Financial modeling 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 is about 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.
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.
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 and financial analysis.
- Business valuation / stock valuation - especially via discounted cash flow, but including other valuation approaches
- Scenario planning and management decision making ("what is"; "what if"; "what has to be done")
- Capital budgeting, including cost of capital (i.e. WACC) calculations
- Financial statement analysis / ratio analysis (including of operating- and finance leases, and R&D)
- Revenue related: forecasting, analysis
- Project finance modeling
- Cash flow forecasting
- Credit decisioning: Credit analysis and Consumer credit risk; impairment- and provision-modelling
- Working capital- and treasury management; asset and liability management
- Management accounting: Activity-based costing, Profitability analysis, Cost analysis, Whole-life cost
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 Corporate finance § Quantifying uncertainty and Financial economics § Corporate finance theory.
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". 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. For the components and steps of business modeling here, see the list for "Equity valuation" under Outline of finance § Discounted cash flow valuation; see also Valuation using discounted cash flows § Determine cash flow for each forecast period for further discussion and considerations.
Although purpose-built business software does exist (see also Fundamental Analysis Software), 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. 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; 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, leverage and/or equity financing. See Sustainable 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 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. (See Valuation using discounted cash flows § Determine equity value.) Other critiques discuss the lack of basic computer programming concepts. More serious criticism, in fact, relates to the nature of budgeting itself, and its impact on the organization  (see Conditional budgeting § Criticism of budgeting).
In quantitative finance, financial modeling entails the development of a sophisticated mathematical model. Models here deal with asset prices, market movements, portfolio returns and the like. A general distinction is between: "quantitative financial management", models of the financial situation of a large, complex firm; "quantitative asset pricing", models of the returns of different stocks; "financial engineering", models of the price or returns of derivative securities; "quantitative corporate finance", models of the firm's financial decisions.
Relatedly, applications include:
- Option pricing and calculation of their "Greeks"
- Other derivatives, especially interest rate derivatives, credit derivatives and exotic derivatives
- Modeling the term structure of interest rates (bootstrapping / multi-curves, short-rate models, HJM) and credit spreads
- Credit scoring and provisioning
- Credit risk, counterparty credit risk, and regulatory capital: exposure at default, probability of default, loss given default, potential future exposure
- Corporate financing activity prediction problems
- Portfolio optimization
- Real options
- Risk modeling (Financial risk modeling) and value at risk
- Credit valuation adjustment, CVA, as well as the various XVA
- 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 Mathematical finance § History: Q versus P, while specific techniques are listed under Outline of finance § Mathematical tools. For further discussion here see also: Financial models with long-tailed distributions and volatility clustering; Brownian model of financial markets; Martingale pricing; Extreme value theory; Historical simulation (finance).
Modellers are generally referred to as "quants" (quantitative analysts), 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, such as the Master of Quantitative Finance, or the more specialized Master of Computational Finance or Master of Financial Engineering; the CQF 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 stage 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 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 Quantitative analysis (finance) § Library quantitative analysis.
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.
Criticism of the discipline (often preceding the financial crisis of 2007–08 by several years) emphasizes the differences between the mathematical and physical sciences, and finance, and the resultant caution to be applied by modelers, and by traders and risk managers using their models. Notable here are Emanuel Derman and Paul Wilmott, authors of the Financial Modelers' Manifesto. Some go further and question whether the mathematical- and statistical modeling techniques usually applied to finance are at all appropriate (see the assumptions made for options and for portfolios). In fact, these may go so far as to question the "empirical and scientific validity... of modern financial theory". Notable here are Nassim Taleb and Benoit Mandelbrot. See also Mathematical finance § Criticism and Financial economics § Challenges and criticism.
- Asset pricing model
- Economic model
- Financial engineering
- Financial forecast
- Financial Modelers' Manifesto
- Financial models with long-tailed distributions and volatility clustering
- Financial planning
- Integrated business planning
- Model audit
- Modeling and analysis of financial markets
- Outline of finance § Education
- Pro forma § Financial statements
- Profit model
- Investopedia Staff (2020). "Financial Modeling".
- Low, R.K.Y.; Tan, E. (2016). "The Role of Analysts' Forecasts in the Momentum Effect" (PDF). International Review of Financial Analysis. 48: 67–84. doi:10.1016/j.irfa.2016.09.007.
- Nick Crawley (2010). Which industry sector would benefit the most from improved financial modeling standards?, fimodo.com.
- Joel G. Siegel; Jae K. Shim; Stephen Hartman (1 November 1997). Schaum's quick guide to business formulas: 201 decision-making tools for business, finance, and accounting students. McGraw-Hill Professional. ISBN 978-0-07-058031-2. Retrieved 12 November 2011. §39 "Corporate Planning Models". See also, §294 "Simulation Model".
