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Advances in Social Sciences Research Journal – Vol. 9, No. 1
Publication Date: January 25, 2022
DOI:10.14738/assrj.91.11663. Jarrett, J. E. (2022). The Influence of the Pandemic of Decision Making. Advances in Social Sciences Research Journal, 9(1). 520-530.
Services for Science and Education – United Kingdom
The Influence of the Pandemic of Decision Making
Jeffrey E. Jarrett, PhD
Professor Emeritus
University of Rhode Island (COB)
ABSTRACT
The abandonment option under various capital budgeting models are discussed in
this study to illustrate the notion that present value of cash flows is often
improperly estimated in financial models utilizing decision analytics in estimation
theory as it applies in financial accounting. In this study, intellectual property rights
and other intangible assets which are often not considered in the accounting
estimation processes utilized in financial accounting. An investor/analyst often
misestimates cash flow resulting in less-than-optimum capital budgeting decisions.
This is especially a problem when actions to abandon for salvage and other similar
decisions improve when the present value of intangibles and property rights are
included in the decision process. This last statement is the goal of this study as well
as to present well-founded processes to improve abandonment and similar
decisions in capital budgeting decisions. The estimation problem in financial
accounting is included in the analysis to accomplish this goal.
Keywords: abandonment, estimation theory, present value of cash flow, distribution of
earnings, normal fiducial deviate, opportunity loss
INTRODUCTION
Financial researchers such as Deschow (1994; Deschow and Strand, 2004) indicated that
employing accrual-based accounting methods creates the capability of accounting-based
earnings projections to control and continuously improve the measures of firm performance
reflected in analysts’ earnings forecasts. The argument was that cash flow accuracy is expected
to suffer from matching, realization, and other timing problems concerning the timing of the
recognition of costs and revenues. Accuracy of financial earnings predictions was studied by
Brandon and Jarrett (1974), Jarrett (1983, 1992), Jarrett and Khumawala (1987), and Lambert,
Matolcsy, and Wyatt (2015). They compared methods of predicting earnings seeking to learn
how forecast models can be compared and possibly improved to produce more accurate results
as to cash flow. Questions posed included sources of accuracy, but accrual accounting alone was
not considered the most important source of inaccurate results. However, no one established a
theoretical link between sources of inaccuracy and the matching principle and the accuracy of
financial analysts’ forecasts although many studied the problem (Jarrett, 1989, 1990; Clement,
1999; Gu and Wu, 2003; Ramnath, Rock, and Shane, 2008; Grosyberg, Healy, Nohria, and
Serafeim, 2011). Accounting reports containing these forecasts of cash flow and rates of return
are, in addition, subject to fluctuations in the interpretation of timing principles utilized by
accountants. However, Gu and Wang (2005) brought up the possibility of another source of
inaccuracy in the forecast of rates of return, cash flow, and earnings. Beneish, Lee, and Nichols
(2013) created a model that uses financial ratios calculated with accounting data of a specific
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company to check if it is likely that the reported earnings for a firm were manipulated—the
goal being to estimate earnings better in financial reports. Last, Lev and Gu (2016) in their study
produced evidence from large-sample empirical analysis that financial documents continuously
deteriorate in relevance to investors’ decisions. Further, they detail why accounting reporting
is losing relevance in today’s decisions related to capital budgeting and the abandonment
option.
Note that decisions about abandonment and salvage utilize normal capital budgeting methods
to determine whether there is a relation among the various capital budgeting options, financial
leverage, and financial estimation by analysts. Illustrating capital budgeting with the
abandonment option; allows us to implement and illustrate how corporations utilizing capital.
In turn, one learns and understands that budgeting process are dynamic and flexible, involving
the information flow throughout an organization that determines the investment and
abandonment decisions at individual stages. With this in mind, one may examine how an
abandonment option influences the optimal timing of information. In particular, one may
compare timely information, where the manager acquires perfect precontract project
information. We examine how the future revenues from intangible and intellectual assets may
affect the level of financial leverage of a firm when not all is known about the economic value
of assets which have not tangible definitions.
