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.
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.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[1] and financial analysis.
Applications include:
- Business valuation and 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"[3])
- Budgeting: revenue forecasting and analytics; production budgeting; operations budgeting
- Capital budgeting, including cost of capital (i.e. WACC) calculations
- 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,[4] P3
- Credit decisioning: Credit analysis, Consumer credit risk; impairment- and provision-modeling
- Management accounting: Activity-based costing, Profitability analysis, Cost analysis, Whole-life cost, Managerial risk accounting
- Public sector procurement[5]
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 (
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 "
MSF with (optional) coursework in "financial modeling".[7] Accounting qualifications and finance certifications such as the CIIA and CFA generally do not provide direct or explicit training in modeling.[8] 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 Outline of finance § Financial modeling; see also Valuation using discounted cash flows § Determine cash flow for each forecast period for further discussion and considerations.
Although purpose-built
"Spreadsheet risk" is increasingly studied and managed;[11] see model audit
.
One critique here, is that model outputs, i.e.
fixed assets and the associated financing, may imbed unrealistic assumptions about asset turnover, debt level 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".[13] Here, in general, modellers "use point values and simple arithmetic instead of probability distributions and statistical measures"[14]
— 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;[15]
see Valuation using discounted cash flows § Determine equity value.
A further, more general critique relates to the lack of basic computer programming concepts amongst modelers,
[16] 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.[17][18]
Quantitative finance
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In
quantitative finance, financial modeling entails the development of a sophisticated mathematical model.[19] Models here deal with asset prices, market movements, portfolio returns and the like. A general distinction [citation needed] is between
:
(i) "quantitative asset pricing", models of the returns of different stocks;
(ii) "financial engineering", models of the price or returns of derivative securities;
(iii) "automated trading
, high-frequency trading, algorithmic trading, and program trading.
Relatedly, applications include:
- multi-curves)
- Other exotic derivatives
- Modeling the term structure of credit spread
- Credit valuation adjustment, CVA, as well as the various XVA
- KMV model
- Structured product design and manufacture
- Quantitative investing more generally; see further re optimization methods employed.
- "sensitivities" analysis
- Corporate finance applications:[21] cash flow analytics,[22] 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
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: 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.
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 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,[23] 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 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,
developed in-house, or whether existing products are to be deployed, will depend on the problem in question.[23]
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.[24]
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".[25]
Notable here are Nassim Taleb and Benoit Mandelbrot.[26]
See also Mathematical finance § Criticism, Financial economics § Challenges and criticism and Financial engineering § Criticisms.
Competitive modeling
Several financial modeling competitions exist, emphasizing speed and accuracy in modeling. The Microsoft-sponsored ModelOff Financial Modeling World Championships were held annually from 2012 to 2019, with competitions throughout the year and a finals championship in New York or London. After its end in 2020, several other modeling championships have been started, including the Financial Modeling World Cup and Microsoft Excel Collegiate Challenge, also sponsored by Microsoft.[6]
Philosophy of financial modeling
Philosophy of financial modeling is a branch of philosophy concerned with the foundations, methods, and implications of modeling science.
In the philosophy of financial modeling, scholars have more recently begun to question the generally-held assumption that financial modelers seek to represent any "real-world" or actually ongoing investment situation. Instead, it has been suggested that the task of the financial modeler resides in demonstrating the possibility of a transaction in a prospective investment scenario, from a limited base of possibility conditions initially assumed in the model.[27]
See also
- All models are wrong
- 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
- Return on modeling effort
- Unreasonable ineffectiveness of mathematics § Economics and finance
References
- ^ a b Investopedia Staff (2020). "Financial Modeling".
- .
- ISBN 978-0-07-058031-2. Retrieved 12 November 2011. §39 "Corporate Planning Models". See also, §294 "Simulation Model".
- ^ See for example: "Renewable Energy Financial Model". Renewables Valuation Institute. Retrieved 2023-03-19.
- ^ Confidential 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
- ^ OCLC 1264716849.
- ^ Example course: Financial Modelling, University of South Australia
- ^ The MiF can offer an edge over the CFA Financial Times, June 21, 2015.
- ^ 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
- ^ a b Best Practice Archived 2018-03-29 at the Wayback Machine, European Spreadsheet Risks Interest Group
- ISBN 978-1-84480-492-4. Retrieved 12 November 2011.
- ISBN 978-0-07-138377-6. 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. 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.
- ^ See discussion here: "Careers in Applied Mathematics" (PDF). Society for Industrial and Applied Mathematics. Archived (PDF) from the original on 2019-03-05.
- S2CID 154138333.
- ^ See David Shimko (2009). Quantifying Corporate Financial Risk. archived 2010-07-17.
- ^ See for example this problem (from John Hull's Options, Futures, and Other Derivatives), discussing cash position modeled stochastically.
- ^ a b c Mark S. Joshi, On Becoming a Quant Archived 2012-01-14 at the Wayback Machine.
- ^ Riccardo Rebonato (N.D.). Theory and Practice of Model Risk Management.
- ISBN 978-0470406755
- ^ 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.
- S2CID 256438018.
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General
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Corporate finance
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Quantitative finance
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