Apportioning active and passive appreciation in divorce. Passive appreciation is claimed, active appreciation is conceded. We present the essentials of this analysis as this can a substantial difference in sharing of the marital estate in a divorce.

1. Active and Passive
Appreciation Attribution
January 2, 2019
Ashok B. Abbott Ph.D.

2. Ashok B. Abbott is an Associate Professor of Finance at West
Virginia University in Morgantown, West Virginia. Professor
Abbott received his MBA in Finance at Virginia Polytechnic
Institute and State University (VPI&SU) in 1984, followed by
a Ph.D. in finance also at VPI&SU, in 1987. His Ph.D.
dissertation title was "The valuation effects of tax
legislation in corporate sell-offs".
He has published extensively in scholarly research journals and
made presentations at national and international
conferences. He serves on the editorial boards of The
Business Valuation Review and The Value examiner.
His focus area of research and consulting in valuation is the
level of price adjustments (discounts/premiums) appropriate
for liquidity, marketability, and apportioning active and
passive appreciation for the interests being appraised.
Professor Abbott consults for valuation divisions of well-known
firms, (Standard & Poor's, Duff & Phelps, Willamette
Management Associates, and Houlihan Valuation Advisors,
among others). He has served as an expert witness in the
business valuation arena for 15 years. You can see his full CV
at www.be.wvu.edu/faculty_staff/cv/ashok_abbott_cv.pdf.

3. Marriage as a ‘Shared’
enterprise
 "Marriage is among other things 'a
shared enterprise or joint undertaking
in the nature of a partnership to which
both spouses contribute—directly and
indirectly, financially and non-
financially—the fruits of which are
distributable at divorce.'"
 —J. Gregory, The Law of Equitable
Distribution (1989) § 1.03 pp. 1-6.

4. Defining the Issue
 A closely held business is often the
single most valuable asset in the
marital estate that needs to be valued
in a divorce.
 One core issue in distribution of the
business’ value is separation of the
total growth in value of the business
during the marriage between growth
attributed to efforts of spouses (active)
and to external factors and market
forces (passive).

5. Five Steps to Attribution of Active
and Passive Components of
Appreciation 1. State whether the business is separate or marital
property.
 2. Assess the value of the non-marital property before it
became subject to the active and passive appreciation
analysis (date of Marriage/Gifting value).
 3. Assess the value of the property at the time of
divorce action (date of separation, filing for divorce, or
another specific valuation date mutually agreed to or
decided by the court).
 4. Calculate the change in value during the period of
marriage as the difference in the valuation at these two
dates.
 5. Determine the proportion of the increase in value of
the
non-marital property as active or passive.

6. Marital Property
Subject to Equitable Distribution
(1) All property and earnings acquired by either
spouse during a marriage, including every valuable right
and interest, corporeal or incorporeal, tangible or
intangible, real or personal, regardless of the form of
ownership.
(2) The amount of any increase in value in the
separate property of either of the parties to a marriage,
which increase results from
(A) an expenditure of funds which are marital
property, including an expenditure of such funds which
reduces indebtedness against separate property,
extinguishes liens, or otherwise increases the net value
of separate property, or

7. Separate Property,
not Subject to Equitable Distribution
(1) Property acquired by a person before marriage; or
(2) Property acquired by a person during marriage,
but excluded from treatment as marital property
by a valid agreement of the parties entered into
before or during the marriage; or
(3) Property acquired by a party during marriage by
gift, bequest, devise, descent or distribution;
and
(4) Any increase in the value of separate property as
defined in subdivision (1), (2), or (3), which is due to inflation
or to a change in market value resulting from conditions
outside the control of the parties.

8. Two Questions That need to be
Answered
◦ Which factors outside the control of the
owner manager(s)of the business, if any,
significantly impacted the (passive)
changes in value of the business?
◦ What proportion of the change in business
value can be explained by these external
factors outside the control of the
manager(s)?

9. Correlation and Causality
 Subject of discussion since Aristotle
 cum hoc ergo propter hoc,
(for "with this, therefore because of
this“)
 Post hoc ergo propter hoc
(for "after this, therefore because of
this").

10. Causality and Correlation
 Related but distinct concepts
 Correlation : a relation existing between
phenomena or things or between economic
or statistical variables which tend to vary, be
associated, or occur together in a way not
expected on the basis of chance alone.
 Causation: Connection between two events
or states such that one produces or brings
about the other; where one is the cause and
the other its effect. Also called causality.

