U.S. patent application number 10/453396 was filed with the patent office on 2004-12-09 for systems, methods and computer program products for modeling uncertain future benefits.
This patent application is currently assigned to The Boeing Company. Invention is credited to Datar, Vinay T., Forgie, Christopher A., Mathews, Scott H..
Application Number | 20040249642 10/453396 |
Document ID | / |
Family ID | 33489535 |
Filed Date | 2004-12-09 |
United States Patent
Application |
20040249642 |
Kind Code |
A1 |
Mathews, Scott H. ; et
al. |
December 9, 2004 |
Systems, methods and computer program products for modeling
uncertain future benefits
Abstract
Systems, methods and computer program products are provided for
modeling future benefits. According to the method, modeling future
benefits begins by defining a growth rate for the good for each
time segment of a period of time, where the period of time includes
a plurality of time segments. An uncertainty for the good is then
determined for each time segment. Next, a benefit distribution is
determined at an end of each time segment based upon the growth
rate and uncertainty for the respective time segment. Finally, a
benefit value is selected at the end of each time segment by
randomly selecting each benefit value based upon a respective
benefit distribution to thereby model future benefits over the
period of time. The method therefore allows the growth rate and/or
the uncertainty to very between time segments. The method can also
account for contingencies at the end of previous time segments.
Inventors: |
Mathews, Scott H.; (Seattle,
WA) ; Datar, Vinay T.; (Mercer Island, WA) ;
Forgie, Christopher A.; (Salt Lake City, UT) |
Correspondence
Address: |
ALSTON & BIRD LLP
BANK OF AMERICA PLAZA
101 SOUTH TRYON STREET, SUITE 4000
CHARLOTTE
NC
28280-4000
US
|
Assignee: |
The Boeing Company
Chicago
IL
|
Family ID: |
33489535 |
Appl. No.: |
10/453396 |
Filed: |
June 3, 2003 |
Current U.S.
Class: |
705/35 |
Current CPC
Class: |
G06Q 40/00 20130101;
G06Q 10/087 20130101; G06Q 10/06 20130101 |
Class at
Publication: |
705/001 |
International
Class: |
G06F 017/60 |
Claims
That which is claimed:
1. A method of modeling future benefits comprising: determining a
benefit distribution at an end of each time segment of a period of
time based upon a growth rate and an uncertainty for the respective
time segment; and selecting a benefit value at the end of each time
segment by randomly selecting each benefit value based upon a
respective benefit distribution to thereby model future benefits
over the period of time.
2. A method according to claim 1 further comprising defining a
growth rate for each time segment before determining a benefit
distribution.
3. A method according to claim 2, wherein defining a growth rate
for each time segment comprises defining a growth rate for each
time segment such that the growth rate for at least one time
segment differs from the growth rate of at least one other time
segment.
4. A method according to claim 2, wherein defining a growth rate
for each time segment comprises defining a growth rate for each
time segment independent of an uncertainty for the respective time
segment, and wherein the method further comprises determining an
uncertainty for each time segment independent of a growth rate for
the respective time segment.
5. A method according to claim 1 further comprising determining an
uncertainty for each time segment such that the uncertainty for at
least one time segment differs from the uncertainty of at least one
other time segment.
6. A method according to claim 1, wherein selecting a benefit value
comprises repeatedly selecting a different benefit value at the end
of each time segment to thereby repeatedly model future
benefits.
7. A method according to claim 1 further comprising modeling bounds
of uncertainty of future benefits, wherein modeling the bounds of
uncertainty comprises determining a mean value and standard
deviation associated with the benefit at the end of each time
segment, and modeling an upper and lower bound of uncertainty for
each time segment based upon the mean value and standard deviation
to thereby model the bounds of uncertainty.
8. A method according to claim 1, wherein modeling the future
benefits comprises modeling the future benefits with a processing
element operating a spreadsheet software program, and wherein the
method further comprises presenting a display of the future benefit
model on a display coupled to the processing element.
9. A method according to claim 7, wherein presenting the display
comprises presenting a display of the future benefit model
comprising a plot of the selected future benefit values and
associated time segments.
10. A method according to claim 1, wherein determining a benefit
distribution comprises determining a benefit distribution at the
end of at least one time segment further based upon execution of a
contingent activity.
11. A method according to claim 10, wherein determining a benefit
distribution comprises determining a benefit distribution at the
end of at least one time segment further based upon execution of a
contingent activity at the end of at least one previous time
segment.
12. A method of modeling bounds of uncertainty of future benefits
comprising: determining a mean value and standard deviation
associated with the benefit for each time segment, wherein the mean
value is determined based upon a growth rate associated with the
benefit for the respective time segment, and wherein the standard
deviation is determined based upon an uncertainty for the good for
the respective time segment; and modeling an upper and lower bound
of uncertainty based upon the mean value and standard deviation for
each time segment to thereby model the bounds of uncertainty.
13. A method according to claim 12 further comprising defining a
growth rate for the good for each time segment before determining
the mean value.
14. A method according to claim 13, wherein defining a growth rate
for each time segment comprises defining a growth rate for each
time segment such that the growth rate for at least one time
segment differs from the growth rate of at least one other time
segment.
15. A method according to claim 13, wherein defining a growth for
each time segment comprises defining a growth rate for each time
segment independent of an uncertainty for the respective time
segment, and wherein the method further comprises determining an
uncertainty for each time segment independent of a growth rate for
the respective time segment.
16. A method according to claim 12 further comprising determining
an uncertainty for each time segment such that the uncertainty for
at least one time segment differs from the uncertainty of at least
one other time segment.
17. A method according to claim 12, wherein modeling an upper and
lower bound of uncertainty comprises modeling an upper and lower
bound of uncertainty further based upon an inverse of a standard
normal cumulative distribution, and wherein the standard normal
cumulative distribution is defined by a probability.
18. A method according to claim 17, wherein modeling an upper and
lower bound further comprises selecting a lower probability
associated with the lower bound and an upper probability associated
with the upper bound, wherein selecting the lower probability
comprises selecting a lower probability higher than zero, and
wherein selecting a higher probability comprises selecting a higher
probability lower than one.
19. A method according to claim 12 further comprising normalizing
the mean value for each time segment based upon the standard
deviation and normalizing the standard deviation for each time
segment based upon the mean value, wherein modeling an upper and
lower bound of uncertainty comprises modeling an upper and lower
bound of uncertainty for each time segment based upon the
normalized mean value and normalized standard deviation.
20. A method according to claim 12, wherein modeling bounds of
uncertainty of future benefits comprises modeling bounds of
uncertainty of future benefits with a processing element operating
a spreadsheet software program, and wherein the method further
comprises presenting a display of the upper and lower bounds of
uncertainty on a display coupled to the processing element.
21. A method according to claim 20, wherein presenting the display
comprises presenting a display of the upper and lower bounds of
uncertainty comprising a plot of the upper and lower bounds of
uncertainty and associated time segments.
22. A method according to claim 12, wherein determining a mean
value comprises determining a mean value for at least one time
segment of the period of time further based upon execution of a
contingent activity.
23. A method according to claim 22, wherein determining a mean
value comprises determining a mean value for at least one time
segment further based upon execution of a contingent activity at an
end of at least one previous time segment.
