Content uploaded by C. Reid Nichols
Author content
All content in this area was uploaded by C. Reid Nichols on Jul 15, 2019
Content may be subject to copyright.
Using Monte Carlo Simulation as a Financial Modeling Tool … 79
Journal of Management for Global Sustainability Volume 7, Issue 1 (2019): 79–103
© 2019 International Association of Jesuit Business Schools
USING MONTE CARLO SIMULATION
AS A FINANCIAL MODELING TOOL TO
SUPPORT SUSTAINABILITY EFFORTS OF
A GOVERNMENT AGENCY
KARYL B. LEGGIO (corresponding author)
Department of Finance
Loyola University Maryland
Baltimore, Maryland, U.S.A.
kbleggio@loyola.edu
C. REID NICHOLS
Marine Information Resources Corporation
Ellicott City, Maryland, U.S.A.
rnichols@mirc-us.com
ABSTRACT
The National Oceanic and Atmospheric Administration (NOAA) collects ecosystem data
to support coastal resource conservation and management activities by studying stressors
that impact estuaries such as the Chesapeake Bay, which is the largest in the United States.
This paper seeks to help NOAA justify its existence and its budget by utilizing Monte Carlo
simulation as a nancial modeling tool, with such simulations providing insights on how to
allocate identied resources. The results of the study offer an innovative method for helping
government managers decide how much money to spend, what to spend it on, and how to
acquire resources for the Chesapeake Bay Interpretive Buoy System. Moreover, this paper
also demonstrates how an experiential project in graduate business education can be used
to support sustainability efforts by addressing community-focused issues while improving
student connection between theory and application at the same time.
KEYWORDS
environmental sustainability; Monte Carlo simulation;
nancial modeling; government funding
Karyl B. Leggio & C. Reid Nichols80
PURPOSE OF THE STUDY
Experiential learning has become increasingly popular as a means for assisting
students in the mastery of concepts and retention of content. Such is the case
in graduate business education where students can aim to create value for their
organizations by fullling course requirements framed in terms of addressing a
company need. This paper discusses one such experiential learning project, one that
supports efforts to protect and preserve the Chesapeake Bay, which produces 500
million pounds of seafood annually and supports two out of ve major shipping
ports in the North Atlantic. It is an attempt to assist the Chesapeake Bay Ofce,
which is part of the National Marine Fisheries Service of the National Oceanic
and Atmospheric Administration (NOAA), in recognizing the potential value that
is present within the connes of the annual congressional budget allocation for
the agency.
In 2016, the Association to Advance Collegiate Schools of Business (AACSB)
formulated a collective vision for business education. They identied one of ve
drivers for change as business schools becoming enablers for global prosperity
(AACSB International, 2016). Business is about more than just wealth creation; it is
a vehicle for having an impact in the creation of a better, more sustainable world.
The AACSB notes the need for business schools to innovate and for business schools
and the business community to have a positive impact upon society.
The future calls for business schools to capitalize on academic strengths in
order to grow and develop the rich space between theory and practice in ways
that positively impact society. To do so, schools will need to pursue operational
models and strategies that firmly position themselves at the intersection of
industry and practice, as conveners and partners in the knowledge creation
ecosystem rather than just suppliers. (AACSB International, 2016)
The content taught in business schools, along with the research created by
faculty in the academy, can thus be integrated to address global issues. This is not
simply a nice idea—it is becoming the expectation of our students, the business
community, and our accreditation organizations. Numerous researchers (Jamison,
Hanushek, Jamison, & Woessmann, 2008; Kim, Tamborini, & Sakamoto, 2015;
Tamborini, Kim, & Sakamoto, 2015) have described the importance of lifelong
learning and demonstrated the value of education and training in sustaining a
healthy economy.
Using Monte Carlo Simulation as a Financial Modeling Tool … 81
In the context of this new perspective on business schools, it is imperative
that graduate education today helps participants learn new skills that will aid
them in making a difference both in their rms and in the world. The Assurance
of Learning Standards conceptualized by AACSB focuses on learning outcomes,
asking the question, “What will our students learn in our program?” (AACSB
International, 2007). At Loyola University Maryland’s Sellinger School of Business,
the Professional’s MBA is customizable and explicitly enables students to acquire a
broader perspective of their organization as they gain real-world experience from
visits to organizations and meetings with business leaders. Students will learn
in an environment where they can explore new ways of thinking and acquire a
deeper prociency in the relationships that power successful organizations, helping
them emerge as condent, competent leaders. This approach to graduate business
education is value-centered and focuses on an ethical commitment that manifests
itself in a series of learning goals designed to encourage student-based experiential
learning.
Creating an often-new-to-the-organization means of deriving recommendations
in support of a project, as is done by incorporating into research the available
databases and tools that were learned in the classroom, is the goal behind pursuing
student-based research projects within the curriculum. The experiential learning
project discussed in this paper involves the development of a system that uses Monte
Carlo simulation to justify the expense of the Chesapeake Bay Interpretive Buoy
System (CBIBS; see http://buoybay.noaa.gov/) based on the value created by the data
that was generated from the instrumented buoys.
