

     

The core objectives of a business are survival, profit and growth; and all three are fraught with danger due to existence of competition (for business). If the company is unable to maintain its market share, gradually it will lose value, and will eventually go out of business. In today’s world of globalisation, market place is becoming increasingly competitive. Particularly, in Indian context, the thrust on foreign direct investment and schemes to encourage start-ups are surely going to result in increased competition.

Therefore, based on Herfindahl index, a popular measure of industry competitiveness and concentration (Lang and Stulz 1992; Rhoades 1993; Januszewski et al. 2002; Jimenez et al. 2007) the following measure of growth/decline is being proposed as an indicator of competition risk.
$$ {\text{Market growth }}\left( {\text{g}} \right) = \sqrt {\frac{{Market\,share_{i,t + 2} }}{{Market\,share_{i,t} }}} - 1 $$
(3.2)
where,
a negative value implies decline and market share of a firm is as measured as \( \frac{{Sales_{i,t} }}{{\sum\nolimits_{i = 1}^{n} {Sales_{i,t} } }} \)
  1. 4.

    Contingency risk:

     

One of the key issues for risk managers is the occurrence of extreme events (those events which occur with low frequency and high severity). Therefore, management should recognise that large losses are possible and develop contingency plans that deal with such losses should they occur. Though it is not practically possible to account for all the risks, the least a company could do is to ensure availability of sufficient funds, should a contingent liability materialise.

Further, contingent liabilities, being an off-balance sheet item, are often neglected. Unavailability of funds at the time of their settlement may hamper a company’s reputation and operations.

Though we could not find any defined ratio, in this regard, in literature, it appears reasonable to state that materialization of any such claim should be paid out of shareholders’ funds. In the event of company being unable to meet its contingent liabilities, it will ultimately be shareholders who will have to bear the brunt. Therefore, logically contingent liabilities are to be met from shareholders’ funds. Accordingly, a new ratio, called contingency coverage ratio has been proposed and the inverse of contingency coverage ratios is proposed to be a measure of contingency coverage risk.1
$$ \text{Inverse}{\text{ contingency coverage ratio }}\left( {\text{ICCR}} \right) \, = \frac{Contingent\,liabilities}{Shareholders\text{'}\,funds} $$
(3.3)
In other words, the ratio measures the proportion of funds available to meet the contingent liabilities, if and when they require settlement.
  1. 5.

    Credit risk

     

Credit risk refers to the risk that the borrower may default in making the payments as and when they become due.

Following the principle of prudence and conservatism, firms are expected to make adequate and timely provisions for bad debts. Further, the balance sheet as prescribed in Part III of Companies Act 2013 requires companies to explicitly classify receivables as good or doubtful, to enable readers to have clear idea of firms’ credit management. As provisions are estimates, prima facie, it may appear unreasonable to base a ratio on these. But, these provisions are believed to be adequate representatives. First, because of principle of prudence or conservatism, they are likely to be not understated. Second, overstating them may deflate companies’ profits, therefore, that is also unlikely. Maechler et al. (2010) used similar methodology for measuring credit risk of banks and found no evidence of overstatement. In addition, audit ensures the reasonableness of all estimates. Therefore, following formula has been used to measure the credit risk of companies.
$$ {\text{Credit risk}} = \frac{{Max \{{{\text{'}}}Doubtful\,recievables\text{'},\,\;Provision\;for\;bad\;and\;doubtful\;recievables{,}\} }}{Recievables} $$
(3.4)
where,
Receivables include, debtors, trade receivables, short term loans given and advances.
  1. 6.

    Exchange rate risk

     

In today’s time of increasing globalisation and cross-country trade, the importance of exchange rate and exchange rate fluctuations as a source of risk cannot be overstated. It is in context of this risk that most sophisticated risk management studies (derivatives, hedging, netting, etc.) are being conducted. But, as data on these parameters is still at nascent stage in Indian context, a rather crude measure has been adopted to measure it. It is also reflective of how efficient the managers have been in its management by employing techniques like hedging and netting etc. It implicitly captures the success of these risk management techniques as the net gain or loss on account of foreign exchange contracts, as well as gain/loss on derivatives as recognised in P&L has been considered.

The coding method is based on the premise that if there is profit, a part may be attributed to gain arising from foreign exchange transactions. If there is a loss in general and gain on foreign exchange transaction, then, had the gain been not there the overall loss would have been even higher. Thus, it has contributed to reduction of loss. Similarly, if there is a loss on foreign exchange dealings and an overall profit, the profit would have been higher had the foreign exchange loss not been there. Therefore, it is a more risky situation than the previous one.

  1. 7.

    Liquidity risk

     

It is the risk signalling that a firm may not be able to meet its short term maturing obligations (i.e. current liabilities) as and when they fall due for payment.

The study makes use of two measures of liquidity. These are discussed below as ‘a’ and ‘b’. The scores obtained on each of the two measures are then averaged to denote the liquidity risk component of the firm.
$$ {\text{Liquidity risk}} = \frac{(a + b)}{2} $$
(3.5)
Where, a and b have been defined as follows:
  1. a.

    Liquidity risk 1 (Inverse of acid-test ratio (IATR))

     
The most widely used measure in literature to indicate liquidity includes current ratio and acid-test ratio (Charitou et al. 2004; Mckee 2003). Acid-test ratio is considered to be a more rigorous measure of firms’ liquidity position than current ratio. Quick assets are current assets net of inventory and pre-paid expenses. Inventory by nature is slow moving and may not be readily convertible to cash. Similarly, pre-paid expenses are not available to pay-off current liabilities. The inverse of acid-test ratio or quick ratio is measured by the following formula:
$$ {\text{Inverse of}}\,{\text{acid-test ratio}}\,\left( {\text{IATR}} \right) = \frac{Cureent\,liabilities}{Quick\,assets} $$
(3.6)
It is worth mentioning, that though higher liquidity is desirable, excessive liquidity may be an indicator of inefficient treasury management and thus may be injurious to financial health of the firm (Khan and Jain 2014). It is believed that firms that have larger cash holdings are “safer.” In particular, cash-rich firms should have a lower probability of default. But, Acharya et al. (2012) present empirical evidence of how a conservative cash policy is indicative of distress. Thus, while constructing the scale, an IATR of less than 0.4 has been considered riskier than an IATR in the range of 0.667–0.8.
  1. b.

    Liquidity risk 2 (Modified defensive interval ratio)

     

Tinoco and Wilson (2013) indicate that higher is the liquidity of a company, lower is the likelihood of financial distress. Also, Agarwal and Taffler (2007) and Taffler (1983), suggest that ‘No credit interval’ is a good measure of liquidity position of a firm. They define ‘No credit interval’ as the length of time for which the company can meet its projected daily expenditure by drawing on its existing liquid assets. While defining daily expenditure, they have ignored interest payments. Also, liquid assets considered are net of current liabilities. Sorter and Benson (1960) have suggested variants of this ratio to suit the needs of various analysts. The ratio basically indicates the number of days a company can continue its current level of operations even with complete cessation of revenue, without resorting to additional financing.

Therefore, the study proposes to use the following modified measure and the term used to denote it is ‘modified defensive interval ratio’.
$$ {\text{Modified defensive interval ratio }}\left( {\text{MDIR}} \right) = \frac{{Quick{\kern 1pt} assets}}{{Daily{\kern 1pt} projected{\kern 1pt} \,cash {\kern 1pt} expenditure}} $$
(3.7)
where,
$$ {\text{Daily projected cash expenditure}} = \frac{{Total{\kern 1pt} cash{\kern 1pt} operating{\kern 1pt} expenditure + interest{\kern 1pt} obligations}}{365} $$
(3.8)
Total cash operating expenditure = Operating expenses − non-cash items, like depreciation

It has been assumed that if the firm will be able to continue to obtain normal short term credit as it carries on its operations, then the average current liabilities at this level of operations will be continually renewed and will not require disbursements. Thus, no deductions on account of current liabilities have been made from quick assets. Also, the component of interest obligation has been considered in calculation of expenses, as it is believed that even with complete cessation of revenue company would be required to pay for its interest obligations.

  1. 8.

    Operating risk:

     

Allen (2013) proposes that business risk can be tied to the fixed nature of many of the costs of engaging in a particular line of business. However, these fixed costs entail the risk of losses to the extent they cannot be met by business operations. Fixed costs are function of time and have to be met regardless of the amount of revenue. High fixed operating costs indicate high operating leverage and high operating risk.

Degree of operating leverage has been proposed as measure of operating risk (Singh et al. 2015).
$$ {\text{Operating risk}}\,{ = }\frac{{\%\,change{\kern 1pt} in {\kern 1pt} EBIT}}{{\%\,change{\kern 1pt} in \,{\kern 1pt} sales}} $$
(3.9)
  1. 9.

    Solvency risk

     

Solvency risk is the risk signalling that a firm may not be able to meet its obligations arising from long-term borrowings. If a firm fails to do so, it may face legal actions eventually leading to bankruptcy.

The study uses two measures of solvency and their average is proposed to be a measure of solvency risk.
$$ {\text{Therefore}},{\text{ solvency risk}} = \frac{(a + b)}{2} $$
(3.10)
where, a and b have been calculated as follows.
  1. a.

