Using Industry Average Multiples For Valuation Finance Essay

Valuation of equity shares of a company is an important exercise and is performed on multiple occasions, be it investment decision in a particular company, merger, acquisition, restructuring, public issue, etc. Using industry average multiple is a common practice, especially when an unlisted security is to be valued.

The study looks at eight industries and attempts to derive (a) which is the most stable industry average multiple by using the statistical tool coefficient of variation and (b) which would be the most important financial performance parameter, which could be driving multiple of a particular security within the industry by using statistical tool of coefficient of correlation.

Executive Summary

A company will get valued/re-valued on multiple occasions such as raising capital, sale of business, swap of shares, issue of stock options, etc. Valuation of publicly traded securities is quite straightforward and often regulated for different events, while valuation of thinly traded or un-traded securities requires some special approaches. There are three main approaches to security valuation such as discounted cash flows, asset based valuation and comparables. Comparables are regarded as one of the most useful and practical method. Ideal approach within comparables is to find out a publicly traded company which is exactly like the company being valued and adopt an appropriate multiple as valuation metric. Finding such a company is a challenge. Even if a company is financially alike, many non-financial factors such as general market reputation, stock liquidity, etc. could be influenced its valuation of a particular stock.

Experts often use industry average multiples to counter this anomaly. They could be used on a stand-alone basis or along-with a set of exact comparables. The articles analyses the concept of industry multiples in eight industries: Private sector banks, Public sector banks, General food processing, Agri Inputs, Edible Oil, Rice, Sugar, Plantations (tea, coffee, flowers) and Auto-components and tries to answer two questions:

Which is the most appropriate industry average multiple? The criterion used is co-efficient of variation. Multiples used are Market Capitalisation (MCap) / Profit After Tax, Enterprise Value (EV) / Earnings Before Interest Taxes Depreciation and Ammortisation (EBITDA), MCap/Book Value, MCap/Sales

Which factor is the major driver of a multiple in a particular industry? The author has calculated co-efficient of correlation between different multiples and factors like revenues, 5 year revenue growth, margins, total assets, provisions, Return on Equity (ROE), Net worth.

EV/EBITDA was the most stable multiple followed by Mcap/PAT (similar to P/E ratio). Revenue, net-worth and margins were important drivers.

Keywords:

Industry average multiple, valuation, market capitalization, book value, coefficient of variation/correlation

Background

There are many situations wherein a company will get valued/re-valued such as raising capital, sale of business, swap of shares, issue of stock options, etc. While, valuation is easy and fairly regulated (SEBI, the regulator in India has defined how a security is to be valued for different purposes) for a publicly traded company, valuation of a thinly traded or un-traded securities requires some special approaches. At times, analysts also value a well-traded company to determine whether it is value fair or if there is any possible up-side. Different approaches to valuation are as described below:

Comparables

Asset Value

EBITDA

PAT

Book Value

Sales, etc.

Equity Value

DCF

Figure 1 Different valuation methods

Asset Value:

Asset based approaches – such as book value (asset less liabilities as reflected in books of accounts) and realizable value (market value of asset less liabilities) are more relevant when the company/vehicle is wound-up or dissolved in any manner.

Discounted Cash Flow (Discounted Cash Flow to the Firm):

Discounted cash flow is, theoretically, the best valuation method. The company calculates its projected financial performance. These projections and their assumptions are vetted against market factors, expert opinions.

Once the parties are confident with projections, cash flows of the company (called Cash Flow to the Firm) are calculated as follows: EBIT X (1-Tax Rate) Less Working Capital Changes Less Capital Expenditure Add Depreciation.

An important component of DCF based valuation is the Terminal Value. Last year in the projection period is capitalized as: Cash flow in terminal year X (1+ perennial growth rate) / (WACC – perennial growth rate). This is again discounted to calculate present value of terminal cash flow.

This approach is well recognized, but is not widely used due to the following limitations:

The model involves a number of assumptions – (i) Entire set of assumptions going into calculation of financial projections, (ii) Market risk premium, (iii) Long term growth rate, etc. which makes it very subjective. The method does not work with firms which have un-utilised assets, are in the process of re-structuring, which do not have positive operating cash flows, etc.