11. Correlation does not imply
Causation
 Correlation does not imply causation
is a phrase often used in science and
statistics to emphasize that a correlation
between two variables does not
necessarily imply that one causes the
other.
 Aristotle discerned two modes of
causation: proper (prior) causation, and
 accidental (chance) causation.

12. Correlation
 the state or relation of
being correlated;
 specifically : a relation existing
between phenomena or things or
between mathematical or statistical
variables which tend to vary, be
associated, or occur together in a way
not expected on the basis of chance
alone

13. Correlation :
Necessary but not Sufficient
 Empirically observed correlation is a
necessary but not sufficient condition
for causality.
 Causation without correlation is
unlikely.
 Causal pathway needs to be
established theoretically and tested
empirically.

14. Real vs. Spurious Correlation
 But first correlations must be
confirmed as real, and then every
possible causative relationship must
be systematically explored. In the end
correlation can be used as powerful
evidence for a cause-and-effect
relationship between a treatment and
benefit, a risk factor and a disease, or
a social or economic factor and
various outcomes.

15. Market Forces : Examples
◦ Market Forces are typically defined in statute and
case law by giving examples of what constitutes
a market force. Some of the economic Indicators
seen in such analyses are
◦ Consumer Confidence
◦ Demographics
◦ GDP Growth
◦ Unemployment
◦ Housing Starts
◦ Interest Rates
◦ Commodity Prices
◦ Consumer Spending
◦ Regulatory Changes

16. Causation
 Connection between two events or
states such that one produces or
brings about the other; where one is
the cause and the other its effect. Also
called causality.

17. Mere Correlation or Real
Causation
 Correlation is not causation is a Hail
Mary pass often lobbed at an expert.
 However, there is no causation without
correlation.
 Empirically observed correlation is a
necessary but not sufficient condition for
causality.
 Causation without correlation is unlikely.
 Causal pathway needs to be established
theoretically and tested empirically.

18. Hill’s Criteria for causation
 Strength (effect size): A small association does not mean that there is not a causal
effect, though the larger the association, the more likely that it is causal.
 Consistency (reproducibility): Consistent findings observed by different persons in
different places with different samples strengthens the likelihood of an effect.
 Temporality: The effect has to occur after the cause (and if there is an expected
delay between the cause and expected effect, then the effect must occur after that
delay).[
 Coherence: The association should be compatible with existing theory and
knowledge.
 Plausibility: A plausible mechanism between cause and effect is needed.
(but Hill noted that knowledge of the mechanism is limited by current knowledge)
 Analogy: The effect of similar factors may be considered.
Austin Bradford Hill, “The Environment and Disease: Association or Causation?,”
Proceedings of the Royal Society of Medicine, 58 (1965): 295-300.

19. Market Forces: Establishing
Proximate Causation
◦ Econometric methodologies have been developed to identify market
forces that reasonably cause changes in value of assets similar to the
subject asset, and to quantify the expected change in the subject
asset attributable to the movements in market forces.
◦ Robert F. Engle, and Clive W.J. Granger shared the 2003 Nobel prize
in Economics for their work in establishing and testing Causal
relationships.
◦ “Messrs. Engle and Granger have a statistical tool named after them,
the Engle-Granger Test, which helped economists tackle a
longstanding problem in the field: how to identify when movements in
economic variables are connected and when they aren't. ” WSJ
October 9, 2003

20. Identifying Casual Market
Forces
 Throwing spaghetti at the wall/Kitchen
Sink Approach
 Take a handful of economic indicators,
run a regression model.
 Get coefficients, apply to subject interest.
 Voila, Regression Alpha is Active
component, rest is Passive.
 We are done.
 NOT REALLY, we have not even started.

21. Causation: Variable
Identification Start by identifying all potential variables of interest.
 Industry reports, IBES analysis, SEC filings are a good
starting point where analysts and management identify
economic factors that influence firm performance.
 Also look for similar factors, for example interest rates
can be treasury, bank prime, mortgage, credit card
rates. One or more of which may be influential in
impacting performance of the subject company.
 Test each variable individually for its impact on the
performance measure, (Revenue, EBIT, NI, Cash
Flow), as well as on each of the other causal variables
being considered to guard against false causation.
This is the design of the Engle-Granger Test.

22. Guarding against False
Causation
 Correlation, by definition, is bi-directional. If x and
y are positively correlated higher values of y are
observed with higher values of x. Conversely if x
and y are negatively correlated higher values of y
are observed with lower values of x. Observation
of correlation between x and y may suggest three
potential causal pathways.
 1. Changes in x may be causing changes in y
 2. Changes in y may be causing changes in x
 3. Changes in a third factor z may be causing
changes in both x and y
 Elimination of 2 and 3 above is the goal of
Engle-Granger Test that we employ in our
analysis.

