< PreviousA PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES20 JANUARY | FEBRUARY 2019 t h e v a l u e e x a m i n e rTABLE 1: DESCRIPTIVE STATISTICS—DECEMBER 2011No. GICS CODEGICS Industry GroupNumber of companiesAverage Mkt Cap (£m)MeanSt DevMean St DevMean St Dev11010ENERGY2612510.721.17480.6119 1.1171 0.41001.2093 0.378821510MATERIALS475706.861.38820.7325 1.2601 0.49081.3620 0.480432010CAPITAL GOODS331178.221.13480.4981 1.0903 0.33371.1082 0.413542020COMMERCIAL & PROFESSIONAL SERVICES441311.481.28940.6113 1.1939 0.40961.2123 0.456252030TRANSPORTATION131965.271.09040.5590 1.0606 0.37461.1885 0.367262510AUTOMOBILES & COMPONENTS21450.232.02390.2495 1.6860 0.16721.7718 0.285772520CONSUMER DURABLES & APPAREL51373.900.85710.3730 0.9042 0.24990.9000 0.304982530CONSUMER SERVICES23938.131.25850.6947 1.1732 0.46541.2393 0.364392540MEDIA232164.170.86560.5680 0.9099 0.38060.9980 0.3572102550RETAILING201175.440.99140.7270 0.9942 0.48711.0020 0.5002113010FOOD & STAPLES RETAILING146299.460.85980.3563 0.9061 0.23870.8548 0.3069123020FOOD, BEVERAGE & TOBACCO2210361.401.11900.7162 1.0797 0.47990.9751 0.3913133030HOUSEHOLD & PERSONAL PRODUCTS55048.650.92250.4387 0.9481 0.29390.9339 0.3840143510HEALTH CARE EQUIPMENT & SERVICES41592.181.32900.8823 1.2204 0.59111.2759 0.7494153520PHARMACEUTICALS, BIOTECHNOLOGY & LIFE SCIENCES816091.690.70640.3212 0.8033 0.21520.7388 0.2840164510SOFTWARE & SERVICES14703.000.85000.3904 0.8995 0.26160.9074 0.3475174520TECHNOLOGY HARDWARE & EQUIPMENT19525.811.22010.8806 1.1475 0.59001.1840 0.5696184530SEMICONDUCTORS & SEMICONDUCTOR EQUIPMENT33194.451.25780.3933 1.1727 0.26351.2352 0.3456195010TELECOMMUNICATION SERVICES129288.090.89150.4662 0.9273 0.31240.9468 0.3693205510UTILITIES78796.790.47980.1819 0.6515 0.12190.5124 0.1860Equal weighting3444583.801.08550.53261.0573 0.35681.07780.3921Market capitalisation weighting0.97120.48330.9807 0.32380.97160.3515High Risk Industries Beta > 1.12233876.951.31950.62701.2141 0.42011.25730.4435Average Risk Industries 0.9> Beta <1.1382729.791.00150.57491.0010 0.38521.04150.4171Low risk Industries Beta < 0.9836388.160.78720.37960.8574 0.25430.83690.3080OLS BetaBlume BetaVasicek BetaOn an initial view, the Blume approach seems to shift the mean Beta closer to the market base of one more than the other techniques do. The Blume estimates also exhibit the lowest standard deviation which implies consistency in the population of Beta predictions. However, imprecision decreases even more when applying the Vasicek adjustment which adds forward estimation accuracy to the results. The firms have been classified into high, medium, and low-risk industries on the basis of the mean OLS Beta in each industry. Firms in the seven low-risk industries display Beta estimates below 0.9, while the companies in the high-risk industries have a Beta greater than 1.1. To highlight the relationship between firm size and inaccuracy, the market capitalization-weighted means and standard errors require particular attention. On a market capitalization basis, both the means of Beta estimates and the standard errors decrease, moving the estimates closer to one. Therefore, it can be noted that the computations that take into consideration the market capitalization display a higher level of accuracy (see Table 1). APPLICATION OF FILTERING MECHANISMSOnce the primary estimation techniques have been employed, the focus moves to the three filtering adjustments. They aim to eliminate observations which do not have a substantial impact on the forecasted Betas. Thus, they intend to reduce imprecision by including only statistically reliable data in the calculations. However, the filters might introduce bias which could offset any potential benefits. The first filtering adjustment excludes the Beta estimates derived from a regression with little explanatory power. The low R2 indicates that the market returns explain very little of the variation in stock returns. The second filter involves the omission of companies having a market capitalization below GBP 100m. The reason behind this filter is represented by the thin trading argument. Stoll and Whaley18 consider that a low market capitalization can 18 Stoll, H. R. and Whaley, R. E. (1983). Transaction costs and the small firm effect. Journal of Financial Economics, 12 (1), pp. 57–79.A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINESt h e v a l u e e x a m i n e r JANUARY | FEBRUARY 2019 21be the cause of thin trading