    Solvency risk 1 (Total debt to equity) (TD/TE)

     
Tinsley (1970) relates higher risk of distress with high leverage. It is noteworthy that leverage may have different connotations, based on the objective for which it is computed. In the present study, leverage has been used in a broader sense. It not only includes long-term liabilities of the company, but also its current liabilities. It is believed that current liabilities are, by and large, in existence on a continuous basis. Some current liabilities like bank credit are renewed year after year and are available on a long-term footing. Also, in the event of liquidation, like long-term lenders, short-term creditors also have a prior right on assets of company. Thus, the following measure has been used as an indicator of solvency risk.
$$ {{\text{TD}} \mathord{\left/ {\vphantom {{\text{TD}} {\text{E}}}} \right. \kern-0pt} {\text{E}}}{\text{ ratio}} = \frac{Total\,outside\,obligations}{Shareholders\text{'}\,funds} $$
(3.11)
where, total outside obligations include current liabilities and long term liabilities.
  1. b.

    Solvency risk 2 (Interest coverage ratio) (ICR)

     
The company may face legal actions if it defaults in repayment of loan. Likewise, non-payment of interest may also create legal and reputational hazards for company. The most widely used measure to gauge company’s ability to service its fixed interest obligations is interest coverage ratio (ICR). It is also known as time-interest earned ratio. The ratio indicates the tolerance limit in terms of fall in EBIT without jeopardising the ability of the company to meet its interest obligations. Tinoco and Wilson (2013), indicate that an ICR value greater than 0.4 may be a sign of distress. Therefore the following formula and scale have been used to measure and denote the solvency risk in terms of inverse of interest coverage ration (IICR):
$$ {\text{Inverse of}}\,{\text{interest coverage ratio}}\,\left( {\text{IICR}} \right) = \frac{Interest}{EBIT} $$
(3.12)
Further, some researchers recommend the use of ICR in conjunction with operating leverage. As the study uses both these measures, evidently, the results are expected to be more credible.
Once the index has been constructed, the analysis is proposed to be carried out as per classifications set out in Fig. 3.1.
Fig. 3.1

Scope of analysis

3.3 Sample Selection

The sample consists of non-financial companies that constitute Nifty 500 index as on March 31, 2014. The study covers a period of 10 years from April 1, 2005 to March, 31, 2015. The period of study is of particular importance as it includes the recession period, which impacted the world economy towards second half of 2008. As per the United Nations Council on Trade and Development (UNCTAD), investment brief (November 1, 2009), the year 2008 marked the end of a growth cycle in global foreign direct investment. Worldwide flows came down by more than 20%. This global financial crisis reduced access to financial resources both internally as well as externally (Singh et al. 2012). Thus, the study considers, two phases, Phase I (pre-recession period) April 1, 2005 to March 31, 2008 (2006–2008) and April 1, 2008 to March, 31, 2015 (2009–2015) as Phase II (post-recession period).

As the findings of past studies suggest that age and industry classification of the company influence the risk of a company, we chose to look at these aspects as well in our analysis. Therefore, the companies have been divided into three age categories—(companies based on year of incorporation) Young companies (bottom 25% (Quartile 1), Middle-aged companies (i.e. middle 50% companies (falling in Quartile 2 and Quartile 3)), and old and established companies (i.e. top 25% companies or companies in Quartile 4). Further, for an industry-wise analysis, companies have been regrouped into 15 industry groups, namely, agriculture, capital goods, chemical, diversified, fast moving consumer goods (FMCG), healthcare, housing, information and communication technology (ICT), media, metal, miscellaneous, oil and gas, power, textile and transport.

The variables of interest were collected from the balance sheet, profit and loss account, and the annual reports of sample companies.

3.4 Empirical Evidence

The normative framework for measuring risk has been empirically tested using the sample described in Sect. 3.3 above. The scope of the analysis is depicted in Fig. 3.1.

3.4.1 Aggregative Analysis

3.4.1.1 Full-Sample Analysis

Table 3.1 depicts year-wise descriptive statistics for the risk index.
Table 3.1

Descriptive statistics of year-wise risk index on full sample, 2005–2015

Year

N

Mean (%)

Median (%)

Std. deviation (%)

Minimum (%)

Maximum (%)

2005–2006

267

41.01

41.11

6.71

25.28

69.72

2006–2007

306

40.69

40.83

6.43

26.39

67.78

2007–2008

333

40.52

40.00

6.70

21.94

61.67

2008–2009

359

42.01

41.39

7.36

25.56

64.44

2009–2010

368

42.59

41.94

7.21

26.11

66.94

2010–2011

389

41.71

40.83

7.74

24.17

77.78

2011–2012

401

42.61

42.22

8.04

22.78

64.44

2012–2013

401

42.90

42.22

8.85

21.94

73.33

2013–2014

379

43.21

42.50

8.62

24.17

66.67

2014–2015

375

44.18

43.33

9.50

22.78

69.72

Total

3578

42.23

41.94

7.92

21.94

77.78

It is evident that the mean risk index of sample companies has increased from 41% in 2005–06 to 44% in 2014–2015. Though the increase is of only 3% points or 1.42 points out of maximum possible 45 points; it is a sign of increasing distress. The marginal fall in average risk index, in two years immediately preceding the period of recession is puzzling. But almost a steady increase in average risk index in each of the years in post-recession period is indicative of lagged effects of recession (Fig. 3.2). The risk index in the range of 40–45% can be interpreted as ‘moderate’ risk levels. In other words, an average risk index in this range suggests that on an average the companies have a risk score of 2 (out of 5) on each of the nine risks. This could be viewed as good risk management, as risk scores in this range appear to be a result of ‘calculated risks’ rather than unattended risks.
Fig. 3.2

Year-wise descriptive statistics of risk index

It is not surprising that Indian companies had these ‘moderate’ risk levels in pre-recession period as well during the recession. This is so because had they had excessive exposures, they might not have been able to survive the downturn. Given the fact that these are India’s topmost companies and they could endure the gloomy market period, it seems unreasonable to assume that they would have had exorbitant levels of risk exposure. In other words, the findings indicate that the Indian corporate sector has the advantage of having moderate risk; it does not suffer the drawback of high risk exposure. It may be considered as a strength of corporate enterprises in India.

But, a steady increase in risk levels of Indian companies, with the highest average risk index being in 2014–2015, is alarming. This could be viewed as both an indicator, and a consequence of ‘contagion’. With increasing globalisation, cross border trades, joint ventures at international level, it would be unjustified to assume that companies work in isolation or closed environment of the country that they are operating in. Companies’ fundamentals, no matter how strong they are, cannot be cushioned against world-wide booms and depressions without adequate risk management.

It is interesting to note that the minimum risk index has declined during the period of study. It is suggestive of dynamic risk management, at least on part of some companies. It is only in the year 2010–2011 that the risk index peaked to a maximum of 78% (approx.). It is noteworthy, as the period of recession is believed to be from 2008 to 2009, so its true effects on risk could only be observed in the financial year 2010–11.

Further, a low standard deviation (within each year) is indicative of the fact that the companies have similar exposures to the risks considered in the study. Since, these exposures implicitly incorporate the risk handling/management mechanism employed for concerned risk; it is also suggestive of somewhat similar risk appetite and tolerance by Indian companies.

3.4.1.2 Phase-Wise Analysis

As is evident from Table 3.2 the mean risk index has increased from approximately 41% in pre-recession period to close to 44% in post-recession period. Whether the increase is significant or not has been tested with the following hypothesis.
Table 3.2

Descriptive statistics of risk index, phase-wise (pre-recession (2005–2008); post-recession (2008–2015))

Particulars

Pre-recession (2005–2008)

Post-recession (2008–2015)

N (number of observations)

906

2672

Mean (%)

40.72

42.74

Std. deviation (%)

6.61

8.26

Hypothesis: Recession has an impact on risk levels of companies.

As the data is not normally distributed, Mann-Whitney U2 test has been applied to test the hypothesis. The statistically significant increase (Table 3.3) in risk can be attributed to the uncertainties that are at play in complex business environment. Also, it is believed that, Indian companies were not immune to after-effects of recession. Their operations worldwide may have taken a hit.
Table 3.3

Mann Whitney U test for difference of mean Risk index (pre-recession, post-recession)

Particulars

Pre-recession (2005–2008)

Post-recession (2008–2015)

N

906

2672

Mean rank

1603.29

1852.64

Sum of ranks

1452585.00

4950246.00

 

Risk index

Mann-Whitney U

1041714.000

Wilcoxon W

1452585.000

Z

−6.279

Asymp. Sig. (2-tailed)

0.000 ***

Note *** Indicates significance at 1% level of significance

3.4.1.3 Age-Wise Analysis

Table 3.4 shows that, prima facie, there is negligible difference among risk index of companies of different age groups. All three age groups-young, middle-aged and, old and established have risk index of around 42%. This leads to our next hypothesis.
Table 3.4

Descriptive statistics of risk index, age-wise (2005–2015)

Age class

Mean (%)

Std. deviation (%)

Minimum (%)

Maximum (%)

Skewness

Kurtosis

Young (Q1)

42.30

8.30

21.94

77.78

0.44

0.29

Middle-aged (Q2 and Q3)

41.14

7.87

22.78

69.72

0.41

0.04

Old and established (Q4)

43.35

7.60

21.94

73.33

0.52

0.37

Hypothesis: Age has an impact on risk levels of companies.

