Investment project valuation based on a fuzzy binomial approach

The typical approaches to project valuation are based on discounted cash flows (DCF) analysis which provides measures like net present value (NPV) and internal rate of return (IRR). DCF-based approaches exhibit two major pitfalls. One is that DCF parameters such as cash flows cannot be estimated precisely in an uncertain decision making environment. The other one is that the values of managerial flexibilities in investment projects cannot be exactly revealed through DCF analysis. Both of them would have significant influence on strategic investment projects valuation. This paper proposes a fuzzy binomial approach that can be used in project valuation under uncertainty. The proposed approach also reveals the value of flexibilities embedded in the project. Furthermore, this paper provides a method to compute the mean value of a project’s fuzzy NPV. The project’s fuzzy NPV is characterized with right-skewed possibilistic distribution because these flexibilities retain the upside potential of profit but limit the downside risk of loss. Finally, this paper discusses the value of multiple options in a project.     

Valuation by using a fuzzy discounted cash flow model

The objective of this paper is to extend the classical discounted cash flow (DCF) model by developing a fuzzy logic system that takes vague cash flow and imprecise discount rate into account. In order to explicitly discuss a more appropriate valuation model, uncertain information will be fuzzified as triangular fuzzy numbers to quantify and evaluate the intrinsic value of a company's financial asset under the framework of DCF approach. We will find that the fuzzy discounted cash flow (FDCF) model proposed in this paper is one extension of classical (crisp) model and should be more suitable to capture the elements of valuation than non-fuzzy models.     

Evaluating methods of investment project and optimizing models of portfolio selection in fuzzy uncertainty

This paper deals with the problems of both project valuation and portfolio selection under the assumption that the investment capitals and the net cash flows of the projects are fuzzy variables. Using the credibilistic expected value and the credibilistic lower semivariance of fuzzy variables, this paper proposes both the credibilistic return index and the credibilistic risk index, which are measures of investment return and investment risk with annuity form for evaluating single project. Moreover, a composite risk-return index for selecting the optimal investment strategy is also presented. Then, we set up a general project portfolio optimization model with fuzzy returns and two specific models: triangle and interval fuzzy returns. Furthermore, we provide two algorithms: the improved heuristic rules based on genetic algorithm and the traversal algorithm. Finally, two numerical examples are presented to illustrate the efficiency and the effectiveness of these proposed optimization methods.     

The valuation of RFID investment using fuzzy real option

Net present value (NPV) and return on investment (ROI) are commonly used to evaluate investment in new technologies. Sometimes, however, measuring the value of investment in new IT becomes very difficult due to its wide scope of application coupled with embedded options in its adoption. Therefore, comprehensive but easily understandable methodologies are needed to solve the complicated problems resulting from the complexity of new technologies. This paper employs a real option analysis to evaluate RFID adoption in the supply chain. Real options analysis should be a better way to evaluate a disruptive technology like RFID. However, the pure (probabilistic) real option rule characterizes the present value of expected cash flows and the expected costs by a single number, which is not realistic in many cases. To solve the problem, this paper considers the real option rule in a more realistic setting, namely, when the present values of expected cash flows and expected costs are estimated by trapezoidal fuzzy numbers. Specifically, it drew out their means and variance and presented a method of calculating fuzzy real options through numerical value examples of RFID investment assuming the current value of expected cash flow and investment costs using trapezoid fuzzy number fuzzy real options. Since advanced information technology such as RFID has very high risk and options such as change, extension, delay and withdrawal, etc., investment valuation using the real options technique should be done, and in the process, in a more realistic and practical approach, the fuzzy real options model presented in this study is judged to be useful.     

Mean-variance model for fuzzy capital budgeting

In an uncertain economic environment, it is usually difficult to predict accurately the investment outlays and annual net cash flows of a project. In addition, available investment capital sometimes cannot be accurately given either. Fuzzy variables can reflect vagueness of these parameters. In this paper, capital budgeting problem with fuzzy investment outlays, fuzzy annual net cash flows and fuzzy available investment capital is studied based on credibility measure. One new mean-variance model is proposed for optimal capital allocation. A fuzzy simulation-based genetic algorithm is provided for solving the proposed optimization problem. One numerical example and an experiment are also presented to show the optimization idea and the effectiveness of the algorithm.     

