Fernando Rodrigues Amorim
Universidade Estadual de Campinas, Brazil
E-mail: fernando.amorim@feagri.unicamp.br
Bianca Regina Ferreira Silveira
Fundação Educacional de Barretos, Brazil
E-mail: biahsilveira3@gmail.com
Edilene Alves dos Santos
Fundação Educacional de Barretos, Brazil
E-mail: edilenealvesds@gmail.com
Pedro Henrique Camargo de Abreu
Centro Paula Souza, Brazil
E-mail: phcamargo1997@gmail.com
Juliana Rosa Tostes
Universidade de São Paulo, Brazil
E-mail: jrosatostes@yahoo.com.br
Submission: 17/04/2017
Revision: 11/05/2017
Accept: 16/05/2017
ABSTRACT
The acquisition of projects aimed at rural tourism
represents an alternative for generating income. The objective of this study
was to evaluate the viability of purchasing a farm that is structured as a
hostel, located in Joanópolis, interior of São Paulo, Brazil. The method was
based on exploratory research based on a case study comparing the economic
viability of this project. However, this viability is surrounded by
uncertainties and risks. With this, the Monte Carlo method was used to analyze
this probability. The data were obtained through the Department of Tourism in
the city of Joanópolis from primary and secondary data. The calculations were
made for work during a year drawn up in a cash flow with the monthly expenses
of the hostel. From the results it was concluded that it is feasible to buy
this hostel in the real and optimistic scenario and in the Monte Carlo method
analyzing the project’s total NPV values.
Keywords: Monte Carlo method; simulation; economic
viability; scenario analysis.
1. INTRODUCTION
Tourism,
in addition to being very favorable for the entrepreneur who ventures on the
scene, also generates expressive results for the community, encourages the
economy and cultural production.
According
to Sanovicz (2011), the tourist is guided by basic reasons that are summarized
in four verbs: eating, sleeping, shopping and visiting. What drives them to
make the decision in choosing a particular location is the reason why it
separates holidays and leisure, business and events, visiting relatives,
tasting a food or a drink, plus dozens of other reasons we can imagine.
However,
tourism generates a set of experiences and perspectives. This sector fits into
the service sector and provides particular, momentary and intangible
experiences.
Kotler
and Keller (2012) define service as any essentially intangible act or
performance that one party can offer to another and that does not result in
ownership of anything; this service may or may not be linked to a concrete
good, there is still the characteristic that there is no way to produce the
service without its consumption being concomitant.
In
other words, Cooper et al. (2011) believe that even though it is inserted in
the services sector, tourism does not only provide the intangible, since it has
tangible areas such as gastronomy, comfort offered by hotel facilities, all
products that the tourist can acquire; in addition to all the infrastructure
offered by the places of visitation.
Still
according to the same authors, despite all the tangible assets, resources and
infrastructure that are necessary for tourism to take place, what has the
greatest value for the tourist is experience; an interrelationship between
producer and consumer is created, where this experience is generated.
Maximiano
(2011) demonstrates that a system is a set of parts that interact and function
as a whole. However, Cooper et al. (2011) consider the most elementary form of
tourism, which can be understood as the set of consumption, production and
experiences generated.
Silva
et al. (2010) report that the vital studies for the success of an enterprise
focused on rural tourism are: quality in services, hygiene and cleanliness,
gastronomy and cultural identity; also consider Brazil favored for the practice
due to the variety of cultures brought by the colonization process and, added
to the diversity, there is the characteristic hospitality of the Brazilian,
especially the one of the inhabitant of the interior.
These
authors also explain that rural tourism provides a new alternative for the
development of rural communities, and complements the family income of the agricultural
units that appropriate the tourism proposal through offers of activities
related to leisure, sports, culture, gastronomy, hosting and productive
techniques.
The
destination that the tourist chooses is central element that contributes to the
consumption and the production of the tourism, and today stands out the
increasing offer of said rural tourism, justified by the increasing demand of
the society in search of places that offer tranquility and simplicity of the
interior, and also by the great incentive of governmental and non-governmental
bodies to such ventures; therefore, the objective of this article is to verify
the economic viability of an investment in a rural hostel, which aims at the
utilization of natural, historical, cultural and gastronomic resources.