DESIGN AND METHODOLOGY
The system that would become a nancial modeling tool was developed in the
context of a graduate course in nance (GB 719) the objectives of which were to
1) study capital budgeting models, 2) build a nancial model, and 3) work with data
from an existing organization. The course began with a review of the applications
of nancial decision tools such as payback period, net present value (NPV), internal
rate of return (IRR), and protability index before moving into learning new ones
such as Monte Carlo simulation for valuation, a tool which had been previously
used in other student case studies (Stretcher, 2015). Monte Carlo simulation allows
students to build a tractable model that provides valuable information to the
Karyl B. Leggio & C. Reid Nichols82
decision maker. It can be used to determine how sensitive a system is to changes
in variables or operating conditions as well as an optimal operating policy or
distribution of resources (Winston, 1996). Company-specic projects are thus good
platforms for applying Monte Carlo simulation since students will be using a new
technique on familiar data—that gathered from within their rms or market areas.
Research shows that student learning is enhanced when the work is relevant to their
lives both inside and outside of the classroom (Kuh, 2016).
Projects are segmented into a series of deliverables to make them more
manageable for students; increase the faculty member’s familiarity with the
student’s company, market, and project as the semester progresses; and assure that
the student is on track through feedback provided by the faculty member prior to
a nal submission.
The rst deliverable for this project is an overview of the rm and market which,
in this case, is complicated by the fact that NOAA’s budget is set by Congress and
has been declining in recent years.
PROJECT BACKGROUND
Founded in 1970, the National Oceanic and Atmospheric Administration
(NOAA) is an agency of the U.S. Department of Commerce whose mission is to
understand and predict changes in climate, weather, the oceans, and coasts; share
that knowledge and information with others; and conserve and manage coastal and
marine ecosystems and resources. Dedicated to the understanding and stewardship
of the environment, NOAA has been a partner in the multi-state and multi-agency
Chesapeake Bay Program which works to protect and restore the Chesapeake
Bay through ecosystem science, coastal and living resource management, and
environmental literacy. Their Chesapeake Bay Ofce (NCBO) supports NOAA’s
National Estuarine Research Reserves (NERRS) network, a system of 28 coastal
sites designated for the protection and study of estuarine systems. NERRS has also
developed partnerships within and outside of NOAA, such as with the National
Parks Service and Environmental Protection Agency (EPA).
The NCBO fullls its statutory mandate through multi-species sheries research,
habitat characterization and assessment, community engagement and outreach, and
Using Monte Carlo Simulation as a Financial Modeling Tool … 83
coordination of NOAA activities under Executive Order (EO) 13508, Chesapeake
Bay Protection and Restoration, which was issued in 2009. This EO states that the
Chesapeake Bay Ofce shall “provide technical assistance on processes impacting
the Chesapeake Bay system, its restoration and habitat protection; develop a strategy
to meet the commitments of the Chesapeake Bay Agreement; and coordinate
programs and activities impacting the Chesapeake Bay, including research and
grants.” The Agreement focuses on collaboration and coordination in watershed
restoration and protection efforts.
The NCBO accomplishes its mission with personnel from several contractors as
well as from NOAA’s Fisheries Service, the National Ocean Service, and the National
Environmental Satellite, Data, and Information Service.
NCBO’s operations include the Chesapeake Bay Interpretive Buoy System
(CBIBS) which was implemented in 20 07. The CBIBS observation network provides
users with information on wind speed and direction, wave measurements, dissolved
oxygen, chlorophyll, and turbidity. These measurements provide the data necessary
for improving marine forecasts which support commercial transportation, shing,
and recreational boating on the Chesapeake Bay. The growing database also provides
information needed for monitoring the health of the Bay. Observations from the
buoys are used in educational settings, and buoys mark locations along the National
Park Service’s Captain John Smith Chesapeake National Historic Trail (National
Park Service, n.d.). Finally, software applications that allow users to obtain real-time
weather and environmental information at any buoy location, such as wind speed,
temperature, and wave height, are also available.
CBIBS supports watershed benefits such as fisheries and tourism which
are estimated to be worth $4.6 billion annually in Maryland’s Chesapeake Bay
region (Phillips & McGee, 2014). To ensure high quality data, eld technicians
who understand the CBIBS system must be capable of completing diagnostics
and repair in both the field and the laboratory. CBIBS buoys require monthly
scheduled maintenance, semi-annual refurbishment, and an unpredictable
amount of unscheduled maintenance (to repair or replace a broken cable or
sensor, for example). Routine tasks include removing biofouling from buoy hulls
and transducers, cleaning and replacing solar panels, and conducting mooring
inspections, among others.
Karyl B. Leggio & C. Reid Nichols84
DETERMINATION OF ECONOMIC VALUE
Since there are no direct revenues associated with the purpose of this study, the
value added by the agency’s existence to constituents was estimated with the help
of data gathered from various agencies and from previous studies that quantied
the value of the agency’s work.
Appropriations to the NCBO for each of the scal years from 2006 through 2016
totaled approximately $6,000,000. Figure 1 provides an estimate of this funding
(NOAA Budget Ofce, n.d.). Buoys cost approximately $150,000 each (an operational
CBIBS buoy deployed in the Severn River is pictured in Figure 2). Four buoys were
lost due to ice damage during the winter of 2014–2015 (the impact of extreme
winter weather on the Potomac Buoy is depicted in Figure 3). Estimated expenses
are provided in Table 1.
Figure 1: CBIBS budget fluctuations. While the CBIBS budget is steady at approximately
$8 million per year, events such as collisions and severe weather can cause unbudgeted
buoy destruction.
