2 Quantitative finance
3 See also
4 Selected books
6 External links
In corporate finance, investment banking and the accounting profession financial modeling is largely synonymous with cash flow forecasting. This usually involves the preparation of detailed company specific models used for decision making purposes and financial analysis. Applications include:
Business valuation, especially discounted cash flow, but including other valuation problems
Scenario planning and management decision making ("what is"; "what if"; "what has to be done")
Cost of capital (i.e. WACC) calculations
Financial statement analysis (including of operating- and finance leases, and R&D)
To generalize as to the nature of these models: firstly, as they are built around financial statements, calculations and outputs are monthly, quarterly or annual; secondly, the inputs take the form of “assumptions”, where the analyst specifies the values that will apply in each period for external / global variables (exchange rates, tax percentage, etc.…) and internal / company specific variables (wages, unit costs, etc.…). Correspondingly, both characteristics are reflected (at least implicitly) in the mathematical form of these models: firstly, the models are in discrete time; secondly, they are deterministic. For discussion of the issues that may arise, see below; for discussion as to more sophisticated approaches sometimes employed, see Corporate finance: Quantifying uncertainty.
Modellers are sometimes referred to (tongue in cheek) as "number crunchers", and are often designated "financial analyst". Typically, the modeller will have completed an MBA or MSF with (optional) coursework in "financial modeling". Accounting qualifications and finance certifications such as the CIIA and CFA generally do not provide direct or explicit training in modeling. At the same time, numerous commercial training courses are offered, both through universities and privately.
Although purpose built software does exist, the vast proportion of the market is spreadsheet-based - this is largely since the models are almost always company specific. Microsoft Excel now has by far the dominant position, having overtaken Lotus 1-2-3 in the 1990s. Spreadsheet-based modelling can have its own problems, and several standardizations and "best practices" have been proposed. "Spreadsheet risk" is increasingly studied and managed.
One critique here, is that model outputs, i.e. line items, often incorporate “unrealistic implicit assumptions” and “internal inconsistencies” (for