In the absence of the real option, the following trade-off arises: If information is timely, the
investment decision can be based on perfect information. Alternatively, if information about
intangible assets is not considered in the abandonment option, the timing and decision
concerning the abandon option may very well be estimated incorrectly. The incorrect
information is the product of the misreporting of factual events associated with intangible
assets, and the error associated with incorrect analysts’ forecasts turn to the estimation
problem in financial accounting and in turn apply it to the relation of analysts’ forecasts and the
bias in estimating earnings and cash flow present in evaluating capital decisions.
CAPITAL BUDGETING METHODOLOGY
Berger, Ofek, and Swary (1996) established the link among analysts’ forecasts, cash flow, the
expected capital asset pricing model (CAPM) return, and the present value of cash flow, which
includes forecasts of earning rather than the distributable cash flow. In addition, Wong (2009)
examined the relation between the abandonment option’s potential effect on a firm’s decision
analysis and the eventual analytics employed to determine the optimal decision and operating
leverage. Furthermore, McDonald (2003) analyzed abandonment options, divestment options,
expansion options, and growth options previously examined in a survey by Triantis and
Borison (2001). These and many more studies revealed that they use real options to the general
problems associated with capital budgeting.
Analysts’ earnings forecasts enable analysts to estimate the present value of cash flow (PVCF).
According to Berger, Ofek, and Swary (1996), the advantage is that analysts’ forecasts of
earnings do not incorporate the value of the abandonment option. If forecasts of distributable
cash flows, cash flows from non-ongoing concern events would be included in the forecasts.
Thus, earnings may not be the same as cash flows. Hence, we adjust because capital
expenditures are not equivalent to depreciation and the growth in working capital is not
subtracted from earnings. No longer is it required to adjust for capital structure changes in the
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environment that such changes cannot be foreseen. Borrowing again from Berger, Ofek, and
Swary (1996), their equation constructs the present value of capital formation (PVCF) that
evolves from the analyst’s discounted forecasts. Included in the equation is the sum of the
present value of analysts’ predicted going-concern cash flows discounted by analyst forecast of
year t after-interest earnings and expected CAPM (capital asset pricing model return),
consensus forecast of five-year earnings growth, the terminal growth rate of earnings, the
number of years for which earnings are forecast, and a year index. The CAPM adjustment
includes the reduction to the present value of analysts’ earnings. The second adjustment to
PVCF is the working capital adjustment, which is a reduction to the present value of analysts’
earnings forecasts to adjust for growth in working capital. Finally, the expected CAPM return is
defined as
r = rf + βe * [rm – rf], (1)
where rf is the risk-free rate, βe is the firm’s beta or systematic risk (from the CRSP beta file),
and (rm – rf) is the risk premium of the stock market minus the risk-free rate.
In implementing Equation (1), we assume that the relevant investment horizon is short term.
Therefore, a useful solution is to use the one-month Treasury-bm rate as a proxy for the risk- free rate and a risk premium (the arithmetic mean from a long period of time from between the
return on the S&P 500 and the return on Treasury bills).
The problem with the above approach is the variable the analysts’ forecasts of earnings. In part,
this is a solution to the problems noted by Pappas (1977) in response to the work by Brief and
Owen (1968, 1969, 1970, 1977; Barnea and Sadan, 1974; Jarrett, 1983, 1992), who used their
work in developing models to adjust analysts’ earnings forecasts in evaluating the
abandonment option. Studies concerning analysts’ forecasts are well known and include a huge
number. In general, as stated by many in the field of financial accounting, earnings forecasts are
dependent on the principles of financial accounting that produce the data for modeling trends
and seasonality (or modeling components). The accuracy of analysts’ forecasts has a long
history and includes works by Clement (1999), Gu and Wu (2003), Ramnath, Rock, and Shane
(2008), Groysberg, Healy, Nohria, and Serafeim (2011), and Makridakis, Spiliotis, and
Assimakopoulos (2017). The last paper suggested that machine learning models may have
better results than self-prepared models for forecasting. The aforementioned studies focused
on a relation between analysts’ forecasts and the magnitude and value of intangible assets.
Intangible assets were not considered in the forecasting method discussed by the researchers
in their many and detailed studies. The value of intangible assets produces a great source of
error if they are not considered in the forecasting methods utilized by analysts in the
production of cash flow, rates of return and earning per share (EPS) forecasts. When
adjustments for intangible assets are included in the analysts’ forecasts, Gu and Wang (2005, p.
