scholarly journals A Comparison of Monte Carlo Simulation and Discounted Cash Flow Investment Appraisal Techniques Using an Office Building in Akure, Nigeria

2017 ◽  
Vol 1 (2) ◽  
pp. 299-308
Author(s):  
S. P. Akinbogun ◽  
O. P. Binuyo ◽  
O. T. Akinbogun
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cash Flow ◽  
Investment Decision ◽  
Single Point ◽  
Investment Analysis ◽  
Point Estimate ◽  
Discounted Cash Flow ◽  
Expected Return ◽  
Level Of Risk

Every rational investor aimed to secure an optimum return from an investment at certain level of risk. However, the factors that affect the realization of the expected return are not known with certainty. Therefore, the reliability of a single point estimate for investment decision is debatable particularly when the likelihood of realizing the expected return is crucial to the investor. This study aimed at examining a better method of analysing the uncertainty in property investment rather than the use of the single point estimate formula. We collected a set of historic data from a multi-tenanted office complex in Akure to carry out the investment analysis using Monte Carlo simulation and Discounted Cash Flow Technique. The results from the study were compared and revealed that the single point estimate accounted for a huge level of risk as probability of realizing the expected return is unknown. The Monte Carlo simulation offers a more robust opportunity for a measured investment decisions by providing a probability of realizing different level of the expected returns. The study concludes that it is difficult to make a smart investment decision on a single point estimate. It therefore recommends the use of Monte Carlo simulation for property investment analysis for a more realistic return.

Using Monte Carlo Simulation to Improve Ceramic R&D Investment Decisions

2010 ◽  
Vol 105-106 ◽  
pp. 798-801
Author(s):  
Bao Cheng He ◽  
Hong Tao Jiang ◽  
Shu Zhi Yao ◽  
Bao Yuan He
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Real Options ◽  
Cash Flow ◽  
Investment Decisions ◽  
Discounted Cash Flow ◽  
Valuation Model ◽  
Managerial Flexibility ◽  
D Investment

The success of ceramic companies is highly dependent on research and development (R&D). Thus, a pivotal aim of management is to allocate resources to the best scientific and financial R&D projects. But the valuation of ceramic R&D is a difficult task for managers. The conventional discounted cash flow (DCF) methods fail to consider the value of managerial flexibility provided by R&D projects. Real options Analysis (ROA) offers a superior way of capturing the value of flexibility. It enables decision-maker to value projects more accurately by incorporating managerial flexibilities into the valuation model. However, ROA can’t effectively deal with the volatility of parameters in itself under high uncertain circumstance. In view of the limitation of ROA, this paper uses Monte Carlo simulation to solve the parameters volatility problems. In the end, the case study proves that Monte Carlo simulation can improve R&D investment decisions, especially for highly unpredictable ceramic R&D projects.

scholarly journals Economic and Risk Analysis to Deep Water Operations

2020 ◽  
pp. 11-16
Author(s):  
C. E. Ubani ◽  
A. O. Oluobaju
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cash Flow ◽  
Net Present Value ◽  
Oil And Gas ◽  
Risk Model ◽  
Flow Analysis ◽  
Discounted Cash Flow ◽  
Exploration And Production

The exploration and production (E&P) operations of oil and gas project in deep waters, is associated with risks. These risks affects return on investment if they are not identified and analyzed to reduce their impact on the project. This study seek to apply Discounted Cash Flow (DCF) analysis, Monte Carlo Simulation and Sensitivity analysis, to an existing field in the Niger Delta region in Nigeria, to ascertain the viability of deepwater project when it is affected by government fiscal terms and technical terms. Economic and risk models were developed to determine profitability indicators and risk associated with the project. Risk simulator software was used to carry out Monte Carlo simulation and the sensitivity analysis. Results obtained showed that the project was economically viable with a Net Present Value (NPV) of $1,621.8 million and Internal Rate of Return (IRR) of 34%. The Monte Carlo Simulation and the sensitivity analysis showed that the Contractor’s NPV and percentage take were most sensitive to tax (under the fiscal terms) with an range of $639.27 million for a variation of by +/- 10% and crude price (under the technical term). The model developed can easily be applied in investment selections and decision makers should make decision based on the outcome of both economic model (cash flow analysis) and the risk model (Monte Carlo Simulation).

scholarly journals Synergies between an Observed Port and a Logistic Company: Application of the Discounted Cash–Flow Model and the Monte Carlo Simulation

2017 ◽  
Vol 8 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Dejan Dragan ◽  
Bojan Rosi ◽  
Toni Avžner
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cash Flow ◽  
Flow Model ◽  
Strategic Partnership ◽  
Discounted Cash Flow ◽  
Strategic Cooperation ◽  
Simulation Based ◽  
Main Motive ◽  
Dcf Model

AbstractThe paper addresses an analysis of potential synergies in collaboration between an observed Port in the Mediterranean Sea and Central-European logistic railway-services based company. Both companies have established a strategic partnership. The main motive was cooperation in rail transport, with a particular emphasis on potential synergies that would a rail traffic have brought to a port’s business. For the purpose of synergies valuation under uncertain conditions, a Monte Carlo simulation-based framework with integrated discounted cash flow (DCF) model is applied. The possible values of future synergies are calculated via the DCF model by simultaneously changing values of different uncertain financial parameters at each repetition of a Monte Carlo scenario-playing mechanism. In this process, predicted forecasts of future synergetic throughputs are also used for various types of observed cargo. As it turned out, the generated synergies’ values follow the approximate normal distribution. Based on statistical inference and analysis of probability intervals it was discovered that there might indeed exist certain important synergies in the collaboration between both companies. This fact has convinced us into a belief in the correctness of companies′ decision to enter into such kind of strategic cooperation.