which ultimately results in an under-estimate of Beta. The final filter attempts to eliminate the Beta estimates which display imprecision in estimation. The t-statistics, calculated as the Beta estimate divided by its standard error, measures the degree of departure of Beta estimate from its notional value and its standard error. The companies with a t-statistics below two are eliminated from the sample as the imprecision relative to the magnitude of Beta estimate is too high.19Despite their ability to reduce imprecision, the filtering adjustments can introduce significant bias in the calculations. This has been documented by Gray et al.,20 while applying 19 Gray, S., Hall, J., Klease, D., and McCrystal, A. (2009). Bias, stability, and predictive ability in the measurement of systematic risk. Accounting Research Journal, 22 (3), pp.220–236.20 Ibid.the same three filters when studying alternative Beta estimation techniques. In their study, almost seventy percent of the estimates have been eliminated by the filtering mechanisms. However, in this study, each filter excludes between approximately nine percent and twenty percent of the sample. Therefore, this examination seems to avoid the negative effects implied by filters. In the next stage, the filtering criteria have been applied to the OLS Beta calculations and the results are reported in Table 2. The first filter eliminates the Betas with a R2 less than ten percent, which indicates a low explanatory power. The second filter adds another requirement regarding the market capitalization which must be greater than GBP 100m. This is reasoned by the bias that might be introduced due to a small-firm effect. Finally, the last filter excludes the estimates which exhibit a t-stat below two, which shows that these Betas have been measured with less precision.TABLE 2: FILTERED STATISTICS— DECEMBER 2011No.GICS CODEGICS Industry GroupMarket Cap (£m)No of companies Mean Market CapNo of companies Mean Market CapNo of companies Mean 11010ENERGY14663.72221.219514663.72221.219514058.43231.283321510MATERIALS6048.06351.38776409.99331.40635583.14371.570232010CAPITAL GOODS1209.42321.11861287.89301.13271178.22331.134842020COMMERCIAL & PROFESSIONAL SERVICES1388.25411.28221527.23371.22911360.13421.317252030TRANSPORTATION2124.67121.20162811.1091.27602124.67121.201662510AUTOMOBILES & COMPONENTS1450.2322.02392842.2512.27341450.2322.023972520CONSUMER DURABLES & APPAREL1373.9050.85711373.9050.85711373.9050.857182530CONSUMER SERVICES948.28211.26181040.92191.2969945.01221.339892540MEDIA2415.90200.99113420.78141.07292415.90200.9911102550RETAILING1348.93170.94951348.93170.94951329.28171.1793113010FOOD & STAPLES RETAILING6761.84130.86207319.55120.88136761.84130.8620123020FOOD, BEVERAGE & TOBACCO9549.21190.93299549.21190.93299075.07201.0195133030HOUSEHOLD & PERSONAL PRODUCTS5048.6550.92256298.5240.75805048.6550.9225143510HEALTH CARE EQUIPMENT & SERVICES1592.1841.32901592.1841.32901592.1841.3290153520PHARMACEUTICALS, BIOTECHNOLOGY & LIFE SCIENCES16091.6980.706416091.6980.706416091.6980.7064164510SOFTWARE & SERVICES752.49130.8520875.89110.9395752.49130.8520174520TECHNOLOGY HARDWARE & EQUIPMENT548.91181.2960577.57171.2985548.91181.2960184530SEMICONDUCTORS & SEMICONDUCTOR EQUIPMENT3194.4531.25783194.4531.25783194.4531.2578195010TELECOMMUNICATION SERVICES10020.04110.952512232.8690.943110967.95101.0385205510UTILITIES8796.7970.47988796.7970.47988796.7970.4798Equal weighting4766.383081.09425162.772811.11204732.453141.1331Market capitalisation weighting0.97510.99491.0146High Risk Industries Beta > 1.11971.31094268.541851.33763898.572041.3571Average Risk Industries 0.9> Beta <1.1341.02453486.18300.99452834.20341.1012Low risk Industries Beta < 0.9770.81447158.78660.84006737.22760.8267R sq>0.1R sq > 0.1 & Mkt Cap > $ 100mt-stat > 2A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES22 JANUARY | FEBRUARY 2019 t h e v a l u e e x a m i n e rIn the last period, the filters eliminate approximately twelve percent of the sample. This percentage is reasonable, and it does not permit the filters to introduce imprecision. The filtered observations display higher OLS Beta than the unfiltered statistics. This can be the result of systematically eliminating firms with very low market capitalization, which may also exhibit low Betas. The second filter seems to shift the estimates closer to the absolute mean and proves to be more accurate. In both sets of descriptive statistics, the industries