- See for example, Valuing Companies by Cash Flow Discounting: Ten Methods and Nine Theories, Pablo Fernandez: University of Navarra - IESE Business School
- Danielle Stein Fairhurst (2009). Six reasons your spreadsheet is NOT a financial model Archived 2010-04-07 at the Wayback Machine, fimodo.com
- Best Practice, European Spreadsheet Risks Interest Group
- Krishna G. Palepu; Paul M. Healy; Erik Peek; Victor Lewis Bernard (2007). Business analysis and valuation: text and cases. Cengage Learning EMEA. pp. 261–. ISBN 978-1-84480-492-4. Retrieved 12 November 2011.
- Richard A. Brealey; Stewart C. Myers; Brattle Group (2003). Capital investment and valuation. McGraw-Hill Professional. pp. 223–. ISBN 978-0-07-138377-6. Retrieved 12 November 2011.
- (2004). Spreadsheets: 25 Years in a Cell, eWeek.
- Prof. Aswath Damodaran. Probabilistic Approaches: Scenario Analysis, Decision Trees and Simulations, NYU Stern Working Paper
- 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.
- 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.
- Low, R.K.Y.; Faff, R.; Aas, K. (2016). "Enhancing mean–variance portfolio selection by modeling distributional asymmetries" (PDF). Journal of Economics and Business. 85: 49–72. doi:10.1016/j.jeconbus.2016.01.003.
- Low, R.K.Y.; Alcock, J.; Faff, R.; Brailsford, T. (2013). "Canonical vine copulas in the context of modern portfolio management: Are they worth it?" (PDF). Journal of Banking & Finance. 37 (8): 3085–3099. doi:10.1016/j.jbankfin.2013.02.036.
- Mark S. Joshi, On Becoming a Quant.
- Riccardo Rebonato (N.D.). Theory and Practice of Model Risk Management.
- Nassim Taleb and Benoit Mandelbrot. "How the Finance Gurus Get Risk All Wrong" (PDF). Archived from the original (PDF) on 2010-12-07. Retrieved 2010-06-15.
- Benninga, Simon (1997). Financial Modeling. Cambridge, MA: MIT Press. ISBN 0-585-13223-2.
- Benninga, Simon (2006). Principles of Finance with Excel. New York: Oxford University Press. ISBN 0-19-530150-1.
- Fabozzi, Frank J. (2012). Encyclopedia of Financial Models. Hoboken, NJ: Wiley. ISBN 978-1-118-00673-3.
- Ho, Thomas; Sang Bin Lee (2004). The Oxford Guide to Financial Modeling. New York: Oxford University Press. ISBN 978-0-19-516962-1.
- Sengupta, Chandan (2009). Financial Analysis and Modeling Using Excel and VBA, 2nd Edition. Hoboken, NJ: John Wiley & Sons. ISBN 9780470275603.
- Winston, Wayne (2014). Microsoft Excel 2013 Data Analysis and Business Modeling. Microsoft Press. ISBN 978-0735669130.
- Yip, Henry (2005). Spreadsheet Applications to securities valuation and investment theories. John Wiley and Sons Australia Ltd. ISBN 0470807962.
- Beech, G. and Thayser, D. (2015). Valuations, Mergers and Acquisitions. Oxford: Oxford University Press. ISBN 0-585-13223-2.CS1 maint: multiple names: authors list (link)
- Day, Alastair (2007). Mastering Financial Modelling in Microsoft Excel. London: Pearson Education. ISBN 978-0-273-70806-3.
- Lynch, Penelope (1997). Financial Modelling for Project Finance, 2nd Edition. Euromoney Trading. ISBN 9781843745488.
- Mayes, Timothy R.; Todd M. Shank (2011). Financial Analysis with Microsoft Excel, 6th Edition. Boston: Cengage Learning. ISBN 978-1111826246.
- Peter K Nevitt; Frank J. Fabozzi (2000). Project Financing. Euromoney Institutional Investor PLC. ISBN 978-1-85564-791-6.
- Ongkrutaraksa, Worapot (2006). Financial Modeling and Analysis: A Spreadsheet Technique for Financial, Investment, and Risk Management, 2nd Edition. Frenchs Forest: . ISBN 0-7339-8474-6.
- Palepu, Krishna G.; Paul M. Healy (2012). Business Analysis and Valuation Using Financial Statements, 5th Edition. Boston: South-Western College Publishing. ISBN 978-1111972288.
- Pignataro, Paul (2003). Financial Modeling and Valuation: A Practical Guide to Investment Banking and Private Equity. Hoboken, NJ: Wiley. ISBN 978-1118558768.