Comparables:

One of the most preferred methods of valuing a company is comparing it with a publicly –

traded company of similar nature – called relative valuation. It is also the most intuitive method – we practice it in pricing almost everything – real estate, items of daily usage, etc. In relative valuation, the value of an asset is derived from the pricing of ‘comparable’ assets, standardized using a common variable such as earnings, cash flows, book value or revenues. (Damodaran on Valuation: Security Analysis for Investment and Corporate Finance, by Ashwath Damodaran, Wiley Finance)

A publicly traded peer is identified and compared to the company under consideration in terms of various valuation parameters like – Price to Earnings, Price to Book, Price to Sales, Enterprise Value / EBITDA which ever is applicable and accordingly the value of the company/security under consideration can be calculated, e.g. If a comparable company is traded at 15 times its earnings, the earnings of the company under consideration are multiplied by 15 to calculate its value.

The approach is fairly simple, however, the challenge lies in finding an exact comparable. There can be many differentiating factors, and some of them could be quite stark.

The pricing of the publicly traded peer would also be influenced by many non-objective factors like: general market perception, promoter reputation, adverse market rumors, low liquidity in specific stock, low level of public holding, etc.

In light of these, many analysts and industry experts use industry-average multiples, on a stand-alone basis as well as to moderate/rationalize multiples of an individual or group of comparables.

This brings us to the questions which the article intends to ponder over:

Which bench-mark should be used? Every industry has two or three popular benchmarks, which appropriately capture financial and operative strengths, such as the tea gardens are valued at certain times of their sales, so are football clubs. Manufacturing industries are valued at certain time of their EBITDA or PAT as the case may be. However, if an industry average is to be used, high degree of variability in the multiple will compromise its reliability.

Another question is what drives a company’s valuation. The range in multiples in many industries tends to be quite high. Some tangible financial factor could be an important driver/differentiator for a company. Which would be the driver in a particular industry?

The article attempts to answer these questions via an exercise on 214 companies in 8 different industries. The author has:

Chosen 8 industries based on his past work experience

Selected different publicly listed companies in each industry

Derived their multiples and financial parameters from various databases

Checked the variability of industry averages of multiples by using the statistical tool – co-efficient of variation to answer the first question (most reliable benchmark)

Run correlation between a particular industry relevant bench-mark such as 5 year growth, margins, etc. and the multiple – e.g. correlation between P/E ratios and book size in banking industry to answer the second question.

The breakup of companies across industries is as follows:

Table 1 Sectors and number of companies used in analysis

Industry

No of companies

Private sector banks

Public sector banks

General food processing

Agri Inputs

Edible Oil

Rice

Sugar

Plantations (tea, coffee, flowers)

Auto-components

Total

214

The following multiples were used:

Market Capitalisation (MCap) / Profit After Tax, Enterprise Value (EV) / Earnings Before Interest Taxes Depreciation and Ammortisation (EBITDA), MCap/Book Value, MCap/Sales. Mcap/PAT is similar to more commonly used Price to Earnings per share (P/E), and Mcap/Book Value is similar to Price to Book value per share (P/B).

The following financial performance parameters were selected for analysis:

Revenues of latest available financial year, 5 year revenue growth, margins (PAT margin for banks and EBITDA margins for others), total assets, provisions, Return on Equity (ROE), Net worth

Analysis

Private Sector Banks

The following banks were analysed within private sector banks:

HDFC Bank Ltd., ICICI Bank Limited, Axis Bank Limited, IndusInd Bank Limited, Yes Bank Ltd, Federal Bank Limited, ING Vysya Bank Limited, The Jammu & Kashmir Bank Limited, Karur Vysya Bank Ltd., South Indian Bank Limited, City Union Bank Ltd., Karnataka Bank Ltd, Development Credit Bank Ltd., Lakshmi Vilas Bank Limited.

