Using Industry Average Multiples For Valuation Finance Essay

Using Industry Average Multiples For Valuation Finance sampleValuation of equity shargons of a company is an historic exercise and is performed on manifold occasions, be it investment decision in a particular company, merger, acquisition, restructuring, existence issue, and so forth Using pains average sixfold is a common practice, e sparely when an unlisted security is to be note valued.The study looks at eight industries and attempts to extrapolate (a) which is the most stable industry average multiple by utilise the statistical tool coefficient of translation and (b) which would be the most important financial surgical process disceptation, which could be driving multiple of a particular security within the industry by using statistical tool of coefficient of correlativityal statistics.Executive compendiumA company will get valued/re-valued on multiple occasions much(prenominal) as raising capital, sale of business, swap of sh atomic number 18s, issue of stock opt ions, etc. Valuation of commonplacely traded securities is quite a straightforward and often regulated for diametrical events, while valuation of thinly traded or un-traded securities requires some peculiar(a) approaches. There atomic number 18 cardinal main approaches to security valuation such as discounted cash flows, asset based valuation and similars. Comparables argon regarded as one of the most recyclable and practical manner. Ideal approach within comparables is to find out a publicly traded company which is exactly like the company being valued and blow up 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 utilize on a stand-alone basis or along-with a set of exact compar ables. The articles analyses the concept of industry multiples in eight industries Private domain banks, Public sector banks, oecumenical food processing, Agri Inputs, Edible inunct, Rice, Sugar, Plantations (tea, coffee, flowers) and Auto-components and tries to answer two questionsWhich 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 respect (EV) / Earnings Before Interest Taxes Depreciation and Ammortisation (EBITDA), MCap/ book Value, MCap/ gross salesWhich factor is the study driver of a multiple in a particular industry? The author has calculated co-efficient of correlation between different multiples and factors like revenues, 5 class revenue harvest-feast, margins, total assets, provisions, go on on Equity (ROE), mesh topology worth.EV/EBITDA was the most stable multiple followed by Mcap/ swob (similar to P/E ratio). tax income, net-wor th and margins were important drivers.KeywordsIndustry average multiple, valuation, market capitalization, book value, coefficient of variation/correlationBackgroundThere 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 slightly regulated (SEBI, the regulator in India has specify 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 to a fault 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 draw belowComparablesAsset ValueEBITDAPAT phonograph recording ValueSales, etc.Equity ValueDCFFigure 1 Different valuation methodsAsset ValueAsset based approaches such as book value (asset less liabilities as reflected in books of a ccounts) and realizable value (market value of asset less liabilities) are much 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 stovepipe valuation method. The company calculates its projected financial feat. 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 final stage Value. Last form in the projection period is capitalized as Cash flow in terminal course of study X (1+ unending 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 hobby limitationsThe 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 maven 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 too 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 psychoanalysis for Investment and somatic Finance, by Ashwath Damodaran, Wil ey Finance)A publicly traded peer is identified and compared to the company under consideration in terms of various valuation parameters like hurt 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 take aim of public holding, etc.In light of these, many analysts and industry experts use industry-average multiples, on a sta nd-alone basis as well as to muffle/rationalize multiples of an individual or group of comparables.This brings us to the questions which the article in bes to ponder overWhich 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, game degree of variability in the multiple will compromise its reliability.An another(prenominal) question is what drives a companys 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 differen t industries. The author hasChosen 8 industries based on his past work have a go at itSelected different publicly listed companies in each industryDerived their multiples and financial parameters from various databasesChecked 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 yr 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 dodge 1 Sectors and number of companies used in analysisIndustryNo of companiesPrivate sector banks14Public sector banks23General food processing16Agri Inputs8Edible Oil17Rice7Sugar17Plantations (tea, coffee, flowers)17Auto-components85Total214The chase multiples were usedMarket Capitalisation (MCap) / Profit After Tax, Enterpris e 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 side by side(p) financial performance parameters were selected for analysisRevenues of latest available financial course, 5 year revenue growth, margins (PAT margin for banks and EBITDA margins for others), total assets, provisions, Return on Equity (ROE), Net worthAnalysisPrivate Sector cambersThe following banks were analysed within private sector banksHDFC blaspheme 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 banksBanks (private)Multiple contentionMcap/PATMcap/AssetsMcap/SalesMcap/Book ValueMean8.400.090.881.19StdEv5.430.080.820.93Coeff of edition0.650.990.930.78Correlation between multiple parameterRevenue0.100.080.090.09Past 5 year growth0.200.340.360.48 strand0.320.590.610.63Total Assets0.070.060.060.07 eatable-0.05-0.10-0.09-0.07ROE0.010.320.340.43Net Worth0.180.180.180.16MCap/PAT, similar to Price to Earnings showed maximum stability. valuation account (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 BanksPublic 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 analysedIndian 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 banksBanks (public)Multiple lineMcap/PATMcap/AssetsMcap/SalesMcap/Book ValueMean5.410.040.450.69StdEv1.360.010.150.16Coeff of Variation0.250.290.320.23Correlation between multiple parameterRevenue0.340.610.650.60Past 5 year growth-0.050.120.130.07Margin-0.460.790.810.76Total Assets0.310.620.700.63Provisions0.440.510.500.46ROE-0.640.570.570.69Net Worth0.290.700.750.65Public sector banks showed a different trend in variability of multiples. The book value multiple seems to show the to the lowest degree 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. pabulum pr ocessing nutrition processing give backs 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 ProcessingMultiple debateMcap/PATEV/EBITDAMcap/SalesMcap/Book ValueMean18.509.390.974.21StdEv14.096.641.617.36Coeff of Variation0.760.711.651.75Correlation between multiple parameterRevenue0.390.640.490.75Past 5 year growth-0.34-0.27-0.17-0.14EBITDA Margin0.020.200.570.26ROE0.500.800.750.90Net Worth0.060.310.340.34EV/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 processingHatson 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,Venkys (India), Waterbase.The companies belonged to multiple sub-sectors like dairy, poultry, consumer goods, ice creams, frozen food, etc .Agri InputsAgri 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 anlysedSukhjit Starch Chemicals, Narmada Gelatines, Sakuma Exports, Vidhi Dyestuffs, Saboo Sodium Chloro, Kaveri Se ed, 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 inputsAgri InputMultipleParameterMcap/PATEV/EBITDAMcap/SalesMcap/Book ValueMean11.278.530.981.68StdEv9.645.111.281.87Coeff of Variation0.860.601.301.11Correlation between multiple parameterRevenue-0.360.250.040.11Past 5 year growth-0.180.250.260.40EBITDA Margin-0.88-0.080.710.11ROE0.02-0.030.400.48Net Worth0.330.660.480.53Edible OilEdible 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 anlysedRuchi 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 IndustriesTable 6 Results of edible oilEdible OilMultipleParameterMcap/PATEV/EBITDAMcap/SalesMcap/Book ValueMean11.106.430.211.53StdEv9.774.180.272.20Coeff of Variation0.880.651.311.44Correlation between multiple parameterRevenue0.43-0.12-0.12-0.03Past 5 year growth-0.28-0.360.99-0.20EBITDA Margin-0.01-0.030.510.16ROE-0.20-0.120.580.61Net Worth0.38-0.14-0.11-0.04EV/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(prenominal) correlation with revenue followed by Net Worth.RiceRice 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, Eur ope and US. The following companies were analysedKhushi 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 riceRiceMultipleParameterMcap/PATEV/EBITDAMcap/SalesMcap/Book ValueMean6.127.940.160.68StdEv2.273.950.130.30Coeff of Variation0.370.500.830.44Correlation between multiple parameterRevenue0.420.680.160.17Past 5 year growth-0.700.47-0.92-0.98EBITDA Margin0.86-0.600.59-0.25ROE-0.770.120.110.73Net Worth1.00-0.170.55-0.30SugarSugar 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 merchandise, concentrated production in Asia and South America, etc. The following companies were ana lysedE.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 SucroseEV/EBITDA showed lowest co-efficient of variation (0.44). The multiple showed highest correlation with net worth, followed by revenue.Table 8 Results of sugarSugarMultipleParameterMcap/PATEV/EBITDAMcap/SalesMcap/Book ValueMean14.386.900.350.80StdEv14.793.070.200.39Coeff of Variation1.030.440.560.48Correlation between multiple parameterRevenue-0.010.200.000.19Past 5 year growth-0.26-0.04-0.420.23EBITDA Margin-0.51-0.430.490.18ROE-0.65-0.690.440.61Net Worth-0.010.440.080.01PlantationsTea 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 analysisKaruturi 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. present 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)MultipleParameterMcap/PATEV/EBITDAMcap/SalesMcap/Book ValueMean15.179.601.091.13StdEv13.195.700.810.87Coeff of Variation0.870.590.750.76Correlation between multiple parameterRevenue0.220.330.090.27Past 5 year growth-0.47-0.38-0.19-0.43EBITDA Margin-0.34-0.420.20-0 .11ROE-0.37-0.270.200.54Net Worth0.180.290.160.21Auto componentsAuto 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 areEngine separate 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 industryBosch, 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 give the axe 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, pr oscription 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-componentsAuto ComponentsMultipleParameterMcap/PATEV/EBITDAMcap/SalesMcap/Book ValueMean12.476.040.671.62StdEv13.094.650.931.67Coeff of Variation1.050.771.401.03Correlation between multiple parameterRevenue0.190.350.180.31Past 5 year growth-0.040.05-0.050.09EBITDA Margin0.030.060.470.12ROE-0.310.040.210.45Net Worth0.130.350.300.24InferencesThe most stable multiples across different industries and their respective coefficients of correlations with different financial parameters were as followsTable 11 Summary of trendsCoefficient of variationCorrelationIndustryCo-efficient of VariationMultipleHighestCorrelationSecond highestCorrelationPrivate sector banks0.65MCAP/PATMargin0.32Past 5 year growth0.20Public sector banks0.23P/BMargin0.76ROE0.69General food processing0.71EV/EBITDAROE0.80Revenue0.64Agri Inputs0.60EV/ EBITDANet worth0.66Revenue0.25Edible Oil0.88MCAP/PATRevenue0.43Net worth0.38Rice0.37MCAP/PATNet worth1.00EBITDA margin0.86Sugar0.44EV/EBITDANet worth0.44Revenue0.20Plantations (tea, coffee, flowers)0.59EV/EBITDARevenue0.33Revenue0.29Auto-components0.77EV/EBITDARevenue0.35Revenue0.35*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 st able multiple.Considering the correlations observed with the most stable multiple, we can infer thatnet 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 oilplantations and auto-components seem to be driven by revenue vis--vis other parameters observedand 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 industriesIndustryMaximum CorrelationRelationshipsPrivate sector banks0.63ROE and Mcap/Book ValuePublic sector banks0.81PAT Margin and Mcap/Book ValueGeneral food processing0.90ROE and Mcap/Book ValueAgri Inputs0.71EBITDA margin and Mcap/SalesEdible Oil0.995 year growth and Mcap/SalesRice1.00Net-worth and Mcap/PATSugar0.61ROE and Mcap/Book ValuePlantations (tea, coffee, flowers)0.54ROE and Mcap/Book ValueAuto-components0.47 EBITDA margin and Mcap/SalesROE 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 analysisParameterMcap/PAT