Using Monte Carlo Simulation as a Financial Modeling Tool … 85
Figure 2: Annapolis CBIBS buoy deployed near the mouth of the Severn River. (Photo
courtesy of C. Reid Nichols)
Karyl B. Leggio & C. Reid Nichols86
Figure 3: The impact of ice loading on a CBIBS buoy like this one in the Potomac River
can confound measurements and destroy sensors. Ice floes can also drag the buoys
out of position. (Photo courtesy of NOAA)
Item Expenses Remarks
Vessel
Operations
$150,000 Ships such as the M/V John C. Widener are
used to recover and redeploy buoys.
MARACOOS $150,000 Data Management, Research & Development
(R&D), Consultants
CRC $300,000 R&D, Buoy Maintenance
Salaries $200,000 NOAA and Consultants
NCBO may move various amounts of money to meet operational and maintenance
needs as research and development is completed. Monies on the order of $20,000
per year, for example, may be available for new components and buoys as data
management software is completed and vessel operations are reduced.
Table 1: Estimated CBIBS expenses.
Based on the numbers provided by NCBO, there is an overall decline in budget
which may be complicated by the need to maintain ageing CBIBS buoys. The system
at present includes ten networked data collection buoys that are sited throughout the
Bay. These buoys and their sensors require routine maintenance as well as the ability
to procure supplies from manufacturers and/or vendors of buoy components. NCBO
Using Monte Carlo Simulation as a Financial Modeling Tool … 87
as such maintains several contracts with multiple vendors who supply appropriate
buoys, basic sensors, spare parts, and consumable materials. To control costs and
ensure efciency of maintenance as CBIBS expands, buoys added to the core system
must be consistent to the greatest possible degree with the standard platform and
complement of sensors currently in use.
Cost management also needs to consider contingency funding on an annual
basis for at least one spare replacement buoy and an inventory of spare parts based
on usage history. If the CBIBS program were to be downsized, buoys could be
removed from the water and stored until repurposed or otherwise re-appropriated to
another agency or organization (Wheeler, 2012). Some cost savings can be achieved
by eliminating stations; others pertaining to salaries, equipment, website expenses,
and facilities are xed and cannot be scaled. These amount to an estimated $450,000
per year. The CBIBS program, on the other hand, may maintain its utility and
operate for many years. According to the NCBO, for instance, nancial resources
to replace aging buoy components will be made available through more efcient
use of vessel services and the elimination of a costly data management contract.
Partners such as Virginia Commonwealth University (VCU) and Dominion Virginia
Power might also deploy or donate similar instrumented buoys that can display
observations through the CBIBS portal.
The presidential budget for fiscal year 2017 included $5.5 million for the
coordination of NOAA programs and activities in the Chesapeake Bay. Activities
included targeted restoration, protection, and monitoring of vital habitats and shery
resources; synthesizing and delivering scientic data to support the management of
oysters, blue crab, striped bass, and other ecologically and commercially important
species; and operating and maintaining CBIBS to deliver information about the Bay
to the public. CBIBS as such continues to provide essential foundations or baseline
data for NCBO operations and resultant reports.
We have used information obtained from U.S. Integrated Ocean Observing
System (U.S. IOOS) studies in our analysis. Direct use values have been documented
by NOAA and organizations such as the Chesapeake Bay Foundation (CBF). These
data, information, and capabilities support the forecasting of harmful algae blooms,
identication of hypoxia, monitoring of pathogens such as Vibrio bacteria, and
essential infrastructure and processes for ecological forecasts. The NCBO, for
example, provides CBIBS data to weather forecast ofces and the National Data
Karyl B. Leggio & C. Reid Nichols88
Buoy Center (NDBC). The CBF uses the CBIBS system for both staff level scientic
observation and analysis such as in the preparation of an annual Bay Report Card.
Passive use values have been estimated—the CBF education program, for example,
uses CBIBS eld collected water quality parameters and CBIBS remotely sensed data
in their Science, Technology, Engineering, and Mathematics (STEM) programs.
CBIBS is introduced annually to over 1,000 secondary school students, their teachers,
and principals, with the buoy system in particular allowing students to understand
the concepts of stratication and eutrophication as it effects hypoxia. This is because
the chlorophyll, bottom dissolved oxygen, and temperature sensors on some buoys
augment data that students can collect from education vessel platforms such as
the schooner Lady Maryland, Chesapeake Buyboats Mildred Belle and Half Shell, and
Skipjacks Sigsbee and Minnie V.
Numerous authors (e.g., Altalo, 2006; Colgan, 2007; Kite-Powell, 2009; ERISS
Corporation & The Maritime Alliance, 2016) have also looked at the U.S. IOOS or
similar observatories and estimated the value of their observations for the benet of
the public. Requirements to safeguard lives and protect property drive the need for
relevant observations and environmental information. These rely on environmental
forecast information for operations in revenue forecasting and load management
to infrastructure siting and supply chain management. Altalo (2006) points out
that market economics is a major driver when there is a need for internalizing
environmental externalities to reduce impact on operations. Systems such as CBIBS
improve environmental forecasts and reduce risks, thereby increasing value for
operations, and provide baseline data for regulators. A partial list of users that
depend on or benet from CBIBS is provided in Table 2.
The present study is the rst one to look at the value of the CBIBS system as a
whole. It addresses the broader question concerning the system’s overall economic
value for other government agencies, academia, industry, and the American public.