Table 2 Results of private sector banks

Banks (private)

Multiple

Parameter

Mcap/PAT

Mcap/Assets

Mcap/Sales

Mcap/Book Value

Mean

8.40

0.09

0.88

1.19

StdEv

5.43

0.08

0.82

0.93

Coeff of Variation

0.65

0.99

0.93

0.78

Correlation between multiple & parameter

Revenue

0.10

0.08

0.09

Past 5 year growth

0.20

0.34

0.36

0.48

Margin

0.32

0.59

0.61

0.63

Total Assets

0.07

0.06

0.07

Provisions

-0.05

-0.10

-0.09

-0.07

ROE

0.01

0.32

0.34

0.43

Net Worth

0.18

0.16

MCap/PAT, similar to Price to Earnings showed maximum stability. Margin (calculated as PAT/Revenue) showed maximum correlation with MCap/PAT, followed by high growth rate. The MCap/Book value Price to Book in popular parlance and Return On Equity showed the maximum correlation across all multiples and parameters.

Margin and ROE showed maximum correlation with MCap/PAT.

Public Sector Banks

Public sector banks tend to have different operating objectives and are often valued differently compared to private sector banks. Mcap/PAT of public sector banks is 5.41 v/s 8.40 as observed in private sector banks. The following public sector banks were analysed:

Indian Overseas Bank, Andhra Bank, Corporation Bank, Central Bank Of India, UCO Bank, Dena Bank, Bank of Maharashtra, State Bank of Bikaner and Jaipur, State Bank of Travancore, State Bank of Mysore, United Bank of India, Punjab & Sind Bank.

Table 3 Results of public sector banks

Banks (public)

Multiple

Parameter

Mcap/PAT

Mcap/Assets

Mcap/Sales

Mcap/Book Value

Mean

5.41

0.04

0.45

0.69

StdEv

1.36

0.01

0.15

0.16

Coeff of Variation

0.25

0.29

0.32

0.23

Correlation between multiple & parameter

Revenue

0.34

0.61

0.65

0.60

Past 5 year growth

-0.05

0.12

0.13

0.07

Margin

-0.46

0.79

0.81

0.76

Total Assets

0.31

0.62

0.70

0.63

Provisions

0.44

0.51

0.50

0.46

ROE

-0.64

0.57

0.69

Net Worth

0.29

0.70

0.75

0.65

Public sector banks showed a different trend in variability of multiples. The book value multiple seems to show the least variation around mean as compared to Mcap/PAT observed in private banks. Within the book value multiple, margins show the highest correlation of 0.76 followed by ROE, 0.69.

Food processing

Food processing falls into manufacturing domain. EV/EBITDA multiple is introduced in place of the Total Assets multiple is relevant to the banking and NBFC company wherein income is primarily driven by book size. EV/EBITDA is one of the most popular multiples in manufacturing sector. It captures the operating strength of a company (EBITDA) v/s Enterprise Value. Enterprise value is a debt and cash neutral metric, calculated by Market Capitalisation + Debt – Cash.

Table 4 Results of food processing (general)

Food Processing

Multiple

Parameter

Mcap/PAT

EV/EBITDA

Mcap/Sales

Mcap/Book Value

Mean

18.50

9.39

0.97

4.21

StdEv

14.09

6.64

1.61

7.36

Coeff of Variation

0.76

0.71

1.65

1.75

Correlation between multiple & parameter

Revenue

0.39

0.64

0.49

0.75

Past 5 year growth

-0.34

-0.27

-0.17

-0.14

EBITDA Margin

0.02

0.20

0.57

0.26

ROE

0.50

0.80

0.75

0.90

Net Worth

0.06

0.31

0.34

EV/EBITDA shows the lowest variation around mean (0.71). ROE is the most important driver for this multiple (0.8 correlation), followed by revenue.

The following companies were considered for analysis in food processing:

Hatson Agro Products REI Agro, Heritage Foods, KSE Limited, Nestle India Ltd., Glaxo SmithKline, Britannia Industries, Zydus Wellness, DFM Foods Ltd., Vadilal Industries, Himalya International, ADF Foods, Anik Industries, Srinivasa Hatcheries, Flex Foods, Bambino Agro, Foods and Inns, Tasty Bite Eatables, Freshtrop Fruits, Temptation Foods, Chordia Food Products. Vadilal Enterprises, Sita Shree Food Products, Simran Farms,

Venky’s (India), Waterbase.

