(Continued)
E. Evaluation and Recommendations
DLOM Job Aid page 76
selection of valuation methods or parameters such should be looked at for the
underlying reasoning applied and the logic and flow of the judge’s thinking not for
the results that were finally reached. No two valuation assignments are identical.
Therefore, basing one’s results on the results of another assignment whether
litigated or not is a failure of proper diligence with regard to the assignment
presently at hand.
4. Sources available to IRS Valuation Analysts
It has been attempted to gather as many of the underlying studies and research
papers referenced in this job aid. Please refer to the Engineer DLOM shared
folder on the IRS intranet see Engineering Program National Shared folder for
information on mapping the network drive where these have been placed to your
computer.. See Exhibit D—DLOM Files on Shared Folder as of September 25,
2009.
DLOM online sources currently available51 are listed below. These have limited
access and have been purchased for one user—contact information is provided
at Engr BV Resources webpage.
Online databases:
• FMV Restricted Stock Study
• Valuation Advisors Pre-IPO database
FMV Restricted Stock Database—Analysis
The FMV Restricted Stock database of transactions is available for purchase,
and is utilized by valuators to estimate DLOM on privately-held business
interests. IRS Engineer, Tom Kelley, AVA, completed an analysis52 of the 475
transactions in the FMV Restricted Stock database in 2009. The purpose was a)
to analyze the FMV model for determining DLOM on private equity, and b) to
determine whether it is possible to develop a statistically valid regression-based
model to determine the DLOM. The conclusions drawn are:
1) FMV Opinions’ model is flawed insofar as explanation of the DLOMs on
the restricted stock transactions in their database;
2) Valuators cannot confidently rely on FMV’s model when determining
DLOMs on restricted stocks, much less on interests in private equity; and
3) Neither FMV’s model nor multivariate regression analysis can be applied
to FMV’s database to confidently determine the DLOM on private equity.
51 Resources require annual funding to maintain the license. Therefore budgetary limitations may require
that these subscriptions be canceled.
52 February 18, 2009 memo from Tom Kelley with the subject, “Update: FMV Opinion’s Model and
Database”. Copy is provided as an Exhibit to this job aid.
E. Evaluation and Recommendations
DLOM Job Aid page 77
Please refer to Exhibit A—Review FMV Restricted Stock Model in this job aid for
information on the process Tom Kelley followed in reaching his conclusions.
F. Summary
DLOM Job Aid page 78
F. Summary and Conclusions
This job aid was prepared to assist IRS valuators understand the numerous
studies and approaches used by valuation professionals to determine DLOM. We
have addressed restricted stock studies, the eldest type of study having been
developed in the early 1970’s. As the need increased to better quantify DLOM,
newer approaches are introduced, such as Liquistat (2007). While many of the
newer approaches are not currently used in professional practice, the
profession’s reliance could change in the future. In total, the job aid has
information on 23 different DLOM approaches.
DLOM has been defined as well as the factors that impact DLOM on a specific
interest. Sample IDR questions are provided. We have summarized each
approach, provided references, identified strengths and weaknesses, how the
Tax Court has ruled on the approach, and how prevalent its use in practice. In
addition, the job aid offers discussion points to use with Taxpayer appraisers to
focus on a specific method.
As is always the key, facts and circumstances surrounding the subject interest
are what determine the level of DLOM, if any. DLOM studies, methods and
models can be complex, can indicate widely diverse conclusions, and may be
appropriate in only certain limited situations. The business valuation profession
does not identify acceptable or unacceptable methods for estimating
marketability discounts, although some individual practitioners have their own
preferences and frequently disagree as to the best approach.
This job aid does not provide guidance on the best DLOM approaches, but is
meant to help the reader understand and make an informed decision about
DLOM. It is current as of the date of this writing.
For recommendations on the content included in this job aid, please contact any
of the members of the DLOM Team who were the developers.
Name POD
Mike Gregory, ASA, AVA
Engineering Territory
Manager St. Paul, MN
Sue Kurzweil, CPA, ASA
National Business Valuation
Issue Coordinator Independence, OH
Jeff Myers, ASA, AVA Engineering Team Manager Columbus, OH
Laura Goldberg, AVA Engineer Plantation, FL
Ernie DeRosa, AVA Financial Analyst New York, NY
James McCann, ASA Financial Analyst San Francisco, CA
Aberdeen Sabo Estate Tax Attorney Independence, OH
G. Bibliography
DLOM Job Aid page 79
G. Bibliography
Articles
Abbott, Ashok. “Empirical Measures of Marketability and Liquidity Discounts,
Discount for Lack of Marketability: An Empirical Analysis and DLOM – Concepts
and Models.” Presentations at Various ASA and NACVA Conferences and on the
BVR Teleconference of April 26, 2006. (available in shared folder)
Abbott, Ashok. “New Abbott Analysis Aids Valuators in Assessing Liquidity
Discounts.” Business Valuation Update. Vol. 13, No.11, Nov. 2007. (available in
shared folder)
Amihud, Yakov and Mendelson, Haim “Asset Pricing and the Bid Ask Spread”,
Journal of Financial Economics v17 (December 1986) pp. 223049. (available in
shared folder)
Bajaj, Mukesh, David J. Denis, Stephen P Ferris & Atula Sarin. Firm Value and
Marketability Discounts. Journal of Corporation Law. Vol 27, No.1. (available in
shared folder)
Barclay, Michael J., Clifford G. Holderness and Dennis P. Sheehan. “Private
Placements and Managerial Entrenchment.” Journal of Corporate Finance Vol.
13, 2007, pp. 461-484. (available in shared folder)
Cost of Flotation of Registered Issues 1971-1972. Securities and Exchange
Commission, 1974.
Dann, L.Y. and H. DeAngelo, 1988, Corporate Financial Policy and Corporate
Control: A Study of Defensive Adjustments in Asset and Ownership Structure,
Journal of Financial Economics, 20, 87-127
Edelman, Richard B. and H. Kent Baker, 1993, The Impact of Company Pre-
Listing Attributes on the Market Reaction to NYSE Listings, The Financial
Review, Vol. 28, No. 3, August 1993, 431-448
Emory, John D. Sr, F.R. Dengel, III. “Discounts for Lack of Marketability, Emory
Pre-IPO Discount Studies 1980-2000 as Adjusted October 10, 2002.” Business
Valuation Review, Vol. 21 No. 4, (December 2002). (available in shared folder)
Feldman, Stanley Jay. “Revisiting the Liquidity Discount Controversy:
Establishing a Plausible Range.” Bentley College and Axiom Valuation Solutions.
(available in shared folder)
G. Bibliography
DLOM Job Aid page 80
Finnerty, John D. “The Impact of Transfer Restrictions on Stock Prices.” Fordham
University, Presentation at the Meeting of the American Finance Association,
2003. (available in shared folder)
Frazier, William. "Non-marketable Investment Company Evaluation (NICE)."
Valuation Strategies. November/December 2006. Vol 10, No 2. (available in
shared folder)
Garland, Pamela J. and Ashley L. Reilly. “Update on the Willamette
Management Associates Pre-IPO Discount for Lack of Marketability Study”.
Available http://www.willametteinsights.com/03/Autumn2003article2.pdf, April 30,
2009.
Gomes, Armando and Gordon Phillips. “Why Do Public Firms Issue Private and
Public Equity, Convertibles and Debt?” Presentation at the Research Seminar in
Law, Economics, and Organization, 2005. (available in shared folder)
Hall, Lance. “New Tactics Required to Prove Discount for Lack of Marketability.”
