Fuzzy Logic and Intelligent Agents: Towards the Next Step of Capital Budgeting Decision Support

The economic life of large investments is long and thus necessitates constant dynamic managerial actions. To be able to act in an optimal way in the dynamic management of large investments managers need the support of advanced analytical tools. They need to have constant access to information about the real time situation of the investment, as well as, access to up-to-date information about changes in the business environment. What is more challenging, they need to integrate qualitative information into quantitative analysis process, and to integrate foresight information into the capital budgeting process. In this paper we will look at how emerging soft computing technologies, specifically fuzzy logic and intelligent agents, will help to provide a better support in such a context and then to frame a support system that will make an integrated application of the aforementioned technologies. We will first develop a holistic framework for an agent-facilitated capital budgeting system using a fuzzy real option approach. We will then discuss how intelligent agents can be applied to collect decision information, both qualitative and quantitative, and to facilitate the integration of foresight information into capital budgeting process. Integration of qualitative information into quantitative analysis process will be discussed. Methods for integrating qualitative and quantitative information into fuzzy numbers, as well as, methods for using the fuzzy numbers in capital budgeting will be presented. A specification of how the agents can be constructed is elaborated.Intelligent Agents, Fuzzy Sets, Capital Budgeting, Real Options, DSS

Capital budgeting problems with fuzzy cash flows

We consider the internal rate of return (IRR) decision rule in capital budgeting problems with fuzzy cash flows. The possibility distribution of the IRR at any r � 0, is defined to be the degree of possibility that the (fuzzy) net present value of the project with discount factor requals to zero. Generalizing our earlier results on fuzzy capital budegeting problems [5] we show that the possibility distribution of the IRR is a highly nonlinear function which is getting more and more unbalanced by increasing imprecision in the future cash flow. However, it is stable under small changes in the membership functions of fuzzy numbers representing the lingusitic values of future cash flows

A genetic algorithm for capital budgeting problem with fuzzy parameters

When an organization utilizes modern technology in its manufacturing process, it needs to update and upgrade its facilities repetitively by efficient ways to stay with great productivity along with efficiency so. Capital Budgeting (CB) problem is one of the most important issues in decision makings about capital in the manufacturing management. Sometimes all variables and parameters are not necessarily deterministic and enough experiments are not available. Current study develops a chance constrained integer programming in the fuzzy environment for capital budgeting. Considering the complexity theory, a good answer could not be found in reasonable time, so that an intelligent Genetic Algorithm (GA) as a metaheuristic approach is provided to trace this problem with satisfying solutions. Thereupon, a fuzzy simulation-based genetic algorithm is provided for solving chance constrained integer programming model with fuzzy parameters

Investment Risk Appraisal

Standard financial techniques neglect extreme situations and regards large market shifts as too unlikely to matter. This approach may account for what occurs most of the time in the market, but the picture it presents does not reflect the reality, as the major events happen in the rest of the time and investors are ‘surprised’ by ‘unexpected’ market movements. An alternative fuzzy approach permits fluctuations well beyond the probability type of uncertainty and allows one to make fewer assumptions about the data distribution and market behaviour. Fuzzifying the present value criteria, we suggest a measure of the risk associated with each investment opportunity and estimate the project’s robustness towards market uncertainty. The procedure is applied to thirty-five UK companies and a neural network solution to the fuzzy criterion is provided to facilitate the decision-making process. Finally, we discuss the grounds for classical asset pricing model revision and argue that the demand for relaxed assumptions appeals for another approach to modelling the market environment

Soft computing in investment appraisal

Standard financial techniques neglect extreme situations and regards large market shifts as too unlikely to matter. Such approach accounts for what occurs most of the time in the market, but does not reflect the reality, as major events happen in the rest of the time and investors are ‘surprised’ by ‘unexpected’ market movements. An alternative fuzzy approach permits fluctuations well beyond the probability type of uncertainty and allows one to make fewer assumptions about the data distribution and market behaviour. Fuzzifying the present value criteria, we suggest a measure of the risk associated with each investment opportunity and estimate the project’s robustness towards market uncertainty. The procedure is applied to thirty-five UK companies traded on the London Stock Exchange and a neural network solution to the fuzzy criterion is provided to facilitate the decision-making process. Finally, we suggest a specific evolutionary algorithm to train a fuzzy neural net - the bidirectional incremental evolution will automatically identify the complexity of the problem and correspondingly adapt the parameters of the fuzzy network