have been divided into high, average, and low risk based on OLS Beta estimates. It can be seen that there are significant differences between high-risk industries and low-risk industries in terms of Beta estimates. Therefore, by considering a specific risk-free rate and market risk premium, one can easily compute a rough cost of equity for each type of industry. This emphasizes the practical application of reporting grouped industries in terms of risk along with their filtered and unfiltered Beta estimates.CONCLUSION PART 1In part two of this study, the performance evaluation criteria of bias, stability, and predictive ability will be considered in critical depth. This will evolve to a detailed analysis of the full results for the Beta modelling techniques to evaluate their ranked effectiveness leading to practical recommendations for practitioners in the future use and application of proxy Betas. Diana Raicov, MSc, earned her master’s degree at the University of Portsmouth in the U.K. After graduating with an MSc Finance awarded with Distinction, she started working for Lloyds Banking Group. Her research interests include investigating risk estimation; mitigation techniques and examples of her work include the assessment and mitigation of credit risk by Romanian Banks, and the effectiveness of various techniques measuring systematic risk. E-mail: diana.Richard Trafford MSc, FCT, CVA, CFE, MAFF, is a visiting fellow at the University of Portsmouth in the U.K. where he supervises a number of doctoral students in the areas of business valuation and financial forensics. He was previously the course director for the postgraduate programs in accounting and finance at the university and taught graduate classes in business valuation and forensic accounting. When in practice, he conducted valuations for mergers and acquisitions, tax purposes, shareholder disputes and damages claims, and was also appointed as joint independent expert in legal disputes requiring share and business valuations. 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Additional users must be added in the shopping cart.Single-user / Yearly Single-user / Monthly§ Multi-user / Yearly# Multi-user / Monthly§# Actual Bundle Costs{$445$745$1,545$2,795$40$70$140$250$685$1,165 $2,445 $4,475$65$105$220$400FREE with NACVA membershipKVDgrid VE J-F19.indd 12/1/19 8:28 PMA PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES24 JANUARY | FEBRUARY 2019 t h e v a l u e e x a m i n e rVALUATION//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////GDPR Compliance Lowers Breach Incidents: An Interview with Leaders in Privacy and DataBy Nancy McCarthy, Senior Editor, The Value Examiner, with Tom Garrubba, Senior Director, Shared Assessments, Adam Laub, Senior VP, Product Marketing, STEALTHbits Technologies, and Christian Vezina, CISO, OneSpanIf data is the new currency, then privacy and data protection is the top concern of organizations around the world. The European Union (EU), to increase the protections on the privacy of the data belonging to EU residents and organizations and individuals doing business in the European Economic Area (EEA), enacted a new set of data protection regulations, The General Data Protection Regulation (GDPR), last May.The GDPR was adopted by the European Parliament in April of 2016. Intended to protect the personal data and privacy of EU citizens for transactions occurring within EU member states, the GDPR also regulates the exportation of personal data outside the EU and within the EEA. Despite some initial concerns, organizations worldwide that invested in maturing their data privacy practices are now realizing tangible business benefits from these investments, according to Cisco’s 2019 Data Privacy Benchmark Study. The study validates the link between good privacy practice and business benefits as respondents report shorter sales delays as well as fewer and less costly data breaches. The Value Examiner took this opportunity to get some feedback from three representatives active in the business of protecting data and preventing data breaks: Tom Garrubba, Senior Director, Shared Assessments, Adam Laub, Senior VP, Product Marketing, STEALTHbits Technologies, and Christian Vezina, CISO, OneSpanBACKGROUND OF THE GDPRValue Examiner: What prompted this new regulation?Tom Garrubba: The GDPR replaces the EU’s Data Protection Directive1 which was enacted in 1995, a decade before the 1 Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals concerning the processing of personal data and on the free movement of such data, Official Journal L 281, 23/11/1995 p. 31–50internet was so central to businesses. Both the EU and enterprises operating in the EU began to realize the pre-digital age directive was not keeping up with the times in terms of the way data is stored, collected, and transferred. Frequent, high-profile, third-party data breaches both in Europe and in the U.S. had been causing concern among EU residents. As a result, there was an increase in vendor oversight regulatory requirements. The patchwork of vendor due diligence programs made it necessary to create a standard—albeit very strict—for companies and other organizations to meet; especially around a vendor’s ability to meet privacy compliance obligations. Another contributing factor is that privacy is increasingly becoming an essential part of the standard vendor assessment process.Value Examiner: Did the U.S. express concerns regarding the European Regulations? Tom Garrubba: Initially, there were some concerns. Reports before the enforcement of GDPR had some U.S. companies wondering if the higher standards in the twenty-eight-member states EU would put them at a competitive disadvantage. The compliance with and the enforcement of the GDPR is forcing U.S. companies to rethink their business strategy in Europe. However, while Europe has had more stringent rules regarding how companies use the personal data of their customers, U.S. vendors and their clients are showing increasing concern about privacy. With each new, high-profile data-breach, businesses suffer. The Value Examiner: How real is the public concern over privacy? Adam Laub: It is significant, and it grows with every new high-profile data breach. In 2017, RSA2 (a data, security, and privacy company) surveyed 7,500 consumers in France, 2 RSA Data Privacy & Security Report, en/e-book/rsa-data-privacy-report.pdfA PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINESt h e v a l u e e x a m i n e r JANUARY | FEBRUARY 2019 25Germany, Italy, the U.K., and the U.S. Eighty percent of the respondents said lost banking and financial data is a top concern. Lost security information (e.g., passwords) and identity information (e.g., passports or driving license) were cited as a concern of seventy-six percent of the respondents.That is an alarming statistic for companies dealing with consumer data. Worse, was that sixty-two percent of the respondents to the RSA report said they would blame the company for their lost data in the event of a breach, not the hacker. The report’s authors concluded “As consumers become better informed, they expect more transparency and responsiveness from the stewards of their data.” The report concluded: “As businesses continue their digital transformations, making greater use of digital assets, services, and big data, they must also be accountable for monitoring and protecting that data daily,” concluded the report.The Value Examiner: Has the GDPR made a difference since it has been enacted?Christian Vezina: I would say yes. Organizations worldwide that invested in maturing their data privacy practices are now realizing tangible business benefits from these investments, according to Cisco’s 2019 Data Privacy Benchmark Study.3 The study validates the link between good privacy practice and business benefits as respondents report shorter sales delays as well as fewer and less costly data breaches.The survey, which was released this past January, shows fifty-nine percent of organizations reported meeting all or most requirements, twenty-nine percent expect to do so within a year, and nine percent will take more than a year.The Value Examiner: Are there any other takeaways from this study?Christian Vezina: Customers are increasingly concerned that the products and services they deploy provide appropriate privacy protections. Those organizations that invested in data privacy to meet GDPR experienced shorter delays due to privacy concerns in selling to existing customers: 3.4 weeks vs. 5.4 weeks for the least GDPR ready organizations. Overall the average sales delay was 3.9 weeks in selling to existing customers, down from 7.8 weeks reported a year ago. GDPR-3 CISCO CYBERSECURITY SERIES 2019, DATA PRIVACY JANUARY 2019, dpbs-2019.pdfready organizations cited a lower incidence of data breaches, fewer records impacted in security incidents, and shorter