Financial modeling What is Financial Modeling?, IBM is the task of building an abstract representation (a model) of a real world financial situation. 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.
Typically, then, financial modeling is understood to mean an exercise in either asset pricing or corporate finance of a quantitative nature. It involves translating a set of hypotheses about the behavior of markets or agents into numerical predictions. At the same time, "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.
Accounting
In corporate finance and the accounting profession, financial modeling typically entails financial statement forecasting; usually the preparation of detailed company-specific models used for decision making purposes, valuation and financial analysis. In entrepreneurial and investment contexts, financial models are widely used to illustrate business viability and capital requirements for funding discussions.How to Create a Financial Model That Secures Funding, Qubit Capital Blog (2024)
Applications include: Business valuation, stock valuation, and project valuation - especially via discounted cash flow, but including other valuation approaches Scenario planning, FP&A and management decision making ("what is"; "what if"; "what has to be done"See §39 "Corporate Planning Models" and §294 "Simulation Model" in: ) Budgeting: revenue forecasting and analytics; production budgeting; operations budgeting Capital budgeting, including cost of capital (i.e. WACC) calculations Cash flow forecasting; working capital- and treasury management; asset and liability management Financial statement analysis / ratio analysis (including of operating- and finance leases, and R&D) Transaction analytics: M&A, PE, VC, LBO, IPO, Project finance,See for example: P3 Credit decisioning: Credit analysis, impairment- and provision-modeling Management accounting: Activity-based costing, Profitability analysis, Cost analysis, Whole-life cost, Managerial risk accounting Public sector procurementConfidential disclosure of a financial model is often requested by purchasing organizations undertaking public sector procurement in order that the government department can understand and if necessary challenge the pricing principles which underlie a bidder's costs. E.g. First-tier Tribunal, Department for Works and Pensions v. Information Commissioner, UKFTT EA_2010_0073, paragraph 58, decided 20 September 2010, accessed 11 January 2024
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....; may be thought of as the model parameters), and for 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 and .
Modelers are often designated "financial analyst" (and are sometimes referred to, tongue in cheek, as "number crunchers"). Typically, the modeler will have completed an MBA or MSF with (optional) coursework in "financial modeling".Example course: Financial Modelling, University of South Australia Accounting qualifications and finance certifications such as the CIIA and CFA generally do not provide direct or explicit training in modeling.The MiF can offer an edge over the CFA Financial Times, June 21, 2015. At the same time, numerous commercial training courses are offered, both through universities and privately. For the components and steps of business modeling here, see ; see also for further discussion and considerations.
Although purpose-built business software does exist, the vast proportion of the market is spreadsheet-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.See for example, Valuing Companies by Cash Flow Discounting: Ten Methods and Nine Theories, Pablo Fernandez: University of Navarra - IESE Business School 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,Danielle Stein Fairhurst (2009). Six reasons your spreadsheet is NOT a financial model , fimodo.com and several standardizations and "best practice"s have been proposed.Best Practice , European Spreadsheet Risks Interest Group Here, professional guidelines emphasize transparent, auditable, and well-documented models. Good practice includes separating input, calculation, and output sheets to enhance traceability and reduce error risk.PwC Global Financial Modeling Guidelines (2023)Financial Modeling Institute – Best Practices (2023) Practical training providers further highlight consistent formatting, clear labeling, and documentation of assumptions as essential for usability and stakeholder confidence.CFI – Free Financial Modeling Guide (2024)ICAEW – Financial Modelling and Forecasting Guidance (2023) "Spreadsheet risk" is increasingly studied and managed; see model audit.
One critique here, is that model outputs, i.e. line items, often inhere "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, debt level and/or equity financing. See .) 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 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"Peter Coffee (2004). Spreadsheets: 25 Years in a Cell, EWeek. — 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;Prof. Aswath Damodaran. Probabilistic Approaches: Scenario Analysis, Decision Trees and Simulations, NYU Stern Working Paper The Flaw of Averages , Prof. Sam Savage, Stanford University. see . A further, more general critique relates to the lack of basic computer programming concepts amongst modelers, Blayney, P. (2009). Knowledge Gap? Accounting Practitioners Lacking Computer Programming Concepts as Essential Knowledge. In G. Siemens & C. Fulford (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2009 (pp. 151-159). Chesapeake, VA: AACE. 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.Loren Gary (2003). Why Budgeting Kills Your Company , Harvard Management Update, May 2003.Michael Jensen (2001). Corporate Budgeting Is Broken, Let's Fix It, Harvard Business Review, pp. 94-101, November 2001.
Quantitative finance
In quantitative finance, financial modeling entails the development of a sophisticated mathematical model.See discussion here: Models here deal with asset prices, market movements, portfolio returns and the like.
Relatedly, applications include: Option pricing and calculation of their "Greeks" (accommodating volatility surfaces - via local / stochastic volatility models - and multi-curves) Other derivatives, especially interest rate derivatives, credit derivatives and exotic derivatives Credit valuation adjustment, CVA, as well as the various XVA Modeling the term structure of interest rates (bootstrapping / multi-curves, short-rate models, HJM framework) and any related credit spread Credit risk, Consumer credit risk, counterparty credit risk and regulatory capital: EAD, PD, LGD, PFE, EE; Jarrow–Turnbull model, Merton model, KMV model Portfolio optimizationSee for example: ; and Quantitative investing more generally; see further re optimization methods employed. Credit scoring and provisioning; Credit scorecards and Structured product design and manufacture Financial risk modeling: value at risk (parametric- and / or historical, CVaR, EVT), stress testing, "sensitivities" analysis (Greeks, duration, convexity, DV01, KRD, CS01, JTD) Corporate finance applications:See David Shimko (2009). Quantifying Corporate Financial Risk. archived 2010-07-17. cash flow analytics,See for example this problem (from John Hull's Options, Futures, and Other Derivatives), discussing cash position modeled stochastically. corporate financing activity prediction problems, and risk analysis in capital investment Real options Actuarial applications: Dynamic financial analysis (DFA), UIBFM, investment modeling
These problems are generally stochastic and continuous in nature, and models here thus require complex algorithms, entailing computer simulation, advanced numerical methods (such as numerical differential equations, numerical linear algebra, dynamic programming) and/or the development of optimization models. The general nature of these problems is discussed under , while specific techniques are listed under . 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 as statistics, physics, engineering, computer science, mathematics or operations research. Alternatively, or in addition to their quantitative background, they complete a finance masters with a quantitative orientation,Mark S. Joshi, On Becoming a Quant . such as the Master of Quantitative Finance, or the more specialized Master of Computational Finance or Master of Financial Engineering; the CQF certificate is increasingly common.
Although spreadsheets are widely used here also (almost always requiring extensive VBA); custom C++, Fortran or Python, or numerical-analysis software such as MATLAB, are often preferred, particularly where stability or speed is a concern. MATLAB is often used at the research or prototyping stageMATLAB for Quantitative Finance and Risk Management, MathWorks because of its intuitive programming, graphical and debugging tools, but C++/Fortran are preferred for conceptually simple but high computational-cost applications where MATLAB is too slow; Python is increasingly used due to its simplicity, and large standard library / available applications, including, for example, QuantLib. Additionally, for many (of the standard) 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. See .