The Maryland Department of Natural Resources, for example, received funding from
NCBO to maintain buoys in Maryland waters while the Virginia Institute of Marine
Sciences was also funded to maintain buoys in Virginia waters. U.S. IOOS funding
for universities and NCBO funding for not-for-prot organizations such as the
Chesapeake Research Consortium (CRC) also contribute to some basic research that
is accomplished by university investigators. The Mid-Atlantic Regional Association
Coastal Ocean Observing System (MARACOOS), a 501(c)3 corporation, has been
funded to help integrate and display CBIBS data in a way that is consistent with
Using Monte Carlo Simulation as a Financial Modeling Tool … 89
the U.S. IOOS. To support data integration with IOOS and acquire redundant server
storage and access, CBIBS data are transmitted to servers maintained by the National
Ocean Service, where processed data are inserted into a relational database and
shared with MARACOOS and the NDBC. Data are quality controlled in accordance
with the Quality Assurance of Real-Time Ocean Data (QARTOD) procedures that
were developed by the NOAA U.S. IOOS Program, delivered to NDBC and appear
on the Global Telecommunications Service within ten minutes of collection,
and periodically transferred to the NOAA National Centers for Environmental
Information for archiving. Finally, for prot companies such as Earth Resources
Technology, Inc. (ERT) provide marine technicians to support many operational
and maintenance tasks of CBIBS.
Such valuation research helps the Chesapeake Bay Program and organizations
such as NCBO to dene with accuracy and inventory the impact of observational
systems such as CBIBS. It also provides an alternative to traditional discounted-
cash-ow (DCF) analysis which, when used alone, may be biased against valuing
projects such as CBIBS that are dependent on congressional appropriations. Rather
than forecast cash ows budget year by budget year and then discount these static
forecasts at the opportunity cost of capital, we will apply a Monte Carlo model,
thereby allowing the reader to visualize inherent risks and their impact upon the
Chesapeake Bay Program. McGinty (2016), for instance, describes how weather
forecasters can use Monte Carlo simulations to compute for reliable probabilities of
hurricane tracks and thus improve the skill of hurricane forecasting.
The allocation of resources is a key driver in CBIBS utility. This paper, moreover,
also considers the policy implications if CBIBS were to be decommissioned.1
A conservative salvage value for a CBIBS buoy—there are ten—is approximately
$150,000 as estimated by Dr. Kilbourne. Abandonment of the system, however, would
negatively impact other agencies such as the NOAA U.S. IOOS Program, U.S. Coast
Guard (USCG), and the National Park Service (NPS) as well as organizations such as
MARACOOS and the CBF that use CBIBS directly. NOAA funded research programs,
such as the Coastal and Ocean Modeling Testbed for example, have also relied on
CBIBS data (in this case, to assess an estuarine hypoxia model) (Luettich et al., 2017).
1The Chesapeake Bay Of fice of NOAA Fisheries and especially Dr. Byron Kilbourne who is
the lead oceanographer responsible for CBIBS provided data and information that was essential
to the completion of this study. Dr. Kilbourne identified the value drivers used therein, and his
expertise assisted in the identification of the appropriate distribution to be used for each variable.
Karyl B. Leggio & C. Reid Nichols90
Sample Organizations Sector/Program Funder
WMO Integrated Global
Observation System Region IV
Global Ocean
Observing System WMO
NDBC, Maryland Department of
Natural Resources (MD DNR),
USACE, USCG
Federal, State, and
Local Government
Department of
Defense, Department
of Commerce, State of
Maryland
University of Delaware, VCU,
Virginia Institute of Marine Science
(VIMS), University of Maryland Horn
Point Environmental Laboratory and
Chesapeake Biological Laboratory
Local Universities
NOAA, Southeastern
Universities Research
Association (SUR A)
CBF, Chesapeake Research
Consortium (CRC), Mid-Atlantic
Regional Association Coastal
Ocean Observing System
(MARACOOS), SURA, U.S. Power
Squadron
Non-Governmental
Organizations
NOAA, State of
Maryland, Private
AXYS Technologies, Caribbean
Wind, LLC, Dominion Virginia
Power, ERT, NORTEC, RPS Group,
WET Labs, etc.
Industry NOAA, Local
Universities
Commercial Fishermen,
Constellation Energy, Crowley
Maritime Corporation, Kingfisher
Environmental Services, Weather
Channel, Weather Underground
Industry NOA A, Private
Recreational Boaters
Power Boats, Work
Boats, Sail Boats,
Kayaks, and other
water craft
Private
Table 2: CBIBS beneficiaries range from local recreational boaters to members of the
World Meteorological Organization (WMO).
Using Monte Carlo Simulation as a Financial Modeling Tool … 91
CBIBS may be partitioned into ve main areas for the analysis of future value
drivers: i) programs that focus on marine operations, ii) programs that focus on
university research and development, iii) recreation opportunities for communities,
iv) protection of natural environments and features that are important to
communities, and v) use by industry. Each of these would be described in terms
of cash ows. CBIBS, for instance, supports the development of research and new
sensors that assist in NOAA’s Ocean Acidication Programs as well as of models
that support the Ecological Forecasting Roadmap. The program must also plan and
budget for risks that require unscheduled maintenance. Indeed, CBIBS has already
experienced ten catastrophic losses since 2007—ve collisions with vessels, ice
damage to four buoys, and vandalism of one buoy. Table 3 below highlights value
drivers for the CBIBS program that impact the number of parameters that are
measured, up time, usage, and data quality.