As the data is not normally distributed, Kruskal-Wallis3 test has been applied to test the hypothesis. The result (Table 3.5) shows that there is a statistically significant difference between risk levels of companies in different age groups. But, a close perusal of Table 3.4 reveals that maximum risk index of 77% is of a Young company. This seems in line with the intuition that young companies are considered to be synonymous with dynamism and vigour and more importantly, aggression. Thus, if it is a conscious decision then, it points towards their higher risk appetite and higher levels of risk tolerance; young companies, by and large, have such a temperament in spite of the fact that they at times struggle to raise funds, face liquidity crunch, may not even have reached their break-even point (in case of infant companies). Whereas, the middle-aged companies can be viewed as in most comfortable position. The possible reason is that they have the right balance of experience and enthusiasm. They have just enough experience to avoid unreasonably high levels of risk and requisite motivation to sustain and establish them with appropriate risk management. However, old and established companies may be looking for avenues for diversification and expansion, and in the process, may take up risky projects. Further, having made a name for themselves they may have a leeway to explore higher risk levels in pursuit of higher gains.
Table 3.5

Kruskal Wallis test for mean difference in risk index among different age groups

Age class

N

Mean rank

Young

959

1794.99

Middle-aged

1752

1778.41

Old and established

867

1805.83

Total

3578

 

Test statistics

Chi-Square

 

0.446

df

 

2

Asymp. Sig.

 

0.048 **

Note ** Indicates significance at 5% level of significance

The difference in exposure levels based on age is suggestive of different risk culture and risk management in Indian companies. This may also be viewed as that age has a significant impact on risk tolerance of companies. In addition, the growing thrust on risk management may not have been taken in the similar stride by young and old companies.

Further, a phase-wise analysis for all three age groups (Tables 3.6 and 3.7) reveals, that there has been a statistically significant increase in risk index of companies, in all three age groups, in post -recession period. This suggests that recession had an adverse impact on Indian companies, notwithstanding their age.
Table 3.6

Descriptive statistics of risk index, age-wise (pre-recession (2005–2008); post-recession (2008–2015))

Particulars

 

Young

Middle-aged

Old and established

N

Total

959

1752

877

Pre-recession

246

437

233

Post-recession

713

1315

644

Mean

Total (%)

42.30

41.14

43.35

Pre-recession (%)

40.55

40.76

40.85

Post-recession (%)

42.90

42.60

42.87

Std. deviation

Total (%)

8.30

7.87

7.60

Pre-recession (%)

6.95

6.38

6.68

Post-recession (%)

8.64

8.25

7.84

Table 3.7

Mann-Whitney U test for difference of mean risk index (pre-recession, post-recession) for each age category

Particulars

 

Young

Middle-aged

Old and established

N

Pre-recession

246

437

233

Post-recession

713

1315

644

Mean rank

Pre-recession

425.17

792.99

386.49

Post-recession

498.92

904.25

450.45

Sum of ranks

Pre-recession

104592.00

346535.50

86186.50

Post-recession

355728.00

1189092.50

290091.50

Mann-Whitney U

 

74211.00

250832.50

61210.50

Wilcoxon W

 

104592.00

346535.50

86186.50

Z

 

−3.60

−3.98

−3.29

Asymp. Sig. (2-tailed)

 

0.000 ***

0.000 ***

0.001 ***

Note *** Indicates significance at 1% level of significance

3.4.1.4 Industry-Wise Analysis

As is evident from Table 3.8, both agriculture and housing and construction industry have maximum risk index with an average of about 44%, whereas, industries, namely, healthcare and media have minimum risk index with an average of 39%.
Table 3.8

Descriptive statistics of risk index, industry-wise (2005–2015)

Industry

Mean (%)

Median (%)

Std. deviation (%)

Minimum (%)

Maximum (%)

Skewness

Kurtosis

Agriculture

44.74

44.44

7.29

25.83

67.78

0.37

0.22

Capital goods

40.55

40.83

6.80

25.28

64.44

0.50

0.51

Chemical

42.00

41.11

6.80

26.39

67.78

0.63

0.72

Diversified

43.89

42.36

8.43

28.33

66.67

0.59

−0.15

FMCG Consumer goods

42.86

43.06

7.62

21.94

65.28

0.01

−0.04

Healthcare

39.48

39.17

7.43

23.89

64.17

0.57

0.31

Housing and Construction

44.30

43.33

8.18

26.67

68.89

0.37

−0.36

ICT

40.87

40.28

8.33

24.17

69.72

0.38

−0.03

Media

39.92

38.75

7.56

22.78

59.72

0.31

−0.55

Metals

43.59

43.06

8.35

26.11

65.28

0.29

−0.36

Miscellaneous

41.55

41.11

7.89

22.78

66.94

0.52

0.29

Oil and gas

43.77

44.17

7.97

26.39

64.44

0.27

−0.16

Power

43.66

42.22

8.53

28.06

77.78

1.19

1.94

Textile

43.87

43.89

8.20

21.94

73.33

0.40

0.85

Transport

41.10

41.11

7.40

24.17

64.44

0.17

−0.28

Agriculture industry is fraught with vagaries of nature and its operations by character are seasonal. Therefore, due to the inherent nature of business, agriculture industry is bound to exhibit higher risk levels. Similarly, housing and construction industry is closely linked to economic conditions in the country. It is well established that the economy is still in the recovery phase with a looming possibility of another phase of recession. Thus, the business of housing industry appears to be in doldrums, as is reflected through higher risk levels. Healthcare industry has witnessed tremendous growth in recent times. This may be attributed to increased innovation, successful research and development, synergistic joint ventures, and efficient use of cutting edge technology. Also, with increased Government focus on health sector as a part of public welfare, the industry is expected to fare well. This reduced uncertainty is very well reflected in the risk index. Similarly, with the advent of 24 h channels, at the turn of the century, media industry is witnessing an unparalleled boom. It is pertinent to note that the calculated risk index is in tune with this.

Schmalensee (1985) and Wernerfelt and Montgomery (1988) conjecture that industry effects have a strong influence on firm’s success. This leads to the next hypothesis:

Hypothesis: Industry group has an impact on risk levels of companies.

A pair-wise comparison (Fig. 3.3) of mean risk levels in industries reveal that healthcare industry has statistically significant different risk levels compared to nine other industries (namely, agriculture, diversified, FMCG, housing and construction, metals, oil and gas, power, textile and chemical). Similarly, housing and construction industry has risk levels, which are significantly different from that of six other industries. These may be attributed to peculiar nature of these industries. Amongst all industry groups, healthcare is one of the most research focussed and regulated industry. Likewise, housing and construction industry is largely affected by macro-economic conditions. Therefore, the fact that these industries have substantially different levels of risk vis-à-vis other industries, is not surprising.
Fig. 3.3

Pair-wise comparison of risk index industry-level

It is noteworthy, that a phase-wise analysis (Tables 3.9 and 3.10) reveals that only four (namely, healthcare, miscellaneous, power and transport) out of 15 industries witnessed a statistically significant increase in risk index in post-recession period. The remaining industries, because of their nature of operations and commendable risk management policies appear to have withstood the testing times.
Table 3.9

Descriptive statistics of risk index, industry-wise, phase-wise (pre-recession, (2005–2008); post-recession (2008–2015))

Particulars

Time-frame

Agriculture (%)

Capital goods (%)

Chemical (%)

Diversified (%)

FMCG (%)

Healthcare (%)

Housing and construction (%)

ICT (%)

Media (%)

Mean

Pre-recession

43.30

39.82

41.27

41.31

42.73

37.79

42.96

40.33

37.46

Post-recession

45.25

40.82

42.29

44.78

42.91

40.18

44.63

41.04

40.59

Std. deviation

Pre-recession

5.31

5.42

7.52

5.61

6.87

5.72

6.04

7.40

6.23

Post-recession

7.82

7.25

6.49

9.05

7.89

7.94

8.60

8.62

7.78

Table 3.10

Mann Whitney U test for difference of mean risk index, industry-wise, phase-wise (pre-recession (2005–2008); post-recession (2008–2015))

Particulars

Time-frame

Agri-culture

Capital goods

Chemical

Diversified

FMCG

Health-care

Housing

ICT

N

2005–08

39

64

87

38

70

87

78

75

N

2008–15

111

169

218

110

192

209

318

229

Mean rank

2005–08

68.32

114.55

139.74

63.61

129.21

131.67

182.24

146.57

Mean rank

2008–15

78.02

117.93

158.29

78.26

132.34

155.50

202.49

154.44

Sum of ranks

2005–08

2664.50

7331.00

12157.50

2417.00

9044.50

11455.50

14214.50

10993.00

Sum of ranks

2008–15

8660.50

19930.00

34507.50

8609.00

25408.50

32500.50

64391.50

35367.00

Mann-Whitney U

 

1884.500

5251.000

8329.500

1676.000

6559.500

7627.500

11133.500

8143.000

Z

 

