Vol.

1(1) ISSN : 2319-9628

Using Industry Average Multiples for Valuation

Variability of industry average multiples in select industries and relation with different financial parameters

Bhargav Maniar
Senior Associate, ICICI securites Ltd.,
Email : maniarbhargav@gmail.com

Introduction
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
Abstract valuation of thinly traded or un-traded securities requires some
special approaches. There are three main approaches to security
Valuation of equity shares of a company is an valuation such as discounted cash flows, asset based valuation
important exercise and is performed on multiple and comparables. Comparables are regarded as one of the most
occasions, be it investment decision in a useful and practical method. Ideal approach within comparables
particular company, merger, acquisition, is to find out a publicly traded company which is exactly like the
restructuring, public issue, etc. Using industry company being valued and adopt an appropriate multiple as
average multiple is a common practice, valuation metric. Finding such a company is a challenge. Even if a
especially when an unlisted security is to be company is financially alike, many non-financial factors such as
valued. general market reputation, stock liquidity, etc. could be influenced
The study looks at eight industries and attempts its valuation of a particular stock.
to derive (a) which is the most stable industry Experts often use industry average multiples to counter this
average multiple by using the statistical tool anomaly. They could be used on a stand-alone basis or along-with
coefficient of variation and (b) which would be a set of exact comparables. The articles analyses the concept of
the most important financial performance industry multiples in eight industries: Private sector banks, Public
parameter, which could be driving multiple of a sector banks, General food processing, Agri Inputs, Edible Oil,
particular security within the industry by using Rice, Sugar, Plantations (tea, coffee, flowers) and Auto-
statistical tool of coefficient of correlation. components and tries to answer two questions:
Which is the most appropriate industry average multiple? The
Keywords: Industry average multiple, valuation, criterion used is co-efficient of variation. Multiples used are Market
market capitalization, book value, coefficient of Capitalisation (MCap) / Profit After Tax, Enterprise Value (EV) /
variation/correlation 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.

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 Vol.1(1) ISSN : 2319-9628

EV/EBITDA was the most stable multiple followed by again discounted to calculate present value of terminal
Mcap/PAT (similar to P/E ratio). Revenue, net-worth and cash flow.
margins were important drivers. This approach is well recognized, but is not widely used
due to the following limitations:
Background The model involves a number of assumptions – (i) Entire
There are many situations wherein a company will get set of assumptions going into calculation of financial
valued/re-valued such as raising capital, sale of projections, (ii) Market risk premium, (iii) Long term
business, swap of shares, issue of stock options, etc. growth rate, etc. which makes it very subjective. The
While, valuation is easy and fairly regulated (SEBI, the method does not work with firms which have un-utilised
regulator in India has defined how a security is to be assets, are in the process of re-structuring, which do not
valued for different purposes) for a publicly traded have positive operating cash flows, etc.
company, valuation of a thinly traded or un-traded
securities requires some special approaches. At times, Comparables:
analysts also value a well-traded company to determine One of the most preferred methods of valuing a company
whether it is value fair or if there is any possible up-side. is comparing it with a publicly -
Different approaches to valuation are as described traded company of similar nature – called relative
below: valuation. It is also the most intuitive method – we
practice it in pricing almost everything – real estate, items
Figure 1 of daily usage, etc. In relative valuation, the value of an
Different valuation methods asset is derived from the pricing of 'comparable' assets,
standardized using a common variable such as
Equity Value earnings, cash flows, book value or revenues.
(Damodaran on Valuation: Security Analysis for
Asset Value DCF Comparables
Investment and Corporate Finance, by Ashwath
PAT
Damodaran, Wiley Finance)
A publicly traded peer is identified and compared to the
EBITDA company under consideration in terms of various
Fig. 1 Book Value valuation parameters like – Price to Earnings, Price to
Sales, etc. 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
Asset Value: times its earnings, the earnings of the company under
Asset based approaches – such as book value (asset consideration are multiplied by 15 to calculate its value.
less liabilities as reflected in books of accounts) and The approach is fairly simple, however, the challenge lies
realizable value (market value of asset less liabilities) in finding an exact comparable. There can be many
are more relevant when the company/vehicle is differentiating factors, and some of them could be quite
wound-up or dissolved in any manner. stark.
Discounted Cash Flow (Discounted Cash Flow to the The pricing of the publicly traded peer would also be
Firm): influenced by many non-objective factors like: general
Discounted cash flow is, theoretically, the best market perception, promoter reputation, adverse market
valuation method. The company calculates its rumors, low liquidity in specific stock, low level of public
projected financial performance. These projections and holding, etc.
their assumptions are vetted against market factors, In light of these, many analysts and industry experts use
expert opinions. industry-average multiples, on a stand-alone basis as
Once the parties are confident with projections, cash well as to moderate/rationalize multiples of an individual
flows of the company (called Cash Flow to the Firm) are or group of comparables.
calculated as follows: EBIT X (1-Tax Rate) Less Working This brings us to the questions which the article intends
Capital Changes Less Capital Expenditure Add to ponder over:
Depreciation. Which bench-mark should be used? Every industry has
An important component of DCF based valuation is the two or three popular benchmarks, which appropriately
Terminal Value. Last year in the projection period is capture financial and operative strengths, such as the tea
capitalized as: Cash flow in terminal year X (1+ perennial gardens are valued at certain times of their sales, so are
growth rate) / (WACC – perennial growth rate). This is
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 Vol.1(1) ISSN : 2319-9628

