Financial modeling is the task of building anabstract representation (amodel) of a real worldfinancial situation.^[1] This is amathematical model designed to represent (a simplified version of) the performance of a financial asset or portfolio of a business,project, or any other investment.

Typically, then, financial modeling is understood to mean an exercise in either asset pricing or corporate finance, of a quantitative nature. It is about translating a set of hypotheses about the behavior of markets or agents into numerical predictions.^[2] At the same time, "financial modeling" is a general term that means different things to different users; the reference usually relates either to accounting andcorporate finance applications or toquantitative finance applications.

Incorporate finance and theaccounting profession,financial modeling typically entailsfinancial statement forecasting; usually the preparation of detailed company-specific models used for^[1] decision making purposes, valuation andfinancial analysis.

Applications include:

• Business valuation,stock valuation, andproject valuation - especially viadiscounted cash flow, but includingother valuation approaches
• Scenario planning,FP&A andmanagement decision making ("what is"; "what if"; "what has to be done"^[3])
• Budgeting:revenue forecasting andanalytics;production budgeting;operations budgeting
• Capital budgeting, includingcost of capital (i.e.WACC) calculations
• Cash flow forecasting;working capital- andtreasury management;asset and liability management
• Financial statement analysis /ratio analysis (including ofoperating- andfinance leases, andR&D)
• Transaction analytics:M&A,PE,VC,LBO,IPO,Project finance,^[4]P3
• Credit decisioning:Credit analysis,Consumer credit risk;impairment- andprovision-modeling
• Management accounting:Activity-based costing,Profitability analysis,Cost analysis,Whole-life cost,Managerial risk accounting
• Public sector procurement^[5]

To generalize^{[citation needed]} as to the nature of these models: firstly, as they are built aroundfinancial statements, calculations and outputs are monthly, quarterly or annual; secondly, the inputs take the form of "assumptions", where the analystspecifies the values that will apply in each period for external / global variables (exchange rates,tax percentage, etc....; may be thought of as the modelparameters), and for internal / company specificvariables (wages,unit costs, etc....). Correspondingly, both characteristics are reflected (at least implicitly) in themathematical form of these models: firstly, the models are indiscrete time; secondly, they aredeterministic.For discussion of the issues that may arise, see below; for discussion as to more sophisticated approaches sometimes employed, seeCorporate finance § Quantifying uncertainty andFinancial economics § Corporate finance theory.

Modelers are often designated "financial analyst" (and are sometimes referred to,tongue in cheek, as "number crunchers"). Typically,^[6] the modeler will have completed anMBA orMSF with (optional) coursework in "financial modeling".^[7] Accounting qualifications and finance certifications such as theCIIA andCFA generally do not provide direct or explicit training in modeling.^[8] At the same time, numerous commercialtraining courses are offered, both through universities and privately.For the components and steps of business modeling here, seeOutline of finance § Financial modeling; see alsoValuation using discounted cash flows § Determine cash flow for each forecast period for further discussion and considerations.

Although purpose-builtbusiness software does exist, the vast proportion of the market isspreadsheet-based; this is largely since the models are almost always company-specific. Also, analysts will each have their own criteria and methods for financial modeling.^[9]Microsoft Excel now has by far the dominant position, having overtakenLotus 1-2-3 in the 1990s. Spreadsheet-based modelling can have its own problems,^[10] and several standardizations and "best practice"s have been proposed.^[11]

Recent professional guidelines emphasize transparent, auditable, and well-documented models. According to PwC and the Financial Modeling Institute, good practice includes separating input, calculation, and output sheets to enhance traceability and reduce error risk.^[12][13] Practical training providers such as the Corporate Finance Institute and ICAEW highlight consistent formatting, clear labeling, and documentation of assumptions as essential for usability and stakeholder confidence.^[14][15]

In entrepreneurial and investment contexts, financial models are sometimes used to illustrate business viability and capital requirements for funding discussions.^[16]"Spreadsheet risk" is increasingly studied and managed;^[11] seemodel audit.

One critique here, is that modeloutputs, i.e.line items, often inhere "unrealistic implicit assumptions" and "internal inconsistencies".^[17] (For example, a forecast for growth in revenue but without corresponding increases inworking capital,fixed assets and the associated financing, may imbed unrealistic assumptions aboutasset turnover,debt level and/orequity financing. SeeSustainable growth rate § From a financial perspective.) What is required, but often lacking, is that all key elements are explicitly and consistently forecasted. Related to this, is that modellers often additionally "fail to identify crucial assumptions" relating toinputs, "and to explore what can go wrong".^[18] Here, in general, modellers "use point values and simple arithmetic instead of probability distributions and statistical measures"^[19] — 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;^[20][21]seeValuation using discounted cash flows § Determine equity value.A further, more general critique relates to the lack of basiccomputer programming concepts amongst modelers,^[22] with the result that their models are often poorly structured, and difficult to maintain. Serious criticism is also directed at the nature of budgeting, and its impact on the organization.^[23][24]

