Assessing investment projects under risk and uncertainty using Discounted Cash-Flow Analysis and Monte Carlo simulation

Territorial transformation projects are subject to specific evaluation procedures that address the question of whether the planned course of actions can achieve objectives in the presence of specific constraints. The uncertainty that characterizes the development process is of particular importance in investment decision-making, Indeed, the model’s input (for example, the costs of construction, the incomes, or the interest rates) can be affected by uncertainty due to the lack of knowledge and poor and imperfect information. It has been noted that this input uncertainty gives uncertain outcomes (i.e., valuation data such as the Net Present Value). To address uncertainty in feasibility studies, probability theory can be used and specific simulations based on the Monte Carlo analysis can be implemented. Starting from a real case study, the paper aims at investigating the role of uncertainty and risk in feasibility studies.

Territorial transformation projects are subject to specific evaluation procedures that address the question of whether the planned course of actions can achieve objectives in the presence of specific constraints. The uncertainty that characterizes the development process is of particular importance in investment decision-making, Indeed, the model’s input (for example, the costs of construction, the incomes, or the interest rates) can be affected by uncertainty due to the lack of knowledge and poor and imperfect information. It has been noted that this input uncertainty gives uncertain outcomes (i.e., valuation data such as the Net Present Value). To address uncertainty in feasibility studies, probability theory can be used and specific simulations based on the Monte Carlo analysis can be implemented. Starting from a real case study, the paper aims at investigating the role of uncertainty and risk in feasibility studies.


ISSN 1121-9041

CiteScore:
2020: 3.8
CiteScore measures the average citations received per peer-reviewed document published in this title.
CiteScore values are based on citation counts in a range of four years (e.g. 2016-2019) to peer-reviewed documents (articles, reviews, conference papers, data papers and book chapters) published in the same four calendar years, divided by the number of these documents in these same four years (e.g. 2016 —19).
Source Normalized Impact per Paper (SNIP):
2019: 1.307
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR)
2019: o.657
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal's impact.
Journal Metrics: CiteScore: 1.0 , Source Normalized Impact per Paper (SNIP): 0.381 SCImago Journal Rank (SJR): 0.163

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