Financial modeling is the task of building an abstract representation (a model) of a real world financial 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.
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 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.^{[3]}
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Transcription
Contents
Accounting
In corporate finance and the accounting profession, financial modeling typically entails financial statement forecasting; usually the preparation of detailed companyspecific models used for decision making purposes^{[1]} and financial analysis.
Applications include:
 Business valuation, especially discounted cash flow, but including other valuation approaches
 Scenario planning and management decision making ("what is"; "what if"; "what has to be done"^{[4]})
 Capital budgeting
 Cost of capital (i.e. WACC) calculations
 Financial statement analysis (including of operating and finance leases, and R&D)
 Project finance
 Cash flow forecasting and asset and liability management related
To generalize^{[citation needed]} 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.^{[citation needed]} At the same time, numerous commercial training courses are offered, both through universities and privately.
Although purpose built business software does exist (see also Fundamental Analysis Software), the vast proportion of the market is spreadsheetbased; this is largely since the models are almost always company specific. Also, analysts will each have their own criteria and methods for financial modeling.^{[5]} (For the components / steps of business modeling here, see the list for "Equity valuation" under Outline of finance #Discounted cash flow valuation.) Microsoft Excel now has by far the dominant position, having overtaken Lotus 123 in the 1990s. Spreadsheetbased modelling can have its own problems,^{[6]} and several standardizations and "best practices" have been proposed.^{[7]} "Spreadsheet risk" is increasingly studied and managed;^{[7]} see model audit.
One critique here, is that model outputs, i.e. line items, often incorporate "unrealistic implicit assumptions" and "internal inconsistencies".^{[8]} (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. Related to this, is that modellers often additionally "fail to identify crucial assumptions" relating to inputs, "and to explore what can go wrong".^{[9]} Here, in general, modellers "use point values and simple arithmetic instead of probability distributions and statistical measures"^{[10]} — 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.^{[11]} Other critiques discuss the lack of basic computer programming concepts.^{[12]} More serious criticism, in fact, relates to the nature of budgeting itself, and its impact on the organization.^{[13]}^{[14]}
The Financial Modeling World Championships, known as ModelOff, have been held since 2012. ModelOff is a global online financial modeling competition which culminates in a Live Finals Event for top competitors. From 20122014 the Live Finals were held in New York City and in 2015, in London.^{[15]}
Quantitative finance
In quantitative finance, financial modeling entails the development of a sophisticated mathematical model.^{[citation needed]} Models here deal with asset prices, market movements, portfolio returns and the like. A general distinction^{[citation needed]} 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, short rate modelling, building "curve sets") and credit spreads
 Credit scoring and provisioning
 Corporate financing activity prediction problems
 Portfolio optimization.^{[16]}
 Real options
 Risk modeling (Financial risk modeling) and value at risk^{[17]}
 Dynamic financial analysis (DFA)
 Credit valuation adjustment, CVA, as well as the various XVA
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, while specific techniques are listed under Outline of finance# Mathematical tools. For further discussion here see also: Financial models with longtailed 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,^{[18]} 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,^{[18]} particularly where stability or speed is a concern. MATLAB is often used at the research or prototyping stage^{[citation needed]} because of its intuitive programming, graphical and debugging tools, but C++/Fortran are preferred for conceptually simple but high computationalcost applications where MATLAB is too slow; Python is increasingly used due to its simplicity and large standard library. 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 inhouse, or whether existing products are to be deployed, will depend on the problem in question.^{[18]}
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.^{[19]}
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 mathematical and statistical modeling may be applied to finance at all, at least with the assumptions usually made (for options; for portfolios). In fact, these may go so far as to question the "empirical and scientific validity... of modern financial theory".^{[20]} Notable here are Nassim Taleb and Benoit Mandelbrot.^{[21]} See also Mathematical finance #Criticism and Financial economics #Challenges and criticism.
See also
 Asset pricing model
 Economic model
 Financial engineering
 Financial forecast
 Financial Modelers' Manifesto
 Financial models with longtailed distributions and volatility clustering
 Financial planning
 Integrated business planning
 Model audit
 Modeling and analysis of financial markets
 Pro forma #Financial statements
 Profit model
 Real options valuation
References
 ^ ^{a} ^{b} http://www.investopedia.com/terms/f/financialmodeling.asp
 ^ Low, R.K.Y.; Tan, E. (2016). "The Role of Analysts' Forecasts in the Momentum Effect". International Review of Financial Analysis. doi:10.1016/j.irfa.2016.09.007.
 ^ Nick Crawley (2010). Which industry sector would benefit the most from improved financial modelling standards?, fimodo.com.
 ^ Joel G. Siegel; Jae K. Shim; Stephen Hartman (1 November 1997). Schaum's quick guide to business formulas: 201 decisionmaking tools for business, finance, and accounting students. McGrawHill Professional. ISBN 9780070580312. 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 20100407 at the Wayback Machine, fimodo.com
 ^ ^{a} ^{b} 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 9781844804924. Retrieved 12 November 2011.
 ^ Richard A. Brealey; Stewart C. Myers; Brattle Group (2003). Capital investment and valuation. McGrawHill Professional. pp. 223–. ISBN 9780071383776. Retrieved 12 November 2011.
 ^ Peter Coffee (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. 151159). 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. 94101, November 2001.
 ^ ModelOff, Financial Modeling World Championships. "ModelOff 2015 Financial Modeling World Championships".
