Financial modeling


Financial modeling is the task of building an abstract representation of a real world financial situation. This is a mathematical model designed to represent 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. 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.

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, valuation 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, and for internal / company specific variables. Correspondingly, both characteristics are reflected 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 and.
Modelers are often designated "financial analyst". Typically, the modeler will have completed an MBA or MSF with 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. 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 ; see also for further discussion and considerations.
Although purpose-built business software does exist, 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. 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, and several standardizations and "best practice"s have been proposed.
Recent professional guidelines emphasize transparent, auditable, and well-documented models. According to PwC and the Financial Modeling Institute, good practice includes separating input, calculation, and output sheets to enhance traceability and reduce error risk. Practical training providers such as the Corporate Finance Institute and ICAEW highlight consistent formatting, clear labeling, and documentation of assumptions as essential for usability and stakeholder confidence.
In entrepreneurial and investment contexts, financial models are sometimes used to illustrate business viability and capital requirements for funding discussions. "Spreadsheet risk" is increasingly studied and managed; see model audit.
One critique here, is that model outputs, i.e. line items, often inhere "unrealistic implicit assumptions" and "internal inconsistencies". 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". Here, in general, modellers "use point values and simple arithmetic instead of probability distributions and statistical measures"
— 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;
see.
A further, more general critique relates to the lack of basic computer programming concepts amongst modelers,
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.

Quantitative finance

In quantitative finance, financial modeling entails the development of a sophisticated mathematical model. Models here deal with asset prices, market movements, portfolio returns and the like.
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 and/or the development of optimization models. The general nature of these problems is discussed under, while specific techniques are listed under.
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. quantitative analysts and typically have advanced 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, such as the Master of Quantitative Finance, or the more specialized Master of Computational Finance or Master of Financial Engineering; the CQF certificate is increasingly common.
Although spreadsheets are widely used here also ;
custom C++, Fortran or Python, or numerical-analysis software such as MATLAB, are often preferred, particularly where stability or speed is a concern.
MATLAB is often used at the research or prototyping stage 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 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.
See.
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.
Criticism of the discipline emphasizes the differences between finance and the mathematical / physical sciences, and stresses 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.
In fact, these may go so far as to question the "empirical and scientific validity... of modern financial theory".
Notable here are Nassim Taleb and Benoit Mandelbrot.
See also, and.

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.

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.