−1.200

−.342

−1.659

−1.818

−.296

−2.183

−1.401

−.673

Asymp. Sig. (2-tailed)

 

0.230

0.732

0.097*

0.069*

0.767

0.029**

0.161

0.501

Particulars

Time-frame

Media

Metal

Miscellaneous

Oil and gas

Power

Textile

Transport

N

2005–08

25

53

91

38

35

36

90

N

2008–15

93

155

279

113

134

103

239

Mean rank

2005–08

48.62

100.40

152.42

70.84

57.77

59.61

147.89

Mean rank

2008–15

62.42

105.90

196.29

77.73

92.11

73.63

171.44

Sum of ranks

2005–08

1215.50

5321.00

13870.50

2692.00

2022.00

2146.00

13310.50

Sum of ranks

2008–15

5805.50

16415.00

54764.50

8784.00

12343.00

7584.00

40974.50

Mann-Whitney U

 

890.500

3890.000

9684.500

1951.000

1392.000

1480.000

9215.500

Z

 

−1.792

−.575

−3.398

−.841

−3.699

−1.799

−2.002

Asymp. Sig. (2-tailed)

 

0.073*

0.565

0.001 ***

0.401

0.000***

0.072*

0.045**

Note ***, **, * Indicates significance at 1%, 5% and 10% level of significance, respectively

3.4.2 Dis-aggregative Analysis

As is evident from aggregative analysis that Indian companies are scoring an average of about 2 (out of 5) on each of the nine risks, inter-se, it would be useful to identify risks which are more dominating than others.

Therefore, an attempt has been made to gauge the frequencies of companies for each of the risk scores, 1, 2, 3, 4 and 5, for each of the nine risks.

3.4.2.1 Full Sample Analysis

Table 3.11 reveals that almost 60% of the observations have a score of 1 or 2 on each of the risks, except on liquidity risk (as measured by inverse of acid-test ratio) and solvency risk (as measured by debt-equity ratio). It is noteworthy that more than half the observations have a score of 5 for IATR. This implies that majority of Indian companies are carrying current liabilities of the amount more than that can be met through quick assets. This is an alarming situation in terms of ability of companies to meet their short-term obligations.
Table 3.11

Summary of frequency distribution of various risks, phase-wise (pre-recession (2005–2008); post-recession (2008–2015))

Risk

Scores

Total

Pre-recession

Post-recession

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Market risk

1

48.02

48.02

46.14

46.14

48.65

48.65

2

8.75

56.76

12.36

58.50

7.52

56.18

3

37.65

94.41

40.40

98.90

36.71

92.89

4

5.53

99.94

1.10

100.00

7.04

99.93

5

0.06

100.00

0.00

100.00

0.07

100.00

Accounting risk

1

77.89

77.89

85.43

85.43

75.34

75.34

2

19.93

97.82

14.13

99.56

21.89

97.23

3

2.10

99.92

0.44

100.00

2.66

99.89

4

0.00

99.92

0.00

100.00

0.00

99.89

5

0.08

100.00

0.00

100.00

0.11

100.00

Competition risk

1

45.25

45.25

42.05

42.05

46.33

46.33

2

44.66

89.91

49.67

91.72

42.96

89.30

3

7.49

97.40

6.29

98.01

7.90

97.19

4

1.93

99.33

1.32

99.34

2.13

99.33

5

0.67

100.00

0.66

100.00

0.67

100.00

Contingency coverage risk

1

36.89

36.89

37.42

37.42

36.71

36.71

2

25.38

62.27

24.61

62.03

25.64

62.35

3

18.11

80.38

20.20

82.23

17.40

79.75

4

10.03

90.41

9.49

91.72

10.22

89.97

5

9.59

100.00

8.28

100.00

10.03

100.00

Credit risk

1

91.76

91.76

96.58

96.58

90.12

90.12

2

3.72

95.47

2.65

99.23

4.08

94.20

3

1.12

96.59

0.33

99.56

1.38

95.58

4

0.75

97.34

0.11

99.67

0.97

96.56

5

2.66

100.00

0.33

100.00

3.44

100.00

Risk

Scores

Total

Pre-recession

Post-recession

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Exchange rate risk

1

24.65

24.65

28.70

28.70

23.28

23.28

2

35.05

59.70

40.18

68.87

33.31

56.59

3

2.21

61.91

0.99

69.87

2.62

59.21

4

35.16

97.07

29.25

99.12

37.16

96.37

5

2.93

100.00

0.88

100.00

3.63

100.00

Liquidity risk I

1.25

8.50

8.50

9.82

9.82

8.05

8.05

2.50

13.00

21.49

19.54

29.36

10.78

18.82

3.75

27.31

48.80

33.11

62.47

25.34

44.16

5.00

51.20

100.00

37.53

100.00

55.84

100.00

Liquidity risk II

1

42.15

42.15

41.61

41.61

42.33

42.33

2

28.45

70.60

30.91

72.52

27.62

69.95

3

24.04

94.63

21.52

94.04

24.89

94.84

4

3.10

97.74

3.53

97.57

2.96

97.79

5

2.26

100.00

2.43

100.00

2.21

100.00

Operating risk

1

23.20

23.20

15.45

15.45

25.82

25.82

2

45.19

68.39

50.11

65.56

43.53

69.35

3

17.89

86.28

22.74

88.30

16.24

85.59

4

6.09

92.37

6.18

94.48

6.06

91.65

5

7.63

100.00

5.52

100.00

8.35

100.00

Solvency risk I

1

12.35

12.35

9.82

9.82

13.20

13.20

2

8.94

21.30

7.51

17.33

9.40

22.60

3

6.01

27.31

5.19

22.52

6.30

28.90

4

7.55

34.85

5.08

27.59

8.40

37.30

5

65.15

100.00

72.41

100.00

62.70

100.00

Solvency risk II

1.25

77.84

77.84

89.62

89.62

73.84

73.84

2.50

10.23

88.07

6.18

95.81

11.60

85.44

3.75

8.69

96.76

3.75

99.56

10.37

95.81

5.00

3.24

100.00

0.44

100.00

4.19

100.00

In addition, higher proportion of financing of assets through external liabilities indicates excessive use of debt. If the companies default on these liabilities, not only their debt will be considered more risky, also the required rate of return on equity will go up, making it difficult for these companies to borrow money.

On the one hand, a high percentage of observations scoring 5 on both the liquidity and solvency ratio I are indicative of distress in terms of payment of short term as well as long term obligations. On the other hand, a whopping percentage (around 70%) of observations scoring up to 2 on MDI and up to 2.5 on IICR is a relief. A score of up to 2 on MDI indicates that these companies can sustain the payment of their operating expenditures and interest obligations for at least 90 days, even with no revenue. Similarly, a score of up to 2.5 on IICR assures that even if the EBIT of these companies fall by 50%, they will still be able to meet their interest obligations.

Thus, it is interesting to note that the combined effect of the two measures of liquidity risk presents a comfortable position.

Further, it is equally interesting to note that a good 92% of observations have a score of 1 in respect of credit risk. This is a clear indication of proactive and efficient credit management on part of Indian companies.

In addition, the fact that close to 40% of observations are more sensitive to market-wide changes, than the market itself, justifies the increasing trend of derivatives usage.

It is noted that not a single sample company has been issued an ‘adverse opinion’ in the entire ten year period of study. It clearly signals that not a single company had misstated such facts that may be material and pervasive to financial statements.

3.4.2.2 Phase-Wise Analysis

Moreover, a phase-wise analysis (Table 3.11) has somewhat startling revelations. The number of companies/observations scoring 5 has increased (from pre-recession to post-recession phase) for almost all the risk categories. The most prominent increase being in liquidity risk 1, from close to 38% observations in Phase I to about 56% observations in Phase II. The liquidity crunch brought about by recession is clearly evident here.

The percentage of companies that suffered a loss on account of foreign exchange transactions as well as an overall loss, increased by 3% in the post-recession period.

In the context of accounting risk, auditors provided a ‘matter of emphasis’ paragraph in 22% annual reports, an increase of 8% from pre-recession phase. It is worth mentioning that not a single sample company was issued a ‘disclaimer of opinion’, in pre-recession phase, whereas three companies have been issued such opinion in post-recession era. Similarly, not a single company had a beta of more than 4 in Phase I, but two companies had become so sensitive to market wide risks that they followed a 1% decrease in index returns with a 4% decrease in their return.

On the positive side, on an average 4% more companies enjoyed ‘growth’ of market share in post-recession phase. This may be attributed to elimination of non-competitive firms, which could not endure recessionary pressures. In addition, the solvency risk 1 witnessed a decline of about 10% in number of observations that had a total debt to equity ratio of more than 0.75. Similarly about 3% more observations seemed to have felt reduced sensitivity to market wide fluctuations, with their betas moving to the lowest score category.