2. MATERIALS AND METHODS
According
to the objective of this work, it is characterized as exploratory research. Gil
(2002) reports that this type of research provides a greater familiarity with
the problem in a way that allows ample and detailed knowledge about a certain
subject.
With
regard to development, this research is considered as a case study, carried out
from the analysis of the investment in the purchase of a hostel, inserted in
the Atlantic Forest and divided into apartments that can be individual, for a
couple, a couple with children and groups of up to 6 people.
It
has all the infrastructure ready to receive the tourist, as well as options
such as convenience store, event room and whirlpool, which allow
diversification and increase the attractiveness of the business.
The
Tourist Resort of Joanópolis is only 120 Km from the Capital São Paulo and is
easily accessible by the Highways Fernão Dias and Dom Pedro I, being famous for
the mild climate with temperatures that do not exceed 19º, it is also part of
the circuit between mountains and waters, an association that encourages the
tourist development of its member cities.
According
to information provided by the Tourism Secretariat, the municipality, which is
the site of the hostel, has about 12,800 inhabitants. It also receives large
numbers of tourists in the times of the typical Feast of St. John and in the
summer months, and smaller quantities in winter, where temperatures are lower.
2.1.
Case
study
The
purpose of the case study is not to provide the precise knowledge of the
characteristics of a population, but to allow a global view of the problem, or
to identify possible factors that influence or are influenced by it. Another
contradiction concerns time, when it says that it takes a long time for its
realization and its results become less consistent (GIL, 2002).
According
to Yin (2001) and Stake (2000), the definitions of a set of steps that can be
used in the so-called case study are: problem formulation; unit-case
definition; determination of the number of cases; elaboration of the protocol;
data collect; evaluation and analysis of the data and preparation of the
report.
Raising
the formulation of the problem is the initial stage of the research, in the
case study it is not appropriate to develop explanations of characteristics of
a population to measure the level of correlation between variables, or to
verify causal hypotheses. It is most commonly used for exploratory and
descriptive studies.
For
example, when you want to check how much a population consumes, a survey is
carried out, and if you want to verify the reasons that determine the
preference for a particular product, the survey may be insufficient and,
therefore, it is suggested to perform a case study (GIL, 2002). Therefore,
based on these procedures mentioned above, the research is characterized as a
case study.
2.2.
Economic
viability
Today,
due to the greatness and complexity of the world economy, and the need for
companies to adapt for the dynamism, the tools that help in the management are
extremely important and of great general use.
When
it comes to investing in a new business project or even a new venture, several
authors consider it of great importance that the economic viability of the
project is verified, so that only investment safety can be achieved. Sviech and
Mantovan (2013) explain that for this there are a set of techniques that seek
to establish these viability indicators and are often used for this purpose,
among them IRR, NPV and Payback.
The
aforementioned authors also explain that it is not interesting to make
decisions based on only one of the tools, since each one has shortcomings in
investment projects that have different useful lives and characteristics.
Therefore,
for this work three tools were used to analyze how feasible is the investment
in the purchase of the said rural inn: Internal Rate of Return (IRR), Net
Present Value (NPV), Payback Period.
According
to Batalha (2009), the most used methods to analyze the selection of investment
opportunities are: NPV, IRR and Payback Period. In order to analyze the
economic viability, it was necessary to draw up a cash flow that demonstrates
the entry and exit of all the products and resources necessary for the
operation of the inn.
Jerônimo
(2013) also considers that the indicators are strongly influenced by the
operational variables, which help in the oscillations of costs, revenues and
market dynamics. Based on this, it was decided to consider scenarios with high
and low seasons, as well as the construction of the scenarios of risks
characterized by the optimistic (+10% of expected result), real and pessimistic
(-10% of expected result) that will generate a more assertive analysis of
investment.
According
to Motta and Calôba (2002), the NPV is expressed by the algebraic sum of
discounted cash flows for the present instant, at an interest rate. According
to Bruni et al. (1998), NPV is characterized as the difference between future
cash flows transferred to a present value by the opportunity cost of capital
and the initial investment.