The Value Examiner, Jan-Feb 2007, pp 36-39. (available in shared folder)
Hall, Lance S. and Timothy C. Polacek, “Strategies for Obtaining the Largest
Valuation Discounts”, Estate Planning, Vol. 21 No. 1, January/February, 1994.
(available in shared folder)
Hall, Lance, 2004, “The Discount for Lack of Marketability: An Examination of Dr.
Bajaj’s Approach”, BV Update, March 2004. (available in shared folder)
Hertzel, Michael and Richard Smith. “Market Discounts and Shareholder Gains
for Placing Equity Privately.” Journal of Finance, Vol. 48, 1993, pp. 459-485.
(available in shared folder)
Keys, Phyllis and Norris Larrymore. “Integration of Private and Public Offerings.”
Presentation at the Conference of the Eastern Finance Association, 2004.
(available in shared folder)
Koeplin, John, Atulya Sarin and Alan Shapiro, 2000, “The Private Company
Discount, Journal of Applied Corporate Finance, Volume 12, Number 4, Winter
2000, 94-101. (available in shared folder)
Longstaff, Francis A. “How much Can Marketability Affect Security Values?” The
Journal of Finance, December 1995, pp. 1767-74. (available in shared folder)
Mitchell, Mark and Mary Norwalk, 2008, “Assessing and Monitoring Bajaj”,
Business Valuation Review 27-1, Spring 2008.
G. Bibliography
DLOM Job Aid page 81
Pratt, Shannon , 2004, “The McCord Case and the Bajaj Method”, Unpublished
Conference Call Pre-Read. (available in shared folder)
Pratt, Shannon , Chris Mercer, Lance Hall and Rob Oliver, 2004, “DLOM: A
Critique of the Bajaj Approach”, BVR Teleconference April 2004
Pratt, Shannon, 2004, “Defending Discounts for Lack of Marketability”, ACTEC
Journal 2004, 276. (available in shared folder)
Pratt, Shannon, 2003, “Discounts for Lack of Marketability: Documentation,
Critique and Defense”, American Society of Appraisers International Appraisal
Conference, July 2003
Reilly, Robert. “Update on Study of Discount for Lack of Marketability”. American
Bankruptcy Institute Journal, June 2005, Value and Cents
Reilly, Robert and Aaron Rotkowski, “Discount for Lack of Marketability: Update
on Current Studies and Analysis of Current Controversies”. Fall 2007, Tax
Lawyer, Vol 61, No. 1. (available in shared folder)
Ritter, Jay R. “The Costs of Going Public.” Journal of Financial Economics, Vol.
19, No. 2 (December 1987), pp. 269-81. (available in shared folder)
Robak, Espen. “Discounts for Illiquid Shares and Warrants: The LiquiStat
Database of Transactions on the Restricted Securities Trading Network.” Pluris
Valuation Advisors White Paper, January 2007. (available in shared folder)
Robak, Espen. Liquidity and Levels of Value: A New Theoretical Framework.”
BV Update, October, 2004. (available in shared folder)
Paschall, Michael A. Discounts for Lack of Marketability: A Review of Studies and
Factors to be Considered. Fair Value, September 1994.
Sanger, Gary C. and John J. McConnell, “Stock Exchange Listings, Firm Value,
and Security Market Efficiency: The Impact of NASDAQ”. The Journal of
Financial and Quantitative Analysis, Volume 21, Issue 1, March 1986, 1-25
Saunders, Philip. “Marketability Discounts and Risk in Transactions Prior to Initial
Public Offerings”. Business Valuation Review. Volume 19, No. 4, December
2000, pp 186-195. (available in shared folder)
Seaman, Ronald M. “Minimum Marketability Discounts—2nd Edition.” Business
Valuation Review. June 2005, pp. 58-64.
G. Bibliography
DLOM Job Aid page 82
Seaman, Ronald M. “Minimum Marketability Discounts—3rd Edition.” September
2007. Available at http://www.dlom-info.com/ August 18, 2009. (available in
shared folder)
Seaman, Ronald M. “Minimum Marketability Discounts—4th Edition, A Study of
Discounts for Lack of Marketability Based on LEAPS Put Options in November
2008.” September 2007. Available at http://www.dlom-info.com/ August 18, 2009.
(available in shared folder)
Silber, William, 1991, “Discounts on Restricted Stock: The Impact of Illiquidity on
Stock Prices”, Financial Analysis Journal, 47, 60-64. (available in shared
folder)
Tabak, David. “A CAPM-Based Approach to Calculating Illiquidity Discounts.”
Available http://www.nera.com/Publication.asp?p_ID=1152 , November 11, 2002.
Trout, Robert R. “Minimum Marketability Discounts.” Business Valuation Review.
September 2003, pp. 124-126. (available in shared folder)
Trout, Robert R. “Estimation of the Discount Associated with the Transfer of
Restricted Securities.” Taxes. June, 1997, pp. 381-384. (available in shared
folder)
Wruck, Karen Hopper. “Equity Ownership Concentration and Firm Value:
Evidence from Private Equity Financings.” Journal of Financial Economics Vol.
23, 1989, pp. 3-28. (available in shared folder)
Books
Kasper, Larry J. Business Valuations: Advanced Topics. Greenwood Publishing
Group. 1997. Chapter 5 Premium and Discounts.
Mercer, Z. Christopher, Quantifying Marketability Discounts: Developing &
Supporting Marketability Discounts in the Appraisal of Closely Held Business
Interests, Peabody Publishing, LP, 1997.
Mergerstat Review, FactSet Mergerstat, LLC, 2006.
Pratt, Shannon P., Robert F. Reilly, Robert P, Schweihs. Valuing A Business.
The Analysis and Appraisal of Closely Held Companies, 5th ed. New York:
McGraw-Hill, 2008.
Pratt, Shannon P. Business Valuation Discounts and Premiums. New York:
John Wiley & Sons, Inc., 2001.
G. Bibliography
DLOM Job Aid page 83
Websites
Howard, Frazier, Barker, Elliot, Inc. (www.hfbe.com)
LEAPS (www.dlom-info.com/articles.html)
NERA (National Economic Research Associates) (www.nera.com)
Options Education (http://www.optionseducation.org/strategy/collar.jsp )
Partnership Profiles (aka “Partnership Spectrum”) (www.partnershipprofiles.com)
Table 1 page 84
Table 1 Analysis of SEC Institutional Investors Restricted Stock Study
TABLE 1
REFERENCE - Institutional Investor Study ("SEC Study") data1
Discounts on Purchase Price of Restricted Common Stock
( see note [2] )
================ Range of Discounts by Respective Groupings ================
-15% to 0% discount 0% to 10% discount 10% to 20% discount 20% to 30% discount 30% to 40% discount 40% to 50% discount 50% to 80% discount
Transactions As a Transactions As a Transactions As a Transactions As a Transactions As a Transactions As a Transactions As a
Average Total in this Percent in this Percent in this Percent in this Percent in this Percent in this Percent in this Percent
Discount Transactions Grouping of Total Grouping of Total Grouping of Total Grouping of Total Grouping of Total Grouping of Total Grouping of Total
26% 398 26 6.5% 67 16.8% 78 19.6% 77 19.3% 67 16.8% 35 8.8% 48 12.1%
Weighted-average "Discount"
( see note [3] ) Grouping Respective Grouping Respective Grouping Respective Grouping Respective Grouping Respective Grouping Respective Grouping Respective
Discount Weighting Discount Weighting Discount Weighting Discount Weighting Discount Weighting Discount Weighting Discount Weighting
-7.5% 6.5% 5.0% 16.8% 15.0% 19.6% 25.0% 19.3% 35.0% 16.8% 45.0% 8.8% 65.0% 12.1%
26% Weighted-Average Discount
greatest weighting within these two groupings
( see note [4] )
1
2
3
4
Source: Quantifying Marketability Discounts, by Z. Christopher Mercer, ASA, CFA, Peabody Publishing, LP , 1997, Exhibit 2-1, page 70.