football clubs. Manufacturing industries are valued at used Price to Earnings per share (P/E), and Mcap/Book
certain time of their EBITDA or PAT as the case may be. Value is similar to Price to Book value per share (P/B).
However, if an industry average is to be used, high The following financial performance parameters were
degree of variability in the multiple will compromise its selected for analysis:
reliability. Revenues of latest available financial year, 5 year
Another question is what drives a company's valuation. revenue growth, margins (PAT margin for banks and
The range in multiples in many industries tends to be EBITDA margins for others), total assets, provisions,
quite high. Some tangible financial factor could be an Return on Equity (ROE), Net worth
important driver/differentiator for a company. Which
would be the driver in a particular industry? Analysis
The article attempts to answer these questions via an Private Sector Banks
exercise on 214 companies in 8 different industries. The The following banks were analysed within private sector
author has: banks:
Chosen 8 industries based on his past work experience HDFC Bank Ltd., ICICI Bank Limited, Axis Bank Limited,
Selected different publicly listed companies in each IndusInd Bank Limited, Yes Bank Ltd, Federal Bank
industry Limited, ING Vysya Bank Limited, The Jammu & Kashmir
Derived their multiples and financial parameters from Bank Limited, Karur Vysya Bank Ltd., South Indian Bank
various databases Limited, City Union Bank Ltd., Karnataka Bank Ltd,
Checked the variability of industry averages of multiples Development Credit Bank Ltd., Lakshmi Vilas Bank
by using the statistical tool - co-efficient of variation to Limited.
answer the first question (most reliable benchmark)
Run correlation between a particular industry relevant Table 2
bench-mark such as 5 year growth, margins, etc. and the Results of private sector banks
multiple – e.g. correlation between P/E ratios and book Banks (private) Multiple
size in banking industry to answer the second question. Mcap/Book
Parameter Mcap/PAT Mcap/Assets Mcap/Sales
The breakup of companies across industries is as Value
follows: Mean 8.40 0.09 0.88 1.19
StdEv 5.43 0.08 0.82 0.93
Table 1 Coeff of Variation 0.65 0.99 0.93 0.78
Sectors and number of companies used in analysis
Correlation between multiple & parameter

Industry No of companies Revenue 0.10 0.08 0.09 0.09

Past 5 year growth 0.20 0.34 0.36 0.48
Private sector banks 14 Margin 0.32 0.59 0.61 0.63
Public sector banks 23 Total Assets 0.07 0.06 0.06 0.07
Provisions -0.05 -0.10 -0.09 -0.07
General food processing 16
ROE 0.01 0.32 0.34 0.43
Agri Inputs 8 Net Worth 0.18 0.18 0.18 0.16

Edible Oil 17 Table 2

Rice 7 MCap/PAT, similar to Price to Earnings showed
maximum stability. Margin (calculated as PAT/Revenue)
Sugar 17 showed maximum correlation with MCap/PAT, followed
Plantations (tea, coffee, flowers) 17 by high growth rate. The MCap/Book value Price to Book
in popular parlance and Return On Equity showed the
Auto-components 85 maximum correlation across all multiples and
Total 214 parameters.
Margin and ROE showed maximum correlation with
Table 1 MCap/PAT.
The following multiples were used:
Market Capitalisation (MCap) / Profit After Tax, Enterprise Public Sector Banks
Value (EV) / Earnings Before Interest Taxes Depreciation Public sector banks tend to have different operating
and Ammortisation (EBITDA), MCap/Book Value, objectives and are often valued differently compared to
MCap/Sales. Mcap/PAT is similar to more commonly
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 Vol.1(1) ISSN : 2319-9628

