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==External links==
==External links==
*[http://www.eusprig.org/best-practice.htm Best Practice], European Spreadsheet Risks Interest Group
*[http://www.eusprig.org/best-practice.htm Best Practice], European Spreadsheet Risks Interest Group
*[http://model-viewer.com/DetailSection/FinancialForecasting Financial Forecasting], Details on Financial Forecast


{{Corporate finance and investment banking}}
{{Corporate finance and investment banking}}

Revision as of 05:17, 21 January 2013

Financial modeling is the task of building an abstract representation (a model) of a financial decision making 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. 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.[2] Typically, financial modelling is understood to mean an exercise in either asset pricing or corporate finance, of a quantitative nature. In other words, financial modelling is about translating a set of hypotheses about the behavior of markets or agents into numerical predictions; for example, a firm's decisions about investments (the firm will invest 20% of assets), or investment returns (returns on "stock A" will, on average, be 10% higher than the market's returns).

Accounting

In corporate finance, investment banking and the accounting profession financial modeling is largely synonymous with cash flow forecasting.[citation needed] This usually involves the preparation of detailed company specific models used for decision making purposes[1] and financial analysis. Applications include:

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.[citation needed] For discussion of the issues that may arise, see below; for discussion as to more sophisticated approaches sometimes employed, see Corporate finance: Quantifying uncertainty.

Modellers are sometimes referred to (tongue in cheek) as "number crunchers", and are often designated "financial analyst". Typically, the modeller will have completed an MBA or MSF with (optional) coursework in "financial modeling".[citation needed] 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 software does exist, the vast proportion of the market is spreadsheet-based [citation needed] - 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 modelling can have its own problems,[4] and several standardizations and "best practices" have been proposed.[5] "Spreadsheet risk" is increasingly studied and managed.[5]

One critique here, is that model outputs, i.e. line items, often incorporate “unrealistic implicit assumptions” and “internal inconsistencies”[6] (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".[7] Here, in general, modellers "use point values and simple arithmetic instead of probability distributions and statistical measures"[8] - 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.[9] Other critiques discuss the lack of adequate spreadsheet design skills,[10] and of basic computer programming concepts.[11] More serious criticism, in fact, relates to the nature of budgeting itself, and its impact on the organization.[12][13]

In December 2012 the inaugural [[ModelOff]] Financial Modelling World Championship was held in New York City.[14] Major judges were Professor Simon Benninga and Bill Jelen (aka "Mr Excel"). Event Sponsors included Microsoft, S&P Capital IQ, Bloomberg Institute, AMT Training and Deloitte. The event was won by Alex Gordon of New Zealand.

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 key distinction[citation needed] is between models of the financial situation of a large, complex firm or "quantitative financial management", models of the returns of different stocks or "quantitative asset pricing", models of the price or returns of derivative securities or "financial engineering" and models of the firm's financial decisions or "quantitative corporate finance". Applications include:

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, 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; see also Financial models with long-tailed distributions and volatility clustering.

Modellers are generally referred to as "quants" (quantitative analysts), and typically have advanced (Ph.D. level) backgrounds in quantitative disciplines such as 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,[15] 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,[15] particularly where stability or speed is a concern. Matlab is the tool of choice for doing economics research because of its intuitive programming, graphical and debugging tools, but C++/Fortran are preferred for conceptually simple but high computational costs applications where Matlab is too slow. 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.[15]

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.[16]

Criticism of the discipline (often preceding the Financial crisis of 2007-2008 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".[17] Notable here are Nassim Taleb and Benoit Mandelbrot.[18] See also "Criticism" under Mathematical finance.

