Financial modeling is the task of building an abstract representation (a model) of a financial decision making situation.This is a mathematical model designed to represent (a simplified version of) the performance of a financial asset or a portfolio, of a business, a 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. Several scholarly books have been written on the topic, in addition to numerous scientific articles.

Accounting

In corporate finance, investment banking and the accounting profession (and generally in Europe, financial modeling is largely synonymous with cash flow forecasting. This usually involves the preparation of detailed company specific models used for decision making purposes; see Financial analyst. 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" .
  • Capital budgeting
  • Cost of capital (i.e. WACC) calculations
  • Financial analysis and / or Financial statement analysis
  • 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. For discussion of the issues that may arise, see below; for discussion as to more sophisticated approaches sometimes employed, see Corporate finance: Quantifying uncertainty.

Modelers are sometimes referred to (tongue in cheek) as "Number crunchers", and are often designated as "Financial analyst". Typically, the modeler will have completed an MBA or MSF with (optional) coursework in "financial modeling". Accounting qualifications and finance certifications such as the CIIA, CEFA, CFA,  generally do not provide direct / 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 modeling can have its own problems ("Spreadsheet Shortcomings"), 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 example, a forecast for growth in revenue but without corresponding increases in working capital, fixed assets and the associated financing, may imbed unrealistic assumptions about asset turnover, leverage and / or equity financing). What is required, but often lacking, is that all key elements are explicitly and consistently forecasted. An extension of this is that modellers often additionally "fail to identify crucial assumptions" relating to inputs, "and to explore what can go wrong". Here, in general, modellers "use point values and simple arithmetic instead of probability distributions and statistical measures" – i.e., as mentioned, the problems are treated as deterministic in nature – and thus calculate a single value for the asset or project, but without providing information on the range, variance and sensitivity of outcomes; . Other critiques discuss the lack of adequate spreadsheet design skills, and of basic computer programming concepts. More serious criticism, in fact, relates to the nature of budgeting itself, and its impact on the organization.

Quantitative finance

In quantitative finance (and generally in the U.S., financial modeling entails the development of a sophisticated mathematical model. Models here deal with asset prices, market movements, portfolio returns and the like. Applications include:

  • Option pricing and "Greeks"; other derivatives
  • Modeling the term structure of interest rates (short rate modeling) and credit spreads; Interest rate derivatives
  • Credit scoring and provisioning
  • Portfolio problems
  • Real options
  • Risk modeling and Value at risk.

These problems are often stochastic and continuous in nature, and models here thus require complex algorithms, entailing computer simulation, advanced numerical methods (such as numerical differential equations and Numerical linear algebra), and / or the development of optimization models. The general nature of these problems is discussed under Modeling and analysis of financial markets, while specific techniques are listed under Outline of finance: Mathematical tools.

Modelers are generally referred to as "quants" (quantitative analysts), and typically have strong (Ph.D. level) backgrounds in quantitative disciplines such as physics, engineering, computer science, mathematics or operations research. Alternatively / additionally they have completed a finance masters with a quantitative orientation, such as the Master of Quantitative Finance, or the more specialized Master of Computational Finance or Master of Financial Engineering.

Although spreadsheets are widely used here also (almost always requiring extensive VBA), custom C++ or numerical analysis software such as MATLAB is often preferred, particularly where stability or speed is a concern. Additionally, for many derivative and portfolio applications, commercial software is available, and the choice as to whether the model is to be developed in-house, or whether existing products are to be deployed, will depend on the problem in question.

The complexity of these models may result in incorrect pricing or hedging or both. This Model risk is the subject of ongoing research by finance academics, and is a topic of great, and growing, interest in the risk management arena.

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