A
positive NPV means that the project will cover the initial investment, already
discounting an expected rate of return and will also generate additional
resources, providing benefits to investors. In the case of negative NPV, it
means that the project will not generate enough resources to meet the invested
capital, considering the required rate of return, and it is clear that its
implementation is not viable.
Payback
is the period it needs to be able to recover its invested capital. In other
words, Gitman (2002) reports that it is possible to verify how long it will
take for the investor to have a return on investment. Payback is used to
calculate how much time it will take to cover spending on the investment.
However, Lapponi (2000) corroborates that payback identifies and specifies the
time required for investment recovery, complementing NPV and IRR.
The
use of the IRR method does not have a specific capital cost as well as the NPV;
but, otherwise, its purpose is to discover an intrinsic rate of income
(SAMANEZ, 2001)
The
IRR is the maximum rate of return that an investment can verify to be viable; a
widely used tool to evaluate the expected return on each investment. To be sure
your investment will have a good return, your IRR has to be greater than zero.
From
the cash flows were determined the NPV, considering a rate of 10%; the IRR,
which makes the present value of the net flow equal to zero at the initial
moment; and finally the Payback Period was made, which establishes the time
necessary for recovery of capital.
The
projection of future cash flows considers the variables that influence the
operation of the company and the projection of the expected results. The
identification of the value drivers of the business was based on the analysis
of the historical statements and on macroeconomic variables that consider the
economic, social and political environment in which the company is inserted.
Regarding
the interpretation and analysis of data, the research has a quantitative
character, as it will demonstrate data on values and costs. However, this type
of research serves to analyze the information and show how everyone can
understand the numbers and/or data, the information acquired so they can
classify and analyze.
However,
the qualitative research aims to quantify the data and criteria of large
representative samples, using statistical analysis (MALHOTRA et al., 2010).
Investment
analysis is based on the use of techniques to identify the best investment
allocation among the various alternatives. When processing the data, equations
and calculations related to the asset, it is possible to see if there is a
profitability, how much it can be and whether the investment is working or not.
Therefore, it can be said that this analysis is indispensable when considering
the realization of an investment, since it proves to be a very good tool to
support the decision of the investor. Since every investment has a risk
involved, the investment analysis will help minimize them.
According
to Correia Neto et al. (2002), the Monte Carlo simulation is the most complete
method of measuring the risk of the company's cash flows, since it is more
dynamic in the analyzes of the volatilities of the flows and captures in a more
efficient way the relationship between the variables that compose the flow of
the company.
2.3.
Monte
Carlo method
The
next step was to use the Crystal Ball software option to run the Monte Carlo
method simulation application. Rodrigues et al. (2010), state that this type of
analysis is randomly generated results and recorded in the worksheet numerous
times, guaranteeing a statistical confidence level of 95%, which means that the
results generated by the simulation will be 95% of the time within the average
range population. The confidence level adopted in the present study is the same
as in the study cited.
For
the application of the Monte Carlo simulation, it was necessary to use the
results obtained from the criteria mentioned above, so that the simulation was
able to take into account the totality of the company due to the project. Only
in this way would it be possible to identify and reach a conclusion on the
feasibility of the investment.
Moore
and Weatherford (2005) argue that in many cases, simulation models are applied
to analyze a decision that involves risk. This risk is present in a model in
which the behavior of one or more factors cannot be defined with certainty. In
this case, these factors are known as random variables, and their behavior is
represented by a probability distribution.
According
to Lustosa et al. (2004, p. 251), the Monte Carlo simulation consists of a
method that "uses the generation of random numbers to assign values to the
system variables to be investigated."
In
order for the Monte Carlo simulation to be correctly applied, Lustosa et al.
(2004) indicate that the simulation should be replicated more than one hundred
times to provide a representative sample. However, there is no rule regarding
the maximum number of simulations to be performed. On the other hand, as basic
instruction, the greatest number of simulations should be applied due to the
processing power of the equipment used.
According
to Doucet (2004), the objective of the Monte Carlo simulation is to present the
probability distribution of the dependent variable analyzed, through the
behavior of the independent variables that somehow affect it. The result
generated is not a single value, but a sample of values obtained through a
random set of data created, from their respective probability of occurrence and
measures.