private sector banks. Mcap/PAT of public sector banks is Table 4

5.41 v/s 8.40 as observed in private sector banks. The Results of food processing (general)
following public sector banks were analysed:
Indian Overseas Bank, Andhra Bank, Corporation Bank, Food Processing Multiple
Central Bank Of India, UCO Bank, Dena Bank, Bank of Mcap/Book
Parameter Mcap/PAT EV/EBITDA Mcap/Sales
Value
Maharashtra, State Bank of Bikaner and Jaipur, State
Bank of Travancore, State Bank of Mysore, United Bank Mean 18.50 9.39 0.97 4.21
of India, Punjab & Sind Bank. StdEv 14.09 6.64 1.61 7.36
Coeff of Variation 0.76 0.71 1.65 1.75
Table 3 Correlation between multiple & parameter
Results of public sector banks Revenue 0.39 0.64 0.49 0.75
Banks (private) Multiple Past 5 year growth -0.34 -0.27 -0.17 -0.14

Parameter Mcap/PAT Mcap/Assets Mcap/Sales

Mcap/Book EBITDA Margin 0.02 0.20 0.57 0.26
Value
ROE 0.50 0.80 0.75 0.90
Mean 5.41 0.04 0.45 0.69 Net Worth 0.06 0.31 0.34 0.34
StdEv 1.36 0.01 0.15 0.16
Table 4
Coeff of Variation 0.25 0.29 0.32 0.23
Correlation between multiple & parameter EV/EBITDA shows the lowest variation around mean
Revenue 0.34 0.61 0.65 0.60 (0.71). ROE is the most important driver for this multiple
Past 5 year growth -0.05 0.12 0.13 0.07 (0.8 correlation), followed by revenue.
Margin -0.46 0.79 0.81 0.76 The following companies were considered for analysis
Total Assets 0.31 0.62 0.70 0.63 in food processing:
Provisions 0.44 0.51 0.50 0.46 Hatson Agro Products REI Agro, Heritage Foods, KSE
ROE -0.64 0.57 0.57 0.69
Limited, Nestle India Ltd., Glaxo SmithKline, Britannia
Industries, Zydus Wellness, DFM Foods Ltd., Vadilal
Net Worth 0.29 0.70 0.75 0.65
Industries, Himalya International, ADF Foods, Anik
Table 3 Industries, Srinivasa Hatcheries, Flex Foods, Bambino
Public sector banks showed a different trend in variability Agro, Foods and Inns, Tasty Bite Eatables, Freshtrop
of multiples. The book value multiple seems to show the Fruits, Temptation Foods, Chordia Food Products.
least variation around mean as compared to Mcap/PAT Vadilal Enterprises, Sita Shree Food Products, Simran
observed in private banks. Within the book value Farms, Venky's (India), Waterbase.
multiple, margins show the highest correlation of 0.76 The companies belonged to multiple sub-sectors like
followed by ROE, 0.69. dairy, poultry, consumer goods, ice creams, frozen food,
etc .
Food processing
Food processing falls into manufacturing domain. Agri Inputs
EV/EBITDA multiple is introduced in place of the Total Agri inputs included seed, special fertilizers and some
Assets multiple is relevant to the banking and NBFC special input companies in food processing industries.
company wherein income is primarily driven by book The larger fertilizer companies, which fall more into
size. EV/EBITDA is one of the most popular multiples in chemicals domain were not considered. The following
manufacturing sector. It captures the operating strength companies were anlysed:
of a company (EBITDA) v/s Enterprise Value. Enterprise Sukhjit Starch & Chemicals, Narmada Gelatines,
value is a debt and cash neutral metric, calculated by Sakuma Exports, Vidhi Dyestuffs, Saboo Sodium
Market Capitalisation + Debt – Cash. 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.