3.4.2.3 Age-Wise Analysis

To gain a better insight into distribution of each of the risks, an age-wise analysis (Table 3.12) has been attempted.
Table 3.12

Summary of frequency distribution of various risks, age-wise

Risk

Scores

Young companies

Middle-aged companies

Old and established companies

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Market risk

1

42.0

42.0

48.6

48.6

53.4

53.4

2

9.6

51.6

8.2

56.8

9.0

62.4

3

41.5

93.1

37.6

94.4

33.4

95.8

4

6.8

99.9

5.5

99.9

4.2

100.0

5

0.1

100.0

0.1

100.0

0.0

100.0

Accounting risk

1

75.1

75.1

78.1

78.1

80.6

80.6

2

21.8

96.9

19.7

97.8

18.3

99.0

3

3.1

100.0

2.1

99.8

1.0

100.0

4

0.0

100.0

0.0

99.8

0.0

100.0

5

0.0

100.0

0.2

100.0

0.0

100.0

Competition risk

1

51.0

51.0

45.9

45.9

37.6

37.6

2

36.6

87.6

43.7

89.6

55.6

93.2

3

9.2

96.8

7.6

97.2

5.3

98.5

4

2.4

99.2

2.0

99.2

1.3

99.8

5

0.8

100.0

0.8

100.0

0.2

100.0

Contingency coverage risk

1

37.3

37.3

36.8

36.8

36.7

36.7

2

20.9

58.2

24.5

61.3

32.1

68.7

3

15.8

74.0

19.5

80.8

17.8

86.5

4

11.3

85.3

9.9

90.8

8.9

95.4

5

14.7

100.0

9.2

100.0

4.6

100.0

Credit risk

1

89.5

89.5

92.5

92.5

92.8

92.8

2

5.4

94.9

3.7

96.1

2.0

94.8

3

1.1

96.0

1.3

97.4

0.8

95.6

4

0.6

96.7

0.5

97.8

1.5

97.1

5

3.3

100.0

2.2

100.0

2.9

100.0

Risk

Scores

Young companies

Middle-aged companies

Old and established companies

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Exchange rate risk

1

31.2

31.2

22.5

22.5

21.8

21.8

2

31.5

62.7

36.4

58.8

36.3

58.1

3

3.6

66.3

1.8

60.7

1.4

59.5

4

29.7

96.0

36.8

97.4

37.9

97.5

5

4.0

100.0

2.6

100.0

2.5

100.0

Liquidity risk I

1.25

7.7

7.7

9.7

9.7

6.9

6.9

2.50

18.7

26.4

12.4

22.1

7.8

14.8

3.75

27.2

53.6

30.1

52.2

21.8

36.6

5.00

46.4

100.0

47.8

100.0

63.4

100.0

Liquidity risk II

1

52.0

52.0

42.4

42.4

30.8

30.8

2

24.3

76.3

30.3

72.6

29.4

60.2

3

17.3

93.6

23.3

95.9

33.0

93.2

4

3.6

97.3

2.6

98.5

3.5

96.7

5

2.7

100.0

1.5

100.0

3.3

100.0

Operating risk

1.0

21.5

21.5

22.5

22.5

26.4

26.4

2.0

46.3

67.8

46.1

68.7

42.1

68.5

3.0

18.4

86.1

18.6

87.2

16.0

84.5

4.0

6.2

92.3

5.7

92.9

6.8

91.3

5.0

7.7

100.0

7.1

100.0

8.7

100.0

Solvency risk I

1

18.8

18.8

12.0

12.0

5.9

5.9

2

10.5

29.3

8.6

20.6

8.0

13.8

3

5.5

34.8

5.9

26.5

6.8

20.6

4

6.7

41.5

7.6

34.1

8.4

29.1

5

58.5

100.0

65.9

100.0

70.9

100.0

Solvency risk II

1.25

73.4

73.4

78.4

78.4

81.7

81.7

2.50

11.2

84.6

10.0

88.4

9.6

91.2

3.75

11.6

96.1

8.2

96.6

6.5

97.7

5.00

3.9

100.0

3.4

100.0

2.3

100.0

It is noteworthy that none of the older companies displayed a beta of more than 4. Also, more than 50% of observations in the category of old and established companies have a beta in the range of 0–0.95. This is suggestive of market risk of older companies being in line with that of overall market.

In the context of accounting risk, it is only the middle-aged companies that have been issued a disclaimer of opinion by auditors. One of them relates to misappropriation of funds; other relates to non-provision of impairment loss on an asset and the third deals with inability of auditors to obtain sufficient documents for the purpose of audit due to non-cooperation of directors.

Though there is not much difference in figures for credit risk across age categories, young companies appear to have higher (marginally) credit risk. They have the least percentage (89 per cent) of observations having less than one-third debts as doubtful. This may be due to lack of experience on part of young companies in assessing credit-worthiness, capacity, character and capability of counter parties.

Interestingly, the results confirm the intuition that majority of observations in the category of young companies have displayed a growth in market share. In other words, dynamic young companies are taking their competitors head on and are on expected growth trajectory. Also, only 0.8% of observations of young companies have shown a decline in market share of more than 50 per cent. In addition, about 55% of observations in the category of old and established companies have witnessed a decline of up to 20% in market share. This may be attributed to increased competition for old companies from new entrants. The flourishing start-up culture coupled with entry of new multinational companies could be a reason.

The ability to meet contingent liabilities is almost similar across all groups. It should be a matter of concern that about 15% observations in the category of young companies may not be able to meet their contingent liabilities, in full, if they materialise. These liabilities mainly include ‘liabilities against company not yet acknowledged’ and liabilities under guarantees. Whereas, only about 10% of observations in the middle-aged group and 5% observations in the old and established group have a score of ‘5’ on this risk.

About 40% of all observations in all the age groups seemed to have suffered a loss on account of foreign exchange transactions. It is noteworthy that young companies appear to have managed this risk better than the other two groups. About two-third of observations in young companies have either recorded no gain-no loss on forex or have recorded a gain on account of forex transactions. Middle-aged as well as old and established companies have the highest percentage of observations (about 36% firm-year observations) in the category where the overall profits have declined because of loss on forex dealings. This calls for more efficient treasury management by these companies.

Since two measures have been used to indicate liquidity risk, for better comprehension, it has been analysed in two parts. The first sub-part relates to inverse of acid-test ratio, for which, four score points are possible, namely, 1.25, 2.5, 3.75 and 5. It is noteworthy that a whopping two-third of observations in the old and established category have more current liabilities than could be financed from their quick assets. They may have to resort to costlier external financing to meet these obligations. In case, they are unable to make timely payment of the current liabilities, their market image is affected adversely and more importantly, they may have to face legal proceedings leading to disruption of operations. In marked contrast, less than half the observations in the young and middle-aged group have an acid test ratio of more than 1.

Second measure of liquidity is modified defensive interval ratio. About three-fourth of companies in the young and middle aged category can sustain their current level of operations, without resorting to additional financing, for at least 90 days. Whereas, only about three-fifth of companies in old and established category can continue operations for a minimum period of 90 days, in the event of complete cessation of revenue. Also, only about 2.7%, 1.5% and 3.3% of companies in the young, middle-aged and old companies respectively, have acute liquidity crunch and can sustain operations for a period of less than 15 days.

It is noteworthy that more than two-third of all the firm-year observations, irrespective of their age group have an operating leverage of less than 1.5. In other words, even in times of declining sales these firms can be assured of meeting their fixed costs.

It is pertinent to note that almost half the observations in all the age categories have a total debt to equity ratio of more than 0.75. It signifies a risky situation for debenture-holders as well as shareholders. Further, old and established companies appear to be relying most on external financing. Only about 6% of observations related to old firms have a debt equity ratio of less than 0.33, the least among all age groups. On the other hand, results on solvency risk 2 shows a comfortable position for firms in terms of their long term obligations. About three-fourth of observations in all age groups have an interest coverage ratio of less than 0.33. Despite an evidence of high reliance on external financing, a low interest burden in relation to EBIT is indicative of lower costs of borrowing. Also, it may be seen as an efficient use of resources to generate sufficient funds to meet the cost of financing. The two measures combined point towards a moderate level of solvency risk for Indian companies.

3.4.2.4 Industry-Wise Analysis

Almost half the industries have at least 50% observations with a beta in the least risky zone (Table 3.13). But about 50% observations in diversified industry, housing and construction, media and power industry have a beta in the range of 1.05–2. In other words these companies are more sensitive to market wide fluctuations than the market as a whole. It is heartening to note that apart from housing and construction industry, none of the industries have even a single company with a beta more than 4.
Table 3.13