Impacts or risks to the budget such as deficits (or surpluses) need to be
understood for CBIBS to remain viable. If NCBO takes in more money than it
spends in a given year, for example, the result could be a surplus for enhancing
the existing CBIBS. The scal year 2017 CBIBS budget, for instance, has reduced
funding for vessel services and the development of a data management system.
Such anticipated changes could free up approximately $100,000 which could be
applied toward replacing aging CBIBS hardware or responding to system losses and
contingencies. NOAA also requested $5.5M for the coordination of their programs
and activities in the Chesapeake Bay region for 2017. It would seem then that
programs such as CBIBS facilitate the transfer of funds, property, and services to
the NOAA Chesapeake Bay Ofce from other federal agencies. We estimated that
the transfers will not exceed $500,000 per year.
The declining NCBO and stable CBIBS budgets are depicted in Figure 4. Based
on an R2 of 0.003, there is no clear association between the two.
Karyl B. Leggio & C. Reid Nichols92
Buoy Location Impact
(Period of Operation) Description
Susquehanna
(S) None (2008–2016)
Buoy is visible from locations along the Harford
and Cecil County shorelines, including Havre de
Grace’s Concord Point and Promenade area.
Patapsco (SN) 2008, 2010 (2008–2016)
Buoy was struck by vessel, resulting
in significant hull damage and flooded
instruments. Buoy was vandalized.
Annapolis
(AN) 2015 (2009–2016) Buoy accumulated ice on superstructure and
capsized in February 2016.
Upper
Potomac (UP) (2010– 2016)
Buoy was impaled by carbon fiber object.
There is a large hole in the hull. Buoy damage
estimated at $30,000.
Gooses Reef
(GR) 2015 (2010–2016)
Buoy accumulated ice on superstructure and
capsized in February 2016. Owing to Hurricane
Matthew damage, it flooded internally, resulting
in low buoyancy which reduced resiliency of the
hull. Buoy damage estimated at $30,00 0.
Potomac (PL) 2015 (2008–2016) Buoy accumulated ice on superstructure and
capsized in February 2016.
Stingray Point
(SR) None (2008–2016) Located near Deltaville, VA and approximately a
mile offshore.
York Spit (YS) None (2016)
Buoy is located near Perrin, VA at the mouth of
the York River. Maintenance activities involve the
CBIBS field and technical team in collaboration
with partners from NOAA Sanctuaries and the
VIMS.
Jamestown (J) 2015 (2007–2016) Buoy accumulated ice on superstructure and
capsized in February.
First Landing
(FL)
2008, 2010, 2012, 2016
(2011–2016)
Buoy struck by vessel and relocated; another
relocation is planned. In October 2016,
vessel collision damaged superstructure and
meteorological sensors; internal flooding
occurred during Hurricane Matthew. Buoy
damage estimated at $50,000.
Table 3: Attribute descriptions—CBIBS. Buoys collect and report information for up
to 37 meteorological and oceanographic parameters. Details were obtained from
http://buoybay.noaa.gov/.
Using Monte Carlo Simulation as a Financial Modeling Tool … 93
CBIBS vs. NCBO
y = -0.0036x + 0.63
R2 = 0.0033
CBIBS Budget ($M)
NCBO Budget ($M)
3 4 5 6 7 8 9 10 11 12 13 14 15 16
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
Figure 4: Budget graphic showing the correlation between the NCBO and CBIBS budgets.
FINANCIAL MODELING USING MONTE CARLO SIMULATION
For the Monte Carlo simulation, different types of distributions were reviewed,
the best t distribution was determined, and the data was inputted into the Monte
Carlo model. The product used for running the simulation, @Risk Monte Carlo
simulation software, contains more than 100 distributions for consideration in
modeling variables. The distributions selected, and the rationale behind their
selection, are discussed below.
Monte Carlo simulation performs risk analysis by building models of possible
results by substituting a range of values— a probability distribution—for any
factor that has inherent uncertainty. It then calculates results over and over,
each time using a different set of random values from the probability functions.
Depending upon the number of uncertainties and the ranges specified for
them, a Monte Carlo simulation could involve thousands or tens of thousands
of recalculations before it is complete. Monte Carlo simulation produces
distributions of possible outcome values.
By using probability distributions, variables can have different probabilities
of different outcomes occurring. Probability distributions are a much more
realistic way of describing uncertainty in variables of a risk analysis.…
Karyl B. Leggio & C. Reid Nichols94
During a Monte Carlo simulation, values are sampled at random from the input
probability distributions. Each set of samples is called an iteration, and the
resulting outcome from that sample is recorded. Monte Carlo simulation does
this hundreds or thousands of times, and the result is a probability distribution
of possible outcomes. In this way, Monte Carlo simulation provides a much
more comprehensive view of what may happen. It tells you not only what could
happen, but how likely it is to happen. (Palisade, n.d.)
The Monte Carlo simulation for this study required the development of scenarios
that included assumptions about the value drivers and factors that are critical to
CBIBS’s success. These value drivers relate to usage of the system by universities,
industries, other agencies, and the general public. Random inputs (within realistic
limits) were used to model CBIBS’s costs and produce probable outcomes of value.