The companies belonged to multiple sub-sectors like dairy, poultry, consumer goods, ice creams, frozen food, etc .

Agri Inputs

Agri inputs included seed, special fertilizers and some special input companies in food processing industries. The larger fertilizer companies, which fall more into chemicals domain were not considered. The following companies were anlysed:

Sukhjit Starch & Chemicals, Narmada Gelatines, Sakuma Exports, Vidhi Dyestuffs, Saboo Sodium Chloro, Kaveri Seed, Advanta India, Basant Agro Tech.

In agri inputs also, EV/EBITDA showed maximum stability, followed by MCap/PAT. EBITDA margin showed highest correlation with EV/EBITDA.

Table 5 Results of specialised agri inputs

Agri Input

Multiple

Parameter

Mcap/PAT

EV/EBITDA

Mcap/Sales

Mcap/Book Value

Mean

11.27

8.53

0.98

1.68

StdEv

9.64

5.11

1.28

1.87

Coeff of Variation

0.86

0.60

1.30

1.11

Correlation between multiple & parameter

Revenue

-0.36

0.25

0.04

0.11

Past 5 year growth

-0.18

0.25

0.26

0.40

EBITDA Margin

-0.88

-0.08

0.71

0.11

ROE

0.02

-0.03

0.40

0.48

Net Worth

0.33

0.66

0.48

0.53

Edible Oil:

Edible oil is a special segment within food processing. The sector is characterized by high level of imports, benchmarking with international prices, low regulations compared to commodities like rice and pulses, etc. The following companies were anlysed:

Ruchi Soya Industries, Sanwaria Agro Oils, Rasoya Proteins, Gujarat Ambuja Exports, Jayant Agro-Organics, JVL Agro Industries, Vippy Industries Limited, Vimal Oil & Foods, Raj Oil Mills, BCL Industries, Hind Industries, Kriti Nutrients, Vijay Solvex, Sam Industries, Modi Naturals, Natraj Proteins, Poona Dal & Oil Industries

Table 6 Results of edible oil

Edible Oil

Multiple

Parameter

Mcap/PAT

EV/EBITDA

Mcap/Sales

Mcap/Book Value

Mean

11.10

6.43

0.21

1.53

StdEv

9.77

4.18

0.27

2.20

Coeff of Variation

0.88

0.65

1.31

1.44

Correlation between multiple & parameter

Revenue

0.43

-0.12

-0.03

Past 5 year growth

-0.28

-0.36

0.99

-0.20

EBITDA Margin

-0.01

-0.03

0.51

0.16

ROE

-0.20

-0.12

0.58

0.61

Net Worth

0.38

-0.14

-0.11

-0.04

EV/EBITDA showed the maximum stability, however, none of the parameters showed any reasonable correlation with the parameter. EV/EBITDA was followed by Mcap/PAT with 0.88 coefficient of variation. This factor showed relatively higher correlation with revenue followed by Net Worth.

Rice

Rice is also a typical sector within food processing. Most of the publicly traded rice companies have focused on basmati rice. Basmati is a famous variety of aromatic rice and has large export market in the middle east, Europe and US. The following companies were analysed:

Khushi Ram Behari La, Usher Agro, LT Food, Lakshmi Energy and Foods, Emmsons International, Chaman Lal Setia Exports, GRM Overseas.

The sector showed better stability of Mcap/PAT followed by Mcap/Book Value. Within Mcap/PAT EBITDA margin showed the highest correlation at 0.86.