Summary of frequency distribution of various risks, industry-wise

Risk

Score

Agriculture

Capital goods

Chemical

Diversified

FMCG

Per cent

Cumulative Per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Market risk

1.0

42.00

42.00

55.36

55.36

54.43

54.43

29.73

29.73

64.89

64.89

2.0

10.67

52.67

11.59

66.95

11.15

65.57

6.76

36.49

6.11

70.99

3.0

42.00

94.67

29.18

96.14

33.11

98.69

45.95

82.43

28.24

99.24

4.0

5.33

100.00

3.86

100.00

1.31

100.00

17.57

100.00

0.76

100.00

5.00

0.00

100.00

0.00

100.00

0.00

100.00

0.00

100.00

0.00

100.00

Accounting risk

1.0

78.67

78.67

87.12

87.12

87.87

87.87

72.30

72.30

82.44

82.44

2.0

17.33

96.00

11.16

98.28

12.13

100.00

25.00

97.30

17.56

100.00

3.0

3.33

99.33

1.72

100.00

0.00

100.00

2.70

100.00

0.00

100.00

4.00

0.00

99.33

0.00

100.00

0.00

100.00

0.00

100.00

0.00

100.00

5.0

0.67

100.00

0.00

100.00

0.00

100.00

0.00

100.00

0.00

100.00

Competition risk

1.0

44.67

44.67

40.77

40.77

43.28

43.28

47.97

47.97

45.42

45.42

2.0

46.00

90.67

51.07

91.85

51.48

94.75

39.86

87.84

43.51

88.93

3.0

7.33

98.00

6.01

97.85

4.59

99.34

9.46

97.30

9.16

98.09

4.0

2.00

100.00

1.29

99.14

0.33

99.67

2.70

100.00

1.53

99.62

5.00

0.00

100.00

0.86

100.00

0.33

100.00

0.00

100.00

0.38

100.00

Risk

Score

Healthcare

Housing and construction

ICT

Metal

Media

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Market risk

1

72.30

72.30

26.01

26.01

51.32

51.32

43.22

43.22

27.88

27.88

2

6.42

78.72

6.31

32.32

6.91

58.22

10.17

53.39

6.25

34.13

3

20.27

98.99

50.51

82.83

39.47

97.70

40.68

94.07

59.62

93.75

4

1.01

100.00

16.92

99.75

1.97

99.67

5.93

100.00

6.25

100.00

5

0.00

100.00

0.25

100.00

0.33

100.00

0.00

100.00

0.00

100.00

Accounting risk

1

87.16

87.16

66.92

66.92

75.66

75.66

66.95

66.95

74.52

74.52

2

12.16

99.32

29.04

95.96

19.41

95.07

31.36

98.31

21.63

96.15

3

0.68

100.00

4.04

100.00

4.61

99.67

1.69

100.00

3.85

100.00

4

0.00

100.00

0.00

100.00

0.00

99.67

0.00

100.00

0.00

100.00

5

0.00

100.00

0.00

100.00

0.33

100.00

0.00

100.00

0.00

100.00

Competition risk

1

40.20

40.20

47.47

47.47

49.34

49.34

46.61

46.61

55.77

55.77

2

50.00

90.20

36.87

84.34

40.46

89.80

40.68

87.29

41.35

97.12

3

8.45

98.65

10.10

94.44

7.24

97.04

12.71

100.00

2.40

99.52

4

1.35

100.00

4.04

98.48

1.64

98.68

0.00

100.00

0.48

100.00

5

0.00

100.00

1.52

100.00

1.32

100.00

0.00

100.00

0.00

100.00

Risk

Score

Miscellaneous

Oil and gas

Power

Textile

Transport

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Market risk

1

55.14

55.14

50.33

50.33

27.81

27.81

40.29

40.29

55.02

55.02

2

12.70

67.84

9.93

60.26

8.28

36.09

6.47

46.76

10.64

65.65

3

28.11

95.95

35.10

95.36

49.70

85.80

51.08

97.84

33.13

98.78

4

4.05

100.00

4.64

100.00

14.20

100.00

2.16

100.00

1.22

100.00

5

0.00

100.00

0.00

100.00

0.00

100.00

0.00

100.00

0.00

100.00

Accounting risk

1

79.46

79.46

74.17

74.17

60.95

60.95

73.38

73.38

84.19

84.19

2

18.92

98.38

23.84

98.01

34.91

95.86

26.62

100.00

14.29

98.48

3

1.35

99.73

1.99

100.00

4.14

100.00

0.00

100.00

1.52

100.00

4

0.00

99.73

0.00

100.00

0.00

100.00

0.00

100.00

0.00

100.00

5

0.27

100.00

0.00

100.00

0.00

100.00

0.00

100.00

0.00

100.00

Competition risk

1

46.22

46.22

50.33

50.33

32.54

32.54

59.71

59.71

37.08

37.08

2

41.62

87.84

44.37

94.70

50.89

83.43

35.97

95.68

52.28

89.36

3

8.11

95.95

2.65

97.35

11.83

95.27

3.60

99.28

7.60

96.96

4

3.51

99.46

2.65

100.00

3.55

98.82

0.00

99.28

1.52

98.48

5

0.54

100.00

0.00

100.00

1.18

100.00

0.72

100.00

1.52

100.00

Risk

Score

Agriculture

Capital goods

Chemical

Diversified

FMCG

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Contingency coverage risk

1

38.00

38.00

38.20

38.20

42.95

42.95

44.59

44.59

29.39

29.39

2

22.67

60.67

28.33

66.52

29.18

72.13

16.22

60.81

30.53

59.92

3

23.33

84.00

17.17

83.69

17.70

89.84

14.19

75.00

25.19

85.11

4

9.33

93.33

7.30

90.99

6.89

96.72

9.46

84.46

7.63

92.75

5

6.67

100.00

9.01

100.00

3.28

100.00

15.54

100.00

7.25

100.00

Credit risk

1

88.67

88.67

97.42

97.42

90.49

90.49

96.62

96.62

94.27

94.27

2

3.33

92.00

1.72

99.14

1.64

92.13

2.03

98.65

3.05

97.33

3

2.00

94.00

0.00

99.14

1.31

93.44

1.35

100.00

1.15

98.47

4

0.00

94.00

0.00

99.14

2.30

95.74

0.00

100.00

0.38

98.85

5

6.00

100.00

0.86

100.00

4.26

100.00

0.00

100.00

1.15

100.00

Exchange rate risk

1

21.33

21.33

25.32

25.32

20.98

20.98

19.59

19.59

19.85

19.85

2

29.33

50.67

36.48

61.80

29.51

50.49

33.78

53.38

34.35

54.20

3

4.67

55.33

    