24. A system for modeling future benefits comprising: a processing
element capable of determining a benefit distribution at an end of
each time segment of a period of time based upon a growth rate and
an uncertainty for the respective time segment, and wherein the
processing element is further capable of selecting a benefit value
at the end of each time segment by randomly selecting each benefit
value based upon a respective benefit distribution to thereby model
future benefits over the period of time.
25. A system according to claim 24, wherein the processing element
is also capable of defining a growth rate for each time
segment.
26. A system according to claim 25, wherein the processing element
is capable of defining a growth rate for each time segment such
that the growth rate for at least one time segment differs from the
growth rate of at least one other time segment.
27. A system according to claim 25, wherein the processing element
is capable of defining a growth rate for each time segment
independent of an uncertainty for the respective time segment, and
wherein the processing element is capable of determining an
uncertainty for each time segment independent of a growth rate for
the respective time segment.
28. A system according to claim 24, wherein the processing element
is also capable of determining an uncertainty for each time segment
such that the uncertainty for at least one time segment differs
from the uncertainty of at least one other time segment.
29. A system according to claim 24, wherein the processing element
is capable of repeatedly selecting a different benefit value at the
end of each time segment to thereby repeatedly model future
benefits.
30. A system according to claim 24, wherein the processing element
is also capable of modeling bounds of uncertainty of future
benefits by determining a mean value and standard deviation
associated with the benefit at the end of each time segment, and
thereafter modeling an upper and lower bound of uncertainty for
each time segment based upon the mean value and standard
deviation.
31. A system according to claim 24, wherein the processing element
is capable of modeling the future benefits by operating at least
one function within a spreadsheet software program, and wherein the
system further comprises: a display coupled to the processing
element, wherein the display is capable of presenting the future
benefit model.
32. A system according to claim 31, wherein the display is capable
of presenting the future benefit model as a plot of the selected
future benefit values and associated time segments.
33. A system according to claim 24, wherein the processing element
is capable of determining a benefit distribution at the end of at
least one time segment further based upon execution of a contingent
activity.
34. A system according to claim 33, wherein the processing element
is capable of determining a benefit distribution comprises at the
end of at least one time segment further based upon execution of a
contingent activity at the end of at least one previous time
segment.
35. A system for modeling bounds of uncertainty of future benefits
comprising: a processing element capable of determining a mean
value and standard deviation associated with the benefit for each
time segment, wherein the processing element determines the mean
value based upon a growth rate associated with the benefit for the
respective time segment, wherein the processing element determines
the standard deviation based upon an uncertainty for the good for
the respective time segment, and wherein the processing element is
capable of modeling an upper and lower bound of uncertainty based
upon the mean value and standard deviation for each time segment to
thereby model the bounds of uncertainty.
36. A system according to claim 35, wherein the processing element
is also capable of defining a growth rate for the good for each
time segment before determining the mean value.
37. A system according to claim 36, wherein the processing element
is capable of defining a growth for each time segment independent
of an uncertainty for the respective time segment, and wherein the
processing element is capable of determining an uncertainty for
each time segment independent of a growth rate for the respective
time segment.
38. A system according to claim 36, wherein the processing element
is capable of defining a growth rate for each time segment such
that the growth rate for at least one time segment differs from the
growth rate of at least one other time segment.
39. A system according to claim 35, wherein the processing element
is capable of determining an uncertainty for each time segment such
that the uncertainty for at least one time segment differs from the
uncertainty of at least one other time segment.
40. A system according to claim 35, wherein the processing element
is capable of modeling the upper and lower bound of uncertainty
further based upon an inverse of a standard normal cumulative
distribution, and wherein the standard normal cumulative
distribution is defined by a probability.
41. A system according to claim 40, wherein the processing element
is capable of modeling an upper and lower bound by further
selecting a lower probability associated with the lower bound and
an upper probability associated with the upper bound, wherein the
processing element is capable of selecting a lower probability
higher than zero, and wherein the processing element is capable of
selecting a higher probability lower than one.
42. A system according to claim 35, wherein the processing element
is also capable of normalizing the mean value for each time segment
based upon the standard deviation and normalizing the standard
deviation for each time segment based upon the mean value, wherein
the processing element is capable of modeling an upper and lower
bound of uncertainty by modeling an upper and lower bound of
uncertainty for each time segment based upon the normalized mean
value and normalized standard deviation.
43. A system according to claim 35, wherein the processing element
is capable of operating at least one function within a spreadsheet
software program to thereby model the bounds of uncertainty of
future benefits, and wherein the system further comprises: a
display capable of presenting the upper and lower bounds of
uncertainty.
44. A system according to claim 43, wherein the display is capable
of the upper and lower bounds of uncertainty as a plot of the upper
and lower bounds of uncertainty and associated time segments.
45. A system according to claim 35, wherein the processing element
is capable of determining a mean value for at least one time
segment further based upon execution of a contingent activity.
46. A system according to claim 45, wherein the processing element
is capable of determining a mean value for at least one time
segment further based upon execution of a contingent activity at an
end of at least one previous time segment.
47. A computer program product for modeling future benefits, the
computer program product comprising a computer-readable storage
medium having computer-readable program code portions stored
therein, the computer-readable program portions comprising: a first
executable portion for determining a benefit distribution at an end
of each time segment of a period of time based upon a growth rate
and an uncertainty for the respective time segment; and a second
executable portion for selecting a benefit value at the end of each
time segment by randomly selecting each benefit value based upon a
respective benefit distribution to thereby model future benefits
over the period of time.
48. A computer program product according to claim 47 further
comprising a third executable portion for defining a growth rate
for the good for each time segment.
49. A computer program product according to claim 48, wherein the
third executable portion defines a growth rate for each time
segment such that the growth rate for at least one time segment
differs from the growth rate of at least one other time
segment.
50. A computer program product according to claim 48, wherein the
third executable portion defines a growth rate for each time
segment independent of an uncertainty for the respective time
segment, and wherein the computer program product further comprises
a fourth executable portion for determining an uncertainty for each
time segment independent of a growth rate for the respective time
segment.
51. A computer program product according to claim 47 further
comprising a third executable portion for determining an
uncertainty for each time segment such that the uncertainty for at
least one time segment differs from the uncertainty of at least one
other time segment.
52. A computer program product according to claim 47, wherein the
second executable portion repeatedly selects a different benefit
value at the end of each time segment to thereby repeatedly model
future benefits.
53. A computer program product according to claim 47 further
comprising a third executable portion for modeling bounds of
uncertainty of future benefits, wherein the third executable
portion models the bounds of uncertainty by determining a mean
value and standard deviation associated with the benefit at the end
of each time segment, and modeling an upper and lower bound of
uncertainty for each time segment based upon the mean value and
standard deviation to thereby model the bounds of uncertainty.
54. A computer program product according to claim 47 further
comprising a third executable portion for generating a display of
the future benefit model.
55. A computer program product according to claim 54, wherein the
third executable portion generates a display of the future benefit
model comprising a plot of the selected future benefit values and
associated time segments.