23. Market Forces : Measuring
Impact
◦ Once the unique causal variables that are independent of
the performance measure and other potential variables
have been identified, using the Engle-Granger Test , we
need to assess their individual and collective impact as the
percentage change in the performance measure for each
one percent change in the causal variable. (partial
elasticity) .
◦ Identified independent variables are ranked in order of their
individual impact from highest to lowest using a rigorous
ranking for noise to information ratio test known as
Akaike's Information Criteria (AIC) test to compare
impact of the possible causal variables and pick the
variable with the lowest AIC score as the starting point.
◦ As explanatory variables are added to the model, we re-
evaluate the model for individual variable significance and
aggregate information content.

24. RULE 702.
TESTIMONY BY EXPERT WITNESSES
 A witness who is qualified as an expert by
knowledge, skill, experience, training, or education
may testify in the form of an opinion or otherwise if:
 (a) The expert’s scientific, technical, or other
specialized knowledge will help the trier of fact to
understand the evidence or to determine a fact in
issue;
 (b) The testimony is based on sufficient facts or
data;
 (c) The testimony is the product of reliable
principles and methods; and
 (d) The expert has reliably applied the principles
and methods to the facts of the case.

25. Regression Method
 Developed by Karl Friedrich Gauss,
and Adrien-Marie Legendre in 1801-
1810 has become the workhorse of
empirical analysis.
 Regression is the tool of choice to
quantify the influence that
independent variable(s) (Xi)exert on
the dependent variable.(Y)

26. Regression Analysis
 Regression analysis is often employed
to identify relationships between the
independent variables and the
dependent variable, and to explore the
nature of these relationships.
 The earliest form of regression was
the method of least squares, commonly
called OLS , which was published
by Legendre in 1805, and by Gauss in
1809.
 OLS has been the workhorse of
empirical testing for 200 years.

27. Building a Regression Model
 Start by identifying potential variables of
interest.
 Test for existence of a statistically
significant causal relationship between
the variables.
 Determine the correlation between the
dependent variable ( e.g. Revenues) and
the independent causal variables.
 Determine the correlation between
independent variables.
 Start by adding the independent causal
variable with highest correlation with the
dependent variable.

28. Building a Regression Model,
contd.
 At each step, select the independent
causal variable with highest
correlation with the dependent
variable and lowest correlation with
the independent causal variables in
the model.
 Test your regression model at each
step.
 Adding variables usually leads to an
increase in R square, watch the
adjusted R square as it will start
declining as additional variables are

29. Building a Regression Model,
contd.
 Watch carefully as you add additional
independent variables.
 Individual independent variables
should all remain significant ( P( t ) <
0.10 for a 90% confidence)
 Regression equation should remain
significant.
 Sign on each independent variable
should remain as indicated by theory.

30. Interpreting Regression Beta
 Beta(s) measure the impact of each
independent factor on the value of the
dependent variable.
 Product of Beta and the average value
of the independent variable is the
contribution of that to the average
value of the dependent variable.

31. Interpreting Regression R
Square
 The coefficient of determination R-
square is the proportion of variability
in a data set that is accounted for by a
statistical model.
 R-square almost always increases
when a new term is added to a model,
therefore it is useful to consider
adjusted R –Square.

32. Interpreting Regression adj. R
Square
 Adjusted R-square is a modification of
R-square that adjusts for the number of
terms in a model. R-square almost
always increases when a new term is
added to a model, but adjusted R-square
increases only if the new term improves
the model more than would be expected
by chance.
 AIC , Akike’s Information criterion is a
well established test for comparing
alternative regression models. When
comparing alternative models the best
model is the one with the lowest AIC
score.

33. Regression applied to Active
Passive Determination
 Model Building Exercise
 Identify variables of interest
 Industry reports
 Economic Data
 Establish potential causality pathway
 Objective Analysis : No Cherry Picking

34. Concluding Thoughts
 Claimed active passive attributions
are being critically examined.
 It is important to provide strong
analytical support that is specific to
the valued interest at the time of
valuation.
Support Support
Support

35. Questions?
Please do not hesitate to contact us for
any Questions/clarifications.
Ashok Bhardwaj Abbott Ph.D.
Email ashok.abbott@bizvalinc.com
Set up a phone call at
https://calendly.com/ashok-abbott
Or just call
Phone 304 692 1385