Financial modeling

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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.

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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]

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:

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. 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. [5] 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, [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 inhere "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. 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". [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] (See Valuation using discounted cash flows § Determine equity value.) 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] (see Conditional budgeting § Criticism of budgeting).

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:

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, [17] 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, [17] 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, 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. [17] 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. [18]

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". [19] Notable here are Nassim Taleb and Benoit Mandelbrot. [20] See also Mathematical finance § Criticism and Financial economics § Challenges and criticism.

See also

Related Research Articles

In finance, discounted cash flow (DCF) analysis is a method of valuing a security, project, company, or asset using the concepts of the time value of money. Discounted cash flow analysis is widely used in investment finance, real estate development, corporate financial management and patent valuation. It was used in industry as early as the 1700s or 1800s, widely discussed in financial economics in the 1960s, and became widely used in U.S. courts in the 1980s and 1990s.

Finance Academic discipline studying businesses and investments

Finance is a term for matters regarding the management, creation, and study of money and investments. Specifically, it deals with the questions of how an individual, company or government acquires money – called capital in the context of a business – and how they spend or invest that money. Finance is then often divided into the following broad categories: personal finance, corporate finance, and public finance.

Financial economics is the branch of economics characterized by a "concentration on monetary activities", in which "money of one type or another is likely to appear on both sides of a trade". Its concern is thus the interrelation of financial variables, such as share prices, interest rates and exchange rates, as opposed to those concerning the real economy. It has two main areas of focus: asset pricing and corporate finance; the first being the perspective of providers of capital, i.e. investors, and the second of users of capital. It thus provides the theoretical underpinning for much of finance.

Financial engineering is a multidisciplinary field involving financial theory, methods of engineering, tools of mathematics and the practice of programming. It has also been defined as the application of technical methods, especially from mathematical finance and computational finance, in the practice of finance.

Volatility risk is the risk of a change of price of a portfolio as a result of changes in the volatility of a risk factor. It usually applies to portfolios of derivatives instruments, where the volatility of its underlying is a major influencer of prices.

A financial analyst is a professional, undertaking financial analysis for external or internal clients as a core feature of the job. The role may specifically be titled securities analyst, research analyst, equity analyst, investment analyst, or ratings analyst. The job title is a broad one: in banking, and industry more generally, various other analyst-roles cover financial management and (credit) risk management, as opposed to focusing on investments and valuation; these are also discussed in this article. The term “financial analyst” is not usually taken to include quantitative analysts, or “quants”.

Monte Carlo methods are used in corporate finance and mathematical finance to value and analyze (complex) instruments, portfolios and investments by simulating the various sources of uncertainty affecting their value, and then determining the distribution of their value over the range of resultant outcomes. This is usually done by help of stochastic asset models. The advantage of Monte Carlo methods over other techniques increases as the dimensions of the problem increase.

Financial risk management is the practice of protecting economic value in a firm by using financial instruments to manage exposure to risk: operational risk, credit risk and market risk, foreign exchange risk, shape risk, volatility risk, liquidity risk, inflation risk, business risk, legal risk, reputational risk, sector risk etc. Similar to general risk management, financial risk management requires identifying its sources, measuring it, and plans to address them.

In financial economics, asset pricing refers to a formal treatment and development of two main pricing principles, outlined below, together with the resultant models. There have been many models developed for different situations, but correspondingly, these stem from either general equilibrium asset pricing or rational asset pricing, the latter corresponding to risk neutral pricing.

Lattice model (finance)

In finance, a lattice model is a technique applied to the valuation of derivatives, where a discrete time model is required. For equity options, a typical example would be pricing an American option, where a decision as to option exercise is required at "all" times before and including maturity. A continuous model, on the other hand, such as Black–Scholes, would only allow for the valuation of European options, where exercise is on the option's maturity date. For interest rate derivatives lattices are additionally useful in that they address many of the issues encountered with continuous models, such as pull to par. The method is also used for valuing certain exotic options, where because of path dependence in the payoff, Monte Carlo methods for option pricing fail to account for optimal decisions to terminate the derivative by early exercise, though methods now exist for solving this problem.