56. A computer program product according to claim 47, wherein the
first executable portion determines a benefit distribution at the
end of at least one time segment further based upon execution of a
contingent activity.
57. A computer program product according to claim 56, wherein the
first executable portion determines a benefit distribution at the
end of at least one time segment further based upon execution of a
contingent activity at the end of at least one previous time
segment.
58. A computer program product for modeling bounds of uncertainty
of future benefits, the computer program product comprising a
computer-readable storage medium having computer-readable program
code portions stored therein, the computer-readable program
portions comprising: a first executable portion for determining a
mean value and standard deviation associated with the benefit for
each time segment, wherein the first executable portion determines
the mean value based upon a growth rate associated with the benefit
for the respective time segment, and wherein the first executable
portion determines the standard deviation based upon an uncertainty
for the good for the respective time segment; and a second
executable portion for modeling an upper and lower bound of
uncertainty based upon the mean value and standard deviation for
each time segment to thereby model the bounds of uncertainty.
59. A computer program product according to claim 58 further
comprising a third executable portion for defining a growth rate
for the good for each time segment.
60. A computer program product according to claim 59, wherein the
third executable portion defines a growth rate for each time
segment such that the growth rate for at least one time segment
differs from the growth rate of at least one other time
segment.
61. A computer program product according to claim 59, wherein the
third executable portion defines a growth rate for each time
segment independent of an uncertainty for the respective time
segment, and wherein the computer program product further comprises
a fourth executable portion for determining an uncertainty for each
time segment independent of a growth rate for the respective time
segment.
62. A computer program product according to claim 58 further
comprising a third executable portion for determining an
uncertainty for each time segment such that the uncertainty for at
least one time segment differs from the uncertainty of at least one
other time segment.
63. A computer program product according to claim 58, wherein the
fourth executable portion models an upper and lower bound of
uncertainty further based upon an inverse of a standard normal
cumulative distribution, and wherein the standard normal cumulative
distribution is defined by a probability.
64. A computer program product according to claim 63, wherein the
fourth executable portion models an upper and lower bound further
by selecting a lower probability associated with the lower bound
and an upper probability associated with the upper bound, wherein
the fourth executable portion selects the lower probability higher
than zero, and wherein the fourth executable selects a higher
probability lower than one.
65. A computer program product according to claim 58 further
comprising a fifth executable portion for normalizing the mean
value for each time segment based upon the standard deviation and
normalizing the standard deviation for each time segment based upon
the mean value, wherein the fourth executable portion models an
upper and lower bound of uncertainty by modeling an upper and lower
bound of uncertainty for each time segment based upon the
normalized mean value and normalized standard deviation.
66. A computer program product according to claim 58 further
comprising a fifth executable portion for generating a display of
the upper and lower bounds of uncertainty.
67. A computer program product according to claim 66, wherein the
fifth executable portion generates a display of the upper and lower
bounds of uncertainty comprising a plot of the upper and lower
bounds of uncertainty and associated time segments.
68. A computer program product according to claim 58, wherein the
first executable portion determines a mean value for at least one
time segment further based upon execution of a contingent
activity.
69. A computer program product according to claim 68, wherein the
first executable portion determines a mean value for at least one
time segment further based upon execution of a contingent activity
at an end of at least one previous time segment.
Description
FIELD OF THE INVENTION
[0001] The present invention relates generally to systems, methods
and computer program products for modeling future benefits and,
more particularly, to systems, methods and computer program
products for modeling the future benefits over time where the
benefits are subject to uncertainty.
BACKGROUND OF THE INVENTION
[0002] In many industries, decisions that have future consequences
generally attempt to account for an amount of uncertainty in the
future consequence. For example, when manufacturers decide whether
to start a project for the development, manufacture and sale of a
good, those manufacturers try to account for future benefits
associated with the good, such as future profits or revenues
generated by the good, and/or units of the good produced. For
example, the future revenues generated by a good can depend in
large part on a number of factors, including an amount of
uncertainty that can result in those future revenues actually being
represented over a range of possible values.
[0003] Traditionally, manufacturers have not been capable of
reliably quantifying a forecast of future revenues for projects
when a significant amount of uncertainty exists. In this regard,
techniques such as the price path formulation associated with
Brownian motion and lattice techniques, have been developed to
model uncertainty, sometimes referred to as a "cone of
uncertainty," with the path of future revenues modeled within the
cone. Whereas such techniques adequately model uncertainty and
future revenues, they have shortcomings in certain, but crucial,
applications. For example, such techniques are typically unable to
easily incorporate changes in uncertainty over time. Also, for
example, such techniques are typically unable to easily account for
contingent decisions that may occur during a given time period.
[0004] Both Brownian motion and lattice techniques typically
operate by defining a constant amount of uncertainty and a constant
amount of growth in revenues over a period of time, and do not
account for contingencies such as internal and/or external
activities or endeavors. It will be appreciated, however, that in
many actual instances, uncertainty and/or growth rate can vary from
time segment to time segment over a period of time. In addition, in
many actual instances, uncertainty and/or growth rate can take into
account internal and external activities or endeavors, which may or
may not be conditional, such as the payout of a dividend, entry of
a competitor into the market, change in governmental regulations,
or agreement for revenue sharing. Thus, conventional techniques
such as the Brownian motion and lattice techniques do not provide
adequate flexibility to thereby accurately model uncertainty for
future revenues.
SUMMARY OF THE INVENTION
[0005] In light of the foregoing background, embodiments of the
present invention provide systems, methods and computer program
products for modeling future benefits over a period of time, such
as future profits, revenues, units or the like, where the future
benefits are subject to an amount of growth and/or uncertainty that
can vary. The systems, methods and computer program products of
embodiments of the present invention permit rapid modeling of
uncertain benefits of a project with minimal inputs, and allow the
uncertainty of the benefits to be tailored according to forecasted
business conditions, which can change over the course of time. The
systems, methods and computer program products of embodiments of
the present invention also allow for visualization of the benefits
within bounds of uncertainty, referred to herein as the "cone of
uncertainty." By modeling the uncertain future benefits, the
systems, methods and computer program products of embodiments of
the present invention are also capable of generating a business
case based upon the modeled future benefits.
[0006] According to one aspect of the present invention, a method
is provided for modeling future benefits. The method begins by
determining a benefit distribution at an end of each time segment
of a period of time based upon a growth rate and an uncertainty for
the respective time segment. In this regard, the benefit
distribution can be determined at the end of at least one time
segment further based upon execution of a contingent activity, such
as a contingent dividend payment paid, at the end of at least one
previous time segment. Then, after determining the benefit
distributions, a benefit value is selected at the end of each time
segment by randomly selecting each benefit value based upon a
respective benefit distribution to thereby model future benefits of
the good over the period of time. Before determining the benefit
distribution, a growth rate associated with the benefit can be
defined for each time segment. Advantageously, the growth rate for
each time segment can be defined such that the growth rate for at
least one time segment differs from the growth rate of at least one
other time segment. In addition to defining the growth rate, an
uncertainty associated with the benefit can be determined for each
time segment. In this regard, like the growth rate, the uncertainty
for each time segment can advantageously be defined such that the
uncertainty for at least one time segment differs from the
uncertainty of at least one other time segment. Further, a growth
rate for each time segment can be defined independent of an
uncertainty for the respective time segment, and vice versa.