Table 7 Results of rice

Rice

Multiple

Parameter

Mcap/PAT

EV/EBITDA

Mcap/Sales

Mcap/Book Value

Mean

6.12

7.94

0.16

0.68

StdEv

2.27

3.95

0.13

0.30

Coeff of Variation

0.37

0.50

0.83

0.44

Correlation between multiple & parameter

Revenue

0.42

0.68

0.16

0.17

Past 5 year growth

-0.70

0.47

-0.92

-0.98

EBITDA Margin

0.86

-0.60

0.59

-0.25

ROE

-0.77

0.12

0.11

0.73

Net Worth

1.00

-0.17

0.55

-0.30

Sugar:

Sugar is one of the largest organized sectors in agri processing. The sector has many large companies like Renuka Sugars, Bajaj Hindustan, etc. The sector also has some typical features like minimum procurement price, cyclical production, concentrated production in Asia and South America, etc. The following companies were analysed:

E.I.D. – Parry, Bajaj Hindusthan, Bannari Amman Sugars, Triveni Engineering, Andhra Sugars, Dhampur Sugar Mills, KCP Sugar, Ponni Sugars (Erode), Ugar Sugar Works, Dalmia Bharat Sugar, Thiru Arooran Sugars, Sri Chamundeswari, Piccadily Agro, Vishnu Sugar Mills, Kesar Enterprises, Piccadily Sugars, Indian Sucrose

EV/EBITDA showed lowest co-efficient of variation (0.44). The multiple showed highest correlation with net worth, followed by revenue.

Table 8 Results of sugar

Sugar

Multiple

Parameter

Mcap/PAT

EV/EBITDA

Mcap/Sales

Mcap/Book Value

Mean

14.38

6.90

0.35

0.80

StdEv

14.79

3.07

0.20

0.39

Coeff of Variation

1.03

0.44

0.56

0.48

Correlation between multiple & parameter

Revenue

-0.01

0.20

0.00

0.19

Past 5 year growth

-0.26

-0.04

-0.42

0.23

EBITDA Margin

-0.51

-0.43

0.49

0.18

ROE

-0.65

-0.69

0.44

0.61

Net Worth

-0.01

0.44

0.08

0.01

Plantations

Tea and Coffee are another specialized area in agri and food industries. The sector has stakes of many large FMCG companies like Tata Tea, Unilever, etc. This sector also has special policies, farming conditions, competitive factors. For the purpose of this analysis, flowers have also been analysed together with tea and coffee. The following companies for part of this analysis:

Karuturi Global, Neha International, Pochiraju Industries, Tata Global Beverage, McLeod Russel India, Tata Coffee, CCL Products India, Warren Tea, Dhunseri Petrochem, Goodricke Group, Jayshree Tea, Assam Company India, Harrisons Malayalam, Russell India, United Nilgiri Tea, Joonktollee Tea, Diana Tea.

Here also, EV/EBITDA showed minimum coefficient of variation, followed by Mcap/Sales. Revenue and net worth showed the highest correlation with EV/EBITDA.

Table 9 Results of plantation (tea, coffee, flowers)

Plantation (tea, coffee flowers)

Multiple

Parameter

Mcap/PAT

EV/EBITDA

Mcap/Sales

Mcap/Book Value

Mean

15.17

9.60

1.09

1.13

StdEv

13.19

5.70

0.81

0.87

Coeff of Variation

0.87

0.59

0.75

0.76

Correlation between multiple & parameter

Revenue

0.22

0.33

0.09

0.27

Past 5 year growth

-0.47

-0.38

-0.19

-0.43

EBITDA Margin

-0.34

-0.42

0.20

-0.11

ROE

-0.37

-0.27

0.20

0.54

Net Worth

0.18

0.29

0.16

0.21

Auto components

Auto components industry comprises of a large number of specialized players focusing on different segments of auto industry. Major segments and their composition in total industry size are:

Engine parts – 31%

Drive transmission and steering parts – 19%

Body and Chassis – 12%

Suspension and braking parts – 12%

Equipments – 10%

Electrical parts – 9%

Miscellaneous – 7%

The industry is estimated at USD 43.5 billion in FY 2011-12. (Auto Components Manufacturers Association of India)

The following companies were anlaysed in the industry:

Bosch, Cummins India, Exide Industries, Motherson Sumi Systems, WABCO, Amtek India, Kirloskar, Amtek Auto Limited, Federal-Mogul, Sundram Fasteners, Wheels India, Shanthi Gears, NRB Bearings, Automotive Axles, Mahindra Forgings, Commercial Engineers, Banco Products, Jamna Auto Industries, Fairfield Atlas, Gabriel India, Lumax Industries, Sundaram-Clayton, India Motor Parts, Saint-Gobain, Steel Strips Wheels,