Data range includes a low of -15% (a negative discount), to a high of 80% discount.
Greatest weighting of transactions occurs within the 15% and 25% implied discount groupings, suggesting a most-common discount of 20%.
Calculated result equals the reported 26% "Average Discount".
Table 2 page 85
Table 2 Analysis of MPI Restricted Stock Study
TABLE 2
REFERENCE - Management Planning Study data1
Analysis of Restricted Stock Discounts by Revenue Size
Average
Number of Revenues Average Range of Discounts
Revenues Observations ($ Millions) Discounts Low High
Under $10 Million ( see note [2] ) 14 $6.6 32.9% 2.8% 57.6%
$10 - $30 Million 11 $22.5 30.8% 15.3% 49.8%
$30 - $50 Million 10 $35.5 25.2% 5.2% 46.3%
$50 - $100 Million 8 $63.5 19.4% 11.6% 29.3%
Over $100 Million (Adjusted) * 4 $224.9 14.9% 0.0% 24.1%
Overall Sample Averages $47.5 27.7% 0.0% 57.6%
Totals 47
* Over $100 Million (Actual Calculation) 2 $187.1 25.1% 0.0% 46.5%
Totals 49
Excludes Sudbury Holdings, Inc., whose private placement consisted of 125% of the pre-transaction shares outstanding.
Excludes Starrett Housing Corp., which is one of the five most thinly traded companies in the sample.
1
2
Source: Quantifying Marketability Discounts, by Z. Christopher Mercer, ASA, CFA, Peabody Publishing, LP , 1997,
Figure 12-1, page 346.
Discounts for the smallest companies occurred over a range of [2.8% – 57.6%].
Exhibit A page 86
Exhibit A—Review FMV Restricted Stock Model
DATE: February 18, 2009
TO: Mike Gregory, Program Manager, Field Specialists West
Sue Kurzweil, National Business Valuation Issue Coordinator
THRU: Robin Ruegg, Manager, Field Specialists Team 1864
FROM: Tom Kelley, Engineer, Field Specialists Team 1864
SUBJECT: Update: FMV Opinions’ Model and Database
This memorandum is a summary of the results of my two-part assignment 1) To
analyze FMV Opinions’ (FMV) model for determining the discount for lack of
marketability (DLOM) on private equity, and 2) To determine whether it is
possible to use FMV’s 475-transaction database to develop a statistically valid
regression-based model to determine this discount.
It begins with a description and critique of FMV’s model, continues with a
discussion of multiple regression, and concludes with a statement as to whether
multiple regression can be applied to FMV’s database in order to confidently
ascertain the DLOM on private equity.
The bottom line of my inquiry is threefold:
1) FMV Opinions’ model is flawed insofar as explanation of the DLOMs on
the restricted stock transactions in their database;
2) Valuators cannot confidently rely on FMV’s model when determining
DLOMs on restricted stocks, much less on interests in private equity; and
3) Neither FMV’s model nor multivariate regression analysis can be applied
to FMV’s database to confidently determine the DLOM on private equity.
LMSB:FS:ENG:Team 1864;TPK February 2009
Exhibit A page 87
Description of FMV Opinions’ Model
My understanding of FMV’s model is based on two articles by Espen Robak,
CFA (Robak): “Marketability discounts: Using four measures of risk and adjusting
for relative liquidity” and “FMV introduces detailed Restricted Stock Study.”53
These are not included, but are available upon request.
Following are four quotes from these two articles that explain FMV’s thinking and
methodology. The first three are from “Marketability discounts: Using four
measures of risk and adjusting for relative liquidity”:
• . . . there are inherent and significant differences between the liquidity of
restricted stock and the liquidity of . . . private equity . . .
• We determine the “restricted stock equivalent” discount, given the financial
characteristics of the company . . . We determine the incremental discount
for the privately held company . . . based on a review of the most illiquid
(i.e., large-block) restricted stock issues.
• We must adjust for the fact that private equity is more illiquid than the
typical block of restricted stock in a public company.
• In terms of liquidity, large blocks are essentially like private equity.
The last is from “FMV introduces detailed Restricted Stock Study”:
• . . . the discounts for restricted stock with longer-than-average holding
periods are particularly applicable to privately held securities. The only
question is “How do we isolate the transactions with longer holding periods
from the rest of the sample? The answer is: “by looking at transactions in
large blocks until 1997.”
Attachment 1 is a 1-page summary of FMV’s two-step methodology as it was
explained in Robak’s article, “FMV introduces detailed Restricted Stock Study.”
With rounding, the 50% DLOM in cell G59 is the same as the final indicated
DLOM in Robak’s article.
In short, FMV’s model is based on the proposition that the DLOM on private
equity is a function of firm characteristics (rows 9-31 in Attachment 1), and the
incremental difference between discounts on the smaller and larger blocks of
restricted stock (rows 36-50). Certain related cells have been identified by
shading to enable the reader to more easily follow the less obvious calculations.
Critique of FMV Opinions’ Model
Here are three major problems with FMV Opinions’ model, and then eight less
consequential questions and comments.
53 The second article appeared in Shannon Pratt’s Business Valuation Update, November 2001, pp. 1-3. I
don’t know
where the first one appeared.
LMSB:FS:ENG:Team 1864;TPK February 2009
Exhibit A page 88
Major Problems
1) FMV has not shown that block size is a statistically reliable proxy for
quantifying the supposed difference in liquidity between restricted stocks
and private equity.
2) The first step in FMV’s analysis (rows 9-31 in my Attachment 1) is
intended to account only for the financial risk characteristics of the
subject entity, but it also reflects block size. The second step (rows 36-
50) is intended to segregate and quantify the impact of block size, but it
is also influenced by the same financial risk characteristics in the first
step. While meant to be separate steps, the two are interrelated and,
therefore, corrupted by each other.
3) According to Robak’s article, the first step in FMV’s analysis is to
“determine the ‘restricted stock equivalent’ discount given the financial
characteristics of the company . . .” The tables in Robak’s article – and
on lines 11-14 in my Attachment 1 - show four such firm characteristics.
The discounts in the 475-transaction survey given me are inconsistent
with this purpose because they are a function of both firm characteristics
(such as Z-score54 and Earnings Before Interest, Taxes, Depreciation
and Amortization (EBITDA)), and issue characteristics (such as pershare
offering amount, and block size as a percent of total shares.) They
are the discounts on particular offerings, and not indicators of
marketability or lack thereof.
Other Questions and Comments
4) There is not a statistically significant relationship between discounts to
market value and the four risk characteristics in Robak’s article. I
regressed the discounts in the database against market values, marketto-
book (MTB) ratios, net profit margins, and dividend yields, and found a
0.0238 R-square55, coefficients’ signs contrary to economic theory, and
an F-statistic low enough that the regression equation could not be relied
on even at the 10% level of significance56.
