Sensitivity Analysis

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DEFINITION of 'Sensitivity Analysis'

A technique used to determine how different values of an independent variable will impact a particular dependent variable under a given set of assumptions. This technique is used within specific boundaries that will depend on one or more input variables, such as the effect that changes in interest rates will have on a bond's price.

Sensitivity analysis is a way to predict the outcome of a decision if a situation turns out to be different compared to the key prediction(s).

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BREAKING DOWN 'Sensitivity Analysis'

Sensitivity analysis is very useful when attempting to determine the impact the actual outcome of a particular variable will have if it differs from what was previously assumed. By creating a given set of scenarios, the analyst can determine how changes in one variable(s) will impact the target variable.

For example, an analyst might create a financial model that will value a company's equity (the dependent variable) given the amount of earnings per share (an independent variable) the company reports at the end of the year and the company's price-to-earnings multiple (another independent variable) at that time. The analyst can create a table of predicted price-to-earnings multiples and a corresponding value of the company's equity based on different values for each of the independent variables.

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RELATED FAQS
  1. What are some examples of ways that sensitivity analysis can be used?

    Sensitivity analysis is an analysis method that is used to identify how much variations in the input values for a given variable ... Read Full Answer >>
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    Use sensitivity analysis to estimate the effects of different variables on investment returns. This form of analysis is designed ... Read Full Answer >>
  3. What variables are most important when making a prediction through sensitivity analysis?

    Sensitivity analysis is used in corporate finance and other fields as a means of making predictions based on changes in variables. ... Read Full Answer >>
  4. What assumptions are made when conducting a t-test?

    The common assumptions made when doing a t-test include those regarding the scale of measurement, random sampling, normality ... Read Full Answer >>
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    The most common types of regression an investor can use are linear regressions and multiple linear regressions. Regressions ... Read Full Answer >>
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