[0007] In one embodiment, the method includes repeatedly selecting
a different benefit value at the end of each time segment to
thereby repeatedly model future benefits. Also, in one embodiment,
the method further includes modeling bounds of uncertainty of
future benefits. In this embodiment, the bounds of uncertainty can
be modeled by determining a mean value and standard deviation
associated with the benefit for each time segment, and modeling an
upper and lower bound of uncertainty for each time segment based
upon the mean value and standard deviation to thereby model the
bounds of uncertainty.
[0008] More particularly as to modeling the bounds of uncertainty,
another aspect of the present invention provides a method of
modeling bounds of uncertainty of future benefits. The method
begins by determining a mean value and standard deviation of the
good for each time segment, where the mean value is determined
based upon a growth rate of the good for the respective time
segment, and the standard deviation is determined based upon an
uncertainty for the good for the respective time segment. In this
regard, before determining the mean value and standard deviation, a
growth rate can be defined, and an uncertainty determined, for each
time segment, as such may be defined and determined according to
above. In one embodiment, the mean value for at least one time
segment of the period of time is determined further based upon
execution of a contingent activity, such as payment of a dividend,
at the end of at least one previous time segment.
[0009] Finally, an upper and lower bound of uncertainty are modeled
based upon the mean value and standard deviation for each time
segment to thereby model the bounds of uncertainty. More
particularly, the method can further include normalizing the mean
value for each time segment based upon the standard deviation and
normalizing the standard deviation for each time segment based upon
the mean value. In such instances, the upper and lower bound of
uncertainty can be modeled for each time segment based upon the
normalized mean value and normalized standard deviation. Also, the
upper and lower bound of uncertainty can be modeled further based
upon an inverse of a standard normal cumulative distribution, where
the standard normal cumulative distribution is defined by a
probability. In this regard, the upper and lower bound can be
modeled by further selecting a lower probability associated with
the lower bound and an upper probability associated with the upper
bound. Advantageously, the lower probability can be selected higher
than zero, and the higher probability can be selected higher
probability lower than one.
[0010] In addition, according to various embodiments of the present
invention, the future benefits and/or bounds of uncertainty are
capable of being modeled with a processing element operating a
spreadsheet software program. In such embodiments, the future
benefit model and/or the upper and lower bounds of uncertainty can
be presented on a display coupled to the processing element. More
particularly, the future benefit model or upper and lower bounds of
uncertainty can be presented as a plot of the selected future
benefits or the upper and lower bounds, respectively, and
associated time segments.
[0011] Systems and computer program products for modeling future
benefits of a good are also provided. Therefore, embodiments of the
present invention provide systems, methods and computer program
products for modeling future benefits of a good over a period of
time, where the future benefits are subject to an amount of growth
and uncertainty that can vary. In this regard, the systems, methods
and computer program products of embodiments of the present
invention allow the uncertainty of the benefits to be tailored
according to forecasted business conditions, which can change over
the course of time.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] Having thus described the invention in general terms,
reference will now be made to the accompanying drawings, which are
not necessarily drawn to scale, and wherein:
[0013] FIG. 1 is a flow chart illustrating the operations performed
by the system, method and computer program product of one
embodiment of the present invention;
[0014] FIG. 2 is a graphical illustration of future benefits such
being subject to a contingent activity, such as a dividend
payout;
[0015] FIG. 3 is a graphical illustration of a revenue distribution
for one time segment, according to one embodiment of the present
invention;
[0016] FIG. 4 is a graphical plot of future revenues of a good as a
function of time according to one embodiment of the present
invention;
[0017] FIG. 5 is another flow chart illustrating the operations
performed by the system, method and computer program product of one
embodiment of the present invention;
[0018] FIG. 6 is a graphical plot of the upper and lower bounds of
uncertainty plotted against the future revenues of FIG. 4 as well
as the mean values of the good according to one embodiment of the
present invention, where the upper and lower bounds, the future
revenues and mean values are plotted as a function of time;
[0019] FIG. 7 is a graphical plot of the upper and lower bounds of
uncertainty over all time segments and future revenues, along with
upper and lower bounds of uncertainty for each time segment,
according to one embodiment of the present invention;
[0020] FIG. 8 is a chart illustrating a business case created
according to one embodiment of the present invention;
[0021] FIG. 9A is a graphical plot illustrating future aircraft
fares according to one embodiment of the present invention;
[0022] FIG. 9B is a graphical plot illustrating future aircraft
seat capacity according to one embodiment of the present
invention;
[0023] FIG. 9C is a graphical plot illustrating future demand for
aircraft seats as a function of fares for years 1, 10 and 19
according to the graphical plots of FIGS. 9A and 9B, according to
one embodiment of the present invention; and
[0024] FIG. 10 is a schematic block diagram of the system of one
embodiment of the present invention embodied by a computer.
DETAILED DESCRIPTION OF THE INVENTION
[0025] The present invention now will be described more fully
hereinafter with reference to the accompanying drawings, in which
preferred embodiments of the invention are shown. This invention
may, however, be embodied in many different forms and should not be
construed as limited to the embodiments set forth herein; rather,
these embodiments are provided so that this disclosure will be
thorough and complete, and will fully convey the scope of the
invention to those skilled in the art.
[0026] According to embodiments of the present invention, a future
benefit is modeled over a period of time, where the period of time
includes a plurality of time segments that each begin and end with
a point in time. The benefit can be any of a number of different
values subject to an amount of future uncertainty and growth. For
example, the benefit can comprise future numbers of units of a
good. Alternatively, for example, the benefit can comprise future
profits in the sale of a good. Further, for example, the benefit
can comprise the future price for which a good is sold. As
described herein, the benefit comprises future revenues of a good.
It should be understood, however, that the description herein of
modeling future revenues of a good is but one example of a benefit
capable of being modeled according to embodiments of the present
invention, and in no way should be taken to limit the scope of the
present invention.
[0027] Referring to FIG. 1, a method of modeling future revenues of
a good (benefits) over a period of time according to one embodiment
of the present invention begins by defining the period of time, as
shown in block 10. In this regard, the period of time can begin at
t=0 and extend to t=T. The period of time can then be divided into
a number of different time segments. In one embodiment, the time
period T is defined such that each time segment can be represented
as an integer divisor of T, i.e., t=0, 1, 2, . . . T. Thus, for
example, the period of time can be defined as a number of years
(i.e., T=30), where the period of time is divided into a number of
one-year time segments which, including the original time t=0,
totals the number of years plus one time segment (i.e., t=0, 1, 2,
. . . 30). As used herein, each time segment begins at point in
time t and ends at point in time t+1 (presuming the time segment is
an integer divisor of T), and is defined by the beginning point in
time t. Thus, time segment t=1 extends from point in time t=1 to
point in time t=2. Similarly, time segment t=2 extends from points
t=2 to t=3.