In
Monte Carlo simulation, at each iteration, the result is stored and, at the end
of all the repetitions, a frequency distribution is generated through the
sequence of results obtained, allowing the calculation of descriptive
statistics, such as: average (expected value), minimum value, maximum value and
standard deviation. Thus, the executor of the simulations, would be responsible
for idealizing future scenarios of operation of the system under analysis
(SARAIVA JÚNIOR et al., 2011).
The
Monte Carlo method can be used in the investment analysis, by means of the
continuous and random generation of numbers that are connected in the inputs
and outputs of cash used in the calculation of the NPV. Such changes in cash
flow work as random scenarios. Randomly generated numbers obey predefined
probability distributions, based on data obtained from the analysis of past
events or using projections for the future. The definition of probability
distributions is made on factors that compose the NPV calculation, such as
sales growth and profit per year, where the act of randomly generating these
factors causes NPV to assume several values (OLIVEIRA, 2008).
In
order to operationalize the SMC, the following steps must be followed:
·
Define the variables involved in the
system under analysis based on past data or subjective estimates of managers;
·
Construct the frequency distributions
(absolute, relative and accumulated) for each one of the defined variables;
·
Define for each variable considered, the
class or incidence intervals of the random numbers, based on projected
cumulative frequency distributions;
·
Generate random numbers;
·
Incident random numbers generated in
intervals of each class of each level;
·
Simulate the experiments.
3. RESULTS AND DISCUSSION
The
initial investment forecast was R$ 3.232.596.92. This value is based on
projects with inn characteristics. However, R$ 4.003,36 of fixed costs was
related.
The
variable costs were R$ 242.917,50, allocated to customer demand variations over
the course of a year.
Cash
flow was projected to 10 (ten) years, and growth rates were included over the
course of these ten years, based on the growth prospects of tourism in the
region, with 10% growth in the first 5 years and 5% in the next 5 years.
We
also considered the real, pessimistic and optimistic risk scenarios that
returned investment expectations between the 5th and 6th year of operation.
Table
1 below shows the result of the actual scenario.
Table 1: Real Scenario
Years |
NPV |
Balance |
|
0 |
-R$
3.232.596,92 |
-R$
3.232.596,92 |
|
1 |
R$
456.542,18 |
-R$
2.776.054,74 |
|
2 |
R$
502.196,40 |
-R$
2.273.858,34 |
|
3 |
R$
552.416,04 |
-R$
1.721.442,30 |
|
4 |
R$
607.657,64 |
-R$
1.113.784,66 |
|
5 |
R$
668.423,41 |
-R$
445.361,26 |
|
6 |
R$
701.844,58 |
R$
256.483,32 |
|
7 |
R$
736.936,80 |
R$
993.420,12 |
|
8 |
R$
773.783,65 |
R$
1.767.203,77 |
|
9 |
R$
812.472,83 |
R$
2.579.676,60 |
|
10 |
R$
853.096,47 |
R$
3.432.773,07 |
|
Estimated
Return Rate |
12,25% |
||
PAYBACK |
5,634558237 |
5
years 7 months and 18 days |
|
NPV |
R$
262.908,86 |
||
IRR |
14% |
||
The
results showed that all the analyzes were positive and satisfactory, being
feasible the investment in this scenario.
It
is possible to compare the values obtained in this scenario with the study of
Ires (2013), which consists of a similar analysis in relation to rural tourism,
where the author obtained a NPV of € 913,749, an IRR of 57.41% and a Payback
Period of 4 years. Therefore, the values obtained in the real scenario have a
lower IRR and Payback Period in the comparison. However, the project can be
considered profitable.
When
comparing with the work of Cabral (2016), the author obtained a NPV of €
304,474 in the Expected Scenario, considered viable by him. In addition, the
author obtained a IRR of 12.43%, and a Payback Period of approximately 6 years
and 7 months.
Table
2 below shows the results of the optimistic scenario, 10% more than the real
one.
