# Explain about sensitivity analysis in financial management.

In a business, decision making is very important aspect. Decision making can direct the business in a successful way or in an unsuccessful way. So, if a business wants to be successful, correct decisions should be taken in given circumstances.

A lot of independent variables are involved in decision making mainly in financial aspects of the firm. So, there is a need of a tool or a technique to take appropriate decisions.

Sensitivity analysis is a tool or a technique which tells about how independent variable impacts a dependent variable under current conditions. Investors use this tool to evaluate the result of their investments by taking necessary conditions into account. It is also known as “WHAT IF ANALYSIS”.

Commonly used methods are as follows −

• Modelling and simulation techniques.
• Scenario management tools through Microsoft excel.

Approach to analyse is mentioned below −

• Local sensitivity analysis − The term local determines about derivatives of a single point. This method is used for only simple functions not for complex models. This tool or technique is used to analyse the impact of only one parameter on the cost function by keeping other parameters constant or fixed.

• Global sensitivity analysis − This technique or tool uses Monte Carlo techniques.

Techniques widely used are explained below −

• Differential sensitivity analysis − It involves solving of simple partial derivatives. It is also called as direct method.

• One at a time sensitivity measures − It is also called local analysis. In this, only one parameter is taken at a time.

• Factorial analysis − Selection of different number of samples for a particular parameter, then run the model to see its combinations.

• Correlation analysis − It defines relation between dependent and independent variables.

• Subjective sensitivity analysis − As the name suggests, it is a subjective method analysing individual parameters.

Uses of sensitivity analysis are as follows −

• To specify sensitivity of model to uncertainties.
• Decision making.
• To assess strategy risk.
• Identify errors in the model.