A quantitative model of CBIBS activities as well as a “transfer equation” based
on NOAA-derived information were developed. Some of the value factors in the
transfer equation were found to follow a normal distribution while others followed
a triangular or uniform one.
Distribution parameters for each input (e.g., the mean and standard deviation for
inputs that follow a normal distribution) were then determined. For the triangular
distribution, the minimum, maximum, and mean variables were found through a
review of historical data as well as by relying upon the expertise and experience of
Dr. Kilbourne. Likewise, the minimum and maximum values for the variables in
constant probability uniform distribution were determined using historical data as
well as CBIBS’s executive expertise.
The value drivers are characterized by relevant distributions. Procurement of
spare parts and buoy components, for example, was modeled using a triangular
distribution with minimum costs of $493,000 annually, most likely outows of
$800,000, and maximum costs of $1,400,000. This distribution and its parameters
were determined by reviewing historical data as well as incorporating replacement
costs; distribution was estimated using actual historical data ranging from a cost of
components of $20,000 when no exceptional events occur to the loss of three buoys
like that which occurred in 2015 with a replacement cost of $450,000.
Likewise, costs incurred by CBIBS were modeled as a triangular distribution
based upon both historic costs and future projections. Buoy procurement is one
example—the practice for CBIBS is to acquire buoys on a regular basis to replace
worn or damaged units and have a small inventory of buoys and buoy parts
Using Monte Carlo Simulation as a Financial Modeling Tool … 95
available. Given the lack of correlation between CBIBS’s needs and NCBO’s budgets
(given that the budget is set by Congress), however, variables such as R&D expense,
extended operations, and new products are funded based on remaining budgetary
allotments available after costs of operations are covered and buoys are procured.
These variables are also modeled using a uniform distribution.
Figure 5: Monte Carlo Simulation flow diagram (adapted from Titman & Martin, 2016). The
simulation was run with incomplete value drivers as a student exercise. The importance
here is in the process of determining value for a public good such as CBIBS.
FINDINGS AND DISCUSSION
The pro forma cost of operating CBIBS resulted in an NPV of $24,307.44 and
an IRR of 10% over the ve-year period (2016–2020) of this study.2 Variables that
2The Office of Management and Budget (OMB) has had a real discount rate of seven percent
for public investment and regulatory analyses since 1992.
Step 1. A spreadshe et model was pre pared using E xcel for the releva nt value driver var iables (e.g. NPV ).
Step 2. Characte rize the value dri vers using a prob ability dist ribution.
Reduction R eported by NCBO Sales Revenue
Step 3. Run th e Simulatio n and Interpret th e Results
Generate
random
numbers for
each driver.
Calculate the e ntire
spreadsheet to
estimate CB IBS Free
Cash Flows ( FCF).
Save the values f or the
key forecast varia bles;
CBIBS F CF for each
yea r.
Summarize t he simulatio n
results (charts, summary
statistics, probability
statements).
Repeat this p rocess until t he maximu m number of ite rations have bee n completed.
Karyl B. Leggio & C. Reid Nichols96
could be used by NCBO for budgeting were estimated using a simple Monte Carlo
simulation based on historical trends and the following distributions for key
variables (Table 4):
Variable Expec te d Va lue
Distributional Assumption
Distribution Parameter Range
Budget
appropriations $800,000 Triangular $351,000 – $912,000
Costs $770,000 Triangular $740,000 – $800,000
Buoy procurement $150,000 Uniform $150,000 – $300,000
R&D $200,000 Uniform $200,000 – $1,000,000
Extended operations $200,000 Uniform $200,000 – $400,000
New products $200,000 Uniform $200,000 – $800,000
Table 4: Monte Carlo simulation assumptions for CBIBS project.
The variables are as follows:
•Budget appropriations represents government funding allocated for
NOAA and consequently to CBIBS every year
•Costs represents the projected annual operating expenses for CBIBS
•Buoy procurement is the line item for the cost of replacement buoys
and replacement buoy parts
•R&D represents research and development costs associated with
ongoing work in search of new ways to enhance the effectiveness
of the buoy program
•Extended operations is the line item for projected overtime costs
•New products represents the cost associated with procuring new
technologies to enhance the value added by the buoy program
The simulation used 10,000 iterations to produce a distribution of projected
cash ow for years 2016 through to 2020. The results are reported in Figure 6.