Table 2: Optimistic Scenario (10% more than the real)
Years |
NPV |
Balance |
|
0 |
-R$
3.232.596,92 |
-R$
3.232.596,92 |
|
1 |
R$
502.196,40 |
-R$
2.730.400,52 |
|
2 |
R$
552.416,04 |
-R$
2.177.984,48 |
|
3 |
R$
607.657,64 |
-R$
1.570.326,84 |
|
4 |
R$
668.423,41 |
-R$
901.903,43 |
|
5 |
R$
735.265,75 |
-R$
166.637,68 |
|
6 |
R$
808.792,32 |
R$
642.154,65 |
|
7 |
R$
889.671,56 |
R$
1.531.826,20 |
|
8 |
R$
978.638,71 |
R$
2.510.464,91 |
|
9 |
R$
1.076.502,58 |
R$
3.586.967,50 |
|
10 |
R$
1.184.152,84 |
R$
4.771.120,34 |
|
Estimated
Return Rate |
12,25% |
||
PAYBACK |
5,206032715 |
5
years 2 months and 14 days |
|
NPV |
R$
858.596,21 |
||
IRR |
18% |
||
The results showed that
NPV in this scenario was higher by R $ 595,687.35 than the real scenario, the
IRR was higher by 4%, and the Payback Period was lower by 154 days.
Table
3 below shows the results of the pessimistic scenario, 10% less than the real
one.
Table 3: Pessimistic Scenario (10% less than the real)
Years |
NPV |
Balance |
|
0 |
-R$
3.232.596,92 |
-R$
3.232.596,92 |
|
1 |
R$
410.887,96 |
-R$
2.821.708,96 |
|
2 |
R$
451.976,76 |
-R$
2.369.732,20 |
|
3 |
R$
497.174,43 |
-R$
1.872.557,77 |
|
4 |
R$
546.891,88 |
-R$
1.325.665,89 |
|
5 |
R$
601.581,07 |
-R$
724.084,82 |
|
6 |
R$
631.660,12 |
-R$
92.424,70 |
|
7 |
R$
663.243,12 |
R$
570.818,42 |
|
8 |
R$
696.405,28 |
R$
1.267.223,70 |
|
9 |
R$
731.225,54 |
R$ 1.998.449,24 |
|
10 |
R$
767.786,82 |
R$
2.766.236,06 |
|
Estimated
Return Rate |
12,25% |
||
PAYBACK |
6,139352671 |
6 years
1 month and 20 days |
|
NPV |
-R$
86.641,72 |
||
IRR |
12% |
||
The results showed
that, in this scenario, NPV was negative, IRR was 2% lower than the real
scenario, and the Payback Period was 341 days higher than the real scenario.
According
to the Payback Period analysis, it is possible to conclude that the investment
will bring returns in all scenarios. In the pessimistic scenario, the return
will be provided in a period 6 months higher when compared to the real
scenario. However, the fact that the estimated NPV is negative, indicates that
the project cannot be treated as feasible.
To
perform the simulation, you must be informed of the number of evaluations that
will be performed. Thus, the number of evaluations for the present study was
50,000 evaluations, a considerably high number and capable of providing
considerable estimates.
Through
the execution of the simulation it was possible to obtain some information, such
as: frequency, minimum, average and maximum project NPV, median, among other
information.
Through
the simulation, it was possible to observe that the average obtained for the
project's total NPV was R$ 3,656,620.33. This value is higher than the NPV of
the real scenario, estimated at R$ 853,096.47. The minimum value found in the
simulation for the project's total NPV was R$ 3,102,679.69, which is R$
2,334,892.87, higher than the sum of the pessimistic NPV. The maximum NPV found
in the simulation was R$ 4,358,873.26, which is R$ 748,279.58 lower than the
sum of the optimistic NPV.
Figure 1: Frequency Graph of Project's
Total NPV forecast
According
to Garcia et al. (2010), the range or amplitude refers to the difference
between the maximum and minimum points. However, in the comparison of this
work, we can see the great distance between the extreme points of the real
values and the simulated ones, as well as the great difference in their
amplitudes.
Following Table 4, it demonstrates the
scenario values and the Project’s NPV Simulation and Distribution columns.
Table 4: Simulation and Distribution of Project's NPV
This same comparison
was made for the Project's Total Balance. After the simulation was carried out,
it was possible to obtain some information, such as: frequency chart, minimum,
average and maximum project balance, median, among other information.