A methodology for the selection of new technologies in the aviation industry

The purpose of this report is to present a technology selection methodology to quantify both tangible and intangible benefits of certain technology alternatives within a fuzzy environment. Specifically, it describes an application of the theory of fuzzy sets to hierarchical structural analysis and economic evaluations for utilisation in the industry. The report proposes a complete methodology to accurately select new technologies. A computer based prototype model has been developed to handle the more complex fuzzy calculations. Decision-makers are only required to express their opinions on comparative importance of various factors in linguistic terms rather than exact numerical values. These linguistic variable scales, such as ‘very high’, ‘high’, ‘medium’, ‘low’ and ‘very low’, are then converted into fuzzy numbers, since it becomes more meaningful to quantify a subjective measurement into a range rather than in an exact value. By aggregating the hierarchy, the preferential weight of each alternative technology is found, which is called fuzzy appropriate index. The fuzzy appropriate indices of different technologies are then ranked and preferential ranking orders of technologies are found. From the economic evaluation perspective, a fuzzy cash flow analysis is employed. This deals quantitatively with imprecision or uncertainties, as the cash flows are modelled as triangular fuzzy numbers which represent ‘the most likely possible value’, ‘the most pessimistic value’ and ‘the most optimistic value’. By using this methodology, the ambiguities involved in the assessment data can be effectively represented and processed to assure a more convincing and effective decision- making process when selecting new technologies in which to invest. The prototype model was validated with a case study within the aviation industry that ensured it was properly configured to meet the

Fuzzy Real Investment Valuation Model for Giga-Investments, and a Note on Giga-Investment Lifecycle and Valuation

Very large industrial real investments are different from financial investments and from small real investments, even so, their profitability is commonly valued with the same methods. A definition of a group of very large industrial real investments is made, by requiring three common characteristics. The decision support needs arising from these characteristics are discussed and a summary of existing methods to value and to provide decision support for large industrial investments is presented. A model built specifically to support investment decisions of very large industrial real investments and a numerical application of the model are presented. The model is discussed and commented. A note is made on an observation regarding the giga-investment lifecycle and its effect on giga-investment valuation.Large industrial investments; Profitability analysis; Fuzzy corporate finance; Capital Budgeting

A pure probabilistic interpretation of possibilistic expected value, variance, covariance and correlation

In this work we shall give a pure probabilistic interpretation of possibilistic expected value, variance, covariance and correlation

Flexibility in Investments: Exploratory Survey on How Finnish Companies Deal with Flexibility in Capital Budgeting

Flexibility is an important issue when investments are being planned and valued. How flexibility inherent in investments is utilised and exploited is, therefore, of great importance to the accuracy of the plans and the valuation. This paper describes an exploratory survey, done with leading Finnish companies, exploring the use of Real Option Valuation (ROV), and the methods that Finnish corporations use to take flexibility into consideration, when planning and valuing investments. We found that real options exist in Finnish investments, but there are very few companies that have an established methodology of identifying, categorizing, or valuing them. We also found that Finnish managers have mixed views about the value of flexibility in investments. Very few Finnish managers seem to be aware of research done in the field, but most seem to have an intuitive understanding of how different variables affect the value of flexibility in an investment.flexibility; real options; capital budgeting

A revised perspective on the evaluation of IT/IS investments using an evolutionary approach

On-going research into the evaluation of Information Technology (IT) / Information Systems (IS) projects has shown that aerospace and supply chain industries are needing to address the issue of effective project investment in order to gain technological and competitive advantage. The evaluative nature of the justification process requires a mapping of interrelated quantities to be optimised. Earlier work by the authors (Irani and Sharif 1997) has presented a theoretical functional model that describes these relationships in turn. By applying a fuzzy mapping to these variables, the optimisation of intangible relationships in the form of a Genetic Algorithm (GA) is proposed as a method for investment justification. This paper revises and reviews these key concepts and provides a recapitulation of this optimisation problem in terms of long-term strategy options and cost implications. Glossary of terms : DC = Direct Costs, FA = Financial Appraisal, FR = Financial Risks, FUR = Functional Risks, HC = Human Costs, IC = Indirect Costs, IR = Infrastructural Risks, OB = Operational Benefits, OC = Organisational Costs, PB = Project Benefits, PC = Project Costs, RF = Risk Factor, SB = Strategic Benefits, SM = Strategic medium-term benefit, SR = Systemic Risks, TB = Tangible Benefits, TC = Tangible Costs, TL = project lead time, TR = Technological Risks, V= Project Value