[0028] Either before, during or after defining the time period, the
mean market value of the good is defined for time segment t=0, as
shown in block 12. After defining the time period, then, the growth
rate of the good can be defined over the time period, as shown in
block 14. The mean market value of the good can be defined in any
of a number of different manners. For example, in one embodiment,
the mean market value can be defined as the maximum gross
profitability in a forecasted market including an optimal number of
units of the good purchased for an optimal price. For a description
of one technique of determining the maximum gross profitability,
see U.S. patent application Ser. No. ______, entitled: Systems,
Methods and Computer Program Products for Modeling Demand and
Associated Profitability of A Good, filed concurrently herewith,
the contents of which are hereby incorporated by reference in its
entirety.
[0029] The growth rate can be determined according to any of a
number of different techniques, such as according to market
forecasts. Advantageously, and in contrast to the price path
formulation associated with the Black-Scholes method, the growth
rate can vary from time segment to time segment over the time
period, typically beginning at time segment t=1. Thus, for example,
the growth rate for time segment t=1 can equal 20%, while the
growth rate for time segment t=2 can equal 20% or, alternatively,
any value greater than or less than 20%.
[0030] Either during or after defining the growth rate, the
uncertainty in the market including the good can be determined for
each time segment, typically beginning with t=1, as shown in block
16. In this regard, the uncertainty can be determined according to
any of a number of different techniques. In one embodiment, for
example, the uncertainty is determined based upon a model of
returns, or growth rate, versus risk, or uncertainty. More
particularly, in one embodiment for example, the returns are
modeled from two risk values and associated return values, as such
may be determined by an estimator or the like.
[0031] Then, assuming a typical linear relationship between risk
and return, the risk can be modeled as a linear function of returns
based upon the two risk values and associated return values. For
example, according to one embodiment, two risk values may comprise
20% and 30%, with associated return values comprising 10.0% and
12.5%, respectively. With such values, risk can be modeled as a
linear function of return as follows:
Risk(Return)=4.times.(Return-5)
[0032] where return and risk are expressed as percentages. For a
further description of modeling risk as a function of returns, see
U.S. patent application Ser. No. ______, entitled: Systems, Methods
and Computer Program Products for Modeling A Monetary Measure for A
Good Based Upon Technology Maturity Levels, filed concurrently
herewith, the contents of which is incorporated by reference in its
entirety. For an example of the uncertainty associated with various
growth rates, as such may have been determined according to the
above, see Table 1.
1 TABLE 1 Growth Rate Uncertainty 10.0% 20% 12.5% 30% 15.0% 40%
17.5% 50% 20.0% 60% 22.5% 70% 25.0% 80% 27.5% 90% 30.0% 100%
[0033] It should be noted that whereas a linear relationship
between risk and return has been assumed above, risk and return
need not have such a relationship. In this regard, a linear
relationship between risk and return is approximately reflective of
current the capital market relationship of risk and return,
embodied in the well-known Capital Asset Pricing Model (CAPM)
theory. In many instances, however, risk and return may not have a
linear relationship. For example, some goods may have a high
projected return with corresponding low risk when compared to
CAPM.
[0034] After the growth rate has been defined and the uncertainty
has been determined for each time segment in the time period, the
revenue for each time segment can be modeled based upon a revenue
distribution for each time segment. Each revenue distribution can
be determined in any of a number of different manners but, in one
embodiment, each revenue distribution can determined based upon the
standard deviation in time for the respective time segment and the
mean value of the good for the time segment, as shown in block 18.
And whereas the mean value of the good for each time segment can be
determined in any of a number of different manners, in one
embodiment the mean value of the good for each time segment is
determined as follows: 1 t = ( 1 + G R t 100 ) .times. t - 1 ( 1
)
[0035] in equation (1), .mu..sub.t represents the mean value of the
good for the current time segment, .mu..sub.t-1 represents the mean
value of the good at the immediately preceding time segment, and
GR.sub.t represents the growth rate for the current time segment,
where the growth rate is expressed in terms of a percentage.
[0036] Like the mean value of the good for each time segment, the
standard deviation for each segment can be determined in any of a
number of different manners. In one embodiment, for example, the
standard deviation can be determined as follows:
.sigma..sub.t=.mu..sub.t.times.{square root}{square root over
(e.sup..sigma..sup..sub.avgt.sup..sup.2.sup..times.1-1)} (2)
[0037] In equation (2), t represents the current time segment, and
at represents the standard deviation for the current time segment.
Also in equation (2), .sigma..sub.avgt represents a running average
of the uncertainty values from t=1 to the current time segment t,
and can be determined, for example, as follows: 2 avgt = i = 1 t U
i 2 t ( 3 )
[0038] where U.sub.i represents the uncertainty for the ith time
segment, and i=1, 2, . . . t. For an example of the values of the
growth rate, mean value of the good, uncertainty and standard
deviation for each time segment in a period of 30 years, where each
time segment represents one year, see Table 2 below.
2TABLE 2 Growth Time (t) Rate (GR.sub.t) Mean (.mu..sub.t)
Uncertainty(U.sub.i) Std. Dev. (.sigma..sub.t) 0 -- $500 -- -- 1
20% $600 60% $395 2 20% $720 60% $739 3 20% $864 60% $1,205 4 20%
$1,037 60% $1,861 5 20% $1,244 60% $2,796 6 20% $1,493 60% $4,135 7
20% $1,792 60% $6,057 8 20% $2,150 60% $8,816 9 20% $2,580 60%
$12,779 10 20% $3,096 60% $18,471 11 18% $3,653 52% $25,034 12 16%
$4,238 44% $32,051 13 14% $4,831 36% $39,026 14 12% $5,411 28%
$45,483 15 10% $5,952 20% $51,056 16 8% $6,428 20% $56,269 17 6%
$6,813 20% $60,866 18 4% $7,086 20% $64,595 19 2% $7,228 20%
$67,234 20 0% $7,228 20% $68,608 21 -2% $7,083 20% $68,609 22 -4%
$6,800 20% $67,209 23 -6% $6,392 20% $64,466 24 -8% $5,880 20%
$60,518 25 -10% $5,292 20% $55,577 26 -12% $4,657 28% $50,880 27
-14% $4,005 36% $46,710 28 -16% $3,364 44% $43,253 29 -18% $2,759
52% $40,631 30 -20% $2,207 60% $38,942
[0039] As will be appreciated, future benefits such as revenues can
be subject to a contingent activity, or event, which can impact the
future benefits (mean and uncertainty values) for subsequent time
segments. For example, revenues over a time segment can be subject
to a contingent dividend payment, which diverts of a portion of
revenues, as shown in FIG. 2 at time t=3. By diverting a portion of
the revenues, the mean value of the good (i.e., .mu..sub.t-1) is
lowered in the determination of the mean value over a subsequent
time segment (i.e., .mu..sub.t). An analogous situation, for
example, is a contingent revenue sharing agreement with a supplier,
or the impact of a competitor entering the market and diverting
revenues. As will also be appreciated, the contingent activity can
itself be represented as a probability distribution. For example,
the contingent activity comprising payment of a dividend can be
represented by a value of the dividend if executed, and an
associated probability of the dividend being executed at the
respective value. Thus, as the mean value and standard deviation
are determined, any contingent activities to which the revenues for
the respective time segment are subject can be accounted for to
adjust the growth rate, uncertainly, mean value and/or standard
deviation accordingly. By linking each time segment to the outcome
of the prior time segment, embodiments of the present invention can
advantageously provide the flexibility to incorporate contingent
activities or endeavors or decisions that may occur at the
transition between time segments.