5) Selection of how many and which particular firm characteristics to
include in the model, appears subjective, and has not been explained.
Robak has only noted “we use different indicators in different situations.”
6) FMV has not shown why survey quintiles are the best way – or even a
reliable way – of extrapolating survey results to a subject outside the
54 Developed in 1968 by Edward I. Altman, Ph.D., Z-score is a formula for predicting bankruptcy and a
numerical indicator of a firm’s financial health.
55 R-square, or coefficient of determination, is a measure of the degree of association between the response
variable (the discount) and independent variables, as a whole. By comparison, the most often quoted
minimum acceptable R-square is 0.80.
56 The F-test is a comparison of the F-statistic and critical values of F at various significance levels. In this
case, 1.293 vs 1.972 F-critical at the 10% significance level means one couldn’t even be 90% certain that
the regression equation explains observed discounts.
LMSB:FS:ENG:Team 1864;TPK February 2009
Exhibit A page 89
survey. Why weren’t quartiles or deciles or multiple regression used
instead?
7) The restricted stock equivalent on line 15 in Attachment 1 is dependent
on whether the analyst chooses to use quintiles or some other measure.
One would get a different result using, say, quartiles or deciles.
8) The example in Robak’s article - and summarized in my Attachment 1 –
did not include the characteristic thought by them57 to be most correlative
with marketability: stock price volatility.
9) FMV Opinions did not explain why they used medians instead of means.
10) FMV Opinions did not explain why they used “additive” and
“multiplicative” calculations to determine the Private Company Increment
in step 2. Why not just one or the other?
11) FMV Opinions didn’t explain how they selected the matching
transactions in rows 17-23. It’s presumed that these were the only
transactions in the same factor quintiles.
The last eight of these factors question FMV’s model. The first three disprove it.
Regression Analysis
Robak refers to regression analysis in his article, “Marketability discounts: Using
four measures of risk and adjusting for relative liquidity”, but he doesn’t explain
why FMV’s model doesn’t use it, or go on to discuss it in either of his articles.
I reviewed FMV’s 475-transaction spreadsheet58 and found two combinations of
firm and issue characteristics that, arguably, do result in regression models that
explain the observed discounts in these transactions. These are in my
Attachments 2 and 3.
Attachment 2 shows that, collectively, stock price volatility, per-share offering
price, offering as a fraction of total shares outstanding, and offering dollar
amount, could be used to make predictions of discounts for restricted stock
transactions outside the database. The 22.20 F-statistic, significant even at the
1% level (22.20>3.36), indicates that one could be 99% confident that this
combination is related to the discount in a restricted stock transaction.
In regard to interests in private equity – which is more often the subject of IRS
valuations - this model is lacking because -
1) It could not be used to determine lack-of-marketability discounts on
interests in private companies; and
57 BVR April 2006 Telephone Conference: “Discounts for Lack of Marketability”, Page 2.
58 Of the 563 trillion combinations of firm and issue characteristics in the 53-column, 49-
“driver”spreadsheet, I tested approximately thirty of the most promising combinations of characteristics.
LMSB:FS:ENG:Team 1864;TPK February 2009
Exhibit A page 90
2) According to a number of authors (most of whom have Ph.D.’s in
economics)59, it does not control for (isolate and quantify) the nonmarketabilty
determinants of observed discounts. Unlike the traditional
Restricted Stock Approach, these two studies suggested that only a
portion of the overall discount represents a lack-of-marketability.
With the second limitation of Attachment 2 in mind, I tested other combinations of
characteristics to see if there might be a combination which does control for nonmarketability
factors. Of the combinations of characteristics I looked at60, the
model in Attachment 3 comes closest to allowing for determination of the DLOM
on interests in private equity.
This model is adequate in that -
• The F test indicates that, collectively, this combination of characteristics is
adequate for prediction of the DLOM on restricted stock.;
• All four coefficients are consistent with economic theory; and
• By differentiating between stocks with registration rights and stocks without
registration rights, the “Registered vs Restricted” term61 serves the
purpose of quantifying the impact of marketability.
Unfortunately, this model, too, is lacking. While it could have been argued that
the DLOM on restricted stock is 2.42%62 were it not for the registration indicator’s
statistically unacceptable 0.188 P-value, the registration indicator’s high P-value
(greater then 0.1) invalidates use of this model for determining DLOMs.
Summary – Attachments 2 and 3
While there are at least two combinations of firm and issue characteristics in
FMV’s database that adequately explain the discounts in it, neither can be used
to predict the lack-of-marketability discount on an interest in a private company.
The regression models in Attachments 2 and 3 both fail because of the difference
between private equity and the restricted stocks in FMV’s database. The model
in Attachment 2 also fails because it does not include “registration indicator”,
arguably the only variable that measures marketability. The model in Attachment
3 also fails because the P-value of the “registration indicator” renders this model
less than a reliable predictor.
59 Bajaj, et al “Firm Value and Marketability Discounts”, 27 J. Corp. L. 89, 98 (2001) and Hertzel and
Smith’s “Market Discounts and Share holder Gains for Placing Equity Privately”, 48 J. Fin. 459 (1993).
60 This included holding period, offering price, block size, stock price volatility, z-score, retained earnings,
market value, revenues, and whether or not registered.
61 Also called the “registration indicator.”
62 The coefficient of “r.”
LMSB:FS:ENG:Team 1864;TPK February 2009
Exhibit A page 91
Conclusion
FMV Opinions’ 2-part model is based on the proposition that the DLOM on
private equity is the sum of –
• The relationship between the financial risk characteristics of, and the
discounts on restricted stocks; and
• The difference between the discounts on smaller and larger blocks of
stock.
Three of the more important problems with FMV Opinions’ model are:
1) FMV has not shown that block size is a statistically reliable proxy for the
supposed difference in liquidity between restricted stocks and private
equity;
2) The two steps in FMV Opinions’ model are interrelated and, therefore,
corrupted in that they are both influenced by firm characteristics and by
block size; and
3) The discounts in FMV’s database are the discounts on particular offerings,
comprised of marketability and non-marketability factors, and not
necessarily indicators of marketability or the lack thereof.
Applying multivariate regression analysis to FMV’s database, I found two
combinations of four firm and issue characteristics which could be used, with
some degree of statistical certainty, to explain the discounts in FMV’s study.63
The difference between them is that the model in Attachment 3 includes
“registration indicator”, the variable certain authors64 believe is the only one
measuring marketability.
Whether looking at the model in my Attachment 2 or the model in my Attachment
3, or whether one agrees with the proposition that the registration indicator,
alone, measures marketability, the fact is:
1) FMV Opinions’ model cannot be used to reliably predict lack-ofmarketability
discounts on restricted stock transactions, much less interests
in private equity; and
2) No one – FMV using their methodology, or someone using regression
analysis – could use FMV’s database to confidently predict lack-ofmarketability
discounts on either restricted stock transactions or interests in
private equity.
If you have any questions, please contact me at 651-726-1512
Tom Kelley
63 The 0.17 R-square notwithstanding. It is less than problematic because of the large number of data
points.
