Bogdan Rebiasz

Fuzziness and randomness in investment project risk appraisal

Risk quantification is one of the most difficult tasks associated with investment project risk management, and computer simulation seems to be an especially effective tool for such risk appraisal. This article presents a method for quantification of project-specific risk. When assessing investment project risk it is very common to apply two analytical methods for describing parameter uncertainty: probability distribution and possibility distribution. This study discusses methods for integrating the above-mentioned approaches into a description of the uncertainty of parameters in calculations of effectiveness and investment project risk. The paper presents an example of a computer simulation used for the purpose of an investment project risk assessment. Uncertainty for some parameters of the effectiveness calculation is defined by a probability distribution and by fuzzy sets for others, and a transformation of possibility distributions into probability distributions is thus done. For comparison, the investment risk assessment is undertaken on the assumption that uncertainty distributions of the effectiveness calculation parameters are presented in the form of fuzzy numbers.

Selection of efficient portfolios-probabilistic and fuzzy approach, comparative study

This paper presents a new method for selection of efficient portfolios in a situation where parameters in the calculation of effectiveness are expressed in form of interactive fuzzy numbers and probability distribution. Fuzzy simulations are used for performing arithmetic operations on interactive fuzzy numbers. The process of selecting investment projects takes into account statistical and economic dependencies of projects. The paper contains comparison of results obtained in selection of efficient portfolios of investment projects in a situation where parameters in calculation of effectiveness were expressed in form of interactive fuzzy numbers or probability distributions.

Advances in Fuzzy Decision Making

This book shows how common operation management methods and algorithms can be extended to deal with vague or imprecise information in decision-making problems. It describes how to combine decision trees, clustering, multi-attribute decision-making algorithms and Monte Carlo Simulation with the mathematical description of imprecise or vague information, and how to visualize such information. Moreover, it discusses a broad spectrum of real-life management problems including forecasting the apparent consumption of steel products, planning and scheduling of production processes, project portfolio selection and economic-risk estimation. It is a concise, yet comprehensive, reference source for researchers in decision-making and decision-makers in business organizations alike.

Hybrid framework for investment project portfolio selection

Project selection is a complex multi-criteria decision making process that is influenced by multiple and often conflicting objectives. The complexity of the project selection problem is mainly due to the high number of projects from which an appropriate collection (an effective portfolio) of investment projects must be selected. This paper presents a new conception of a hybrid framework for construction of an effective portfolio of investment projects. The parameters of the considered model are described using both probability distributions and fuzzy numbers (possibility distributions). The proposed framework enables to take into account stochastic dependencies between model parameters and economic dependencies between projects. As a result, a set of Pareto optimal solutions is obtained. The performance of the proposed method is illustrated using an example from metallurgical industry.

Comparison of Classical Multi-Criteria Decision Making Methods with Fuzzy Rule-Based Methods on the Example of Investment Projects Evaluation

In the process of investment decision making, next to financial indicators many other aspects of investment projects are increasingly often considered. This leads to the multi-criteria evaluation of a project. In the work one compared results of multi-criteria evaluation of the investment projects realized by using TOPSIS and AHP methods with results obtained at the use of rule-based methods, especially fuzzy reasoning techniques. To comparisons were used chosen investments in the metallurgical industry. The work finish conclusions defined on the basis carried out calculations.

Hybrid Data in the Multiobjective Evaluation of Investments

Abstract In the process of investment decision making, next to financial indicators many other aspects of investment projects are increasingly often considered. This leads to the multicriteria evaluation of a project. The last decades have shown that the number and complexity of dependencies both inside and outside a company makes it difficult to use the probability theory to represent all kinds of the uncertainty appearing in case of the evaluation of investment projects. Many authors have applied the alternative description of the uncertainty. First of all, fuzzy numbers may be mentioned as an example of the above. This leads to the hybrid description of the uncertainty in the process of the evaluation of the investment. This paper reports an investigation into the design and implementation of hybrid rule-based systems based on the Dempster–Shafer theory of evidence to investment project evaluation. As a result of the investigation, a new methodology will be proposed for multi-criterion project evaluation in the hybrid environment.

Financial Valuation of Production Diversification in the Steel Industry Using Real Option Theory

Steel industry is subject to significant volatility in its output prices and market demands for different ranges of products. Therefore, it is common practice to invest in various assets, which gives the opportunity to diversify production and generate valuable switch options. This article investigates the incremental benefit of product switch options in steel plant projects. The options are valued using Monte Carlo simulation and modeling the prices of and demand for steel products as Geometric Brownian Motion (GBM). Our results show that the product switch option can generate a significant increase in the net present value (NPV) of metallurgical projects.

Joint Treatment of Imprecision and Randomness in the Appraisal of the Effectiveness and Risk of Investment Projects

This paper proposes a new method for evaluating the effectiveness and risk of investment projects in the presence of both fuzzy and stochastic uncertainty. The main novelty of the proposed approach is the ability to take into account dependencies between uncertain model parameters. Thanks to this extra feature, the results are more accurate. The method combines non-linear programming with stochastic simulation, which are used to model dependencies between stochastic parameters, and interval regression, which is used to model dependencies between fuzzy parameters (possibility distributions). To illustrate the general idea and the effectiveness of the proposed method, an example from metallurgical industry is provided.

Data Mining Methods for Long-Term Forecasting of Market Demand for Industrial Goods

This paper proposes a new method for long-term forecasting of level and structure of market demand for industrial goods. The method employs k-means clustering and fuzzy decision trees to obtain the required forecast. The k-means clustering serves to separate groups of items with similar level and structure (pattern) of steel products consumption. Whereas, fuzzy decision tree is used to determine the dependencies between consumption patterns and predictors. The proposed method is verified using the extensive statistical material on the level and structure of steel products consumption in selected countries over the years 1960–2010.

Multi-attribute Decision Making Process and Its Application

This chapter proposes the integrated fuzzy approach to solve Multi Attribute Decision Problems. Fuzzy Analytical Hierarchy Process (FAHP) is used to assign relative weights to criteria, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is employed to rank the alternatives. The use of the proposed approach is illustrated using a real case from a steel industry.