Risk analysis model for regional railroad investment. Issue 1 (6th March 2017)

• Record Type: Journal Article
• Authors: Suh, Sunduck; Suh, Wonho; Kim, Jung In
• Is Part Of: Engineering computations. Volume 34:Issue 1(2017)
• Page Start: 164
• Page End: 173
• Publication Date: 2017-03-06
• Subjects: Risk analysis -- Financial analysis -- Regional railroad investment -- Risk model; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-10-2015-0323
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: The purpose of this study is to model risks in financial analysis. These risks associated with uncertainties in the projects should be properly addressed to ensure proper decision regarding the projects. Performance indicators should be developed and assessing risks has high priority. All these activities comprise appraisal, and based on these, a proper course of action should be recommended. Design/methodology/approach: To analyze the attractiveness of a project for foreign regional railroad investment or participation, the project should be analyzed in a systematic way. First, the project's goals and objectives should be evaluated for compatibility. Also, criteria of acceptability for stakeholders should be checked against output from the project. Usually, a project can have many alternatives, and impacts of each alternative should be analyzed in terms of quantitative and qualitative forecasts of impacts. Benefits and costs need to be counted in proper units of measurement per goals and objectives. Findings: This paper shows that risk modeling can reflect uncertainty in decision-making and provide robustness of modeling process and improved communication. Also, challenges are presented in using risk analysis. Originality/value: To overcome the shortcomings of traditional mathematical optimization model in identifying best sets of projects for private application, the proposed model finds ways to incorporate risk management components for the optimization procedure. Based on simulation results, a brute force solution procedure using enumeration can be used. Another approach is recommended to use the genetic algorithm process to reduce the number of alternatives to search to reach an optimal solution.