1.0 Title
The working title of this research is initially drafted as – Multi-Objective Project Finance Using Fuzzy Approach: Corporate Investment Portfolio Management.
2.0 Background of the Research
Multi-objective optimization (or programming) refers to the process of simultaneous optimization of two or more conflicting objectives subjected to certain constraints. Multi-objective optimization is applied to various fields including product and process design and aircraft design as well as in the industry of finance, oil and gas, automobile or where optimal decision-making must be consider despite of the evident trade-offs between two conflicting objectives.
Multi-objective optimization follows the fuzzy logic to deal with reasoning that is approximate rather than precise. Compared to the classic predicate logic, in fuzzy logic, the degree of truth to a statement can range between 0 and 1 and not true or false. The net present value (NPV) is given in the form of a fuzzy number. As such, an efficient fuzzy approach could minimize the cost-time quality of the project and support the decision-maker.
Investment portfolio, on the other hand, is created through asset allocation across different classes of investments including cash and equivalents, fixed income investments and equity investments. Basically, investment portfolio purports on spreading the risk and diversification. Apart from professional money managers and financial planners, investors’ decision-making could be supported by technical know-how. The multi-objective of corporate portfolio management is to balance high/low risks investments while also maximising returns. Capital budgeting, capital structure and payout decision will be addressed.
This research maintains that the Monte Carlo Simulation is the most appropriate fuzzy technique in managing corporate investment portfolio since this project valuation technique considers NPV, profitability index, internal rate of return and payback. On a fuzzy basis, investment portfolios and the associated are usually measured based on Pareto analysis. With due respect to the Pareto technique, the paper proposes a computer-based Monte Carlo Simulation in investment analysis. For the case we are going to consider, Monte Carlo method could provide a greater number of scenarios to be analysed as well as of probabilities of NPV scenarios that could be estimated.
3.0 Objectives of the Study
The main aim of the study is to apply the Monte Carlo Simulation fuzzy technique to the case of multi-objective corporate investment portfolio management. In lieu with this, the research will seek to address the following specific objectives.
§ To evaluate the efficiency of Monte Carlo Simulation technique when applied to corporate investment portfolio management
§ To distinguish possible risks, challenges and limitations of the Monte Carlo Simulation technique
§ To compare the Monte Carlo Simulation technique with other fuzzy technique that corporate investors are using
§ To analyse how Monte Carlo Simulation technique benefits the corporate investors in managing their investment portfolio and decision-making
4.0 Research Methodology
Generally, the purpose of the research is to conduct an experimental study on determining the applicability and extent of utilisation of the Monte Carlo Simulation technique for the corporate investors. The experimental method is the method that can be used to establish cause-and-effect relationships. One group, called the experimental group gets the treatment that the researcher believes will cause something to happen (this treatment is formally called the independent variable).
The experimental and control groups are compared on some variable that is presumed to reflect the effects of the treatment, or outcome. This is formally referred to as the dependent variable. In this case the independent variable is the Monte Carlo Simulation technique. The researcher will sample 2 corporate investors. The research will be presented in written form with the addition of data charts which will present the project’s results. Pie charts and network charts will be needed to illustrate some of the analyzed data. This cannot be confirmed, however, until the research data have been analyzed.
Credit:ivythesis.typepad.com
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