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Table 5 Rice
Results of specialised agri inputs Rice is also a typical sector within food processing. Most
of the publicly traded rice companies have focused on
Agri Input Multiple basmati rice. Basmati is a famous variety of aromatic rice
Mcap/Book and has large export market in the middle east, Europe
Parameter Mcap/PAT EV/EBITDA Mcap/Sales
Value
and US. The following companies were analysed:
Mean 11.27 8.53 0.98 1.68 Khushi Ram Behari La, Usher Agro, LT Food, Lakshmi
StdEv 9.64 5.11 1.28 1.87 Energy and Foods, Emmsons International, Chaman Lal
Coeff of Variation 0.86 0.60 1.30 1.11 Setia Exports, GRM Overseas.
Correlation between multiple & parameter The sector showed better stability of Mcap/PAT followed
Revenue -0.36 0.25 0.04 0.11 by Mcap/Book Value. Within Mcap/PAT EBITDA margin
Past 5 year growth -0.18 0.25 0.26 0.40
showed the highest correlation at 0.86.
EBITDA Margin -0.88 -0.08 0.71 0.11
Table 7
ROE 0.02 -0.03 0.40 0.48
Results of rice
Net Worth 0.33 0.66 0.48 0.53
Rice Multiple
Table 5 Mcap/Book
Edible Oil: Parameter Mcap/PAT EV/EBITDA Mcap/Sales
Value
Edible oil is a special segment within food processing. Mean 6.12 7.94 0.16 0.68
The sector is characterized by high level of imports, StdEv 2.27 3.95 0.13 0.30
benchmarking with international prices, low regulations
Coeff of Variation 0.37 0.50 0.83 0.44
compared to commodities like rice and pulses, etc. The
following companies were anlysed: Correlation between multiple & parameter
Ruchi Soya Industries, Sanwaria Agro Oils, Rasoya Revenue 0.42 0.68 0.16 0.17
Proteins, Gujarat Ambuja Exports, Jayant Agro- Past 5 year growth -0.70 0.47 -0.92 -0.98
Organics, JVL Agro Industries, Vippy Industries Limited, EBITDA Margin 0.86 -0.60 0.59 -0.25
Vimal Oil & Foods, Raj Oil Mills, BCL Industries, Hind ROE -0.77 0.12 0.11 0.73
Industries, Kriti Nutrients, Vijay Solvex, Sam Industries, Net Worth 1.00 -0.17 0.55 -0.30
Modi Naturals, Natraj Proteins, Poona Dal & Oil
Table 7
Industries
Sugar:
Table 6 Sugar is one of the largest organized sectors in agri
Results of edible oil processing. The sector has many large companies like
Edible Oil Multiple
Renuka Sugars, Bajaj Hindustan, etc. The sector also
Mcap/Book
has some typical features like minimum procurement
Parameter Mcap/PAT EV/EBITDA Mcap/Sales
Value price, cyclical production, concentrated production in
Mean 11.10 6.43 0.21 1.53 Asia and South America, etc. The following companies
StdEv 9.77 4.18 0.27 2.20
were analysed:
Coeff of Variation 0.88 0.65 1.31 1.44
E.I.D. – Parry, Bajaj Hindusthan, Bannari Amman Sugars,
Triveni Engineering, Andhra Sugars, Dhampur Sugar
Correlation between multiple & parameter
Mills, KCP Sugar, Ponni Sugars (Erode), Ugar Sugar
Revenue 0.43 -0.12 -0.12 -0.03 Works, Dalmia Bharat Sugar, Thiru Arooran Sugars, Sri
Past 5 year growth -0.28 -0.36 0.99 -0.20 Chamundeswari, Piccadily Agro, Vishnu Sugar Mills,
EBITDA Margin -0.01 -0.03 0.51 0.16 Kesar Enterprises, Piccadily Sugars, Indian Sucrose
ROE -0.20 -0.12 0.58 0.61 EV/EBITDA showed lowest co-efficient of variation
Net Worth 0.38 -0.14 -0.11 -0.04 (0.44). The multiple showed highest correlation with net
worth, followed by revenue.
Table 6

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.
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Table 8 Engine parts – 31%

