inancial modeling is the task of building an abstract representation (a model) of a financial decision-making situation.[1] This is a mathematical model designed to represent (a simplified version of) the performance of a financial asset or portfolio of a business, project, or any other investment. Financial modeling is a general term that means different things to different users; the reference usually relates either to accounting and corporate finance applications, or to quantitative finance applications. While there has been some debate in the industry as to the nature of financial modeling - whether it is a tradecraft, such as welding, or a science - the task of financial modeling has been gaining acceptance and rigor over the years.[2] Typically, financial modelling is understood to mean an exercise in either asset pricing or corporate finance, of a quantitative nature. In other words, financial modelling is about translating a set of hypotheses about the behavior of markets or agents into numerical predictions; for example, a firm's decisions about investments (the firm will invest 20% of assets), or investment returns (returns on "stock A" will, on average, be 10% higher than the market's returns).
Contents [hide]
1 Accounting
2 Quantitative finance
3 See also
4 Selected books
5 References
6 External links
[edit]Accounting

In corporate finance, investment banking and the accounting profession financial modeling is largely synonymous with cash flow forecasting.[citation needed] This usually involves the preparation of detailed company specific models used for decision making purposes[1] 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")[3]
Capital budgeting
Cost of capital (i.e. WACC) calculations
Financial statement analysis (including of operating- and finance leases, and R&D)
Project finance.
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.[citation needed] 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".[citation needed] Accounting qualifications and finance certifications such as the CIIA and CFA generally do not provide direct or explicit training in modeling.[citation needed] 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[citation needed] - 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,[4] and several standardizations and "best practices" have been proposed.[5] "Spreadsheet risk" is increasingly studied and managed.[5]
One critique here, is that model outputs, i.e. line items, often incorporate “unrealistic implicit assumptions” and “internal inconsistencies”[6] (for