[0040] After determining the mean value of the good and standard
deviation over each time segment, a revenue distribution can be
determined for each time segment by defining each revenue
distribution according to the respective mean value of the good and
standard deviation. The revenue distribution can be represented as
any of a number of different types of distributions but, in one
embodiment, the revenue distribution is defined as a lognormal
distribution. In this regard, FIG. 3 illustrates the revenue
distribution defined at time t=1 for the example in Table 2.
[0041] Thus, to model the future revenues for the good, a revenue
value for each time segment is selected from the revenue
distribution for the respective time segment, as shown in block 20
of FIG. 1. Advantageously, the revenue value can be selected
according to a method for randomly selecting the revenue value,
such as the Monte Carlo method. As known to those skilled in the
art, the Monte Carlo method is a method of randomly generating
values for uncertain variables to simulate a model. In this regard,
the Monte Carlo method is applied to the revenue distributions to
select a revenue value for each time segment. The model of future
revenues can be represented in any one of a number of manners but,
in one embodiment, the model of future revenues is represented by
plotting the revenue values for the time segments over the period
of time, such as is shown in FIG. 4 where the revenue distributions
are based upon the mean values of the good and standard deviations,
as well as the forecasted revenue values, of Table 4 (described
below). And as also described below, by repeatedly selecting
different revenue values for one or more time segments,
corresponding future revenues for the good can be modeled for each
selected set of revenue values.
[0042] As will be appreciated, the values (e.g., mean and standard
deviation) determined above for each time segment can be determined
in any manner and any order. For example, each respective value for
all time segments in time be determined at one time, with the
remaining values for all time segments determined thereafter.
Alternatively, all values for each time segment in time can be
determined at one time, with all the values for the remaining time
segments determined thereafter. In other terms, the values can be
determined on a value-by-value basis or on a time segment-by-time
segment basis, for example.
[0043] In addition to modeling uncertain revenues for a good,
aspects of the present invention are also capable of modeling the
uncertain revenues within bounds of uncertainty, referred to herein
as the "cone of uncertainty." It will be appreciated, however, that
at the lowest bounds of possible revenues, goods are typically
dropped or re-scoped into different goods. Similarly, at the
highest bounds of possible revenues, several factors, such as
competitors entering the market and reaching capacity of the
market, act to limit the revenues. In this regard, whereas the cone
can be determined to define the range of possible revenues for each
time segment, in one embodiment the cone is determined by
truncating the lowest and highest bounds, such as by truncating the
each bound by a predefined percentage (e.g., 2.5%), so that the
cone is determined to define the rage of most likely revenues for
each time segment.
[0044] With reference now to FIG. 5, the cone of uncertainty, or
lower and upper bounds, can be determined by initially, as before,
defining the period of time, defining the mean market value of the
good is defined for time t=0, defining the growth rate of the good
over the time period, and determining the uncertainty in the market
including the good for each time segment, as shown in blocks 22,
24, 26 and 28, respectively. Then, the mean value of the good .mu.
and the standard deviation .sigma. for each time segment t within
the time period can be determined, such as is described above and
shown in block 30. It will be appreciated, however, that at the
inception of the good (i.e., t=0), no revenues will be generated
and, as such, the lower and upper bounds at t=0 will typically be
zero. After determining the mean value of the good and the standard
deviation for each time segment, the mean and standard deviations
can be normalized based on one another, as shown in block 32. The
mean and standard deviations can be normalized in any of a number
of different manners, but in one embodiment, the mean value of the
good for each time segment can be normalized to .mu..sub.log t as
follows: 3 l og t = - 1 2 .times. ln ( ( ( t / t ) 2 + 1 ) / t 2 )
( 4 )
[0045] Similarly, in one embodiment, the standard deviation for
each time segment can be normalized to .sigma..sub.log t as
follows:
.sigma..sub.log t={square root}{square root over
(ln((.sigma..sub.t/.mu..s- ub.t).sup.2+1))} (5)
[0046] After normalizing the mean value of the good and the
standard deviation for each time segment, the lower and upper
bounds of uncertainty, defining the cone of uncertainty, can be
determined, as shown in block 34. In this regard, the bounds can be
determined based upon the normalized mean values and standard
deviations. Further, the bounds can be determined based upon an
inverse of a standard normal cumulative distribution, where the
distribution is defined by a probability and has a mean of zero and
a standard deviation of one. The lower and upper bounds can be
defined in any of a number of different manners, but in one
embodiment, the lower and upper bounds can be determined according
to the following equations (6) and (7), respectively:
LBound.sub.t=e.sup..mu..sup..sub.log t.sup.+.sigma..sup..sub.log
t.sup..times.NormsInv(p.sup..sub.t.sup.) (6)
UBound.sub.t=e.sup..mu..sup..sub.log t.sup.+.sigma..sup..sub.log
t.sup..times.NormsInv(p.sup..sub.u.sup.) (7)
[0047] In equations (6) and (7), NormsInv(p) represents the inverse
of the standard normal cumulative distribution for a defined
probability, or percentage p.
[0048] As the bounds define the lower and upper bounds of revenues,
the lower percentage p.sub.l can be set at 0% (i.e., probability of
0.0) for the lower bound, and the upper percentage p.sub.u set at
100% (i.e., probability of 1.0) for the upper bound. But as
indicated above, in one embodiment the bounds of the cone are
truncated by a predefined percentage (or probability) so that the
cone is determined to define the rage of most likely revenues for
each time segment. As such, the lower percentage p.sub.l can be set
at a value above 0%, such as at 2.5%. Similarly, the upper
percentage p.sub.u can be set at a value below 100%, such as 97.5%.
For an example of the lower and upper bounds, defined at 2.5% and
97.5%, respectively, and determined based upon the mean values of
the good and standard deviations in the example of Table 2, see
Table 3 below.
3TABLE 3 Time (t) 2.5% Min 97.5% Max 0 -- -- 1 $155 $1,624 2 $95
$2,650 3 $66 $3,860 4 $48 $5,302 5 $36 $7,015 6 $28 $9,037 7 $23
$11,409 8 $18 $14,177 9 $15 $17,386 10 $12 $21,091 11 $11 $24,935
12 $11 $28,880 13 $11 $32,848 14 $11 $36,722 15 $12 $40,350 16 $12
$43,528 17 $12 $46,081 18 $12 $47,861 19 $12 $48,748 20 $12 $48,675
21 $11 $47,626 22 $10 $45,645 23 $9 $42,831 24 $8 $39,333 25 $7
$35,332 26 $6 $30,970 27 $4 $26,444 28 $3 $21,946 29 $2 $17,648 30
$1 $13,695
[0049] The cone of uncertainty can be represented in any one of a
number of manners. In one embodiment, shown in FIG. 6, the cone of
uncertainty is represented by plotting the lower bounds (designated
line 40) and the upper bounds (designated line 42) for the times
segments over the period of time where, as shown, the bounds are
determined based upon the mean values of the good and standard
deviations of Table 2. The cone can then be defined as the
cross-hatched region 44 between the lower and upper bounds, and can
be plotted against the model of future revenues (designated line
46) as well as the mean values of the good (designated line 48). As
shown, one or more of the times segments can have a revenue that
exceeds the upper bound and, although not shown, one or more of the
time segments can have a revenue below the lower bound. In this
regard, it will be appreciated that the bounds define the range of
most likely revenues and, as such, the bounds are defined to not
include a number of possible revenue values.