Frank J. Fabozzi is an American economist, educator, writer, and investor, currently Professor of Finance at EDHEC Business School and a Member of Edhec Risk Institute. He was previously a Professor in the Practice of Finance and Becton Fellow in the Yale School of Management, and a Visiting Professor of Finance at the Sloan School of Management at the Massachusetts Institute of Technology. He has authored and edited many books, three of which were coauthored with Nobel laureates, Franco Modigliani and Harry Markowitz. He has been the editor of the Journal of Portfolio Management since 1986 and is on the board of directors of the BlackRock complex of closed-end funds.

Valuation using discounted cash flows is a method of estimating the current value of a company based on projected future cash flows adjusted for the time value of money. The cash flows are made up of those within the “explicit” forecast period, together with a continuing or terminal value that represents the cash flow stream after the forecast period. In several contexts, DCF valuation is referred to as the "income approach".

The following outline is provided as an overview of and topical guide to finance:

A masters degree in quantitative finance concerns the application of mathematical methods to the solution of problems in financial economics. There are several like-titled degrees which may further focus on financial engineering, computational finance, mathematical finance, and/or financial risk management..

In finance, model risk is the risk of loss resulting from using insufficiently accurate models to make decisions, originally and frequently in the context of valuing financial securities. However, model risk is more and more prevalent in activities other than financial securities valuation, such as assigning consumer credit scores, real-time probability prediction of fraudulent credit card transactions, and computing the probability of air flight passenger being a terrorist. Rebonato in 2002 defines model risk as "the risk of occurrence of a significant difference between the mark-to-model value of a complex and/or illiquid instrument, and the price at which the same instrument is revealed to have traded in the market".

A master's degree in Financial Economics provides a rigorous understanding of theoretical finance and the economic framework upon which that theory is based. The degree is postgraduate, and usually incorporates a thesis or research component. Programs may be offered jointly by the business school and the economics department.

Quantitative analysis is the use of mathematical and statistical methods in finance and investment management. Those working in the field are quantitative analysts (quants). Quants tend to specialize in specific areas which may include derivative structuring or pricing, risk management, algorithmic trading and investment management. The occupation is similar to those in industrial mathematics in other industries. The process usually consists of searching vast databases for patterns, such as correlations among liquid assets or price-movement patterns. The resulting strategies may involve high-frequency trading.

Mathematical finance, also known as quantitative finance and financial mathematics, is a field of applied mathematics, concerned with mathematical modeling of financial markets. See Quantitative analyst.

In finance, a contingent claim is a derivative whose future payoff depends on the value of another “underlying” asset, or more generally, that is dependent on the realization of some uncertain future event. These are so named, since there is only a payoff under certain contingencies. Any derivative instrument that is not a contingent claim is called a forward commitment.

Riccardo Rebonato is Professor of Finance at EDHEC Business School and EDHEC-Risk Institute, and author of journal articles and books on Mathematical Finance, covering derivatives pricing, risk management and asset allocation. Prior to this, he was Global Head of Rates and FX Analytics at PIMCO.

References

  1. 1 2 "How Financial Modeling Works".
  2. 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.
  3. Nick Crawley (2010). Which industry sector would benefit the most from improved financial modeling standards?, fimodo.com.
  4. 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".
  5. See for example, Valuing Companies by Cash Flow Discounting: Ten Methods and Nine Theories, Pablo Fernandez: University of Navarra - IESE Business School
  6. Danielle Stein Fairhurst (2009). Six reasons your spreadsheet is NOT a financial model Archived 2010-04-07 at the Wayback Machine , fimodo.com
  7. 1 2 Best Practice, European Spreadsheet Risks Interest Group
  8. 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. 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. Peter Coffee (2004). Spreadsheets: 25 Years in a Cell, eWeek.
  11. Prof. Aswath Damodaran. Probabilistic Approaches: Scenario Analysis, Decision Trees and Simulations, NYU Stern Working Paper
  12. 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. Loren Gary (2003). Why Budgeting Kills Your Company, Harvard Management Update, May 2003.
  14. Michael Jensen (2001). Corporate Budgeting Is Broken, Let's Fix It, Harvard Business Review, pp. 94-101, November 2001.
  15. 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.
  16. 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.
  17. 1 2 3 Mark S. Joshi, On Becoming a Quant.
  18. Riccardo Rebonato (N.D.). Theory and Practice of Model Risk Management.
  19. http://www.fooledbyrandomness.com/Triana-fwd.pdf
  20. 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.

Bibliography

General

Corporate finance

Quantitative finance