4.05

57.43

1.53

55.73

4

42.00

97.33

37.77

99.57

48.85

99.34

38.51

95.95

41.22

96.95

5

2.67

100.00

0.43

100.00

0.66

100.00

4.05

100.00

3.05

100.00

Risk

Score

Healthcare

Housing and construction

ICT

Metal

Media

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Contingency coverage risk

1.00

39.53

39.53

36.62

36.62

33.88

33.88

56.78

56.78

27.40

27.40

2.00

28.04

67.57

18.94

55.56

25.00

58.88

17.80

74.58

28.37

55.77

3.00

17.23

84.80

13.13

68.69

16.12

75.00

11.86

86.44

17.31

73.08

4.00

8.45

93.24

12.12

80.81

14.80

89.80

5.08

91.53

15.38

88.46

5.00

6.76

100.00

19.19

100.00

10.20

100.00

8.47

100.00

11.54

100.00

Credit risk

1.00

94.93

94.93

95.20

95.20

84.54

84.54

88.14

88.14

86.06

86.06

2.00

1.35

96.28

1.52

96.72

10.53

95.07

5.08

93.22

2.88

88.94

3.00

0.00

96.28

0.51

97.22

1.97

97.04

3.39

96.61

2.88

91.83

4.00

0.00

96.28

1.52

98.74

0.99

98.03

1.69

98.31

0.96

92.79

5.00

3.72

100.00

1.26

100.00

1.97

100.00

1.69

100.00

7.21

100.00

Exchange rate risk

1.00

10.81

10.81

43.94

43.94

16.78

16.78

25.42

25.42

25.00

25.00

2.00

47.64

58.45

22.73

66.67

42.76

59.54

27.12

52.54

40.38

65.38

3.00

4.05

62.50

1.01

67.68

2.96

62.50

6.78

59.32

1.44

66.83

4.00

33.78

96.28

30.56

98.23

34.21

96.71

33.05

92.37

30.29

97.12

5.00

3.72

100.00

1.77

100.00

3.29

100.00

7.63

100.00

2.88

100.00

Risk

Score

Miscellaneous

Oil and gas

Power

Textile

Transport

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Contingency coverage risk

1.00

35.68

35.68

26.49

26.49

36.09

36.09

32.37

32.37

40.43

40.43

2.00

23.24

58.92

29.14

55.63

24.26

60.36

27.34

59.71

27.96

68.39

3.00

18.65

77.57

21.85

77.48

14.79

75.15

21.58

81.29

22.19

90.58

4.00

9.73

87.30

12.58

90.07

12.43

87.57

10.79

92.09

7.90

98.48

5.00

12.70

100.00

9.93

100.00

12.43

100.00

7.91

100.00

1.52

100.00

Credit risk

1.00

89.73

89.73

89.40

89.40

91.72

91.72

94.96

94.96

92.71

92.71

2.00

5.14

94.86

5.96

95.36

4.73

96.45

0.00

94.96

5.47

98.18

3.00

0.27

95.14

2.65

98.01

1.18

97.63

0.72

95.68

0.61

98.78

4.00

1.08

96.22

0.00

98.01

0.00

97.63

0.72

96.40

0.30

99.09

5.00

3.78

100.00

1.99

100.00

2.37

100.00

3.60

100.00

0.91

100.00

Exchange rate risk

1.00

28.38

28.38

15.23

15.23

28.40

28.40

15.83

15.83

33.13

33.13

2.00

35.68

64.05

39.07

54.30

37.28

65.68

41.73

57.55

32.22

65.35

3.00

3.24

67.30

2.65

56.95

2.37

68.05

0.72

58.27

1.52

66.87

4.00

30.27

97.57

37.09

94.04

27.81

95.86

35.25

93.53

31.00

97.87

5.00

2.43

100.00

5.96

100.00

4.14

100.00

6.47

100.00

2.13

100.00

Risk

Score

Agriculture

Capital goods

Chemical

Diversified

FMCG

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Liquidity risk

1.25

6.00

6.00

10.30

10.30

6.56

6.56

7.43

7.43

5.73

5.73

2.50

12.00

18.00

9.87

20.17

3.93

10.49

13.51

20.95

6.87

12.60

3.75

13.33

31.33

37.77

57.94

25.90

36.39

33.11

54.05

19.08

31.68

5.00

68.67

100.00

42.06

100.00

63.61

100.00

45.95

100.00

68.32

100.00

Liquidity risk II

1.00

30.00

30.00

45.49

45.49

14.10

14.10

49.32

49.32

12.60

12.60

2.00

25.33

55.33

44.64

90.13

43.93

58.03

21.62

70.95

27.86

40.46

3.00

37.33

92.67

9.44

99.57

39.34

97.38

20.95

91.89

48.85

89.31

4.00

7.33

100.00

0.00

99.57

2.30

99.67

4.73

96.62

4.58

93.89

5.00

0.00

100.00

0.43

100.00

0.33

100.00

3.38

100.00

6.11

100.00

Operating risk

1.00

26.00

26.00

18.88

18.88

22.95

22.95

31.76

31.76

19.47

19.47

2.00

36.67

62.67

45.92

64.81

47.54

70.49

40.54

72.30

48.09

67.56

3.00

25.33

88.00

20.60

85.41

18.36

88.85

12.16

84.46

14.89

82.44

4.00

4.67

92.67

5.58

90.99

3.93

92.79

6.08

90.54

11.45

93.89

5.00

7.33

100.00

9.01

100.00

7.21

100.00

9.46

100.00

6.11

100.00

Risk

Score

Healthcare

Housing and construction

ICT

Metal

Media

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Liquidity risk I

1.25

12.50

12.50

7.58

7.58

11.84

11.84

16.10

16.10

4.81

4.81

2.50

18.58

31.08

12.37

19.95

21.71

33.55

18.64

34.75

19.23

24.04

3.75

37.16

68.24

24.75

44.70

36.84

70.39

31.36

66.10

21.15

45.19

5.00

31.76

100.00

55.30

100.00

29.61

100.00

33.90

100.00

54.81

100.00

Liquidity risk II

1.00

56.76

56.76

57.32

57.32

53.62

53.62

67.80

67.80

45.19

45.19

2.00

35.14

91.89

21.97

79.29

29.28

82.89

22.03

89.83

23.56

68.75

3.00

7.77

99.66

18.94

98.23

12.50

95.39

7.63

97.46

25.96

94.71

4.00

0.34

100.00

1.01

99.24

0.66

96.05

2.54

100.00

0.96

95.67

5.00

0.00

100.00

0.76

100.00

3.95

100.00

0.00

100.00

4.33

100.00

Operating risk

1.00

18.92

18.92

24.49

24.49

20.72

20.72

36.44

36.44

23.56

23.56

2.00

40.54

59.46

48.74

73.23

46.71

67.43

35.59

72.03

48.56

72.12

3.00

24.32

83.78

14.90

88.13

19.08

86.51

14.41

86.44

14.90

87.02

4.00

6.42

90.20

5.05

93.18

5.26

91.78

5.08

91.53

4.33

91.35

5.00

9.80

100.00

6.82

100.00

8.22

100.00

8.47

100.00

8.65

100.00

Risk

Score

Miscellaneous

Oil and gas

Power

Textile

Transport

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Liquidity risk I

1.25

10.81

10.81

4.64

4.64

12.43

12.43

5.76

5.76

5.17

5.17

2.50

15.68

26.49

7.95

12.58

10.06

22.49

9.35

15.11

12.77

17.93

3.75

27.03

53.51

21.19

33.77

33.14

55.62

25.90

41.01

20.06

37.99

5.00

46.49

100.00

66.23

100.00

44.38

100.00

58.99

100.00

62.01

100.00

Liquidity risk II

1.00

44.05

44.05

32.45

32.45

79.29

79.29

25.90

25.90

28.57

28.57

2.00

31.08

75.14

16.56

49.01

17.75

97.04

20.86

46.76

25.23

53.80

3.00

19.46

94.59

27.15

76.16

2.96

100.00

49.64

96.40

35.56

89.36

4.00

2.43

97.03

18.54

94.70

0.00

100.00

0.72

97.12

7.29

96.66

5.00

2.97

100.00

5.30

100.00

0.00

100.00

2.88

100.00

3.34

100.00

Operating risk

1.00

19.46

19.46

31.13

31.13

24.85

24.85

25.90

25.90

22.49

22.49

2.00

48.11

67.57

42.38

73.51

47.34

72.19

36.69

62.59

46.50

69.00

3.00

19.46

87.03

11.92

85.43

14.20

86.39

24.46

87.05

17.02

86.02

4.00

7.57

94.59

3.97

89.40

7.69

94.08

8.63

95.68

5.47

91.49

5.00

5.41

100.00

10.60

100.00

5.92

100.00

4.32

100.00

8.51

100.00

Risk

Score

Agriculture

Capital goods

Chemical

Diversified

FMCG

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Solvency risk I

1.00

2.67

2.67

9.87

9.87

1.31

1.31

10.81

10.81

9.54

9.54

2.00

6.67

9.33

10.30

20.17

6.56

7.87

5.41

16.22

8.02

17.56

3.00

3.33

12.67

8.15

28.33

7.54

15.41

4.73

20.95

7.63

25.19

4.00

5.33

18.00

7.73

36.05

9.84

25.25

2.03

22.97

7.25

32.44

5.00

82.00

100.00

63.95

100.00

74.75

100.00

77.03

100.00

67.56

100.00

Solvency risk II

1.25

51.33

51.33

88.84

88.84

94.43

94.43

75.00

75.00

83.59

83.59

2.50

24.00

75.33

5.15

93.99

4.59

99.02

7.43

82.43

7.25

90.84

3.75

20.67

96.00

5.58

99.57

0.66

99.67

13.51

95.95

6.87

97.71

5.00

4.00

100.00

0.43

100.00

0.33

100.00

4.05

100.00

2.29

100.00

Risk

Score

Healthcare

Housing and construction

ICT

Metal

Media

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Solvency risk I

1.00

20.27

20.27

8.84

8.84

27.96

27.96

23.73

23.73

20.19

20.19

2.00

16.55

36.82

8.08

16.92

15.79

43.75

9.32

33.05

7.21

27.40

3.00

7.09

43.92

4.04

20.96

5.92

49.67

9.32

42.37

7.69

35.10

4.00

11.49

55.41

5.56

26.52

9.54

59.21

16.10

58.47

6.25

41.35

5.00

44.59

100.00

73.48

100.00

40.79

100.00

41.53

100.00

58.65

100.00

Solvency risk II

1.25

87.84

87.84

53.54

53.54

89.14

89.14

78.81

78.81

77.40

77.40

2.50

6.76

94.59

14.65

68.18

5.59

94.74

8.47

87.29

5.77

83.17

3.75

4.05

98.65

22.47

90.66

3.29

98.03

10.17

97.46

8.65

91.83

5.00

1.35

100.00

9.34

100.00

1.97

100.00

2.54

100.00

8.17

100.00

Risk

Score

Miscellaneous

Oil and gas

Power

Textile

Transport

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Per cent

Cumulative per cent

Solvency risk I

1.00

14.59

14.59

5.30

5.30

8.28

8.28

5.04

5.04

11.25

11.25

2.00

10.54

25.14

5.96

11.26

2.37

10.65

2.16

7.19

8.21

19.45

3.00

8.38

33.51

3.97

15.23

2.37

13.02

1.44

8.63

4.86

24.32

4.00

7.30

40.81

9.27

24.50

3.55

16.57

2.16

10.79

7.60

31.91

5.00

59.19

100.00

75.50

100.00

83.43

100.00

89.21

100.00

68.09

100.00

Solvency risk II

1.25

74.05

74.05

86.75

86.75

60.36

60.36

74.82

74.82

83.59

83.59

2.50

13.51

87.57

5.96

92.72

23.08

83.43

17.99

92.81

10.33

93.92

3.75

9.19

96.76

6.62

99.34

12.43

95.86

4.32

97.12

4.56

98.48

5.00

3.24

100.00

0.66

100.00

4.14

100.00

2.88

100.00

1.52

100.00

Close to 80% firm-year observations in 12 out of 15 industries have unmodified auditor’s opinion. It is only in housing and construction, ICT, and power industry that about one-third companies have been issued an ‘emphasis of matter’ paragraph. The fact that emphasis of matter paragraphs are concentrated in particular industries indicates some serious accounting/operational issues in these industries. Further, the only companies that have been issued a disclaimer of opinion belong to agriculture industry and ICT industry.