system downtimes. They also were much less likely to have a significant financial loss from a data breach. Beyond this, seventy-five percent of respondents cited that they are realizing multiple broader benefits from their privacy investments, which include greater agility and innovation resulting from having appropriate data controls, gaining competitive advantage, and improved operational efficiency from having data organized and cataloged.The Value Examiner: Any final thoughts?Christian Vezina: Regulations such as the GDPR have put even stricter due diligence requirements on organizations, especially around vendors’ ability to meet applicable privacy compliance obligations. Privacy is starting to be an essential part of standard vendor assessment processes.Service organizations having a higher level of privacy maturity will benefit from a shortened sales cycle, as they will be in a position not only to demonstrate their compliance but to assist their customers in meeting their own compliance obligations.Tom Garrubba: In reviewing the CISCO study, I see this as a positive in that 1) outsourcers are performing proper due diligence in getting a better understanding as to how third-party organizations are handling their customer data, and 2) service providers have anticipated these requests and built this into their internal compliance; thus cutting down on the sales delays. This leads me to believe that organizations (both outsourcers and service providers) have seen the hefty fines for GDPR non-compliance and do not want to become an additional breach statistic, so they have taken privacy compliance most seriously and have thus embedded privacy compliance into their business model. Adam Laub: We all knew the EU GDPR aimed to bring the subject of data privacy to a whole new level. We just did not know if it was going to have any teeth. It appears from Cisco’s study that it just may. The most encouraging finding is that GDPR-ready companies appear to be reducing the cost of a breach if compromised in comparison with those organizations that are not. This may imply that the measures organizations are employing to achieve compliance with GDPR requirements are actually leading to much more A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES26 JANUARY | FEBRUARY 2019 t h e v a l u e e x a m i n e rsignificant security outcomes than past regulations have been able to produce. Given other entities like the State of California in the U.S. have and are using the GDPR as a template for similar regulations, we may have finally had the breakthrough we have all needed and achieved a new level of maturity in data security and privacy.Tom Garrubba, Senior Director, Shared Assessments, is an experienced professional in IT risk and controls with extensive experience in developing, maintaining, and consulting on third-party risk programs. He is an internationally recognized subject matter expert, lecturer, author, and contributor, and instructs the Certified Third Party Risk Professional (CTPRP) program. Previously, Mr. Garrubba was Senior Privacy Manager at a Fortune 10 healthcare company where he instituted and managed their third-party risk program. He’s an experienced professional with over twenty years of experience in IT security, privacy, audit, and risk and compliance in various industries and public consultingChristian Vezina, CISO, OneSpan, leads the overall OneSpan corporate information security strategy. With more than thirty years of IT experience in government, financial, manufacturing, engineering, and technology environments, he has dedicated the last fifteen years to information security. Mr. Vezina holds a bachelor’s degree in Information Systems Management, as well as multiple audits, information security, risk management, and privacy-related professional certifications. Adam Laub, Senior VP, Product Marketing, STEALTHbits Technologies, sets STEALTH-bits’ product strategy, defines roadmaps, drives strategic sales engagements, supports demand generation activities, and enables the sales organization and all aspects of product evangelism. Since joining STEALTHbits in 2005, Mr. Laub has held multiple positions within the organization, including sales, marketing, and op-erational management roles. VEI Want to Sell My BusinessI Need Estate PlanningI'm Getting a DivorceCertified Valuation AnalystI'm Missing OutMy Partnership is DissolvingCo-Sponsored by the National Association of Certified