673) stated that “the rise of intangible assets in size and contribution to corporate growth over
the last two decades poses an interesting dilemma for analysts. Most intangible assets are not
recognized in financial statement, and current accounting rules do not require firms to report
separate measures for intangibles.” Intangibles include trademarks, brand names, patents, and
similar properties that have value but are generally not listed in the financial reports of firms.
Many of these items are technology based and are very important in financial decisions such as
in mergers and acquisitions (M&A). They are an intricate part of the growth of firms and
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therefore are shown to be related in the statistical sense to the overall estimates made by
accounting and analysts.
In another study concerning analysts’ forecasts, Matolcsy and Wyatt (2006) found that an
association between EPS forecast, growth rates forecast error, and measures of technological
conditions in the firm’s industry. They found that as the forecast horizon increases, the
technological conditions and current EPS are statistically associated with analysts’ forecasts.
Long horizon creates the conditions for within one to conclude that interactions between
technological conditions and current EPS are associated with analysts’ EPS and growth
forecasts. This conclusion aligns itself with Jung, Shane, and Yang (2012), who suggested that
analysts’ growth forecasts effect efforts to evaluate analysts’ forecasts may produce
optimistically biased long-term forecasts. Because intangible assets that are often technology
based take up more of the balance sheet of many firms, it is likely that analysts’ forecasts may
produce less accurate predictions of earnings, cash flow, and rate of return. The conclusions of
Deschow (1992) become less important. Balance sheets usually have little or no involvement
with the value of intangibles, although there are some practices by accounting that are still used.
Thus, in the remaining portions of this analysis, we propose a method by which one can
estimate earnings such that the value of intangible assets is valued and earnings estimate are
not biased by serious errors of omission such that the capital budgeting model expressed
earlier in equations by Berger, Ofek, and Swary (1996, p. 264) are not unduly biased.
INTELLECTUAL PROPERTY AND TRADITIONAL ACCOUNTING
As noted by Brief and Owen (1969, 1970, 1977), Jarrett (1971, 1974, 1983), Roberts and
Roberts (1970), and Barnea and Sadan (1974), the timing of recognition of revenue for
intellectual property rights (IPR) in financial statements of ten are not featured in merger-and- acquisition activity. The Financial Accounting Standards Board (FASB) provides for such
activities; however, they are often ignored due to their evasiveness or are not fully
informational in their normally structured rules. Recognizing future performance is a goal of
matching and timing but are unrelated to recognizing cash flow and similar items in the
historical performance of a firm. Nonprofit entities often do not use accrual rules at all because
the goal of these are related to achieving high rates of return. Often IPR for nonprofits would
differ from the same item for profit-maximizing entities because the goal of seeking high rates
of return does not enter the strategic planning process for nonprofits (World Trade
Organization, 2016). The purpose here is to consider intellectual property (IP) as intangible
assets, as a product of intellect that law protects from unauthorized use by those not
responsible for the IPR. Hence, IPR are characterized as the protection of distinguished signs
such as trademarks for goods and services, patents, and other similar items that are under
protection from unauthorized use. This includes art, music, creations by authors including the
authorship of computer software, and similar items such as discoveries, inventions, phrases,
symbols, and design. Obviously, a writer and conductor of music such as Leonard Bernstein and
Daniel Barenboim would have created IP that differ greatly from physicists such as Lise
Meitner, Niels Bohr, or Albert Einstein.
Presently, accounting suggests two methods to determine the value of IPR to produce better
estimates of from accounting analysts’ forecasts. The convention of the “lower of cost or
market” is based on the rule of conservatism in valuing assets to anticipate future losses instead
of future gains. The policy tends to understate rather than overstate the value of net assets and
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could therefore lead to an understatement of income, cash flow, earnings, and rates of return.