[0050] Just as the growth rate, mean value of the good, uncertainty
and standard deviation for each time segment in a period of 30
years can be modeled over all time segments, each time segment can
have its own distribution, contingent on the distribution
parameters of the prior time segment. Referring now to FIG. 7,
according to one embodiment, the bounds of uncertainty for time
segment t (i.e., between point in time t and time t+1) can be
modeled by first selecting a future revenue for point in time t,
such as from the revenue distribution for time segment t, as
described above. As will be appreciated, as the future revenue at
time t has been selected, no uncertainty is associated with the
revenues at time t.
[0051] From the future revenue at time t, a mean value at point in
time t=t+1 can be determined, such as according to equation (1)
above where .mu..sub.l-1 represents the future revenue for time t,
.mu..sub.t represents the mean value at time t=t+1, and GR.sub.t
represents the growth rate for time t=t+1. After determining the
mean value at time t=t+1, the standard deviation at time t=t+1 can
be determined, such as according to equation (2) above, where
.sigma..sub.t represents the standard deviation at time t=t+1, t
represents the time at t=t+1, and .sigma..sub.avgt represents a
running average of the uncertainty values from t=1 to time t=t+1
(see equation (3)). As can be seen, then, the mean and standard
deviation values define a distribution. Thereafter, the upper and
lower bounds of uncertainty at time t=t+1 can be determined, such
as in a manner described above in conjunction with equations (4)
through (7). As shown in FIG. 7, then, the upper and lower bounds
at time t=t+1 appear as points 51a and 51b, respectively, at the
respective point in time.
[0052] The bounds of uncertainty between time t and time t=t+1 can
then be modeled by connecting the bounds of uncertainty at time t
(i.e., the selected revenue value at time t) with the bounds of
uncertainty at time t=t+1. As shown, and as will be appreciated, at
time progresses, the uncertainty associated with a future benefit,
such as revenues, increases outwardly. For an example of the values
of the growth rate, mean value of the good, uncertainty, standard
deviation and forecasted revenue for each time segment in a period
of 30 years where each time segment has its own distribution, see
Table 4 below.
4TABLE 4 Un- Forecasted Time Growth certainty Std. Dev. Revenue (t)
Rate (GR.sub.t) Mean (.mu..sub.t) (U.sub.i) (.sigma..sub.t)
(.mu..sub.t-1) 0 -- $500,000 -- -- -- 1 20% $418,748 60% $329,139
$348,957 2 20% $551,432 60% $275,652 $459,526 3 20% $623,987 60%
$362,995 $519,989 4 20% $424,238 60% $410,757 $353,532 5 20%
$264,392 60% $279,266 $220,327 6 20% $580,200 60% $174,044 $483,500
7 20% $1,049,674 60% $381,933 $874,729 8 20% $1,780,455 60%
$690,977 $1,483,712 9 20% $4,733,620 60% $1,172,033 $3,944,683 10
20% $2,926,677 60% $3,116,036 $2,480,235 11 18% $3,974,778 52%
$1,630,788 $3,426,533 12 16% $4,246,647 44% $1,837,065 $3,725,129
13 14% $3,237,536 36% $1,579,689 $2,890,657 14 12% $2,785,884 28%
$924,571 $2,532,622 15 10% $2,683,323 20% $562,795 $2,484,558 16 8%
$3,127,509 20% $542,076 $2,950,481 17 6% $3,234,680 20% $631,809
$3,110,269 18 4% $2,745,234 20% $653,460 $2,691,406 19 2%
$3,003,969 20% $554,583 $3,003,969 20 0% $2,230,074 20% $606,852
$2,275,586 21 -2% $1,931,483 20% $450,512 $2,011,961 22 -4%
$1,931,148 20% $390,192 $2,054,413 23 -6% $1,714,646 20% $390,124
$1,863,746 24 -8% $1,609,526 20% $346,387 $1,788,362 25 -10%
$1,597,992 20% $325,151 $1,815,900 26 -12% $1,150,298 28% $456,353
$1,337,556 27 -14% $1,029,353 36% $427,894 $1,225,420 28 -16%
$764,977 44% $475,747 $932,898 29 -18% $339,436 52% $426,256
$424,295 30 -20% $500,000 60% $223,443 $847,962
[0053] At this point it should be made clear that the model of
uncertain future revenues up to this point has been tied to one
forecasted revenue for each time segment selected according to a
method for randomly selecting a predefined number of units of the
good, such as the Monte Carlo method. As such, after modeling the
future revenues including a selected revenue for each time segment,
the method can then be repeated a plurality of times by selecting
different forecasted revenues for each time segment. Then, if so
desired, the forecasted revenues for each time segment can be
organized into a distribution for the respective time segment. The
distributions can then be defined, such as by a curve type and a
mean and associated standard deviation. In one possible
implementation of such a technique, termed mean-reverting, the path
of forecasted revenues will exhibit a tendency to return to
forecasted mean values for each time segment.
[0054] From the distributions, then, a business case for the good
can be created. For example, the business case can receive the
distributions for the future revenue for each time segment. Based
upon the distributions, then, the market value of the project can
be determined and plotted over time, as shown in FIG. 8. As shown,
the business case can plot the nonrecurring costs associated with
the project (shown below zero for years three through five).
Additionally, the business case can plot the profit associated with
the project, which can be determined as described above. As
described, the business case can be created from distributions
organized from selecting different future revenues for the time
segments by repeating the method a plurality of times. It will be
appreciated, however, that the business case can be created from
performing the method once to select one forecasted revenue for
each time segment.
[0055] As described above, the benefit can be any of a number of
different values subject to an amount of future uncertainty and
growth, including future numbers of units of a good and the future
price for which a good is sold. Thus, in accordance with
embodiments of the present invention, future numbers of units of a
good can be modeled over a period of time, as shown in FIG. 9A for
future seating capacity onboard aircraft. Also, the future price
for which the good is sold can be modeled over the period of time,
as shown in FIG. 9B for future fares associated with aircraft
flights. With such models, then, future demand for aircraft seats
as a function of fares can be modeled, as shown in FIG. 9C for
years 1, 10 and 19. For more information on a method by which such
demand can be modeled, see U.S. patent application Ser. No. ______,
entitled: Systems, Methods and Computer Program Products for
Modeling Demand and Associated Profitability of A Good, filed
concurrently herewith.
