Financial Modeling Guide

Financial models are often developed over the course of months and years, and many financial analysts get caught up the grind of building, auditing and maintaining existing financial models on a daily basis, losing the big picture of understanding best practice modeling solutions used in business and economic decision analysis.

It is therefore useful for a good financial analyst to take a step back, examine the broad categories of financial models that are commonly used, and determine the optimal approach for the financial and business modeling of different scenarios and situations.

Let us first re-visit the basics, and look at how financial models can be related to its usage in modeling an economy, industry or company.

Macroeconomic Financial Models

The models are usually econometric analysis based, built by government departments, universities or economic consulting firms, and used to forecast the economy of a country. Macroeconomic models are used to analyze the like effect of government policy decisions on variables such as foreign exchange rates, interest rates, disposable income and the gross national product (GNP).

Industry Financial Models

Industry models are usually econometric based models of specific industries or economic sectors. Industry models are often similar to macroeconomic models, and typically used by industry associations or industry research analysts to forecast key performance indicators within the industry in question.

Corporate Financial Models

Corporate financial models are built to model the total operations of a company, and often perceived to be critical in the strategic planning of business operations in large corporations and startup companies alike.

Almost all corporate financial models are built in Excel, although specialized financial modeling software are increasingly being used especially in large corporations to ensure standardization and accuracy of multiple financial models, or to comply with spreadsheet management requirements imposed by the Sarbanes Oxley financial reporting act.

Now that we’ve looked at the context of financial models from an economic and financial analysis perspective, let us now examine financial models specifically from a financial modeling build perspective. Financial models can generally be classified into 3 categories:

Deterministic Financial Models

In a deterministic model, a financial analyst enters a set of input data into a spreadsheet, programs the spreadsheet to perform a series of mathematical calculations, and displays an output result.

Most deterministic financial models are built by performing an analysis on historical data to derive the relationship between key forecast variables. In a corporate context, historical accounting relationships are often used to forecast key revenue and cost variables.

Most deterministic models use one or two dimensional sensitivity analysis tables built into the model to analyze the question of risk and uncertainty in the model’s output results. Each sensitivity analysis table allows a financial analyst to perform a “what if” analysis on 1 or 2 variables at a time. The advantage of sensitivity tables are its simplicity and ease of integration into existing deterministic financial models that have already been built.

Multiple sensitivity analysis tables can be combined in a scenario manager. The scenario manager is useful when there are interdependencies between the changing variables, as financial analysts can configure and change multiple variables in each scenario.

In certain scenarios, multiple regression analysis is used to determine the mathematical relationship between multiple variables in a deterministic financial model, and such analysis is termed econometric analysis.

The deterministic model is probably the most common type of financial model used in business and finance today. Most financial forecasting models used for revenue management, cost management and project financing are primarily deterministic based financial models.

Simulation Based Financial Models

While a deterministic financial model is normally structured in such a way that a single point estimate is used for each input variable, simulation based financial models work by entering the likely distribution of key inputs defined by the mean, variance and type of distribution.

Simulation models use these range of inputs to recalculate the defined mathematical equation in the financial model through a few hundred iterations, normally 500 or more. The results of the analysis will produce the likely distribution of the result, therefore providing an indication of the expected range of results instead of a single point estimate.

Where risk is a dominant factor in the financial modeling scenario being analyzed, a reliable estimation of the likely range of results is often more useful than a single point estimate. Simulation based financial models therefore allows a financial analyst to model the question of risk and uncertainty using a higher level of granularity.

Specialized Financial Models

Specialized financial models are narrower in scope and essentially sophisticated calculators built to address a specific business problem or financial computation. Cost management models, marginal contribution analysis models and option pricing models are examples of specialized financial models.

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