Setco Automotive, Minda Industries, Suprajit Engineering, Rane Holdings, ZF Steering Gear, Munjal Showa, Sona Koyo Steering, Munjal Auto, Lumax Auto Technology, Autoline Industries, India Nippon, FIEM Industries, L. G. Balakrishnan, Subros, Pricol, Hindustan Composites, Ucal Fuel Systems, Rane Madras, Rico Auto Industries, Jay Bharat Maruti, Shivam Autotech, Omax Autos, IST, Bimetal Bearings, Rane Engine Valves, REIL Electricals, Rane Brake Lining, Precision Pipes, Automotive Stampings, Harita Seating, JMT Auto, Alicon Castalloy, JBM Auto, Bharat Gears, Menon Pistons, Talbros Automotive, Triton Valves, Aunde India, Clutch Auto, Pix Transmissions, Bharat Seats, Lakshmi Precision, Menon Bearings, Simmonds Marshall, Kar Mobiles, IP Rings, Jay Ushin, Gujarat Automotive, Competent Automobile, Lumax Automotive Systems, Autolite India, ANG Industries, Hindustan Hardy, Raunaq Automotive, Remsons Industries, Porwall Auto Components, Spectra Industries,

Kew Industries, Jagan Lamps, Coventry Coil-O Matic.

In this industry again, EV/EBITDA is the most stable multiple. EV/EBITDA shows maximum correlation with revenue and net-worth.

Table 10 Results of auto-components

Auto Components

Multiple

Parameter

Mcap/PAT

EV/EBITDA

Mcap/Sales

Mcap/Book Value

Mean

12.47

6.04

0.67

1.62

StdEv

13.09

4.65

0.93

1.67

Coeff of Variation

1.05

0.77

1.40

1.03

Correlation between multiple & parameter

Revenue

0.19

0.35

0.18

0.31

Past 5 year growth

-0.04

0.05

-0.05

0.09

EBITDA Margin

0.03

0.06

0.47

0.12

ROE

-0.31

0.04

0.21

0.45

Net Worth

0.13

0.35

0.30

0.24

Inferences:

The most stable multiples across different industries and their respective coefficients of correlations with different financial parameters were as follows:

Table 11 Summary of trends

Coefficient of variation

Correlation

Industry

Co-efficient of Variation

Multiple

Highest

Correlation

Second highest

Correlation

Private sector banks

0.65

MCAP/PAT

Margin

0.32

Past 5 year growth

0.20

Public sector banks

0.23

P/B

Margin

0.76

ROE

0.69

General food processing

0.71

EV/EBITDA

ROE

0.80

Revenue

0.64

Agri Inputs

0.60

EV/EBITDA

Net worth

0.66

Revenue

0.25

Edible Oil

0.88

MCAP/PAT

Revenue

0.43

Net worth

0.38

Rice

0.37

MCAP/PAT

Net worth

1.00

EBITDA margin

0.86

Sugar

0.44

EV/EBITDA

Net worth

0.44

Revenue

0.20

Plantations (tea, coffee, flowers)

0.59

EV/EBITDA

Revenue

0.33

Revenue

0.29

Auto-components

0.77

EV/EBITDA

Revenue

0.35

Revenue

0.35

*In edible oil, lower coefficient was observed in EV/EBITDA. P/E was chosen because EV/EBITDA showed no correlation with any of the parameters studied.

Co-efficient of variation was minimum in public sector banks and highest in auto-components. Industry multiple of public sector banks, hence, stands as the most reliable industry multiple among the industries observed. The co-efficient would be high if there is considerable heterogeneity within the industry in terms of size, profitability, product portfolio, promoter background, etc.

Earnings based multiples – EV/EBITDA and P/E showed minimum coefficient of variation in all industries, except public sector banks, which showed Mcap to Book Value as the most stable multiple.