Using Monte Carlo Simulation as a Financial Modeling Tool … 97
Simulation Summary Information
Workbook Name 160907 CBIBs Monte Carlo data.xlsx
Number of Simulations 1
Number of Iterations 10000
Number of Inputs 12
Number of Outputs 1
Sample Type Latin Hypercube
Simulation Start Time 9/ 7/ 2016 17:29
Simulation Duration 0:00:05
Random # Generator Mersenne Twister
Random Seed 127563525
Minimum - $1,885,0 47.14
Maximum -$121,371.62
Mean -$869,917.43
Std Dev $253,000.27
Values 10000
NPV
-0.471-1.305
5.0% 5.0%90.0%
-2.00
-1.80
-1.60
-1.40
-1.20
-1.00
-0.80
-0.60
-0.40
-0.20
0.00
Values in Millions ($)
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
NPV
Values x 10^-6
Karyl B. Leggio & C. Reid Nichols98
Summary Statistics for NPV
Statistics Percentile
Min ($1,88 5, 047.14) 5% ($1, 304,761.15)
Max ($121, 371. 62) 10% ($1, 207,151.67)
Mean ($869,917.43) 15% ($1,136,259.22)
Std Dev $253,00 0.27 20% ($ 1, 0 8 4 ,17 8 . 91 )
Var 64 00 9139143 25% ($1,037,992.17)
Skew - 0.219 30% ($995,729.74)
Kurtosis 2.897 35% ($ 9 5 6 , 6 7 5 .1 0)
Median ($858,026.36) 40% ($924,500.8 4)
Mode ($784,096.28) 4 5% ($890,222.31)
Left X ($1,30 4,761.15) 50% ($858,026.36)
Left P 5% 55% ( $ 8 26 , 6 3 2 .11)
Right X ($ 471, 050.14) 60% ($792,572.10)
Right P 95% 65% ($763,378.93)
Diff X $833,711.01 70% ($730,094.76)
Diff P 90% 75% ($691,877.19)
#Errors 080% ($ 6 5 2 , 4 5 4.1 3)
Filter Min Off 85% ($605,992.93)
Filter Max Off 90% ($549,966.46)
#Filter 095% ($ 471,05 0.14)
Figure 6: Simulation results using @RISK Course Version with a spreadsheet NPV model.
Minimum - $1,885 ,047.14
Ma ximum - $121,371.62
Mean -$869,917.43
Std Dev $253,000.27
Values 10000
NPV
NPV
-0.471
-1.305
-2.00
-1.80
-1.60
-1.40
-1.20
-1.00
-0.80
-0.60
-0.40
-0.20
0.00
Values in Millions ($)
5.0% 90.0% 5.0%
1.0
0.8
0.6
0.4
0.2
0.0
Using Monte Carlo Simulation as a Financial Modeling Tool … 99
The average cost of running CBIBS is $869,917.43 every year with a standard
deviation of $253,000.27. This compares favorably with an estimated created value
of $4.6 billion which in turn generates a positive NPV of $3.7 billion. The simulation
provides objective data on the value of CBIBS—the project is a benet to multiple
agencies, universities, and organizations.
Sensitivity analysis can help determine which variables have the greatest
potential impact for CBIBS and therefore have the greatest chances of inuencing
project value. This Tornado diagram (see Figure 7) compares the relative importance
of the variables—the Y- a xis contains each type of uncertainty at base values and
the X-axis contains the spread or correlation of the uncertainty to the studied
output. Each uncertainty contains a horizontal bar and is ordered vertically from
most to least impactful to show uncertainties with decreasing spread from the base
values. The top ve variables most critical to CBIBS are, not surprisingly, the budget
appropriations for each of the ve years under study. Cuts in these budgets create
the largest impact on the value CBIBs is able to create for its constituents.
Figure 7. Tornado diagram for CBIBS. Each variable was independently considered for
estimated net present value.
Karyl B. Leggio & C. Reid Nichols100
CONTRIBUTIONS OF THIS STUDY
This student project suggests a methodology integrated with operations and
management that can track CBIBS costs in a way not previously done by the agency.
Using accurate and consistent cost information, the Monte Carlo simulation can
be applied to help make informed investment decisions and especially to prepare
better for the costs of unscheduled maintenance. This is particularly important since
the budget is a congressional appropriation—the Congressional Budget Committee
appreciates transparency in models such as the Monte Carlo simulation and can
see its sophistication in modeling variables with realistic distributions. Finally,
this work also provides tangible insights into the value of CBIBS for stimulating
local economies.
The National Oceanic and Atmospheric Administration eventually deemed this
student experiential learning project to be substantial and sophisticated enough to
assist it in justifying its budget request. The study was thus submitted to Congress
to help rationalize the allocations requested by the Chesapeake Bay Ofce of the
NOAA. Monte Carlo simulation was also deemed to be a modeling approach that
could be applied by NOAA managers for budget justications in the future.
Working with live data in the classroom, moreover, helps students to see the
challenges of actually gathering the data and developing a nancial model for data
analysis. It also enhances student learning and improves retention and recall of
theory when presented with the opportunity to apply such in the future. Finally,
the outcome of the study can be used to introduce new modeling techniques to
agencies and then have those techniques be adopted eventually by them.
There is immediate value creation for the student and potentially for the
organization when experiential learning is accomplished through projects that
benefit particular organizations (an environmental one in this case). Students
will typically have a better understanding of the challenges associated with
completing a comprehensive analysis. They have the opportunity to contextualize
it, and they report more success in transferring classroom learning to their work
world. Employers gain workers who are exposed to new theories and technologies
and therefore are more productive and require less management. Students with
advanced skills thus increase theirearning potentialby developing and rening
their capabilities.
Using Monte Carlo Simulation as a Financial Modeling Tool … 101
ACKNOWLEDGEMENTS
We would like to thank the Chesapeake Bay Office of NOAA Fisheries and
especially Dr. Byron Kilbourne, who is the lead oceanographer responsible for
CBIBS, for their efforts to provide data and information that was essential for the
completion of this study.
REFERENCES
AACSB International. 2007. AACSB assurance of learning standards: An
interpretation. Available at https://naspaaaccreditation.files.wordpress.
com/2014/04/aacsb.pdf.
AACSB International. 2016. A collective vision for business education. Available at
http://communications.aacsb.edu/les/amf_highroad_solution/workspace_43/
BEI_Report_Files/collective-vision-for-business-education.pdf.