[0056] In addition to creating a business case and modeling future
numbers of units of a good, cost of the good and/or demand for the
good, the method of the present invention can be performed to draw
any of a number of different conclusions, and can additionally be
utilized in conjunction with other similar methods to formulate a
more complex financial modeling tool. For example, in one
embodiment, a contingent claim can be valued and thereafter input
into the model of uncertain future benefits, such as the mean value
of the good, number of units of the good (e.g., seats on an
aircraft, number of aircraft, etc.) or the like, for time segment
t=0. Whereas the contingent claim can be valued in any of a number
of different manners, in one embodiment, the contingent claim is
valued by initially determining the present value distribution of
contingent future benefits that is attributable to the exercise of
a contingent claim. In this regard, the distribution of contingent
future benefits can be discounted according to a first discount
rate, such as the weighted average cost of capital (WACC). The
present value of a contingent future investment required to
exercise the contingent claim is also determined based upon another
appropriate discount rate, such as a risk-free rate of discounting.
An average of the difference between the present value distribution
of contingent future benefits and the present value of the
contingent future investment can be determined. For more
information on such a method of valuing a contingent claim, see
U.S. patent application Ser. No. 09/902,021 entitled: Systems,
Methods and Computer Program Products for Performing A Generalized
Contingent Claim Valuation, filed Jul. 10, 2001; and U.S. patent
application Ser. No. ______ entitled: Systems, Methods and Computer
Program Products for Performing A Contingent Claim Valuation, filed
Dec. 4, 2002, the contents of both of which are hereby incorporated
by reference in their entirety.
[0057] Therefore, embodiments of the present invention provide
systems, methods and computer program products for modeling future
revenues of a good over a period of time, where the future revenues
are subject to an amount of growth and uncertainty that can vary.
In this regard, the systems, methods and computer program products
of embodiments of the present invention allow the uncertainty of
the revenues to be tailored according to forecasted business
conditions, which can change over the course of time. As such,
embodiments of the present invention also allow for visualization
of the revenues within bounds of uncertainty, referred to herein as
the "cone of uncertainty."
[0058] As shown in FIG. 10, the system of the present invention is
typically embodied by a processing element and an associated memory
device, both of which are commonly comprised by a computer 41 or
the like. In this regard, as indicated above, the method of
embodiments of the present invention can be performed by the
processing element manipulating data stored by the memory device
with any one of a number of commercially available computer
software programs. In one embodiment, the method can be performed
with data that is capable of being manipulated and/or presented in
spreadsheet form. For example, the method can be performed by the
processing element manipulating data stored by the memory device
with Excel, a spreadsheet software program distributed by the
Microsoft Corporation of Redmond, Wash., including Crystal Ball, a
Monte Carlo simulation software program distributed by
Decisioneering, Inc. of Denver, Colo. The computer can include a
display 42 for presenting information relative to performing
embodiments of the method of the present invention, including the
various distributions, models and/or conclusions as determined
according to embodiments of the present invention. To plot
information relative to performing embodiments of the method of the
present invention, the computer can further include a printer
44.
[0059] Also, the computer 41 can include a means for locally or
remotely transferring the information relative to performing
embodiments of the method of the present invention. For example,
the computer can include a facsimile machine 46 for transmitting
information to other facsimile machines, computers or the like.
Additionally, or alternatively, the computer can include a modem 48
to transfer information to other computers or the like. Further,
the computer can include an interface (not shown) to a network,
such as a local area network (LAN), and/or a wide area network
(WAN). For example, the computer can include an Ethernet Personal
Computer Memory Card International Association (PCMCIA) card
configured to transmit and receive information to and from a LAN,
WAN or the like.
[0060] In one advantageous technique applicable to embodiments of
the present invention, the methods according to embodiments of the
present invention may be embodied in a software or data module,
component, portfolio or the like, that can be manipulated or
otherwise operated within a spreadsheet software program such as
Excel. Such a technique may be advantageous in a number of
different contexts, such as in the context of financial modeling
and analysis. In this regard, modules, components and/or portfolio
that perform various financial modeling functions can be combined
to gain a more complete understanding of a financial context. A
brief description of such a technique as such may be applied to the
present invention will now be described below.
[0061] According to such a technique, data capable of being
manipulated to perform at least a portion of the methods of the
present invention can be embodied in a module, which can thereafter
be linked or otherwise associated with other portions of the
methods of the present invention embodied in other modules so as to
formulate a component. Then, if so desired, the component can be
linked or otherwise associated with other components capable of
performing other related methods to thereby form a portfolio. For
example, methods of modeling future revenues according to
embodiments of the present invention can be embodied in one module
while methods of modeling nonrecurring cost according to
embodiments of the present invention can be embodied in another
module. The two modules can then be linked or otherwise associated
with one another to formulate a component capable of generating a
business case capable of modeling the market value of the good
based upon the future revenues and the nonrecurring cost. Then, if
so desired, the component for generating the business case can be
linked or otherwise associated with another component to perform
another function.
[0062] According to one aspect of the present invention, the system
of the present invention generally operates under control of a
computer program product. The computer program product for
performing the methods of embodiments of the present invention
includes a computer-readable storage medium, such as the
non-volatile storage medium, and computer-readable program code
portions, such as a series of computer instructions, embodied in
the computer-readable storage medium. It should be understood that
the computer-readable program code portions may include separate
executable portions for performing distinct functions to accomplish
methods of embodiments of the present invention. Additionally, or
alternatively, one or more of the computer-readable program
portions may include one or more executable portions for performing
more than one function to thereby accomplish methods of embodiments
of the present invention.
[0063] In this regard, FIGS. 1 and 4 are a flowchart of methods,
systems and program products according to the invention. It will be
understood that each block or step of the flowchart, and
combinations of blocks in the flowchart, can be implemented by
computer program instructions. These computer program instructions
may be loaded onto a computer or other programmable apparatus to
produce a machine, such that the instructions which execute on the
computer or other programmable apparatus create means for
implementing the functions specified in the flowchart block(s) or
step(s). These computer program instructions may also be stored in
a computer-readable memory that can direct a computer or other
programmable apparatus to function in a particular manner, such
that the instructions stored in the computer-readable memory
produce an article of manufacture including instruction means which
implement the function specified in the flowchart block(s) or
step(s). The computer program instructions may also be loaded onto
a computer or other programmable apparatus to cause a series of
operational steps to be performed on the computer or other
programmable apparatus to produce a computer implemented process
such that the instructions which execute on the computer or other
programmable apparatus provide steps for implementing the functions
specified in the flowchart block(s) or step(s).
[0064] Accordingly, blocks or steps of the flowchart support
combinations of means for performing the specified functions,
combinations of steps for performing the specified functions and
program instruction means for performing the specified functions.
It will also be understood that each block or step of the
flowchart, and combinations of blocks or steps in the flowchart,
can be implemented by special purpose hardware-based computer
systems which perform the specified functions or steps, or
combinations of special purpose hardware and computer
instructions.
[0065] Many modifications and other embodiments of the invention
will come to mind to one skilled in the art to which this invention
pertains having the benefit of the teachings presented in the
foregoing descriptions and the associated drawings. By way of but
one example, the contingent claims valued by the system, method and
computer program product of the present invention may be American
style calls, as opposed to the European style calls referenced in
the above-described examples. Therefore, it is to be understood
that the invention is not to be limited to the specific embodiments
disclosed and that modifications and other embodiments are intended
to be included within the scope of the appended claims. Although
specific terms are employed herein, they are used in a generic and
descriptive sense only and not for purposes of limitation.
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