Valuators and Analysts® (NACVA®) Visit www.theCTI.com/BVTC or Call (800) 677-2009Early registration discounts available. Dates and locations subject to change.Consultants’ Training Institute®Business Valuation Certification and TrainingMay 6–11, 2019 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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Salt Lake City, UTLive online broadcasts from select locations are also available.BVTCadVE-S-O18.v2.indd 112/11/18 10:13 AM28 JANUARY | FEBRUARY 2019 t h e v a l u e e x a m i n e rA PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINES///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////ACADEMIC RE VIEWACADEMIC RESEARCH BRIEFSBy Peter L. Lohrey, PhD, CVA, CDBV///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////The purpose of this column is to provide the readers of The Value Examiner summaries of contemporary research in business valuation and forensic accounting. The manuscripts covered are selected from numerous academic research outlets that include relevant topical coverage of valuation and related forensic accounting issues. The objective is to illustrate the core of this novel research while increasing awareness among the community of the subject matter. As this column evolves, I encourage the readership to forward relevant manuscripts or working papers for consideration. Please send links and/or files the subject line. THE PRIVATE COMPANY DISCOUNTAuthors: John Koeplin, University of San Francisco; Atulya Sarin, Santa Clara University; and Alan C. Shapiro, University of Southern California.Source: Journal of Applied Corporate Finance, Vol. 12, No. 4, Winter 2000, pp. 94–101.SummaryKoeplin, Sarin, and Schapiro’s (KSS) paper titled, “The Private Company Discount” (PCD) attempts to determine if the price paid for private companies is less than the price paid for public companies. They match private versus public company transactions for both domestic and foreign transactions. KSS limit the timing of the matched transactions to within one year of each other. They match the transactions based on both four-digit SIC codes and sales revenue.They found that domestic private companies were acquired at approximately a twenty to thirty percent discount from the prices paid for similar public companies. This discount was greater when they used earnings multiples rather than book value multiples. KSS also found that PCDs were larger for foreign companies than domestic U.S. companies.Literature Considered in the Study KSS point out that two types of empirical studies have been used to estimate the discount for lack of marketability (DLOM) when valuing private companies—restricted stock studies and pre-IPO discounts. The restricted stock studies compare the price of publicly traded companies’ stock to those of private placements—where trading in these shares is restricted under Rule 144 for a period of six months or one year—depending on whether the company is considered a “reporting company” subject to the 1934 Act. Restricted shares are often claimed to be identical to public shares—except for their lack of liquidity due to the required holding period. KSS report that the mean and median discounts in these studies range from twenty two percent to thirty five percent. They also point out that a major defect in most of these studies is they ignore the fact that the private placement discount is incurred due to several different factors—not just the lack of liquidity.The pre-IPO studies compare the original offering price of the stock to previous transactions that took place prior to the offering. KSS cite a study by Emory (1994) that found a mean and median discount of forty-seven percent and forty-six percent respectively. The authors point out that there are several flaws with these studies due to: 1) transactions used are with insiders and are not arm’s length; 2) most of the transactions are for restricted options issued to management and do not represent an exchange of shares for cash; and 3) many of these studies suffer from selection bias.Design and Execution of the StudyThis study used eighty-four domestic and 108 foreign company matched pairs over the period 1984 to 1998 taken from the SDC Merger and Acquisition Database (SDC). They removed financial firms, regulated utilities, and minority interest transactions from their original group of transactions to arrive at their final sample. Four separate A PROFESSIONAL