Results of sugar Drive transmission and steering parts – 19%
Body and Chassis – 12%
Sugar Multiple
Suspension and braking parts – 12%
Mcap/Book
Parameter Mcap/PAT EV/EBITDA Mcap/Sales Equipments – 10%
Value
Electrical parts – 9%
Mean 14.38 6.90 0.35 0.80
Miscellaneous – 7%
StdEv 14.79 3.07 0.20 0.39
The industry is estimated at USD 43.5 billion in FY 2011-
Coeff of Variation 1.03 0.44 0.56 0.48
12. (Auto Components Manufacturers Association of
Correlation between multiple & parameter India)
Revenue -0.01 0.20 0.00 0.19 The following companies were anlaysed in the industry:
Past 5 year growth -0.26 -0.04 -0.42 0.23 Bosch, Cummins India, Exide Industries, Motherson
EBITDA Margin -0.51 -0.43 0.49 0.18 Sumi Systems, WABCO, Amtek India, Kirloskar, Amtek
ROE -0.65 -0.69 0.44 0.61
Auto Limited, Federal-Mogul, Sundram Fasteners,
Wheels India, Shanthi Gears, NRB Bearings, Automotive
Net Worth -0.01 0.44 0.08 0.01
Axles, Mahindra Forgings, Commercial Engineers,
Table 8 Banco Products, Jamna Auto Industries, Fairfield Atlas,
Plantations Gabriel India, Lumax Industries, Sundaram-Clayton,
Tea and Coffee are another specialized area in agri and India Motor Parts, Saint-Gobain, Steel Strips Wheels,
food industries. The sector has stakes of many large Setco Automotive, Minda Industries, Suprajit
FMCG companies like Tata Tea, Unilever, etc. This sector Engineering, Rane Holdings, ZF Steering Gear, Munjal
also has special policies, farming conditions, Showa, Sona Koyo Steering, Munjal Auto, Lumax Auto
competitive factors. For the purpose of this analysis, Technology, Autoline Industries, India Nippon, FIEM
flowers have also been analysed together with tea and Industries, L. G. Balakrishnan, Subros, Pricol, Hindustan
coffee. The following companies for part of this analysis: Composites, Ucal Fuel Systems, Rane Madras, Rico
Karuturi Global, Neha International, Pochiraju Industries, Auto Industries, Jay Bharat Maruti, Shivam Autotech,
Tata Global Beverage, McLeod Russel India, Tata Coffee, Omax Autos, IST, Bimetal Bearings, Rane Engine Valves,
CCL Products India, Warren Tea, Dhunseri Petrochem, REIL Electricals, Rane Brake Lining, Precision Pipes,
Goodricke Group, Jayshree Tea, Assam Company India, Automotive Stampings, Harita Seating, JMT Auto, Alicon
Harrisons Malayalam, Russell India, United Nilgiri Tea, Castalloy, JBM Auto, Bharat Gears, Menon Pistons,
Joonktollee Tea, Diana Tea. Talbros Automotive, Triton Valves, Aunde India, Clutch
Here also, EV/EBITDA showed minimum coefficient of Auto, Pix Transmissions, Bharat Seats, Lakshmi
variation, followed by Mcap/Sales. Revenue and net Precision, Menon Bearings, Simmonds Marshall, Kar
worth showed the highest correlation with EV/EBITDA. Mobiles, IP Rings, Jay Ushin, Gujarat Automotive,
Competent Automobile, Lumax Automotive Systems,
Table 9 Autolite India, ANG Industries, Hindustan Hardy, Raunaq
Results of plantation (tea, coffee, flowers) Automotive, Remsons Industries, Porwall Auto
Components, Spectra Industries,
Plantation (tea, coffee flowers) Multiple
Kew Industries, Jagan Lamps, Coventry Coil-O Matic.
Mcap/Book
Parameter Mcap/PAT EV/EBITDA Mcap/Sales In this industry again, EV/EBITDA is the most stable
Value
Mean 15.17 9.60 1.09 1.13
multiple. EV/EBITDA shows maximum correlation with
revenue and net-worth.
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
Table 9
Auto components
Auto components industry comprises of a large number
of specialized players focusing on different segments of
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auto industry. Major segments and their composition in
total industry size are:
 Vol.1(1) ISSN : 2319-9628