64 Bajaj et al and Hertzel and Smith.
LMSB:FS:ENG:Team 1864;TPK February 2009
Exhibit A page 92
ATTACHMENT 1
FMV OPINIONS’ 2 STEP MODEL PER ESPEN ROBAK ARTICLE
B C D E F G
Step 1 - Restricted Stock Equivalent Basis
9 Dimension Variable Subject Company Median Discount -
10 Value Quintile Subject Company's Quintile
11 Size Market Value $15,000,000 5 37.3%
12 Risk Market-to-Book Ratio 8.5X 2 29.5%
13 Profitability Net Profit Margin 9% 1 15.5%
14 Distributions Dividend Yield 5% 1 13.1%
15 Average = Indicated Restricted Stock Equivalent, Part A 24%
16
17 Matching Quintiles Private
18 Companies Market Market-to-Book Net Profit Dividend Placement
19 Value Ratio Margin Yield DLOM
20 Company A 5 = 5th quintile 2 = 2nd quintile 1= 1st quintile 1= 1st quintile 29.3%
21 Company B 5 2 1 1 42.6%
22 Company C 5 2 1 1 18.9%
23 etc. 5 2 1 1
24 Reconciled/Indicated Restricted Stock Equivalent - Part B 32%
25
26
27 Restricted Stock Equivalent Discount Indicated by Quintile Medians, Part A 24%
28 Restricted Stock Equivalent Discount Indicated by Matching Companies, Part B 32%
29 Restricted Stock Equivalent Discount, Average of Parts A and B
30 (The discount at which the company would issue restricted stock, if it were a public company 28%
31 with the same financial risk characteristics
32
33
34 Step 2 - Private Company Increment
35
36 Percent of Shares Placed Median Additive Multiplicative Indicated
37 Discount DLOM
38 Small Block Sample 27.2% NA NA NA
39 More than 25% 35.3% 8.1% 1.3 35%
40 More than 30% 47.7% 20.5% 1.8 49%
41 More than 35% 59.0% 31.8% 2.2 60%
42
43 Additional Private Company Adjustment
44 Percent of Shares Placed ( Adjustment for the fact that private equity is more illiquid than the
45 typical block of restricted stock in a public company. Based on
46 presumption that large blocks are essentially like private equity.)
47 Small Block Sample NA
48 More than 25% 8%
49 More than 30% 22%
50 More than 35% 33%
51
52
53 Reconciliation - Selected Discount for Lack of Marketability - Private Equity
54
55 Private Company Increment
56 Step 1 Restricted Stock Median Discount, Step 2 Indicated
57 Equivalent Discount Small Block Indicated Step 2 Difference DLOM
58 Sample DLOM (Step1 + Step 2)
59 28% 27.2% 49% 22% ≈ 49% - 27.2% 50% = 28% +
22%
LMSB:FS:ENG:Team 1864;TPK February 2009
Exhibit A page 93
ATTACHMENT 2
Following are the regression equation and a tabular presentation of model
validity statistics regarding a regression-based model that, arguably, explains the
discounts in FMV’s 475-transaction database.
The relatively low R-square term notwithstanding, this model is arguably
adequate because -
• Compared to the 22.2 F-statistic, the critical values of F indicate that,
collectively, these four variables explain the observed discounts;
• All of the coefficients have signs that are consistent with economic theory;
and
• The P-values are such that there is a 95% probability that there is a strong
relationship with the discount for three of them, and a 90% probability that
there is a strong relationship for the fourth.
d = 0.1505 + 0.0490v – 0.0016s + 0.3948p – 0.000000000636a
where d = discount
v = stock price volatility
s = offering price per share
p = offering as a % of pre-offering shares outstanding
a = offering amount
R- Square Term * 0.1612
F- Statistic 22.20
10% 1.96
F-critical @ ___ 5% 2.39
Significance Level 2.5% 2.21
1% 3.36
Volatility 8.81E-11
P-Values Offering $/Share 0.0357
Block Size - % of Total Outstanding 0.0001
Offering Amount 0.0601
* Note: While 80% is the most widely quoted minimum R-square, the 16% Rsquare
in this case is less important than the F-test and the P-values.
LMSB:FS:ENG:Team 1864;TPK February 2009
Exhibit A page 94
ATTACHMENT 3
A Regression-based Model That 1) Controls for the Non-marketability
Components in Discounts to Market Price in Restricted Stock
Transactions, and 2) Isolates the Lack-of-Marketability Component
Regression Equation
d = 0.1143 + 0.0878v – 0.0020s + 0.4186p – 0.0242r
where d = discount
v = stock price volatility
s = offering price per share
p = offering as a % of pre-offering shares outstanding
r = registration indicator (1 if the stock was issued with
registration rights, and 0 if the stock was NOT
issued with registration rights)
Model Validity Statistics
R- square Term 0.1703
F- Statistic 16.98
10% 1.96
F-critical @ ___ 5% 2.40
Significance Level 2.5% 2.82
1% 3.38
Registered vs Restricted 1.88E-01
P-Values Offering $/Share 3.45E-04
Block Size - % of Total Outstanding 7.50E-05
Volatility 4.38E-07
Comments
• These four variables explain 17% of the variation in the discounts in this
sample. While 80% is the most widely quoted minimum, the 17% R-square
in this case is less important than the F-test and the P-values.
• The 16.98 F-statistic, significant even at the 1% level (16.98>>3.38),
indicates that collectively, one can be 99% confident that these four
characteristics influence discounts.
• The P-values of three of these are far less than 1%.
• The reason why this model is arguably better than the model in Attachment
2, but still inadequate for determining the DLOM on interests in private
equity, is discussed on Page 5.
Exhibit B page 95
Exhibit B—Pre-IPO Studies
Willamette Management Associates65 (WMA) Original: A series of
studies on the prices of private stock transactions relative to those of
public offerings of stock of the same companies. The studies covered the
years 1975 through 1997.
(*) Excludes the highest and lowest deciles of indicated discount
Willamette Management Associates (WMA) Subsequent: Over the last
several years, Willamette Management Associates conducted 12 studies
on the prices of private stock transactions relative to those of subsequent
offerings of stock of the same companies. The 12 studies covered the
years 1975 through 1992. Each private transaction was compared with the
subsequent public offering price. In one of the studies, Willamette checked
trading prices six months after the initial public offering to see whether the
IPO prices were upwardly or downwardly biased compared to a more
seasoned market price. While after six months some prices increased and
some decreased, the average change from the IPO price was
insignificant.
65 Willamette’s studies are unpublished. However, the summary tables are presented in Business Valuation
Discounts and Premiums, Chapter 5, by Shannon Pratt (New York: John Wiley & Sons, Inc., p. 85).
Time
Period
Number of
Companies
Number of
Transactions
Standard Mean
Discount
Trimmed
Mean
Discount(*)
Median
Discount
Standard
Deviation
1975-78 17 31 34.0 43.3 52.5 58.6
1979 9 17 55.6 56.8 62.7 30.2
1980-82 58 113 48.0 51.9 56.5 29.8
1983 85 214 50.1 55.2 60.7 34.7
1984 20 33 43.2 52.9 73.1 63.9
1985 18 25 41.3 47.3 42.6 43.5
1986 47 74 38.5 44.7 47.4 44.2
1987 25 40 36.9 44.9 43.8 49.9
1988 13 19 41.5 42.5 51.8 29.5
1989 9 19 47.3 46.9 50.3 18.6
1990 17 23 30.5 33.0 48.5 42.7
1991 27 34 24.2 28.9 31.8 37.7
1992 36 75 41.9 47.0 51.7 42.6
1993 51 110 46.9 49.9 53.3 33.9
1994 31 48 31.9 38.4 43.0 49.6
1995 42 66 32.2 47.4 58.7 76.4
1996 17 22 31.5 34.5 44.3 45.4
1997 34 44 28.4 30.5 35.2 46.7
Exhibit B page 96
The findings of the Willamette studies are summarized in the following
table.