The purpose of this study and its conclusive result is to neither understate nor overstate cash
flow so as to produce a rate of return on cash flow that is commensurate with the goal of
producing accurate prediction of cash flow and its rate of return for financial and decision- making purposes. Stated differently, the purpose is not to violate accounting policy but to
ensure the M&A that cash flow is estimated properly. Traditionally, when accounting writes
policy about intangible assets as a residual, by “residual” they mean a buyer is ready to value a
firm in excess of the value of the tangible assets. This value is often referred as “goodwill”
(White, Sandhi, and Fried, 1994), which is an imperfect method. This notion of goodwill is
estimated as a residual value. If the valuation of intangible property is imperfect because it
considers part of the solution of a bargaining process, in this case, the buyer and seller may
have different market power, which greatly affects the residual of the bargaining process and
produces an imperfect or biased estimate of the value of the intangible assets. One may examine
the case of the sale of Superman by struggling comic book artists to a much larger corporate
power who could market the character to comic books, television, and the film industry. The
near-destitute conditions of the original artists who created the intangible product could never
cope with the business and marketing (power) of those who purchased the name Superman.
Thus, goodwill becomes a vague valuation system that justifies the bringing of data analysis and
science into the valuation process.
Accountants often suggest during the M&A process is to simply list the patents, trademarks,
brands, and similar items of IP in the financial reporting of the firm. Following this initiative
and suggestion of the accounting principles board provide little aid concerning the economic
value of IPR and products for a firm during the M&A events. In the final step of the problem, the
evaluation may biases of the reading of the financial reports. Accountants forecast the overall
rate of return for a firm but do not ignore the convention of “conservatism.” Accounting practice
values the IPR for a firm each year for each and every IPR under consideration. The principle of
goodwill is not to be used during M&A activity to account for the value of IPR. IP may induce
greater asset values but it also affects the rate of return on cash flow because the denominator
of the rate of return will change. (To understand the gravity of ignoring or improperly valuing
IPR, see Jarrett, 2016, 2017a, 2017b.) This result, debated previously (Brief and Owen, 1969;
Brief, 1977; Pappas, 1977), indicated that including earnings risks may not fully reflect all risks
in estimating earnings, but at least reflects that part of risk from the variation in earnings.
Furthermore, Helliar, Lonie, Power, and Sinclair (2001) summarized attitudes of managers
toward risk in the following way. The abandonment option may be extremely appointment
when considering the survival of a firm or nonprofit entity. Survival is often the goal of the
abandonment option, indicating that risks that are taken in special situations such as
catastrophes when the survival of whole areas of an industry may be under threat (Shleifer and
Vishny, 1992; Liu and Liu, 2011) may be different from those taken in more usual
environments. An entity in decline may avoid innovative options and concentrate on immediate
short-term options rather than riskier longer-term projects with more difficult goals to be
accomplished. In addition, the choice may rapidly increase the rate up the process decline and
result in managers becoming more risk averse and not employing greater use of intangible
assets and IP.
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ESTIMATION MONETARY VALUES FOR INTANGIBLE AND SIMILAR ASSETS
One illustrates the size of the bias in estimating earnings when the monetary equivalent of
values of intangible assets is not considered by analysts in estimating future earnings. Note that
failure to estimate future earnings affects PVCF, resulting in errors in assessing the
abandonment option. Intangible assets including patents, trademarks, copyrights, and similar
items are usually overlooked and/or not estimated properly in many financial statements.
These statements are considered fundamental information in determining PVCF in
abandonment decisions, M&A, and similar financial decisions analysis and analytics.
To illustrate the case of monetarizing property rights and other intangibles often referred to by
the acronym IPR, let us consider the specific problem of a firm abandoning or selling IPR
through a direct acquisition and the effect on debt as part of its holdings. Obviously, the ratio of
common equity to total capital stock will be changed during the financial operation. In turn, the
effects of financial leverage on total financial risk will also be part of the problem. The rate of
return to common shareholders is related to the measure of financial risk utilized in any
decision of this type. We assume that the firm is motivated to finance the acquisition by leverage
instead of issuing new common share nor a strict loan from a financial institution or similar
institution is the result of an economic optimization policy. Define T as the sum of debt and
common stock. To illustrate simply, preferred share and other financial instruments are valued
at zero to avoid complications that may hinder the explanation. S is the monetary value of
outstand common, and D is the amount of debt. X is the amount of earning in a future time
period. X is a random variable, and E(X) is the mean of the random variable. V(X) is the variance,
and S(X) is the square root or standard deviation. The cost of the debt per dollar is I; the interest
rate. The mean earning per dollar of S is
E(Y) = E(X)/S = E(X)/ (T–D) (1)
Note that Y is also a random variable with mean E(Y). Mean (or expected) earning is defined as
follows:
E (X’) = E(X) – iD for D >0; (2)
Hence, E (X’) = E(X), for D = 0 (2’)
The variance of total earnings is
V (X’) = V(X) for D ≥0 (iD)
and is a constant) (2’’)
The financial decision-optimum to fund the purchase is an example of decision analytics where
the decisions are to substitute debt for common stock or not to substitute debt. Using data
analytical language, for this decision problem the states of nature are defined by
E(X) >ID or E(X) ≤ID (3)
We define the opportunity loss function as an integral approximation the firm’s view toward
choosing a nonoptimal decision. No loss occurs when earnings are greater than the cost of debt
because management will benefit from the strategy of leverage financing.