A Time‐Perception Approach for the Treatment of Risk in Projects Appraisal

This paper presents a novel methodology to introduce the risk in the valuation of random investment projects. Traditionally, a constant risk‐adjusted premium has been added up to the interest rate to include the uncertainty associated with the project. Nevertheless, this method is not objective because the choice of this parameter is not directly identified with a risk measure inherent to the project. Our approach is based on a model that describes the perception that the lender (supplier) has about the expected time to obtain the payment of debts (which will be identified with the cash flows). Thus we will be able to eliminate the subjectivity of traditional valuation methods when considering the risk in investment projects.     

Valuation of expansion flexibility in flexible manufacturing system investments using sequential exchange options

In this paper, a valuation approach that modifies traditional discounted cash flow (DCF) methodology is presented to incorporate the option premium of expansion flexibility while evaluating a flexible manufacturing system investment. Expansion flexibility allows for changing the production capacity in response to deviations in demand, while disregarding the assumption that management makes an irrevocable decision based on its future market expectations. Expansion flexibility provides a key strategic advantage by avoiding the large financial commitment at the initial investment stage and enabling investment in a phased manner according to the changes in market conditions. The aim of this paper is to present a thorough valuation methodology that accounts for both the benefits of keeping the option to expand alive and the loss of market share to competing firms if the expansion investment were delayed. In this paper, the value of expansion flexibility is computed using sequential exchange options. The proposed method employs an analytic approximation scheme for valuing American exchange options on dividend-paying assets. A numerical example demonstrates the application of the valuation framework. The options approach that incorporates the expansion flexibility option into the analysis results in a higher value than the standard DCF approach, which ignores the value of the option to expand. We also perform sensitivity analyses to see whether the expansion option increases in value in cases of high uncertainty where management can respond flexibly to new market information, and where the investment analysis ignoring flexibility yields a marginally positive net present value. The results of the proposed valuation framework are presented in comparison with a previous model that ignores the opportunity cost of delaying expansion investment.     

Optimal multinational capital budgeting under uncertainty

This paper discusses the multinational capital budgeting problem — when there are some candidate foreign projects, which project(s) should the investor choose? In the paper, special cash flows and value sources of foreign projects are introduced. Regarding project parameters such as construction costs, annual net operating cash flows, terminal values of the projects as well as the foreign exchange rates as uncertain variables, the paper proposes one new uncertain zero-one integer model for optimal multinational project selection. To solve the problem, a hybrid intelligent algorithm integrating the 99 Methods and genetic algorithm is provided. As an illustration, an application example is also presented.     

Capital investment appraisal: a new risk premium model

Net Present Value (NPV) is the principal valuation model of the financial literature. Firms are accordingly directed, as a matter of good practice, to adopt the model for selecting investment projects, yet questionnaire surveys show that the adoption rate has been very slow and the quality of usage questionable. In particular, alternative risk measures are popular amongst practitioners. In this paper we remodel the treatment of risk in the NPV model based on assumptions that seem realistic in an organizational or operational, as opposed to a personal, investment context. We derive formulas for calculating: the appropriate discount rate, a 'risk horizon' (where the risk premium exceeds the expected value), and a maximum default hazard point for projects. These measures provide a rationale for non-NPV approaches to risk measurement in questionnaire responses and offer a practical benefit to investors.     

Scheduling Multi-Mode Projects under Uncertainty to Optimize Cash Flows: A Monte Carlo Ant Colony System Approach

Project scheduling under uncertainty is a challenging field of research that has attracted increasing attention. While most existing studies only consider the single-mode project scheduling problem under uncertainty, this paper aims to deal with a more realistic model called the stochastic multi-mode resource constrained project scheduling problem with discounted cash flows (S-MRCPSPDCF). In the model, activity durations and costs are given by random variables. The objective is to find an optimal baseline schedule so that the expected net present value (NPV) of cash flows is maximized. To solve the problem, an ant colony system (ACS) based approach is designed. The algorithm dispatches a group of ants to build baseline schedules iteratively using pheromones and an expected discounted cost (EDC) heuristic. Since it is impossible to evaluate the expected NPV directly due to the presence of random variables, the algorithm adopts the Monte Carlo (MC) simulation technique. As the ACS algorithm only uses the best-so-far solution to update pheromone values, it is found that a rough simulation with a small number of random scenarios is enough for evaluation. Thus the computational cost is reduced. Experimental results on 33 instances demonstrate the effectiveness of the proposed model and the ACS approach.     