STUDY
PERIOD
NUMBER OF IPO
PROSPECTUSES
REVIEWED
NUMBER OF
QUALIFYING
TRANSACTION
DISCOUNT
MEAN
DISCOUNT
MEAN
1991-1993 443 54 45% 44%
1990-1992 266 35 42% 40%
1989-1990 157 23 45% 40%
1987-1989 98 27 45% 45%
1985-1986 130 21 43% 45%
1980-1981 97 13 60% 55%
All Studies 1,191 173 N/A N/A
Of the twelve (12) studies conducted by Willamette, each compared pre-
IPO transactions to the IPO price in estimating the marketability discount.
One study went one-step further by observing price changes in the
publicly-traded stocks six-months subsequent to the IPO. Although this
one study found that after six months the change in the subject stock
prices from the IPO prices were insignificant; a six month time period
allows for market and company specific conditions to change. It should be
noted that no conclusions regarding marketability discounts were based
on the post-IPO pricing.
Robert W. Baird & Company Studies (Emory) Original: John D. Emory
of Robert W. Baird & Company conducted pre-IPO studies. The studies
covered various time periods from 1981 through 1997. The basic
methodologies for the eight studies were identical. The population of
companies in each study consisted of initial public offerings during the
respective period in which Baird & Company either participated or
received prospectuses. The prospectuses of these over 2,200 offerings
were analyzed to determine the relationship between (1) the price at which
the stock was initially offered to the public and (2) the price at which the
latest private transaction occurred up to five months prior to the IPO.
In 2002, John Emory updated his studies in an article titled, "Discounts for
Lack of Marketability, Emory Pre-IPO Discount Studies 1980-2000 as
Adjusted October 10, 2002.”66 This source also has “expanded” and “Dot-
Com” data for 1997-2000.
66 Business Valuation Review, Vol. 21 No. 4 (December, 2002).
Exhibit B page 97
A summary of Emory’s Pre-IPO studies is shown below:
STUDY
PERIOD
NUMBER OF IPO
PROSPECTUSES
REVIEWED
NUMBER OF
QUALIFYING
TRANSACTION
DISCOUNT
MEAN
DISCOUNT
MEDIAN
1997-2000 1847 36 48 44
1995 - 1997 732 91 43 42
1994 - 1995 318 46 45 45
1991- 1993 443 54 45 44
1990 - 1992 266 35 42 40
1989 - 1990 157 23 45 40
1987 - 1989 98 27 45 45
1985 - 1986 130 21 43 43
1980 - 1981 97 13 60 66
All 9 studies 4,088 346 46 % 45 %
Exhibit C page 98
Exhibit C–Analytical Approach Revisited
Other Reviewed Studies
An introduction to the analytical approach to estimating the discount for lack of
marketability was provided in the main body of this job aid. This Exhibit provides
summaries of six additional studies that utilize an analytical approach. These
summaries are included herein for the interested reader who may want a further
background in this growing area of analysis into understanding the mechanics of
corporate capital formation.
The authors and dates of the included studies are:
John D. Finnerty – 2003
Michael J. Barclay, Clifford G. Holderness and Dennis P. Sheehan – 2007
Phyllis Keys and Norris Larrymore – 2004
Armando Gomes and Gordon Phillips – 2005
Stanley Jay Feldman – Undated
Espen Robak – 2007
The study summaries are followed by a commentary on the strength and
weaknesses of these types of studies. However, these studies are basically
academic in nature and are not traditionally used by the valuation community to
set discount numbers. Nor have they been vetted in any meaningful way by the
courts.
John D. Finnerty, 2003, The Impact of Transfer Restrictions on Stock
Prices, Fordham University, Presentation at the Meeting of the
American Finance Association
Finnerty was interested in the impact of transfer restrictions on stock prices and
set about to investigate the factors responsible for the discount when
unregistered shares of common stock are privately placed. He analyzed a
sample of 101 private placements of transfer-restricted stock all of which involved
unregistered shares. Finnerty postulated that the following factors influenced the
size of required placement discounts:
-Volatility of the publicly traded stock of the entity
-Length of the restriction period
-Risk-free interest rate
-Dividend yield
-Information requirements
-Ownership concentration effects
Exhibit C page 99
His premise was that, beyond marketability concerns, a discount compensates
an investor for the due diligence and monitoring costs required by the
investment, the effects due to ownership structure change and the implied
certification effect of having a private investor choose the firm for investment.
Finnerty asked the question that, if the proper discount is as large as is implied
by the so-called benchmark studies, why don’t arbitrageurs enter the market, use
hedging opportunities and capture a guaranteed profit? He believed that the
existence of the equity derivative markets has acted to significantly reduce the
discounts that might have existed in earlier years.
Finnerty’s analysis covered private placements during the period January 1, 1991
through February 3, 1997 and involved 101 placements of which 77 were either
traded on NASDAQ or over-the-counter. His measurement dates were 10 trading
days prior to the announcement date and the day prior. The average discounts
calculated were 20.13% for the day prior measurement and 18.41% for the 10
day prior measurement. The respective median discounts were 15.50% and
16.74%.
A regression analysis was performed which indicated that the most significant
variables influencing total discounts are the volatility of the stock and the length
of the restriction period. Finnerty concludes based on this analysis that a range of
discounts from 25% to 35% is proper for moderate volatility stocks (30% to
120%) but that this range is too high for low volatility stocks (<30%). For low
volatility stocks (20% to 30%), he found the following ranges to apply:
-Dividend paying: 11.5% to 16.0%
-Non-Dividend paying: 15.8% to 20.1%
These ranges apply assuming a 2 year holding period; if a 1 year holding period
is assumed the discounts are reduced by about 50%.
Finnerty found no significant relationship between the discount amount and the
regression variable representing ownership concentration. This is consistent with
the findings of Hertzel & Smith but contrary to the findings of Wruck.
Michael J. Barclay, Clifford G. Holderness and Dennis P. Sheehan,
2007, Private Placements and Managerial Entrenchment, Journal of
Corporate Finance 13, 461-484,
These researchers studied the question of private placements and managerial
entrenchment. Their premise was that many private placements are made to
facilitate management entrenchments rather than for benefiting the firm from
monitoring and certification services provided by the involved investors. The
thesis of using private placements at discounts to entice investment by allies to
management was earlier advanced by Dann & DeAngelo and by Wruck.
Exhibit C page 100
Whereas the monitoring and certification effects hypotheses are favorable to
existing shareholders, the management entrenchment hypothesis is not.
Barclay et al believe that although private placements result in short-term positive
effects on firm value, the long-term effect is negative and leads to firm value
declines. Most private placement investors are seen as passive with only about
12% estimated to be active participants in firm governance and management
persuasion efforts.
These researchers analyzed data from the years 1979 through 1997 and
considered only those placements involving more than 5% of outstanding
common stock. Some 594 placements were identified with the investors
classified as 12% active, 5% managerial and 83% passive. The average discount
found was 18.7% from traded value with a median discount of 17.4%. The
various kinds of investors had discount ranges as follows:
-Active: 1.8% average and 7.5% median
-Managerial: 24.2% average and 18.2% median
-Passive: 20.8% average and 19.5% median
Barclay et al found that registration status is not a substantial factor in the
discount amounts.
Private placements reduce the chances of a downstream acquisition or merger
by about 50%. There is no distinction in this statistic based on the kinds of
investors involved.
