Financial Modeling

Centreville Consultants creates sophisticated models and processes that quantify real world business situations.

Common Types of Models:

  1. Three-Statement Model:  A three statement model brings together all of a business inputs and joins them dynamically to create (as the name implies), a balance sheet, income statement, and statement of cash flows.  This allows for the flow of anticipated expenses, revenues and any myriad of variables to create a financial projection that can demonstrate sensitivities based on the change to any number of inputs.
    See example
  2. Discounted Cash Flow (DCF):  The discounted cash flow model typically builds upon an existing model of varying complexity to forecast a companies anticipated future cash flows.  Metrics such as Net Present Value (NPV), Internal Rate of Return (IRR), and Return on Investment (ROI) are some common metrics that are derived from a Discounted Cash Flow Analysis.
    See example
  3. Capital Projects:  A capital project model aims to determine the validity and suitability of an investment from a purely financial perspective.  Construction or enhancement of a facility, the addition/subtraction of technology and/or equipment, or the shutdown of a production facility are some common examples of scenarios that contain many variables, and could produce any number of outcomes. Quantifying the change of these variables allows for a scientific approach to decisions concerning capital.
  4. Forecasting:  Attempting to predict the future in any situation is challenging at best.  Financial forecasts can be used to predict any number of situations in a methodical, quantitative manner.  Predicting sales, volume, gross profit, delinquency/losses, and pricing are just a few of the categories commonly projected. The reality is that virtually anything measurable can be forecasted incrementally using quantitative methods.
  5. Statistical Analysis:  Many business situations require the use of advanced statistical methods in order to reach educated conclusions.  Probability, correlation analysis, and regression analysis (among others) are often necessary to gauge more complex quantitative data.
  6. Ad-Hoc Analysis:  As businesses evolve it is common for random situations to arise that inevitably create questions and concerns.  These situations often involve carving out a subset of a business population such as a particular product line, asset class, etc.  The creation of a model that is the hybrid of any number of typical methods or a smaller analysis allows businesses to move forward with greater confidence in their conclusions.


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