Inquantitative finance,financial modeling entails the development of a sophisticatedmathematical model.^[25] Models here deal with asset prices, market movements, portfolio returns and the like. Relatedly, applications include:

• Option pricing and calculation oftheir "Greeks" (accommodatingvolatility surfaces - vialocal /stochastic volatility models - andmulti-curves)
• Otherderivatives, especiallyinterest rate derivatives,credit derivatives andexotic derivatives
• Credit valuation adjustment, CVA, as well as the variousXVA
• Modeling the term structure ofinterest rates (bootstrapping /multi-curves,short-rate models,HJM framework) and any relatedcredit spread
• Credit risk,counterparty credit risk, andregulatory capital:EAD,PD,LGD,PFE,EE;Jarrow–Turnbull model,Merton model,KMV model
• Portfolio optimization^[26] andQuantitative investing more generally; see further reoptimization methods employed.
• Credit scoring andprovisioning;Credit scorecards andIFRS 9 § Impairment
• Structured product design and manufacture
• Financial risk modeling:value at risk (parametric- and / orhistorical,CVaR,EVT),stress testing,"sensitivities" analysis (Greeks,duration,convexity,DV01,KRD,CS01,JTD)
• Corporate finance applications:^[27]cash flow analytics,^[28]corporate financing activity prediction problems, andrisk analysis in capital investment
• Real options
• Actuarial applications:Dynamic financial analysis (DFA),UIBFM,investment modeling

These problems are generallystochastic andcontinuous in nature, and models here thus requirecomplex algorithms, entailingcomputer simulation, advancednumerical methods (such asnumerical differential equations,numerical linear algebra,dynamic programming) and/or the development ofoptimization models. The general nature of these problems is discussed underMathematical finance § History: Q versus P, while specific techniques are listed underOutline of finance § Mathematical tools. For further discussion here see also:Brownian model of financial markets;Martingale pricing;Financial models with long-tailed distributions and volatility clustering;Extreme value theory;Historical simulation (finance).

Modellers are generally referred to as "quants", i.e.quantitative analysts (or "rocket scientists") and typically have advanced (Ph.D. level) backgrounds in quantitative disciplines such asstatistics,physics,engineering,computer science,mathematics oroperations research. Alternatively, or in addition to their quantitative background, they complete afinance masters with a quantitative orientation,^[29] such as theMaster of Quantitative Finance, or the more specializedMaster of Computational Finance orMaster of Financial Engineering; theCQF certificate is increasingly common.

Although spreadsheets are widely used here also (almost always requiring extensiveVBA); customC++,Fortran orPython, ornumerical-analysis software such asMATLAB, are often preferred,^[29] particularly where stability or speed is a concern. MATLAB is often used at the research or prototyping stage^[30] because of its intuitive programming, graphical and debugging tools, but C++/Fortran are preferred for conceptually simple buthigh computational-cost applications where MATLAB is too slow; Python is increasingly used due to its simplicity, and largestandard library /available applications, includingQuantLib. Additionally, for many (of the standard) derivative and portfolio applications,commercial software is available, and the choice as to whether the model is to bedeveloped in-house, or whether existing products are to be deployed, will depend on the problem in question.^[29]SeeQuantitative analysis (finance) § Library quantitative analysis.

The complexity of these models may result in incorrect pricing orhedging or both. ThisModel risk is the subject of ongoing research by finance academics, and is a topic of great, and growing, interest in therisk management arena.^[31]

Criticism of the discipline (often preceding the2008 financial crisis by several years) emphasizesthe differences between finance and the mathematical / physical sciences, and stresses the resultant caution to be applied by modelers, and by traders and risk managers using their models. Notable here areEmanuel Derman andPaul Wilmott, authors of theFinancial Modelers' Manifesto. Some go further and question whether themathematical- andstatistical modeling techniques usually applied to finance are at all appropriate (see the assumptions madefor options andfor portfolios). In fact, these may go so far as to question the "empirical and scientific validity... ofmodern financial theory".^[32] Notable here areNassim Taleb andBenoit Mandelbrot.^[33] See alsoMathematical finance § Criticism,Financial economics § Challenges and criticism andFinancial engineering § Criticisms.

Several financial modeling competitions exist, emphasizing speed and accuracy in modeling. TheMicrosoft-sponsored ModelOff Financial Modeling World Championships were held annually from 2012 to 2019, with competitions throughout the year and a finals championship in New York or London. After its end in 2020, several other modeling championships have been started, including theFinancial Modeling World Cup andMicrosoft Excel Collegiate Challenge, also sponsored byMicrosoft.^[6]

Philosophy of financial modeling is a branch of philosophy concerned with the foundations, methods, and implications of modeling science.

In the philosophy of financial modeling, scholars have more recently begun to question the generally-held assumption that financial modelers seek to represent any "real-world" or actually ongoing investment situation. Instead, it has been suggested that the task of the financial modeler resides in demonstrating the possibility of a transaction in a prospective investment scenario, from a limited base of possibility conditions initially assumed in the model.^[34]

• Financial models
• Actuarial science
• Mathematical finance
• Corporate finance
• Computational fields of study

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