Trades of significant sized blocks of stock in the public market were also studied
with 204 data points considered. Investors acquiring interests in such trades
were more likely to take an active role in the firm and these investments were
more likely to lead to a downstream acquisition. These types of trades were also
more likely to be opposed by management since it has no control over who the
buyers might be.
Per Barclay et al, the cost of private placements is about two times the cost of a
seasoned (non-IPO) equity offering when discounts and out-of-pocket costs are
considered. This is not the most cost effective way to raise capital and gives
support to the thesis that management is proceeding in this way for its own
protection and betterment. Passive investors are considered the norm for private
placements per Barclay since such represent over 80% of the investors in the
transactions study. Thus, they believe that the discounts required by passive
investors are most representative of reality.
Exhibit C page 100
Phyllis Keys and Norris Larrymore, 2004, Integration of Private and
Public Offerings, Presentation at the Conference of the Eastern
Finance Association
Keys and Larrymore studied the question of integrating public and private share
offerings to receive the best overall effect with regard to the raising of capital.
They note that private placements relate to a less restrictive offering process but
also involve asymmetry of information between investors and management.
Public offerings require more complete information to be available to investors
but also require more disclosure and a less flexible issuing procedure. SEC Rule
155 instituted in March 2001 grants issuers more flexibility to offer securities
privately following a public offering and vice versa. In general, the market
responds positively to private offerings and negatively to seasoned public
offerings.
Keys and Larrymore studies a total of about 1,020 equity offerings for 2000 and
2002 (before Rule 155 and after Rule155). Some 710 of these were public
offerings and about 310 were private offerings. They found that there is a
significant difference in the two day market reaction to private as compared to
public offerings both before and after Rule 155. There is, however, no significant
long-term (120 day) difference.
This conclusion relates to the work of Wruck, Hertzel & Smith and Bajaj et al as
these researchers made their measurements very close to the announcement
date and did not include a longer-term perspective. Analyzing the Keys result
would indicate that much of the discount found for private offerings may be due
to a short term bounce effect that gradually disappears.
Armando Gomes and Gordon Phillips, 2005, Why Do Public Firms
Issue Private and Public Equity, Convertibles and Debt? Presentation
at the Research Seminar in Law, Economics and Organization
Gomes and Phillips (G & P) were interested in the question of why public firms
issue various kinds of equity, convertibles and debt using both public and private
issuance modes. They believe that asymmetric information effects and moral
hazard problems play a large role in the public versus private market choice and
the security type choice. In the private market, they find that firms with high
measures of asymmetric information are more likely to issue equity where the
public market would prefer debt. Firms with high risk, low profitability and good
investment opportunities for acquired capital are more likely to issue equity
and/or convertibles publicly than to do so privately. Private securities give
investors more incentives to produce information and also to monitor the firm.
Exhibit C page 102
Gomes and Phillips studied 13,000+ security issuances with more than half in the
private market where public companies were the issuers. They used the
accuracy of analyst earnings predictions and the dispersion of analyst errors as a
proxy for asymmetric information effects for private equity and convertibles and
for public debt. They hypothesized that public debt and all kinds of private
offerings serve a “disciplining” function for management due to increased
monitoring.
Per Gomes and Phillips, the level of abnormal returns (returns above average)
should be positively correlated with the degree of information asymmetry for
private offerings; the more information sensitive the security’s value, the stronger
the correlation should be. As risk rises in a firm’s operations it will tend to move
from public debt to private debt and from private debt to convertibles to raise
capital. Private equity is issued when risk levels rise to the point where it is the
last alternative. Agency problems between management and shareholders
increase the need for the use of debt or private equity placements to prevent
management distortions and abuses.
The G & P database consisted of 13,282 issuances during the period January
1995 through December 2003 and involved 4,137 different firms. No secondary
offerings or short-term offerings were included. It was determined that the market
reacts most favorably to private equity issuances and least favorably to public
equity issuances. Once again, this finding indicates that the discount found with
regard to private placements as opposed to public traded prices may result from
a short-term bounce effect that then disappears with time. Marketability may not
be a significant concern in the overall picture.
Stanley Jay Feldman, Revisiting the Liquidity Discount Controversy:
Establishing a Plausible Range, Bentley College and Axiom
Valuation Solutions
Feldman focused his attention on the liquidity discount controversy and worked
to establish a plausible range for such discounts. He found the following in
summary:
-Minority privately held C Corp shares have a liquidity discount in the area
of 14%
-Minority S Corp shares are less liquid than C Corp shares
-Control shares of a C Corp have discounts in the area of 20%
-Discounts >30% for any share blocks are not supported by research
Feldman believes that liquidity may be as important as risk in determining stock
returns. Investors demand discounts for lack of liquidity as compensation for their
higher costs of trading. Liquidity can be measured by the increase in share price
if an OTC firm gets listed on the New York Stock Exchange (NYSE) but such a
Exhibit C page 103
rise in stock price can also be due to other factors such as information signaling
(the willingness to disclose) and certification by seasoned investor buy-ins.
Feldman examines abnormal returns to control for the effects of overall market
movements. He reviews the data from a 1966-1970 study by Sanger and
McConnell on movements from OTC trading to the NYSE since in this timeframe
the NASDAQ did not yet exist. In this study an implied liquidity discount of 20%
was found based on an overall set of cumulative abnormal returns of 25.68%. Of
these abnormal returns analysis indicates that about 14% are due to lack of
liquidity and about 11% are due to information signaling. The liquidity effect is
estimated based on a 1982-1989 study by Edelman and Baker on stocks moving
from the NASDAQ to the NYSE.
The 14% level of abnormal returns converts to a straight liquidity discount of
about 12%. This is seen as being a floor for liquidity discount since there are
shares that need to be analyzed that are not traded on any exchange. For these
shares, Feldman believes that a 3% additional abnormal return amount is
appropriate implying an added discount of 2.5% and resulting in an overall
liquidity discount of 14.5% for these kinds of shares.
Feldman notes that this is close to the discount derived by H & S for lack of
marketability (13.5%) but is much lower than the discounts implied by the pre-
IPO and Restricted Stock Studies of >35%. He believes that there are a myriad
of problems with these latter studies that make them very unreliable including
high ranges among results and wide coefficients of dispersion that make the use
of measures of central tendencies (averages and medians) generally
unreasonable.
Feldman cites a study by Koeplin et al that estimates marketability discounts for
control interests in the range of 18% to 30% with a reasonable estimate of the
average at 20%. He believes that an additional increment of <5% should be
added on for S Corp shares. Finally, he concludes that for pure liquidity
estimation purposes, control blocks are less liquid than minority blocks with the
difference being about 5.5% (20% for control versus 14.5% for minority).
However, he admits that minority blocks might give rise to higher overall
discounts due to the effects of information signaling and certification.
Espen Robak, 2007, Discounts for Illiquid Shares and Warrants: The
LiquiStat Database of Transactions on the Restricted Securities
Trading Network, Pluris Valuation Advisors White Paper, January
2007
Robak postulates that it is not possible to precisely sort out the effect of illiquidity
from all of the factors contributing to total discounts in private placement
transactions, be they of registered shares or unregistered shares. Instead, he
analyzes a specially constructed database referred to as LiquiStat which
incorporates data on investor to investor trades of restricted securities rather than
Exhibit C page 104
trades involving the issuer as the seller. By using investor to investor trades he
believes that he has eliminated the effects of information asymmetry, of firm
financial condition and of assessment and monitoring costs since both buyer and
seller should be on equal ground as far as knowledge of the firm and its financial
conditions are concerned. He argues that this is the pricing concept that should
be involved in fair market value where both buyer and seller are hypothetical
persons and are considered to be equally knowledgeable of the conditions
surrounding the trade transaction.