The Use of Quantitative Methods in Investment Decisions

2019 ◽  
pp. 256-275 ◽  
Author(s):  
Serkan Eti
Keyword(s):  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Quantitative Methods ◽  
Simulation Analysis ◽  
Investment Decision ◽  
Tree Algorithms ◽  
Artificial Neural

Quantitative methods are mainly preferred in the literature. The main purpose of this chapter is to evaluate the usage of quantitative methods in the subject of the investment decision. Within this framework, the studies related to the investment decision in which quantitative methods are taken into consideration. As for the quantitative methods, probit, logit, decision tree algorithms, artificial neural networks methods, Monte Carlo simulation, and MARS approaches are taken into consideration. The findings show that MARS methodology provides a more accurate results in comparison with other techniques. In addition to this situation, it is also concluded that probit and logit methodologies were less preferred in comparison with decision tree algorithms, artificial neural networks methods, and Monte Carlo simulation analysis, especially in the last studies. Therefore, it is recommended that a new evaluation for investment analysis can be performed with MARS method because it is understood that this approach provides better results.

Monte Carlo Simulation-Based Benefit-Cost Analysis Combined with Analytical Hierarchy Process to Support ITS Investment with Consideration of Connected Vehicle Technology

2018 ◽  
Vol 2672 (19) ◽  
pp. 1-12 ◽  
Author(s):  
Samaneh Khazraeian ◽  
Mohammed Hadi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Analytical Hierarchy Process ◽  
Return On Investment ◽  
Intelligent Transportation System ◽  
Investment Analysis ◽  
Connected Vehicle ◽  
Freeway Traffic ◽  
Benefit Cost Analysis ◽  
Hierarchy Process

Decisions to invest in alternative intelligent transportation system (ITS) technologies are expected to increase in complexity, particularly with the introduction of connected vehicles (CV) and automated vehicles (AV) in the coming years. Traditional alternative analyses based on deterministic return on investment analysis are unable to capture the risks and uncertainties associated with the investment problem. In addition, these methods cannot account for agency preferences and constraints that cannot be converted to dollar values. This study utilizes a combination of a stochastic return on investment and a multi-criteria decision analysis method referred to as the Analytical Hierarchy Process (AHP) to select between ITS deployment alternatives considering emerging technologies. The approach is applied in a case study of the selection between using CV data and point detector data to support the freeway traffic data collection and monitoring service. The four objectives specified in the AHP analysis are providing the required functions, providing the required performance, minimizing the risks and constraints, and maximizing the return on investment. A stochastic return-on-investment analysis using a Monte Carlo simulation was used to calculate the return on investment values for input to the AHP method.

Evaluation of Probability Point Estimate Methods for Uncertainty Analysis of Hydrocarbon in Place as an Alternative Techniques to Monte Carlo Simulation

2019 ◽  
Author(s):  
Hossein Algdamsi ◽  
Ahmed Amtereg ◽  
Ammar Agnia ◽  
Gamal Alusta ◽  
Ahmed Alkouh
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Uncertainty Analysis ◽  
Point Estimate ◽  
Alternative Techniques

Instance Analysis On Investment Decision of Port Engineering

2011 ◽  
Vol 282-283 ◽  
pp. 342-345
Author(s):  
Bo Zhang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Investment Decision ◽  
Investment Risk ◽  
Project Investment ◽  
Engineering Project ◽  
Decision Methods ◽  
Instance Analysis ◽  
Port Engineering ◽  
Risk Decision

Advantages and disad vantages of every kind of risk decision methods are analysed in this paper with a risk controlling form. The method of engineering project investment risk decision synthesized by CIM model and Monte-Carlo simulation is proved feasible and effective by the conclusion of the simulation case of certain port with the software crystal ball7.22.

Monte Carlo Simulation–Based Assessment of Risks Associated with Public–Private Partnership Investments in Toll Highway Infrastructure

2017 ◽  
Vol 2670 (1) ◽  
pp. 59-67 ◽  
Author(s):  
Zhe Han ◽  
Juan Diego Porras-Alvarado ◽  
Jingran Sun ◽  
Zhanmin Zhang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Investment Decision ◽  
Quantitative Information ◽  
Decision Makers ◽  
Control Measures ◽  
Highway Projects ◽  
Monte Carlo Simulation Technique ◽  
Funding Mechanism ◽  
Private And Public

The demands for delivering highway services keep growing worldwide. However, funding from government and public agencies alone cannot cover the capital needed to operate and maintain existing highway systems, much less to construct new ones. Public–private partnerships (PPPs) are an innovative funding mechanism for highway agencies to use private capital and expertise in transportation infrastructure projects so as to increase funding options to bridge the budget gap. Even though parties involved in PPPs take different roles and responsibilities, there are still risks taken or shared by the public and private sectors. In particular, assessing risks associated with the potential returns of investments is of great importance to the private and public sectors. This paper presents a methodological framework for assessing the investment risks of PPP toll highway projects, which may help decision makers. The financial viability associated with the components of a project is considered and analyzed, and the Monte Carlo simulation technique is applied to evaluate the overall project risks. Finally, a numerical case study is conducted to demonstrate the application of the proposed method. The risk analysis provides statistical distribution of investment returns for the project under analysis, which will supply decision makers with direct information to estimate the project’s overall financial risks and develop corresponding risk control measures. The risk simulation results are interpreted so that quantitative information can be provided to agencies to establish investment decision criteria.

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