Considering the correlations observed with the most stable multiple, we can infer that:

net margins are the main drivers of multiples in banks (both public and private) among the parameters observed,

ROE was most influential in food processing and edible oil

plantations and auto-components seem to be driven by revenue vis-à-vis other parameters observed

and agri inputs, rice and sugar were influenced by net-worth of respective companies.

The following table shows the maximum correlation observed in a particular industry.

Table 12 Maximum correlations across industries

Industry

Maximum Correlation

Relationships

Private sector banks

0.63

ROE and Mcap/Book Value

Public sector banks

0.81

PAT Margin and Mcap/Book Value

General food processing

0.90

ROE and Mcap/Book Value

Agri Inputs

0.71

EBITDA margin and Mcap/Sales

Edible Oil

0.99

5 year growth and Mcap/Sales

Rice

1.00

Net-worth and Mcap/PAT

Sugar

0.61

ROE and Mcap/Book Value

Plantations (tea, coffee, flowers)

0.54

ROE and Mcap/Book Value

Auto-components

0.47

EBITDA margin and Mcap/Sales

ROE and Mcap/Book Value showed highest correlation in four out of nine industries, followed by EBITDA margin and Mcap/Sales. The results were quite intuitive – a company generating higher returns on invested capital (ROE), or a company operating at a higher margin should be valued more than its peers.

Table 13 Results of general correlation analysis

Parameter

Mcap/PAT

EV/EBITDA

Mcap/Sales

Mcap/Book Value

Mcap/Total Assets

Revenue

-0.03

0.19

0.08

0.01

-0.10

Past 5 year growth

-0.07

-0.09

0.01

-0.03

0.34

EBITDA /PAT Margin

0.01

0.00

0.44

0.11

0.59

ROE

-0.10

0.15

0.37

0.63

0.32

Net Worth

-0.04

0.27

0.11

-0.02

0.18

Total Assets

0.07

0.06

0.07

0.06

Provisions

-0.05

-0.09

-0.07

-0.10

Across all industries, highest correlation was showed by ROE and Mcap/Book Value, followed by PAT margin and Mcap/Total Assets (relevant to banking industry).

—————-

The analysis can be replicated for different industries in different geographical contexts. While co-efficient of variation would be helpful in convincing which industry average multiple should be used or which should not be used, correlations can help derive what would investors be considering while choosing a particular company within an industry.

A possible extension to the analysis could be using a set of parameters as influencing variables to determine multiple of a particular company as a dependent variable. Such derived multiple can be used to test the prevailing multiple and provide guidance about future movement of a particular stock.

Limitations

The following are some limitations of this analysis.

Many companies used in the analysis would be facing unique challenges/opportunities which could have very high influence on their multiples

The author had not analysed the liquidity and volume of trading in the selected stocks, hence the reliability of prices and multiples cannot be ascertained

The figures were collated on October 8, 2011. Scenarios would have changed thereafter

The results are relative to the set used in the analysis, i.e. when the author concludes that EBITDA margin showed maximum influence/correlation, it means the EBITDA margin showed higher correlation compared to other parameters. The significance or otherwise of observed correlation has not been commented upon/captured

The figures were taken from Bloomberg, any errors in the same could have influenced the analysis

List of tables:

Table 1 Sectors and number of companies used in analysis 5

Table 2 Results of private sector banks 6

Table 3 Results of public sector banks 7

Table 4 Results of food processing (general) 8

Table 5 Results of specialised agri inputs 9

Table 6 Results of edible oil 10

Table 7 Results of rice 10

Table 8 Results of sugar 11

Table 9 Results of plantation (tea, coffee, flowers) 12

Table 10 Results of auto-components 13

Table 11 Summary of trends 14

Table 12 Maximum correlations across industries 15

Table 13 Results of general correlation analysis 15

Figure:

Figure 1 Different valuation methods 3

Abbreviations:

Coeff – Coefficient

EBITDA – Earnings Before Interest Tax Depreciation and Ammortisation

DCF – Discounted Cash Flows

P/E – Price to earnings per share

P/B – Price to book value per share

Mcap – Market Capitalisation

PAT – Profit after Tax

ROE – Return on Equity

StdEv – Standard Deviation

SEBI – Securities and Exchange Board of India

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