- Proctor, Scott (2009). Building Financial Models with Microsoft Excel: A Guide for Business Professionals, 2nd Edition. Hoboken, NJ: Wiley. ISBN 978-0-470-48174-5.
- Rees, Michael (2008). Financial Modelling in Practice: A Concise Guide for Intermediate and Advanced Level. Hoboken, NJ: Wiley. ISBN 978-0-470-99744-4.
- Soubeiga, Eric (2013). Mastering Financial Modeling: A Professional's Guide to Building Financial Models in Excel. New York: McGraw-Hill. ISBN 978-0071808507.
- Swan, Jonathan (2007). Financial Modelling Special Report. London: Institute of Chartered Accountants in England & Wales.
- Swan, Jonathan (2008). Practical Financial Modelling, 2nd Edition. London: . ISBN 978-0-7506-8647-1.
- Tham, Joseph; Ignacio Velez-Pareja (2004). Principles of Cash Flow Valuation: An Integrated Market-Based Approach. Amsterdam: Elsevier. ISBN 0-12-686040-8.
- Tjia, John (2003). Building Financial Models. New York: McGraw-Hill. ISBN 0-07-140210-1.
- Hirsa , Ali (2013). Computational Methods in Finance. Boca Raton: CRC Press. ISBN 9781439829578.
- Brooks, Robert (2000). Building Financial Derivatives Applications with C++. Westport: Praeger. ISBN 978-1567202878.
- Brigo, Damiano; Fabio Mercurio (2006). Interest Rate Models - Theory and Practice with Smile, Inflation and Credit (2nd ed.). London: Springer Finance. ISBN 978-3-540-22149-4.
- Clewlow, Les; Chris Strickland (1998). Implementing Derivative Models. New Jersey: Wiley. ISBN 0-471-96651-7.
- Duffy, Daniel (2004). Financial Instrument Pricing Using C++. New Jersey: Wiley. ISBN 978-0470855096.
- Fabozzi, Frank J. (1998). Valuation of fixed income securities and derivatives, 3rd Edition. Hoboken, NJ: Wiley. ISBN 978-1-883249-25-0.
- Fabozzi, Frank J.; Sergio M. Focardi; Petter N. Kolm (2004). Financial Modeling of the Equity Market: From CAPM to Cointegration. Hoboken, NJ: Wiley. ISBN 0-471-69900-4.
- Shayne Fletcher; Christopher Gardner (2010). Financial Modelling in Python. John Wiley and Sons. ISBN 978-0-470-74789-6.
- Fusai, Gianluca; Andrea Roncoroni (2008). Implementing Models in Quantitative Finance: Methods and Cases. London: Springer Finance. ISBN 978-3-540-22348-1.
- Haug, Espen Gaarder (2007). The Complete Guide to Option Pricing Formulas, 2nd edition. McGraw-Hill. ISBN 978-0071389976.
- M. Henrard (2014). Interest Rate Modelling in the Multi-Curve Framework. Springer. ISBN 978-1137374653.
- Hilpisch , Yves (2015). Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging. New Jersey: Wiley. ISBN 978-1-119-03799-6.
- Jackson, Mary; Mike Staunton (2001). Advanced modelling in finance using Excel and VBA. New Jersey: Wiley. ISBN 0-471-49922-6.
- Jondeau, Eric; Ser-Huang Poon; Michael Rockinger (2007). Financial Modeling Under Non-Gaussian Distributions. London: Springer. ISBN 978-1849965996.
- Joerg Kienitz; Daniel Wetterau (2012). Financial Modelling: Theory, Implementation and Practice with MATLAB Source. Hoboken, NJ: Wiley. ISBN 978-0470744895.
- Kwok, Yue-Kuen (2008). Mathematical Models of Financial Derivatives, 2nd edition. London: Springer Finance. ISBN 978-3540422884.
- Levy, George (2004). Computational Finance: Numerical Methods for Pricing Financial Instruments. Butterworth-Heinemann. ISBN 978-0750657228.
- London, Justin (2004). Modeling Derivatives in C++. New Jersey: Wiley. ISBN 978-0471654643.
- Löeffler, G; Posch, P. (2011). Credit Risk Modeling using Excel and VBA. Hoboken, NJ: Wiley. ISBN 978-0470660928.
- Rouah, Fabrice Douglas; Gregory Vainberg (2007). Option Pricing Models and Volatility Using Excel-VBA. New Jersey: Wiley. ISBN 978-0471794646.
- Antoine Savine and Jesper Andreasen (2018). Modern Computational Finance: Scripting for Derivatives and xVA. Wiley. ISBN 978-1119540786.
- Alexander Sokol (2014). Long-Term Portfolio Simulation - For XVA, Limits, Liquidity and Regulatory Capital. Risk Books. ISBN 978-1782720959.
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