DEVELOPMENT JOURNAL for the CONSULTING DISCIPLINESt h e v a l u e e x a m i n e r JANUARY | FEBRUARY 2019 29enterprise value transaction multiples1 were used to arrive at PCD estimate. These multiples were estimated using the percentage difference between the means and medians of the transaction multiples.KSS analyzed four separate enterprise value multiples (Enterprise Value, EBIT Multiple, Sales Multiple, and Book Multiple) for both the private and public company transactions. They used a cross-sectional regression analysis to analyze the differences in size and historical earnings before interest and taxes (EBIT) growth rates between the private and public companies in their sample. They found that earnings multiples, not revenue multiples, provided statistically significant evidence in estimating the PCD. They also point out that one problem with interpreting their results lies in the fact that the acquired private companies in their sample were smaller in size—based on revenue—and had different EBIT growth rates than the public companies they were matched with.Conclusion and ImplicationsKSS found that U.S. domestic private companies were purchased at a twenty to thirty percent discount compared to comparable public companies when using earnings multiples for comparisons. The mean discount using book value multiples was lower and significant for only domestic transactions. There were no significant differences between the revenue multiples for both private and public companies that were acquired.KSS point out that one problem with their study lies in the possible differences in employment contracts for key management in a private company acquisition versus a public company acquisition. Particularly, when the private company owners receive part of their compensation in the form of an employment contract. In cases where such employment contracts represent higher than going market rates for compensation, the private company valuations that are observed may be below fair market value. Hence, they assert that their estimates should be considered at the upper end of the range for the private company discount.1 The four estimates of enterprise value multiples equaled the number of targeted shares, times the offering price, plus book values of: 1) short-term debt, 2) straight debt, 3) convertible debt, and 4) preferred stock minus marketable securities.A NEW EXAMINATION OF THE PRIVATE COMPANY DISCOUNT: THE ACQUISITION APPROACHAuthors: Maher Kooli, Senior Research Analyst; Mohamed Kortas, Senior Research Analyst; and Jean-Francois L’Her, Vice President of Research, Caisse de depot et placement du Quebec.Source: The Journal of Private Equity, Vol. 6, No. 3, Summer, 2003, pp. 48–55.SummaryThis study provides additional private company discount (PCD) evidence by comparing private company transaction multiples to public company transaction multiples. Kooli, Kortas, and L’Her (KKL) introduce the use of a portfolio of guideline public company transactions as the metric for public company comparison instead of a single public company transaction. They use a sample of 331 private company transactions in the U.S. from 1995 to 2002 taken from the Done Deals2 and SDC Mergers and Acquisitions3 databases. The primary innovation that KKL introduce is the use of a portfolio of public companies as a market comparable for a single private company transaction. They utilize the same methodology that Brav, Geczy, and Gompers (2000)4 used to select different portfolios of public companies. KKL found that transaction multiples were mostly greater for public companies compared to the transaction multiples of private companies. Their results revealed deal multiples based on sales, earnings, and cash flow that were seventeen percent, thirty-four percent, and twenty percent greater for public companies than private ones. KKL conclude their study by using regression analysis to identify firm characteristics and industry classification as the primary items that explain PCD variation.Literature Considered in the Study KKL begin by summarizing the four approaches used to estimate the discount for lack of marketability (DLOM): 1) the restricted stock approach; 2) the IPO approach; 3) the expected exit multiple approach; and 4) the acquisition approach.2 donedeals/3 4 Brav, A., C. Geczy, and P. Gompers. “Is Abnormal Return Following Equity Issuance Anomalous?” Journal of Financial Economics, 56 (2000), pp. 209–249.Next >