 ^ Low, R.K.Y.; Faff, R.; Aas, K. (2016). "Enhancing mean–variance portfolio selection by modeling distributional asymmetries". Journal of Economics and Business. 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?". Journal of Banking & Finance. 37 (8). doi:10.1016/j.jbankfin.2013.02.036.
 ^ ^{a} ^{b} ^{c} Mark S. Joshi, On Becoming a Quant.
 ^ Riccardo Rebonato (N.D.). Theory and Practice of Model Risk Management.
 ^ http://www.fooledbyrandomness.com/Trianafwd.pdf
 ^ "Archived copy" (PDF). Archived from the original (PDF) on 20101207. Retrieved 20100615.CS1 maint: Archived copy as title (link)
Bibliography
General
 Benninga, Simon (1997). Financial Modeling. Cambridge, MA: MIT Press. ISBN 0585132232.
 Benninga, Simon (2006). Principles of Finance with Excel. New York: Oxford University Press. ISBN 0195301501.
 Fabozzi, Frank J. (2012). Encyclopedia of Financial Models. Hoboken, NJ: Wiley. ISBN 9781118006733.
 Ho, Thomas; Sang Bin Lee (2004). The Oxford Guide to Financial Modeling. New York: Oxford University Press. ISBN 9780195169621.
 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 9780735669130.
 Yip, Henry (2005). Spreadsheet Applications to securities valuation and investment theories. John Wiley and Sons Australia Ltd. ISBN 0470807962.
Corporate finance
 Day, Alastair (2007). Mastering Financial Modelling in Microsoft Excel. London: Pearson Education. ISBN 0273708066.
 Mayes, Timothy R.; Todd M. Shank (2011). Financial Analysis with Microsoft Excel, 6th Edition. Boston: Cengage Learning. ISBN 9781111826246.
 Ongkrutaraksa, Worapot (2006). Financial Modeling and Analysis: A Spreadsheet Technique for Financial, Investment, and Risk Management, 2nd Edition. Frenchs Forest: Pearson Education Australia. ISBN 0733984746.
 Palepu, Krishna G.; Paul M. Healy (2012). Business Analysis and Valuation Using Financial Statements, 5th Edition. Boston: SouthWestern College Publishing. ISBN 9781111972288.
 Pignataro, Paul (2003). Financial Modeling and Valuation: A Practical Guide to Investment Banking and Private Equity. Hoboken, NJ: Wiley. ISBN 9781118558768.
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 Soubeiga, Eric (2013). Mastering Financial Modeling: A Professional's Guide to Building Financial Models in Excel. New York: McGrawHill. ISBN 9780071808507.
 Swan, Jonathan (2007). Financial Modelling Special Report. London: Institute of Chartered Accountants in England & Wales.
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 Tham, Joseph; Ignacio VelezPareja (2004). Principles of Cash Flow Valuation: An Integrated MarketBased Approach. Amsterdam: Elsevier. ISBN 0126860408.
 Tjia, John (2003). Building Financial Models. New York: McGrawHill. ISBN 0071402101.
Quantitative finance
 Brooks, Robert (2000). Building Financial Derivatives Applications with C++. Westport: Praeger. ISBN 9781567202878.
 Brigo, Damiano; Fabio Mercurio (2006). Interest Rate Models  Theory and Practice with Smile, Inflation and Credit (2nd ed.). London: Springer Finance. ISBN 9783540221494.
 Clewlow, Les; Chris Strickland (1998). Implementing Derivative Models. New Jersey: Wiley. ISBN 0471966517.
 Duffy, Daniel (2004). Financial Instrument Pricing Using C++. New Jersey: Wiley. ISBN 9780470855096.
 Fabozzi, Frank J. (1998). Valuation of fixed income securities and derivatives, 3rd Edition. Hoboken, NJ: Wiley. ISBN 9781883249250.
 Fabozzi, Frank J.; Sergio M. Focardi; Petter N. Kolm (2004). Financial Modeling of the Equity Market: From CAPM to Cointegration. Hoboken, NJ: Wiley. ISBN 0471699004.
 Shayne Fletcher; Christopher Gardner (2010). Financial Modelling in Python. John Wiley and Sons. ISBN 9780470747896.
 Fusai, Gianluca; Andrea Roncoroni (2008). Implementing Models in Quantitative Finance: Methods and Cases. London: Springer Finance. ISBN 3540223487.
 Haug, Espen Gaarder (2007). The Complete Guide to Option Pricing Formulas, 2nd edition. McGrawHill. ISBN 9780071389976.
 Hilpisch , Yves (2015). Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging. New Jersey: Wiley. ISBN 9781119037996.
 Jackson, Mary; Mike Staunton (2001). Advanced modelling in finance using Excel and VBA. New Jersey: Wiley. ISBN 0471499226.
 Jondeau, Eric; SerHuang Poon; Michael Rockinger (2007). Financial Modeling Under NonGaussian Distributions. London: Springer. ISBN 9781849965996.
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 Kwok, YueKuen (2008). Mathematical Models of Financial Derivatives, 2nd edition. London: Springer Finance. ISBN 3540422889.
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 Löeffler, G; Posch, P. (2011). Credit Risk Modeling using Excel and VBA. Hoboken, NJ: Wiley. ISBN 9780470660928.
 Rouah, Fabrice Douglas; Gregory Vainberg (2007). Option Pricing Models and Volatility Using ExcelVBA. New Jersey: Wiley. ISBN 9780471794646.
 Antoine Savine and Jesper Andreasen (2018). Modern Computational Finance: Scripting for Derivatives and xVA. Wiley. ISBN 9781119540786.
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