Figure 2: Frequency Graph of Project's
Total Balance forecast
Through
the simulation, it was possible to observe that the average obtained for the
project's total balance was R$ 2,032,881.16. This amount is greater than the
Balance of the actual scenario, estimated at R$ 3,432,773.07. The minimum value
found in the simulation for the project's total balance was R$ 4,338,842.46.
This amount was higher than the pessimistic balance that was R$ 2,766,236.06.
The maximum balance found in the simulation was R$ 657,440.14. This amount is
lower than the optimistic balance that was R$ 4,771,120.34.
According
to Rodrigues et al. (2010), Monte Carlo simulation acts as a decision-making
tool in the decision-making of investment in risk. This fact justifies the
reason why this tool makes mathematical simulations that refer to a real
system, allowing investors the decision to evaluate the impacts of the independent
variables, represented by the assumptions arbitrated to the project, in the
dependent variables, mentioned as decisive variables, significantly improving
the quality of their decisions.
Table
5 below demonstrates the scenario values and the Project’s Balance Simulation
and Distribution columns.
Table 5: Simulation and Distribution of Project's Total Balance
The application of
Monte Carlo simulation provided the generation of estimates and projections for
the project, allowing a statistical analysis of the viability, but this cannot
be the only source to be considered by the decision makers, since their results
are generated from values collected and analyzed by an individual, evidencing
that the critical knowledge originates from the individuals involved in the
project.
4. FINAL CONSIDERATIONS
After
analyzing all the economic opportunities to acquire a hostel, it was observed
that Joanópolis-SP is a very busy city and with projections of growth. Because
it has the title of tourist resort, the tendency is that more and more people
will go to cities with the characteristics of the tourist resorts to be able to
escape the busy life. In the present work, it can be concluded that the
investment in this hostel is viable, in the real and optimistic scenario, due
to its satisfactory results in Payback, IRR and NPV and with this, its results
were higher than expected, when compared to financial income of Fixed income.
It
can be concluded that the possibilities of the NPV and the Total Balance of the
project reached the most pessimistic or optimistic indexes within the
simulation, is 0% because of the previously established triangular
distribution. On the other hand, it is worth mentioning that each simulation in
Crystal Ball will present different indicators (minimum, average, maximum,
median, etc.) among them, even if they are performed with the same input
values.
In
addition, the work may conclude that the Monte Carlo simulation provided us
with accurate information to assist in the decision making of this investment.
REFERENCES
BATALHA, M. O. (2009) Gestão
Agroindustrial, 3 ed. São Paulo: Atlas.
BRUNI, A. L.; FAMÁ, R.; SIQUEIRA, J. O.
(1998) Análise do risco na avaliação de projetos de investimento: uma aplicação
do Método de Monte Carlo. Cadernos de Pesquisa em Administração, v. 1,
n. 6, p. 62-74.
CABRAL, B. M. C. (2016) Analysis of the Viability of a Rural Tourism Investment. Dissertation (Master in Financy). Lisbon: School of Economics and
Management.
COOPER, C.; HALL, M.; TRIGO, L. G. G. (2011) Turismo Contemporâneo. Rio de Janeiro: Elsevier.
CORREIA NETO, J. F.;
MOURA, H. J.; FORTE, S. H. A. C. (2002) Modelo prático de previsão de fluxo de
caixa operacional para empresas comerciais considerando os efeitos do risco,
através do método de Monte Carlo. REAd -
Revista Eletrônica de Administração, v. 8, n. 3, p. 1-23.
DOUCET, A. (2004) Sequential Monte Carlo methods. In: (Ed.). Encyclopedia of statistical sciences.
Hoboken,
NJ: John Wiley & Sons, Inc.
GALLON, A. V; SILVA, T. D; HEIN, N.;
OLINQUEVITCH, J. L. (2006) Utilização da análise de investimento nas empresas
de tecnologia do vale de Itajaí/SC. In: SIMPÓSIO DE GESTÃO DA INOVAÇÃO
TECNOLÓGICA, 24, Gramado, Proceedings...
Gramado: ANPAD, 2006.