Fuzzy R&D portfolio selection of interdependent projects

Global competition of markets has forced firms to invest in targeted R&D projects so that resources can be focused on successful outcomes. A number of options are encountered to select the most appropriate projects in an R&D project portfolio selection problem. The selection is complicated by many factors, such as uncertainty, interdependences between projects, risk and long lead time, that are difficult to measure. Our main concern is how to deal with the uncertainty and interdependences in project portfolio selection when evaluating or estimating future cash flows. This paper presents a fuzzy multi-objective programming approach to facilitate decision making in the selection of R&D projects. Here, we present a fuzzy tri-objective R&D portfolio selection problem which maximizes the outcome and minimizes the cost and risk involved in the problem under the constraints on resources, budget, interdependences, outcome, projects occurring only once, and discuss how our methodology can be used to make decision support tools for optimal R&D project selection in a corporate environment. A case study is provided to illustrate the proposed method where the solution is done by genetic algorithm (GA) as well as by multiple objective genetic algorithm (MOGA).     

A fuzzy linguistic ontology payoff method for aerospace real options valuation

In this paper, we present a fuzzy linguistic ontology payoff method for the valuation of real options in the aerospace industry. Using real data, we apply the fuzzy linguistic approach to determine the credibility measures and the credibilistic expected value for the fuzzy real options valuation payoff method. This approach is used to obtain a multi-scenario modeling process by envisioning three scenarios: optimistic, most likely, and pessimistic. In addition, our experience with the scenario estimates is premised on results in an operating profit forecast. This forecast corresponds to a plausible outcome within the aerospace licensing maintenance, repair, and overhaul market and provides a decision-making tool. This tool can be utilized for determining real options for project valuation of aerospace licensing revenues based on unit costs, recurring costs, and quantity of units sold.     

Knowledge integration using problem spaces: A study in resource-constrained project scheduling

The need to develop schedules for projects with resource constraints and cash flows arises in organizational settings ranging from construction planning to research and development. Given the intractable nature of the problem, a variety of knowledge sources relevant to the project scheduling task have been identified in the Operations Management literature. These include a large number of heuristic procedures that can be used to generate feasible project schedules as well as recent neural network-based approaches that can select appropriate heuristic procedures to apply to a specific instance of the project scheduling problem. While integrated application of these knowledge sources is required to effectively support scheduling, previous work has focussed on developing and implementing them in isolation. The problem space computational model presented in this paper addresses this shortcoming by integrating these various knowledge sources, thus enabling the development of decision support systems for resource constrained project scheduling. More generally, the modeling approach used in this paper can be applied to create systems to assist knowledge intensive tasks that arise in many organizational settings.     

A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations

A resource investment problem with discounted cash flows (RIPDCF) is a project-scheduling problem in which (a) the availability levels of the resources are considered decision variables and (b) the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, the RIPDCF in which the activities are subject to generalized precedence relations is first modeled. Then, a genetic algorithm (GA) is proposed to solve this model. In addition, design of experiments and response surface methodology are employed to both tune the GA parameters and to evaluate the performance of the proposed method in 240 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is relatively well.     

Activity-based justification of IT investments

Ever since investments in IT moved from operational to decision support, academics and practitioners have been looking for ways to justify the costs. Traditional approaches, such as NPV, have been shown inadequate in capturing qualitative and quantitative benefits, and techniques developed to address the issue have proved limited in their ability to link investment justification with impact assessment. Recent literature has called for the use of business processes to assess the impact of IT and the same processes can be used to justify investments in IT. The activity-based costing (ABC) approaches in the field of accounting were developed to relate investments to product profitability by allocating the burden to those that receive the most benefit. Thus, this paper uses the ABC approach to relate investments to activities, so that it can be used to justify IT investment. The paper provides a model to determine when the approach, referred to as activity-based justification, is most appropriate and its value is illustrated using a case study.     

The incremental funding method: data-driven software development

Software development projects don't get funded unless they return clearly defined value to the business. Demands for shorter investment periods, faster time-to-market, and increased agility require new, radical software development approaches. These approaches must draw on the expertise of both software architects and financial stakeholders and open the traditional black box of software development to rigorous financial analysis. We can accomplish this only by positioning software development as a value-creation activity in which business analysis is integral. The incremental funding method is a financially informed approach to software development. IFM maximizes returns by delivering functionality in "chunks" of customer-valued features, carefully sequenced to optimize the project's net present value (NPV). We derived the IFM concepts from several years' experience in winning competitive contracts for large-systems integration and application development projects.     

