Decision-support analysis for risk management


Demir H. , Bostancı B.

AFRICAN JOURNAL OF BUSINESS MANAGEMENT, cilt.4, ss.1586-1604, 2010 (SSCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 4
  • Basım Tarihi: 2010
  • Dergi Adı: AFRICAN JOURNAL OF BUSINESS MANAGEMENT
  • Sayfa Sayısı: ss.1586-1604

Özet

Generally, a project is an investment suggestion, which requires making a series of investment expenditures (cash outflow) in a planned manner to obtain more cash inflow in the future Therefore, the basic objective of project appraisal should be to make prior decisions on the feasibility of investment advice The results of project feasibility can be classified into two categories uncertainty and risk Risks related to investment and financial markets are also closely related with the audit and supervision authorities One of the main objectives of regulatory and control authorities is to achieve economic stability in the market and to minimize systematic risks This requires that all institutions define the risks they will encounter, measure these risks via risk analysis techniques and assess the potential impacts of these risks on the institution Today, projects within the housing sector -which has been heavily hit by the recent economic crisis- are one of the areas subject to risk analysis This article aims to determine and discuss risks factors within the housing project development process by applying discounted cash flow analysis (DCF), Monte Carlo Simulation (MCS) and sensitivity analysis to a housing sector with an integrated approach Two different discounted cash flow models were developed as part of a scenario analyzing a housing development project These models were subjected to risk analysis based on MCS, one of the many methods analyzing risk distribution Thus, data from the probability distributions are envisaged to strengthen the trust of the manager in the value and acceptance of the project, and to concretize the attitude to risk of the decision making group In conclusion, the study defined important variables for efficient risk management of housing development projects and developed a risk-decision support model, which incorporates scenario analysis and MCS