As an example, consider cash flow to be greater than the cost of debt management, and in turn,
the loss function would change, reflecting the goal of optimum decision analytics. The basic
structure of the acquisition strategy would not change except for the substitution of cash flow
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for earnings. To calculate the opportunity loss function associated with this strategy, we
estimate some probability density function (PDF) that approximates the PDF for future
earnings. Before we consider all PDFs, let the firm focus on the normal distribution or T- distribution having a very large number of degrees of freedom, which approximates the
standard normal distribution. The opportunity loss at breakeven (X b) becomes
X b = E (X’) – Z ((S (X’)) (4)
Z refers to the normal fiducial deviate. S (X’) is the standard deviation. By rearrangement, we
find E(X) = E (X’) – iD. The next step is to determine the size and distribution of the loss function
for the distribution of future earnings, which is all in line with objectives of the timing of the
realization revenues discussed before (Jarrett, 1971, 1992and 2018). In Table 1, we preview
one of three methods to estimate the monetary value of IPR. The E(X) is $4,200, and the S(X)
increases by given amounts ($100). Column 3 contains the cost of debt of $3,200. The Z (the
normal deviate) calculation is accomplished column 4 with column 5 containing the cumulative
normal probability. In turn, the IPR monetary value is simply the normal probability multiplied
by E(X) and is contained in column 6. The $IPR is thus calculated for a variety of circumstances.
Table 1: Estimation of Monetary Value of IPR Changes in standard deviation of earnings (or size
of variation in earnings)
E(X) S(X) Cost of debt Z-Score Cum. Prob. $IPR
4,200 400 3,200 2.50000 0.993790 4,174
4,200 500 3,200 2.00000 0.977250 4,104
4,200 600 3,200 1.66667 0.952210 3,999
4,200 700 3,200 1.42857 0.923436 3,878
4,200 800 3,200 1.25000 0.894350 3,756
4,200 900 3,200 1.11111 0.866740 3,640
4200 1000 3200 1.00000 0.841345 3534
A second example of estimating the monetary value of IPR (Table 2), E(X), column 1 is constant
from row to row; column 2, S(X), remains the same ($600) from row to row; and column 3, the
cost of debt changes from row to row due to the change in the interest rate and other costs
associated with debt. In column 4, the standard normal deviate, Z, decreases in value from row
to row, and in column 5, the cumulative probability from the normal curve decreases from row
to row. The dollar value of the IPR will continually decrease from the top row to the bottom row
in Table 2.
Table 2: The Dollar Value of IPR Changes in Interest Rates and the Cost of Debt
E(X) S(X) Debt cost Z Score Normal
probability
$IPR
2,100 600 500 2.66667 0.996170 2,091.96
2,100 600 1,200 1.50000 0.933193 1,959.70
2,100 600 1,400 1.16667 0.878327 1,844.49
2,100 600 1,600 0.83333 0.797672 1,675.11
2,100 600 1,800 0.50000 0.691462 1,452.07
2,100 600 2,000 0.16667 0.566184 1,188.99
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One last example, Table 3: we alter the example by comparing the monetary value of IPR when
the cost of debt and debt: equity ratio in columns 1 and 2 of Table 3. In turn, both columns 3
and 4 (cost of debt and net cash, respectively) change from row to row. The Z-statistics and
normal probabilities change, and the monetary value of IPR changes from row to row with the
highest in row 1 and descending thereafter.
