Sensitivity Analysis of Large Spreadsheet Models for Industrial

Sensitivity Analysis of Large Spreadsheet Models for Industrial
Investment Evaluation
Emanuele Borgonovo1
IMQ, Bocconi University
Viale Isonzo 25
20135 Milano, Italy
When firms and banks are confronted with investment decisions involving large projects,
business planning involves the creation of dedicated financial models aimed at estimating the
investment returns. The financial modeling effort is usually both time and resource
consuming and leads to the development of models that, due to the desired accuracy level,
contain a large number of parameters. In this work, we show how by employing the
appropriate sensitivity analysis techniques management can unveil additional information
embedded in the model. We illustrate the analysis by means of a financial model created for
the valuation of an investment project in a parking lot, containing 428 parameters. We show
that by utilizing the differential importance measure (D), the decision maker obtains
information on what parameters matter in the decision, and is capable of screening the nonrelevant ones. Being the number of parameters high, for communication purposes between
analysts and upper management, parameters need to be grouped in their respective categories
(i.e. revenue, fiscal, technical assumptions etc.) We show that thanks to the additivity
property of D the grouping of parameters in the respective categories is straightforward. We
perform the analysis with attention on two different valuation criteria utilized on the two
opposite sides of an investment in a large industrial project: the net present value utilized by
the sponsors and the debt service coverage ratio, adopted by lenders. Results of the analysis
show that parameters relevant for sponsors are not necessarily as relevant for lenders, and
enables to identify the drivers of the project economic and financial performance.
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