See also

Selected books

  • 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.
  • Brooks, Robert (2000). Building Financial Derivatives Applications with C++. Westport: Praeger. ISBN 978-1567202878.
  • Brigo, Damiano (2006). Interest Rate Models - Theory and Practice with Smile, Inflation and Credit, 2nd Edition. London. ISBN 978-3-540-22149-4. {{cite book}}: Text "Springer Finance" ignored (help)CS1 maint: location missing publisher (link)
  • Clewlow, Les (1998). Implementing Derivative Models. New Jersey: Wiley. ISBN 0-471-96651-7. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  • Day, Alastair (2007). Mastering Financial Modelling in Microsoft Excel. London: Pearson Education. ISBN 0-273-70806-6.
  • 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. {{cite book}}: Cite has empty unknown parameter: |coauthors= (help)
  • Fabozzi, Frank J. (2012). Encyclopedia of Financial Models. Hoboken, NJ: Wiley. ISBN 978-1-118-00673-3. {{cite book}}: Cite has empty unknown parameter: |coauthors= (help)
  • Fabozzi, Frank J. (2004). Financial Modeling of the Equity Market: From CAPM to Cointegration. Hoboken, NJ: Wiley. ISBN 0-471-69900-4. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  • Fusai, Gianluca (2008). Implementing Models in Quantitative Finance: Methods and Cases. London: Springer Finance. ISBN 3-540-22348-7. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  • Haug, Espen (2006). The Complete Guide to Option Pricing Formulas. New York: McGraw-Hill. ISBN 0-07-138997-0. {{cite book}}: Cite has empty unknown parameter: |coauthors= (help)
  • Ho, Thomas (2004). The Oxford Guide to Financial Modeling. New York: Oxford University Press. ISBN 978-0-19-516962-1. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  • Jackson, Mary (2001). Advanced modelling in finance using Excel and VBA. New Jersey: Wiley. ISBN 0-471-49922-6. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  • Jondeau, Eric (2007). Financial Modeling Under Non-Gaussian Distributions. London: Springer. ISBN 978-1849965996. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  • Kwok, Yue-Kuen (2008). Mathematical Models of Financial Derivatives, 2nd edition. London: Springer Finance. ISBN 3540422889. {{cite book}}: Cite has empty unknown parameter: |coauthors= (help)
  • London, Justin (2004). Modeling Derivatives in C++. New Jersey: Wiley. ISBN 978-0471654643.
  • Ongkrutaraksa, Worapot (2006). Financial Modeling and Analysis: A Spreadsheet Technique for Financial, Investment, and Risk Management, 2nd Edition. Frenchs Forest: Pearson Education Australia. ISBN 0-7339-8474-6.
  • 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.
  • Rouah, Fabrice Douglas (2007). Option Pricing Models and Volatility Using Excel-VBA. New Jersey: Wiley. ISBN 978-0471794646. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  • Swan, Jonathan (2007). Financial Modelling Special Report. London: Institute of Chartered Accountants in England & Wales.
  • Swan, Jonathan (2008). Practical Financial Modelling, 2nd Edition. London: CIMA Publishing. ISBN 0-7506-8647-2.
  • Tham, Joseph (2004). Principles of Cash Flow Valuation: An Integrated Market-Based Approach. Amsterdam: Elsevier. ISBN 0-12-686040-8. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  • Tjia, John (2003). Building Financial Models. New York: McGraw-Hill. ISBN 0-07-140210-1.
  • Vladimirou, Hercules (2007). Financial Modeling (Volume 151 of Annals of operations research). Norwell, MA: Springer. {{cite book}}: External link in |title= (help)
  • Mantegna, Rosario N. (2010). "Focus on Statistical Physics Modelling in Economics and Finance". New Journal of Physics. {{cite journal}}: Cite has empty unknown parameter: |month= (help); Unknown parameter |coauthors= ignored (|author= suggested) (help)

References

  1. ^ a b http://www.investopedia.com/terms/f/financialmodeling.asp
  2. ^ Nick Crawley (2010). Which industry sector would benefit the most from improved financial modelling standards?, fimodo.com.
  3. ^ 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".
  4. ^ Danielle Stein Fairhurst (2009). Six reasons your spreadsheet is NOT a financial model, fimodo.com
  5. ^ a b Best Practice, European Spreadsheet Risks Interest Group
  6. ^ Krishna G. Palepu; Paul M. Healy; Erik Peek (2007). Business analysis and valuation: text and cases. Cengage Learning EMEA. pp. 261–. ISBN 978-1-84480-492-4. Retrieved 12 November 2011. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  7. ^ 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.
  8. ^ Peter Coffee (2004). Spreadsheets: 25 Years in a Cell, eWeek.
  9. ^ http://pages.stern.nyu.edu/~adamodar/pdfiles/papers/probabilistic.pdf
  10. ^ http://www.cluteinstitute.com/Programs/Las_Vegas_2009/Article%20323.pdf [dead link]
  11. ^ 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.
  12. ^ Loren Gary (2003). Why Budgeting Kills Your Company, Harvard Management Update, May 2003.
  13. ^ Michael Jensen (2001). Corporate Budgeting Is Broken, Let's Fix It, Harvard Business Review, pp. 94-101, November 2001.
  14. ^ www.modeloff.com
  15. ^ a b c Mark S. Joshi, On Becoming a Quant.
  16. ^ Riccardo Rebonato (N.D.). Theory and Practice of Model Risk Management.
  17. ^ http://www.fooledbyrandomness.com/Triana-fwd.pdf
  18. ^ http://www.fooledbyrandomness.com/fortune.pdf
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