The LiquiStat database was compiled from April 2005 to December 2006 and
contains information on 61 investor to investor trades. All investors are
independent with no firm affiliates represented in any of the trades. Analysis of
the data provides the following results with regard to discounts for the traded
shares as compared to the present public market price of those shares.
Average Discount 32.8%
1st Quartile Discount 19.1%
Median Discount 34.6%
3rd Quartile Discount 44.0%
The average fraction traded in the population was 0.47%, the average stock
volatility was 89% and the average remaining restriction period was 138 days.
Robak notes that these results are higher than those found by Hertzel & Smith,
Bajaj et al and Finnerty and hypothesizes that this is because the shares studied
were more volatile and had longer required holding periods than is often the case
for restricted stock private placements where registration is often a condition of
the placement. Per the LiquiStat data, there is a clear and increasing relationship
between the discount and the remaining required holding period, although the
increase tends to flatten out over time. For a 25 day holding period the discount
range is 16% to 24% while for a one year holding period the discount range is
30% to 57%.
Based on his data, instead of supporting the premise that others have advanced
that private placement discounts are too high to represent the effects of illiquidity
alone, Robak suggests that they instead may be too low. Having eliminated the
other causes of discounting through the construction of his database, he feels
that the statistics derived should represent a measure of the illiquidity discount in
isolation.
Exhibit C page 105
General Strengths and Weakness of Exhibit C Studies
One of the strengths that is attributable to the analytical approach to DLOM and
DLOL is that the studies underlying the approach were not made for tax
purposes or by practitioners in the generally recognized tax valuation community.
They were instead primarily academic exercises aimed at better understanding
operating company capitalization choices and effects. Thus, there was generally
no attempt made to either support or criticize the traditional benchmark study
approach or to promote a new approach that could be marketed for application in
tax service engagements.
A second strength is the attempt to pursue the question in an analytical manner
and to parse the discounts found into contributing components. Although overall
discounts were found and summary statistics identified, the researchers were
more interested in the causes of the discounts than in the pure size of the
discounts. Various theses were advanced as to why discounts exist with DLOL
and DLOM only being one set of contributing factors. Also mentioned were
assessment costs, monitoring costs, management entrenchment motives,
investor expectations and certification compensation. The attempt to break out
actual discount portions for these components was not necessarily successful
but the conclusion that all of a given measured discount may not measure simply
the lack of marketability is important.
Another potential strength is that the several researchers took several different
approaches to sample selection and measurement such that the effects of these
parts of the discount estimation process can be considered as part of the overall
discount evaluation process. A valuator can make his or her own determination
based on the facts and circumstances under study as to how data selection
should be handled and how discount measurement can best be made.
The studies all suffer from the same kinds of weaknesses that make the actual
numerical results achieved difficult to rely upon. These weaknesses result from
problems in sample selection, problems in sample point classification, problems
in discount measurement point selection, problems in variable selection,
problems in variable estimation and the use of certain proxy variables with a
binary quantification attribute. The various models also result in less than
impressive data fits as measured by such things as R2. In many cases the
number of available data points is small and large time frames are required to
yield an adequate number of data points for analysis. Many of the studies do not
consider factors that would seem from a common sense point of view to have
significant impact on discounts such as required holding periods and the volatility
of the stock as publicly traded. Finally, although the fact that the studies were not
made for tax purposes was cited as a possible strength, it is also a potential
weakness in that it is dangerous to apply the results of a study made for one
purpose to entirely different purposes.
Exhibit D page 106
Exhibit D—DLOM Files on Shared Folder
(as of September 25, 2009)
Folder Subfolder Filename DLOM
Job Aid
reference
Benchmark
Apprchs
Restricted Stock
Studies
FMV Opinions 2001 study.pdf D.1.a
FMV Opinions-Hall & Polacek 1994articles.rtf
Gelman.TIF
Johnson-QuantitativeSupportforDLOMBVReviewDec1999.
pdf
Maher.TIF
Management Planning Inc.TIF
Moroney.TIF
SEC Investor Study (1).pdf
Silber Impact on Liquidity.pdf
Standard Research Consultants.TIF
Trout.TIF
Pre-IPO Studies Philip Saunders Associates D.1.b
EmoryPreIPODiscount.pdf D.1.b
Cost of Flotation ritter cost of going public modified version.pdf D.1.d
Ritter cost of going public original.pdf D.1.d
N/A Robak-LiquidityFramework-BVUOct2004.pdf
(Restricted Stock Equivalent Analysis)
D.1.c
Option-Based
Apprchs
LEAPS BVWire #76-3 Seaman comments LEAPS
Jan2009.htm
D.2.a
BVWire #76-4 LEAPS-Fuhrman responds to
Seaman.htm
Full_Report_2008_Study.pdf
LEAPS_Full_Report_2007_Study.pdf
LEAPS-BVUpdate-article-May09.tif
LEAPS-Trout-BVReview-Sep2003.tif
N/A chaffee dlom article.pdf D.2.c
N/A Longstaff-How Much Can Marketability Affect
Security Values.pdf
D.2.b
Analytical
Apprchs
N/A Abbott-BVUpdate-
NewAbbottAnalysisAidsValuatorsinAssessingLiquidit
yDiscounts.pdf
D.3.d
N/A Abbott-
EmpiricalMeasuresofMarketability&LiquidityDiscount
s.ppt
D.3.d
Bajaj Bajaj Denis Ferris Sarin.pdf D.3.c
Subfolder: Response to Bajaj
(Contains 4 articles)
D.3.c
Exhibit D page 107
(continued)
Folder Subfolder Filename DLOM
Job Aid
reference
N/A Hertzel & Smith.doc D.3.b
N/A Wruck Article.pdf D.3.a
Other Analytical
Apprchs
Subfolder: Liquistat
(Contains 1 article)
Exhibit C
Finnerty-
TheImpactofTransfer_Restrictions_on_StockPrices.pdf
Koeplin-Sarin-Shapiro article-Prvt Co Disct.pdf
PrivatePlacementsManagerialEntrenchment(Barclay).p
df
Public and Private Placements(Keys).pdf
Public v. Private Issuances(Gomes).pdf
Revisitingtheliquiditydiscount(Feldman).pdf
Other
Apprchs
NERA NERA Tabak’s working paper.pdf D.4.c
NICE 1 of 4 NICE.pdf
2 of 4 NICE.pdf
3 of 4 NICE.pdf
4 of 4 NICE.pdf
D.4.b
NICE_Frazier_2008ASA-AICPA_presentation.pdf D.4.b
Other DLOM
Articles
N/A CurrentStateofMarketabilityDiscounts.pdf n/a
Hall-NewTacticstoProveDLOM-Value Examiner Jan-
Feb2007.pdf
n/a
LiquidityandLevelsofValueANewTheoreticalFramewor
k.pdf
n/a
reilly rotkowski DLOM update on current studies.pdf n/a
Rotkowski Current Contrversies Regarding DLOM.pdf n/a
BVR DLOM
Guide-2008
Contains 15 files n/a
Files provided
at 09-18-08
DLOM
Summit in
San Diego
Numerous files of seminar presentations n/a
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