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A brief introduction to sensitivity analysis and margin of safety

Sensitivity analysis:

Sensitivity analysis is used to determine the effect of different values of an independent variable on the dependent variable. For example, the study of the effect of change in interest rate on bond prices is an example of sensitivity analysis. It predicts the outcome of a decision.

Margin of Safety:

Margin of Safety defines a condition when the investor invests in buying securities when the market price is lower than the estimation of intrinsic value. In other words, the difference amidst intrinsic value and market price is known as margin of safety.

Use of sensitivity analysis and margin of safety

Sensitivity analysis and margin of safety assignment help experts will discuss the usage of the analysis of sensitivity and study margin of safety.

  • It is used for predicting the outcome of a decision and compare the decision with the key predictions.
  • It helps the accessing the risks which are associated with a strategy.
  • It studies the dependence of the output on the input and helps in making decisions. The analysis of the dependency helps in assessing the risks associated with the decision.
  • It eliminates the chances of error.
  • It is used for arriving at the optimal solution.

Techniques used in sensitivity analysis

The sensitivity index is used to calculate the output percentage difference by varying the input parameter from maximum to minimum. Sensitivity analysis and margin of safety homework help experts use the following techniques:

  • Differential sensitivity analysis:

It is the direct method which involves solving simple partial derivatives.

  • Subjectivity sensitivity analysis:

It is a subjective and qualitative method which involves analysis of individual parameters.

  • Factorial sensitivity analysis:

The first step involves the selection of samples for getting a specific parameter. And then the model is run for combinations. The outcome obtained is used for determining the sensitivity.

  • Correlation analysis:

This analysis is used for establishing a relationship between input and output.

  • Regression analysis:

This method of analysis is used in complex models.

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