基于模糊集的风险聚类预测方法

复杂社会技术系统存在许多不确定性的因素,这些因素给社会决策、项目过程管理带来了巨大的障碍和风险,因此有效的风险预测方法变得十分重要.根据风险项目的风险因素向量,利用模糊等价类的方法,对风险项目的历史数据进行模糊聚类,进而通过对新的风险项目和历史数据的模糊匹配实现了项目的风险聚类预测方法.分析和实践表明,该模型有效地解决了风险项目中诸多不确定性因素分类问题.该方法适合于政府决策、电子商务、软件项目管理等方面的风险管理应用.     

A modular Decision Support System for optimum investment selection in presence of uncertainty: Combination of fuzzy mathematical programming and fuzzy rule based system

In this paper, we have developed a modular Decision Support System (DSS) in order to select an optimum portfolio of several chances for investments in presence of uncertainty. The investments are considered as the projects so as their initial investment costs, profits, resource requirement, and total available budget are assumed to be uncertain. This uncertainty has been modeled using fuzzy concepts. The proposed DSS has two main modules. The first one is a fuzzy binary programming model which represents the mathematical model of the associated fuzzy capital-budgeting problem. It involves finding optimum combination of investment portfolio considering a multi-objective measurement function and subject to several set of constraints. The results of optimistic and pessimistic analysis of the aforementioned fuzzy binary programming model plus a managerial Confidence Level (CL) value are treated as input of a fuzzy rule based system which is the second module of the proposed DSS. Although some projects are simple to make a decision about at the final step of the first module but the unique output of the second module of the proposed DSS is Risk of Investment (ROI) for all remained project. The logic relations between precedence parts of the rules as well as CL value will work in favor of computational efforts in second module through diminishing some unessential rules. This will help to define a complete set of fuzzy IF-THEN rules more efficiently. The proposed DSS can help the decision makers to select an optimum investment portfolio with minimum risk in a complete ambiguous condition.     

A hybrid fuzzy multiple criteria group decision making approach for sustainable project selection

In this paper, a new hybrid fuzzy multiple criteria group decision making (FMCGDM) approach has been proposed for sustainable project selection. First, a comprehensive framework, including economic, social, and environmental effects of an investment, strategic alliance, organizational readiness, and risk of investment has been proposed for sustainable project selection. As the relative importance of the criteria of the proposed framework are hard to find through several conflictive preferences of a group of Decision Makers (DMs) so, a goal programming (GP) has been supplied to this aim considering multiplicative and fuzzy preference relation. Then, a fuzzy TOPSIS method has been developed to assess the fitness of investment chances. It is based on Preference Ratio (PR), which is known as an efficient ranking method for fuzzy numbers, and a fuzzy distance measurement. The properties of proposed hybrid approach make it robust for modeling real case of uncertain group decision making problems. The FMCGDM has been developed through a linkage between Lingo 11.0, MS-Excel 12.0, and Visual Basic 6.0. The proposed hybrid approach has been applied in a real case study called Iranian financial and credit institute for sustainable project selection.     

Optimal divestment time in supply chain redesign under oligopoly: evidence from shale oil production plants

This paper investigates the optimal timing for the closure of production plants in the context of supply chain redesign. We consider producers operating under oligopoly and analyze their optimal decisions with the aid of real options. We contextualize this study in the energy supply chain, where revenue is heavily influenced by the Organization of the Petroleum Exporting Countries (OPEC). It is optimal to divest from a production if certain values pass below the liquidation cost. The optimal time to divest is, then, the optimal time to exercise a perpetual American put option on the total value of the project, where the strike price is the liquidation value and expenses. The OPEC oligpolistic power is modeled with the aid of a hidden market in a Hidden Markov Model, since the state of the Markov chain is not directly visible to the production manager. The reported results can be used by supply chain managers in decision making on production network redesign in the oligopolistic environment considering present value of plants and cash flows in the supply chain. The advantage of the modeling approach proposed is its analytical tractability, especially for project valuation and real‐option problems.