Table 3: Comparison of the Debt to Equity Ratio) (Equity = $200,000)
Debt D:E ratio Debt cost Cash
inflow
S(X) Z Score Normal
prob.
$IPR
50,000 0.25 2,000 2,300 230 1.304 0.903942 180,788
60,000 0.30 2,400 1,900 190 –2.632 0.004249 850
70,000 0.35 2,800 1,500 150 –8.667 0.000000 0
80,000 0.40 3,200 1,100 110 –19.091 0.000000 0
90,000 0.45 3,600 700 70 –41.429 0.000000 0
100,000 0.50 4,000 300 30 123.333 0.000000 0
Note: $IPR is the Dollar value of IPR
These examples show that estimation theory in financial accounting is a fundamental
ingredient in correcting financial reporting data. Now, financial analysts now have a complete
set of data to work with when making earnings forecasts and other decisions. Our finding does
not dispute that of others.
ADDITIONAL EVIDENCE CONCERNING ESTIMATION THEORY AND METHODS
Estimation and timing of the recognition and matching of costs and revenues is dependent on
the underlying analysis of data that corroborates its use. Although one cannot examine all data
but only samples of data previously analyzed by Berger, Ofek, and Swary (1996). In their study,
they obtained data from the International Brokers Estimate System (IBES) that have forecasts
of earnings and growth in earnings. In Table 4, we provide their descriptive information on the
sample information obtained. The information obtained describes the distribution of PVCF for
three separate forecasting methods. In analyzing these data, one calculates the skewness
coefficient and presents the results in the expanded table. The analytics indicates the symmetry
in the distributions of the PVCF data.
A previous study by Berger, Ofek, and I. Swary(1996 )the distribution of the sample data for
rates of return is probably close to a symmetrical one and, in turn, likely to be distributed
similar to a normal distribution process. If not exactly normally distributed, there are many
ways one can estimate the distribution of the PVCF data, bringing more credibility to the
process. One last point concerning the distribution of PVCF of Berger concerns the kurtosis in
the sample data in Berger’s study. Westtfall (2014) notes, “it’s only an unambiguous
interpretation in terms of the tail extremity; i.e., either existing outliers (for the sample
kurtosis) or propensity to produce outliers (for the kurtosis of a probability distribution).’ The
logic is simple: Kurtosis is the average (or expected value) of the standardized data raised to
the fourth power. Any standardized values that are less than 1 (i.e., data within one standard
deviation of the mean, where the “peak” would be) contribute virtually nothing to kurtosis,
because raising a number that is less than one to the fourth power makes it closer to zero. The
only data values (observed or observable) that contribute to kurtosis in any meaningful way
are those outside the region of the peak, stated differently, the outliers. Therefore, kurtosis
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measures outliers only; it measures nothing about the “peak.” Without the original data, one
cannot measure the exact kurtoses for the data. However, one can observe that the mean of data
and minimum and maximum values do not differ by huge amounts. Hence, the exact likelihood
of long tails in the distribution of data about the mean does not exist. The likelihood is therefore,
if such an observation indicates that at all, the measures of kurtoses would be relatively small
and approach a normal distribution when examining the population from which the sample
was chosen. Hence, the normal approximation when the sample size is large as in the cases
observed indicates the validity of the normal approximation. This also the case if one has
evidence that the data are distributed according to another probability distribution function
and that one could be used in evaluating the value of IPR. Other probability distibutions may be
used when sample data indication we would be more wise to use them. Hence we utilize normal
probabilities when they are most as in this analysis.
SUMMARY AND CONCLUSIONS
Firms entering into decisions in times of financial distress are often confronted with failure and
survival. These decisions concern the abandonment of assets. The problems associated with
valuing intangible assets and IPR are similar to those involved in decisions about M&A. The
firm’s environment may be different in each case, but the problems associated with predicting
cash flow and earnings by analysts still prevail. This study suggests ways of estimating earnings
and PVCF when considering the effects of IPR and other intangible assets in the process. The
proposal studied meets the requirements of the estimation theory in financial account, which
is consistent with accounting conservatism and the goals of financial accounting. Additional
methods exist for estimating the value of intangibles, which include using the distribution of
financial earnings when the normal distribution does not apply. This will be the focus of new
and additional research.
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