GARCIA, S.; LUSTOSA, P. R. B.; BARROS, N. R. (2010) Aplicabilidade do método de simulação de Monte Carlo
na previsão dos custos de produção de companhias industriais: o caso da
Companhia Vale do Rio Doce. Revista
de Contabilidade e Organizações, v. 4, n. 10, p. 152-173.
GIL, A. C. (2002) Como elaborar projetos de pesquisa, 4 ed. São Paulo: Atlas.
GITMAN, L. J. (2002) Princípios de
administração financeira, 7 ed. São Paulo: Harbra.
IRES, A. S. P. C. (2013) Plano de Negócios: Turismo em Espaço Rural na Serra de Tomar.
Dissertation (Master in Tourism). Estoril: ESHTE.
JERÔNIMO, C. E. M. (2013) Estudo de
viabilidade econômica aplicado a um projeto agroindustrial: Análise de
sensibilidade. Revista de Administração
de Roraima, v. 3, n. 2, p. 156-180. doi:
http://dx.doi.org/10.18227/rarr.v3i2.790
KOTLER, P.; KELLER, K. L. (2012) Administração de marketing, 14 ed. São
Paulo: Pearson.
LAPPONI, J. C. (2000) Projetos de
investimento: construção e
avaliação do fluxo de caixa: modelos em Excel. São Paulo: Laponni.
LUSTOSA, P. R. B.; PONTE, V. M. R.; DOMINAS,
W. R. (2004) Simulação. In: CORRAR, L. J.; THEÓPHILO, C. R. (Org.). Pesquisa Operacional para decisão em
contabilidade e administração. São Paulo: Atlas.
MALHOTRA,
N. K. (2010) Marketing research: an
applied orientation, 6 ed. Upper Saddle River, NJ: Prentice Hall.
MARQUEZAN, L. H. F.; BRANDONI, G. (2006)
Análise de Investimentos. Revista
Eletrônica de Contabilidade, v. 3, n. 1, p. 1-15. doi: http://dx.doi.org/10.5902/198109466137
MAXIMIANO, A. C. A. (2011) Introdução à administração, 2 ed. São
Paulo: Atlas.
MOORE, J. H.; WEATHERFORD, L. R. (2005) Tomada de Decisão em Administração com
Planilhas Eletrônicas, 6 ed. Porto Alegre: Bookman.
MOTTA, R. R.; CALÔBA, G. M. (2002) Análise
de investimentos: tomada de decisão
em projetos industriais. São Paulo: Atlas.
OLIVEIRA, M. H. F.
(2008) A avaliação
econômico-financeira de investimentos sob condições de incerteza: uma comparação entre o
método de Monte Carlo e o VPL fuzzy. Dissertation
(Master in Production Engineering). São Paulo: USP.
RODRIGUES, E. M.; NUNES, R. V.; ADRIANO, N. A. (2010) A simulação de Monte Carlo como instrumento
para a análise econômico-financeira em investimentos de risco - O caso de uma
decisão de investimento na abertura de uma filial para revenda de equipamentos
pesados no Estado do Ceará. In: CONGRESSO BRASILEIRO DE CUSTOS, 17, Belo
Horizonte, Proceedings... Belo
Horizonte: CBC, 2010.
SAMANEZ, C. P. (2011) Matemática Financeira: aplicações à análise e investimentos.
São Paulo: Prentice Hall.
SANOVICZ, E. (2011) Turismo em Cidades. São Paulo:
Campus.
SILVA, N. P.; FRANCISCO, A. C.; THOMAZ, M. S.
(2010) Turismo rural como fonte de renda das propriedades rurais: um estudo de
caso de uma pousada rural na Região dos Campos Gerais no Estado do Paraná. Caderno Virtual de Turismo, v. 10, n.
2, p. 22-37.
STAKE,
R. E. (2000) Case studies. In: DENZIN, N. K.; LINCOLN, Y. (Eds.). Handbook of
qualitative research, 2 ed. Thousand Oaks: Sage.
SVIECH, V.; MANTOVAN, E. A. (2013) Análise de
investimentos: controvérsias na utilização da TIR e VPL na comparação de
projetos. Percurso, v. 13, n. 1, p.
270- 298.
YIN, R. K. (2001) Estudo
de Caso: planejamento e
métodos, 3 ed. Porto Alegre: Bookman.