Economic analysis of risky projects by ANNs

Abstract

Multilayer perceptron (MLP) and radial basis function (RBF) artificial neural networks (ANN) are used to model economic analysis of risky projects and are presented in this paper. Analytical models of risky projects are investigated and neural network function approximation results are compared. A general, problem independent ANNs are developed for the normalized input values for risky projects. The expected cost value and variance are the outputs of the ANNs. The simulation results of RBF and MLP with respect to a mathematical model are shown and concluded.

Introduction

The artificial neural network (ANN) model development is a technique heavily researched and used in applications for engineering and scientific fields for various purposes ranging from control systems to artificial intelligence. Its generalization powers have not only received admiration from the engineering and scientific fields, but also in recent years, the finance researchers and practitioners are taking interest in the application of ANN [28]. Risk assessment application is one of the areas that is heavily researched in the financial field.

In dictionary definition terms ‘risk’ means: “hazard, chance of bad consequences, loss, exposure to chance of injury or loss” (Concise Oxford Dictionary). Such definitions illustrate one problem with the term ‘risk’—its ambiguous use as a synonym of probability or chance in relation to an event or outcome, the nature of an outcome, or its cause. Dowie [6] argues that the term ‘risk’ is an obstacle to improved decision and policy making. Its multiple and ambiguous usages persistently jeopardize the separation of the tasks of identifying and evaluating relevant evidence on the one hand, and eliciting and processing necessary value judgements on the other. The term ‘risk’ contaminates all discussions of probability because of the implicit value judgement/s that the term always brings with it, just as it contaminates all discussions of value assessment because of the implicit probability judgement/s that it contains [6], [32].

Risk management is a critical part of project management as ‘unmanaged or unmitigated risks are one of the primary causes of project failure’ [29]. While numerous papers have been written on the subject of risk management, little current information exists on the actual use of risk management in practice [22].

The management of risk in projects is currently one of the main topics of interest for researchers and practitioners working in the area of project management [27]. The concept of risk most widely used in project evaluation is the variability of return, which is measured by variance (or standard deviation). The use of variance as a measure of risk implies that deviations below the expected value are regarded in the same way as deviations above the expected value [24]. Most project managers are risk averse, in the sense of utility theory. Hence, they would most likely not like the variation in project costs that would occur if projects were accepted as investments based on their expected cost (or profit) [15]. Identification of project risk is, in the main, achieved by analysing the financial data through the measurement of its ‘sensitivity’ to variations and by identifying a project’s payback period [19].

According to McMillan [23], cost estimation is difficult in certain industries, often leading to considerable cost overruns. The explanations are that there is often large uncertainty—often related to new technology—and that the uniqueness of the project, limits the learning process. One might expect that the cost overruns have the same probability as completing the project below the cost estimate. However, observations clearly indicate an overrepresentation of cost overruns. This is due to two types of selection bias : (1) project selection; it is typically the projects with the most optimistic internal cost estimates that are being pursued by an investor, and (2) tender selection; competition sees to it that tenders with pessimistic and realistic cost estimates are ruled out [7].

In this paper, we present an artificial neural network model for economic analysis of risky projects. MLP and RBF are used for approximating the risky projects. The performance of Radial Basis Function was compared with multilayer perceptrons.