Table 10 considerable heterogeneity within the industry in terms

Results of auto-components of size, profitability, product portfolio, promoter
Auto Components Multiple background, etc.
Mcap/Book Earnings based multiples – EV/EBITDA and P/E showed
Parameter Mcap/PAT EV/EBITDA Mcap/Sales
Value minimum coefficient of variation in all industries, except
Mean 12.47 6.04 0.67 1.62 public sector banks, which showed Mcap to Book Value
StdEv 13.09 4.65 0.93 1.67 as the most stable multiple.
Coeff of Variation 1.05 0.77 1.40 1.03 Considering the correlations observed with the most
stable multiple, we can infer that:
Correlation between multiple & parameter
(a) Net margins are the main drivers of multiples in
Revenue 0.19 0.35 0.18 0.31 banks (both public and private) among the
Past 5 year growth -0.04 0.05 -0.05 0.09 parameters observed,
EBITDA Margin 0.03 0.06 0.47 0.12 (b) ROE was most influential in food processing and
ROE -0.31 0.04 0.21 0.45 edible oil
Net Worth 0.13 0.35 0.30 0.24 (c) Plantations and auto-components seem to be driven
by revenue vis-à-vis other parameters observed
Table 10 (d) And agri inputs, rice and sugar were influenced by
Inferences: net-worth of respective companies.
The most stable multiples across different The following table shows the maximum correlation
industries and their respective coefficients of observed in a particular industry.
correlations with different financial parameters
Table 12
were as follows:
Maximum correlations across industries
Table 11
Maximum
Summary of trends Industry Relationships
Correlation

Coefficient of variation Correlation

Private sector banks 0.63 ROE and Mcap/Book Value
Public sector banks 0.81 PAT Margin and Mcap/Book Value
Co-efficient Second
Industry Multiple Highest Correlation highest Correlation General food processing 0.90 ROE and Mcap/Book Value
of Variation
Past 5 year Agri Inputs 0.71 EBITDA margin and Mcap/Sales
Private Edible Oil 0.99 5 year growth and Mcap/Sales
sector banks 0.65 MCAP/PAT Margin 0.32 growth 0.20
Rice 1.00 Net-worth and Mcap/PAT
Public sector
banks 0.23 P/B Margin 0.76 ROE 0.69 Sugar 0.61 ROE and Mcap/Book Value
General food Plantations
processing 0.71 EV/EBITDA ROE 0.80 Revenue 0.64 (tea, coffee, flowers) 0.54 ROE and Mcap/Book Value
Agri Inputs 0.60 EV/EBITDA Net worth 0.66 Revenue 0.25 Auto-components 0.47 EBITDA margin and Mcap/Sales
Edible Oil 0.88 MCAP/PAT Revenue 0.43 Net worth 0.38
Table 12
Rice 0.37 MCAP/PAT Net worth 1.00 EBITDA 0.86
margin
ROE and Mcap/Book Value showed highest correlation
Sugar 0.44 EV/EBITDA Net worth 0.44 Revenue 0.20
in four out of nine industries, followed by EBITDA margin
Plantations
(tea, coffee, and Mcap/Sales. The results were quite intuitive – a
flowers) 0.59 EV/EBITDA Revenue 0.33 Revenue 0.29 company generating higher returns on invested capital
Auto- (ROE), or a company operating at a higher margin
components 0.77 EV/EBITDA Revenue 0.35 Revenue 0.35
should be valued more than its peers.
Table 11

*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
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 Vol.1(1) ISSN : 2319-9628

Table 13 Abbreviations:
Results of general correlation analysis Coeff – Coefficient
EBITDA – Earnings Before Interest Tax Depreciation
Mcap/Book Mcap/Total and Ammortisation
Parameter Mcap/PAT EV/EBITDA Mcap/Sales
Value Assets
DCF – Discounted Cash Flows
Revenue -0.03 0.19 0.08 0.01 -0.10
P/E – Price to earnings per share
Past 5 year growth -0.07 -0.09 0.01 -0.03 0.34
P/B – Price to book value per share
EBITDA /PAT Margin 0.01 0.00 0.44 0.11 0.59
Mcap - Market Capitalisation
ROE -0.10 0.15 0.37 0.63 0.32
PAT – Profit after Tax
Net Worth -0.04 0.27 0.11 -0.0 20.18
Total Assets 0.07 NA 0.06 0.07 0.06
ROE – Return on Equity
Provisions -0.05 NA -0.09 -0.07 -0.10
StdEv – Standard Deviation
Table 13 SEBI – Securities and Exchange Board of India

Across all industries, highest correlation was showed by References:

ROE and Mcap/Book Value, followed by PAT margin and Damodaran on Valuation: Security Analysis for
Mcap/Total Assets (relevant to banking industry). Investment and Corporate Finance, by Ashwath
The analysis can be replicated for different industries in Damodaran, Wiley Finance
different geographical contexts. While co-efficient of Bloomberg terminal
variation would be helpful in convincing which industry Moneycontrol.com
average multiple should be used or which should not be Google finance
used, correlations can help derive what would investors Auto Components Manufacturers Association of India
be considering while choosing a particular company Reuters finance
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

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