Financial modeling

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. Financial modeling is a general term that means different things to different users; the reference report is for accounting and corporate finance applications, or for quantitative finance applications. While there is some debate in the industry to the nature of financial modelingtradecraft , such as welding, or a science -the task of financial modeling has been gaining acceptance and rigor over the years. [2] Typically, financial modeling is understood to be an exercise in financial asset pricing, or a quantitative nature. In other words, financial modeling is about translating a set of hypotheses about the behavior of markets or agents into numerical predictions; for example, a firm’s decisions on investments (or the firm will invest 20% of assets), or investment returns [3] (returns on “stock A” will, on average, be 10% higher than the market’s returns).

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 [1] and financial analysis .

Applications include:

  • Business valuation , Especially discounted cash flow , profit Including other valuation problems
  • Scenario planning and management decision making (“what is”; “what if”; “what has to be done” [4] )
  • Capital budgeting
  • Cost of capital (ie WACC ) calculations
  • Financial statement analysis (including operating and finance leases , and R & D )
  • Project finance

To generalize citation needed ] to the nature of these models: firstly, 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 SPECIFIED the gains 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 theMathematical models form of thesis : Firstly, the models are in discrete time ; secondly, they are deterministic . For discussion of the issues that may arise, see below; for debate as to more sophisticated approaches Sometimes employed, see Corporate finance # Quantifying uncertainty , and Financial Economics #Corporate finance theory .

Modelers are Sometimes Referred to ( tongue in cheek ) as “number crunchers”, and are designated Often ” financial analyst “. Typically, the modeler will have completed an MBA or MSF with (optional) coursework in “financial modeling”. Accounting and finance skills certifications Such As the CIIA and CFA Generally do not live gold Provide explicit training in modeling. citation needed ] At the same time, many commercial training courses are offered, both through universities and privately.

Well developed software does exist, the vast proportion of the market is spreadsheet -based; this is widely available since Also, analysts will have their own criteria and methods for financial modeling. [5] Microsoft Excel now has the dominant position, having overtaken Lotus 1-2-3 in the 1990s. Spreadsheet-based modeling, [6] and several standardizations and ” best practices ” have been proposed. [7] “Spreadsheet risk” is being studied and managed. [7]

One criticism here is that model outputs , ie line items , often include “unrealistic implicit assumptions” and “internal inconsistencies”. [8] (For example, a forecast for growth in revenue purpose 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, purpose Often lacking, it is always clear. 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, the use of simple and simple arithmetic methods of calculating the probability of a particular problem. [10] – ie, 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 reviews 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, has been held since 2012. Model is a global online financial modeling competition which culminates in a Live Event Finals for top competitors. From 2012-2014 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 with market values, 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 modeling ) 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)
  • Pairs trading [18]
  • Credit valuation adjustment , CVA, more 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 long-tailed distributions and volatility clustering ; Brownian model of financial markets ; Martingale pricing ; Extreme value theory; Historical simulation (finance) .

Modellers are 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 quantitative Their background, They have full finance masters with a quantitative orientation, [19] Such as the Master of Quantitative Finance , or the more Specialized Master of Computational Finance Golden Master of Financial Engineering ; the CQF is more common.

ALTHOUGH spreadsheets are Widely used here aussi (Almost always Requiring extensive VBA ) custom C ++ , Fortran or Python , or numerical analysis software Such As MATLAB , are preferred Often, [19] PARTICULARLY Where stability or speed is a concern. MATLAB is the tool of choice for doing economics research citation needed ] Because of ict intuitive programming, and graphical debugging tools, purpose C ++ / Fortran are preferred for conceptually simple but high-cost computational applications Where MATLAB is too slow; Python is widely used due to its simplicity and broadnessstandard library . Additionally, for many (of the standard) derivatives and softwareapplications, commercial software is available, and the choice is to be made in the future. question. [19]

The complexity of these models may result in incorrect pricing or hedging or both. This Model is the subject of ongoing research by finance academics, and is a topic of great, and growing, interest in the risk management arena. [20]

Criticism of the discipline (PRECEDING Often the financial crisis of 2007-08 by Several years) emphasizes the differences entre les mathematical and physical sciences, and finance, and the resulting deposit 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 that is mathematical and statistical modeling may be used for the most part ( for options , for portfolios). In fact, these may go so far as to question the empirical and scientific validity of modern financial theory . [21] Notable here are Nassim Taleb and Benoit Mandelbrot . [22] See also Mathematical Finance #Criticism and Financial Economics #Challenges and criticism .

See also

  • Economic model
  • Financial engineering
  • Financial forecast
  • Financial Modelers’ Manifesto
  • Financial models with long-tailed distributions and volatility clustering
  • Financial planning
  • Integrated business planning
  • LBO valuation model , valuation of the current value of a business based on the business’s forecast financial performance
  • Model audit
  • Modeling and analysis of financial markets
  • Profit model
  • Real options valuation

References

  1. ^ Jump up to:b http://www.investopedia.com/terms/f/financialmodeling.asp
  2. Jump up^ Nick Crawley (2010). Which industry sector would be the most popular financial modeling? , fimodo.com.
  3. Jump up^ Low, RKY; 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 .
  4. Jump up^ Joel G. Siegel; Jae K. Shim; Stephen Hartman (November 1, 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”.
  5. Jump up^ See for example,Valuing Companies by Cash Flow Discounting: Ten Methods and Theories Nine, Pablo Fernandez: University of Navarra – IESE Business School
  6. Jump up^ Danielle Stein Fairhurst (2009). Six Reasons your spreadsheet is NOT a financial model Archived2010-04-07 at theWayback Machine., Fimodo.com
  7. ^ Jump up to:b Best Practice , European Spreadsheet Risks Interest Group
  8. Jump up^ 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 .
  9. Jump up^ 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 .
  10. Jump up^ Peter Coffee(2004). Spreadsheets: 25 Years in a Cell ,eWeek.
  11. Jump up^ http://pages.stern.nyu.edu/~adamodar/pdfiles/papers/probabilistic.pdf
  12. Jump up^ 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.
  13. Jump up^ Loren Gary (2003). Why Budgeting Kills Your Company , Harvard Management Update, May 2003.
  14. Jump up^ Michael Jensen(2001). Corporate Budgeting Is Broken, Let ‘s Fix it,Harvard Business Review, pp. 94-101, November 2001.
  15. Jump up^ ModelOff, Financial Modeling World Championships. “ModelOff 2015 Financial Modeling World Championships” .
  16. Jump up^ Low, RKY; 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 .
  17. Jump up^ Low, RKY; 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 .
  18. Jump up^ Rad, Hossein; Low, Rand Kwong Yew; Faff, Robert (2016-04-27). “The profitability of peers trading strategies: distance, cointegration and copula methods” . Quantitative Finance . 0 (0): 1-18. doi : 10.1080 / 14697688.2016.1164337 . ISSN  1469-7688 .
  19. ^ Jump up to:c Mark S. Joshi , On Becoming a respect .
  20. Jump up^ Riccardo Rebonato(ND). Theory and Practice of Model Risk Management .
  21. Jump up^ http://www.fooledbyrandomness.com/Triana-fwd.pdf
  22. Jump up^ “Archived copy” (PDF) . Archived from the original (PDF) on 2010-12-07 . Retrieved 2010-06-15 .