It is seen that about two-fifth observations in all industries have witnessed a growth in market share in the years under study. Further more than half the companies are on growth trajectory in ICT, media, oil and textile industry. This implies that most companies in these sectors are pro-active to manage their competition well. It may also be noted that these are the industries that are dominated by few large business. Thus, it may also be possible that small firms and new entrants are unable to survive in these industries. Further, hardly any companies have witnessed a decline of more than 50% in market share. This is an intuitive result in the light of the fact that these sample companies constitute top 500 companies of India.

More than half the observations in all the industries have a contingency coverage ratio in the range of 0–0.25. It means that even if all the contingent liabilities materialise the shareholders’ funds in these companies would not fall by more than 25 per cent. Also, the proportion of companies having contingent liabilities more than shareholders’ funds is almost negligible. The minimum figure being 1.5% companies in transport industry. But, a good 20% of observations in housing and construction industry are facing the danger of wipe out of shareholders’ funds should the contingent liabilities materialise.

Indian companies seem to be having excellent credit management teams. Only 4 out of 15 industries have less than 90% of their firm-year observations with a credit risk score of 1. Further almost all the industries have at least four-fifth observations with a provision for debts to receivables ratio of less than 0.55.

Exchange rate risk, being governed by uncontrollable factors, is one of the most difficult risks to manage. This is evident for Indian companies also. All the industries have about 30% observations recording a foreign exchange loss. Had this loss not been there, the profits for these companies would have been higher by the amount of loss recorded on account of forex loss.

As regards the operating leverage, it is noteworthy that it is less than 1.5 for two-thirds of observations in all industry groups. Thus, even with decline in sales, most of the firms will be able to meet their fixed cost obligations.

Almost half the observations in half the industry groups have an acid-test ratio of more than 1. This potential inability of firms to meet their short-term obligations may jeopardise their operations as well as ability to raise additional funds. The highest concentration of these risky companies has been observed in agriculture (68% firm-year observations) and FMCG (68% firm-year observations) industry. Whereas, ICT industry has the least number of companies with ‘more current liabilities than quick assets’. In terms of modified defensive interval (MDI) ratio, almost all the industries have negligible number of companies with a risk score of 5. Further, metal industry has about 68% and power industry has about 79% companies that can sustain their operations for a period more than 180 days, even with no revenue and no additional financing. Given, the nature of these two industries, it appears to be a strategic move. Further, only about 12% observations in FMCG industry and 26% observations in textile industry have a MDI ratio of more than 180 days. Considering the fast moving nature of the products of these industries, it appears to be a deliberate move on part of companies to maintain less amount of quick assets in relation to cash expenses.

It is startling to note that in 12 out of 15 industry groups; more than 50% firm-year observations have total debt to equity ratio of more than 0.75. In textile industry, as much as 89% observations have a TD/TE of more than 0.75. Whereas, only about 45% of observations in healthcare industry, 41% in ICT and about 42% in metal industry fall in this category. Such a high proportion of observations in most of the industries can be construed as an excessive use of debt, which is a cheaper source of finance. This is so because solvency risk 2 that measures IICR indicates a very comfortable position in respect of interest payments. It is only in housing and construction industry that about 10% firm-year observations have an IICR of more than 1. This may be because of excessive debt and slow moving product, resulting in lower levels of profit and consequently a higher ICR.

3.5 Implications

  1. 1.

    For policy makers—The lack of reliable data highlights the need for ‘subscription databases’ for Indian companies. These databases usually include description and analysis of operational risk events which are derived from legal and regulatory sources and news articles. Further, consortium based risk event services that offer central data repository and benchmarking services in context of various risks need to be promoted. In addition, there is an urgent need to expand the canvas of accounting standards to ensure disclosure on potential sources of risk like human capital, innovation, etc. As is evident from poor scores on inverse of contingency coverage ratio, there is a need to formulate Basel-like norms for non-financial companies. If certain restrictions in terms of maintenance of certain levels of ratios are imposed, firms’ options for excessive and reckless business propositions may be controlled.

     
  2. 2.

    For investors—A normative framework and easy to compute measure of risk will facilitate investors in comparing firms. Investors are expected to diversify the firm specific risk by choosing appropriate securities. The index may thus help in portfolio construction and optimisation. It will also be useful in gauging the risk appetite and risk tolerance levels of various firms. Consequently, the investors may choose securities of companies whose appetite aligns best with theirs.

     
  3. 3.

    For the company itself—First and foremost, being a normative framework based on sound tenets of theory and validated by expert opinion, it may serve as a benchmark against which companies can gauge their risk levels. Further, the companies may rank their risks based on actual exposure levels. This will help companies to focus on the risks with greater exposure levels, thus facilitating management by exception. Further, it is suggested that if ‘responsibility centres are created based on the identified risks’ and compensation is linked to exposure levels, then participation of all employees in risk management process can be ensured and may lead to lowered risk levels. With demonstrated lowered risk levels, investors’ confidence will be boosted and their required rate of return will fall. This will enable companies to borrow money at lower costs of capital. The increasing thrust on risk appetite and tolerance is imperative and does not require any detailed emphasis. Therefore, the methodology used in index construction may be used by firms as a starting point in developing and defining their risk appetite and tolerance levels.

     
  4. 4.

    For other stakeholders—Creditors/lenders may use the index to gauge the margin of safety available to them. Similarly, index may be of use to auditors in identification of potential areas of misstatements.

     
  5. 5.

    For academicians—As the index is following a composite approach to measure risk, it may be seen as a step towards construction of a proxy for the variable—Enterprise Risk Management (ERM).

     

3.6 Limitations of the Study

The study has a limitation in terms of epistemic risk, i.e., risk arising out of lack of information and knowledge in specific circumstances. This lack of information in the current circumstances may be attributed to non-availability of reliable data, for most of the Indian companies, in respect of a number of potential sources of risk. For example, attrition risk could not be considered due to unavailability of data on number of employees and innovation risk could not be considered due to unavailability of reliable data on research and development expenses (in annual reports).

3.7 Concluding Observations

An attempt has been made to construct a risk index for non-financial companies. The index is in the form of a normative framework, delineating comfortable and distress levels of risks, through the use of ratios. The index is comprehensive as it covers market risk, accounting risk, competition risk, contingency risk, credit risk, exchange rate risk, liquidity risk, operating risk and solvency risk.

It is worth mentioning that a new ratio, ‘contingency coverage ratio’ has been attempted. The inverse of contingency coverage ratio is expected to be a good measure of company’s ability to meet its contingent liabilities, (in case they arise), without jeopardising shareholders’ value. Further, a variant of popular ‘no credit interval’ called ‘modified defensive interval’ ratio has also been conceived. This modified version also considers interest obligations, an often neglected item in this context.

The empirical analysis reveals that Indian companies, by and large, have ‘moderate’ risk levels. It is noteworthy that there has been a statistically significant increase in risk levels in post-recession period. This may be attributed to increased cross border trade and globalisation. Further, the age of a company also appears to have statistically significant impact on risk levels. But, in terms of impact of industry group in which the company operates, there is evidence of companies belonging to agriculture as well as housing and construction industry, being more prone to risks.

It is heartening to note that none of the sample companies were accorded an adverse auditor opinion during the period of study. But, three companies had a ‘disclaimer of opinion’ in post-recession period. It is pertinent to note that all these companies are middle-aged and are affiliated to agriculture and/or ICT industry. It is worth mentioning that the researcher is of the opinion, that every study that makes use of accounting data should incorporate auditor’s opinion to control for risk of misstatement in the data so used.

Interestingly, most of the companies have struggled with the inverse of acid-test ratio but have comfortable positions in terms of modified defensive interval ratio, thereby balancing their liquidity positions. Similarly, majority of the companies appear to rely heavily on external financing, leaving lower margin of safety for creditors. But the same companies are generating sufficient profits to cover their interest obligations.

It is pertinent to note that about 30% profitable companies have experienced a loss on account of foreign exchange transactions. This raises questions on effectiveness of treasury departments of these companies. On the contrary, the best managed risks appear to be credit risk and business risk. Majority of the companies have demonstrated least scores for these risks.

Thus, the study has important implications for regulators, investors and the management of companies.

To conclude, Indian companies have endured recession due to their ‘moderate’ risk levels. But, in post-recession period, increasing risk levels call for better, pro-active, more efficient and more effective risk management.

Footnotes

  1. 1.

    Inverse of contingency coverage ratio and not the contingency coverage ratio has been taken as a proxy of risk, for the reason that throughout the study, the element that may lead to unwanted consequences for the company has been taken in the numerator and the element out of which the risky part is expected to be met-out has been taken in the denominator.

  2. 2.

    Mann-Whitney U test is a non-parametric counterpart of t-test, which is used to compare two groups when the data is not normally distributed.

  3. 3.

    Kruskal Wallis test is a non-parametric counterpart of ANOVA, which is used to compare more than two groups when the data is not normally distributed.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • M. V. Shivaani
    • 1
    Email author
  • P. K. Jain
    • 2
  • Surendra S. Yadav
    • 3
  1. 1.Indian Institute of Management (IIM), VNIT CampusNagpurIndia
  2. 2.Department of Management StudiesIndian Institute of Technology DelhiNew DelhiIndia
  3. 3.Department of Management StudiesIndian Institute of Technology DelhiNew DelhiIndia

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