Altalo, M. 2006. Applications of ocean forecast information for economic
advancement in developed and developing societies. In E. P. Chassignet &
Jacques Verron (Eds.), Ocean weather forecasting: An integrated view of
oceanography: 483–505. Dordrecht, The Netherlands: Springer.
Colgan, C. S. 2007. A guide to the measurement of the market data for the ocean
and coastal economy in the National Ocean Economics Program. Mo nte r e y, C A:
National Ocean Economics Program. Available at http://www.oceaneconomics.
org/Download/Market_Guide.asp.
ERISS Corporation & The Maritime Alliance. 2016. The ocean enterprise: A study
of US business activity in ocean measurement, observation, and forecasting.
Silver Spring, MD: National Oceanic and Atmospheric Administration / U.S.
Integrated Ocean Observing System. Available at https://cdn.ioos.noaa.gov/
media/2017/12/oceanenterprise_feb2017_secure.pdf.
Jamison, D. T., Hanushek, E. A., Jamison, E. A., & Woessmann, L. 2008. Education
and economic growth: It’s not just going to school, but learning something
while there that matters. Education Next, 8(2): 62 –71.
Karyl B. Leggio & C. Reid Nichols102
Kim, C., Tamborini, C. R., & Sakamoto, A. 2015. Field of study in college and lifetime
earnings in the United States. Sociology of Education, 88(4): 320 – 339.
Kite-Powell, H. L. 2009. Economic considerations in the design of ocean observing
systems. Oceanography, 22(2): 44–49.
Kuh, G. D. 2016. Making learning meaningful: Engaging students in ways that
matter to them. New Directions for Teaching and Learning, 145: 49– 56.
Luettich Jr., R. A., Wright, L. D., Nichols, C. R., Baltes, R., Friedrichs, M. A., Kurapov,
A., van der Westhuysen, A. J., Fennel, K., & Howlett, E. 2017. A test bed for
coastal and ocean modeling. Eos, 98. Available at https://eos.org/project-
updates/a-test-bed-for-coastal-and-ocean-modeling.
McGinty, J. C. 2016. As forecasts go, you can bet on Monte Carlo. The Wall Street
Journal, August 12. Available at https://www.wsj.com/articles/as-forecasts-go-
you-can-bet-on-monte-carlo-1470994203 (accessed August 12, 2016).
National Park Service. n.d. Captain John Smith Chesapeake National Historic Trail.
Available at http://smithtrail.net/ (accessed July 25, 2016).
NOAA Budget Ofce. n.d. Historical budget information. Available at http://www.
corporateservices.noaa.gov/nbo/historical_budget.html (accessed July 27, 2016).
Palisade. n.d. How Monte Carlo simulation works. Available at https://www.
palisade.com/risk/monte_carlo_simulation.asp.
Phillips, S., & McGee, B. 2014. The economic benets of cleaning up the Chesapeake:
A valuation of the natural benets gained by implementing the Chesapeake
clean water blueprint. Chesapeake Bay Foundation. Available at https://
conservationtools-production.s3.amazonaws.com/library_item_les/1328/1219/
FINALBenetsOfTheBlueprint_TechReport_and_Summary20141002.pdf.
Stretcher, R. 2015. Net present value simulation: A case study. Journal of Business
Strateg ies, 32(2): 139 –150.
Tamborini, C. R., Kim, C., & Sakamoto, A. 2015. Education and lifetime earnings
in the United States. Dem og r a p h y, 52(4): 1383–1407.
Using Monte Carlo Simulation as a Financial Modeling Tool … 103
Titman, S., & Martin, J. D. 2016. Valuation: The art and science of corporate
investment decisions (3rd ed.). Boston, MA: Pearson.
Wheeler, T. 2012. Bay “smart” buoys scuttled by Obama. The Baltimore Sun,
February 17. Available at http://articles.baltimoresun.com/2012-02-17/features/
bal-bay-smart-buoys-face-budget-cut-20120217_1_buoys-oyster-restoration-bay-
program (accessed July 27, 2016).
Winston, W. L. 1996. Simulation modeling using @Risk. Belmont, CA: Duxbury Press.
Karyl B. Leggio is Professor of Finance at Loyola University Maryland. She received
her Ph.D. at the University of Kansas, her MBA at East Tennessee State University,
and her undergraduate degree from Virginia Tech. Her primary area of research is in
deregulating industries, specifically in the area of risk management. Additional avenues
of active research are in the areas of real options, corporate restructuring, mergers, and
individual risk management. Dr. Leggio has been awarded numerous grants for her
research projects as well as being an award-winning teacher. She previously served as
Dean of the Sellinger School of Business (2008–2014).
C. Reid Nichols is the president of Marine Information Resources Corporation,
a Maryland veteran-owned small business. He is a physical oceanographer with an
M.S. from North Carolina State University and an M.B.A. in international business from
Loyola University Maryland. He has served as a physical oceanographer for the National
Oceanic and Atmospheric Administration and currently provides applied oceanographic
solutions to a variety of commercial and government customers. Nichols joined the U.S.
Marine Corps Reserves in 1977 as a combat engineer and, after commissioning, served
in positions as platoon, company, and battalion commander and then as a senior staff
officer until his retirement in 2011 as a colonel.
<$103>
