Sensitivity_Analysis_Presentation.pptx...

SENSITIVITY

1. Sensitivity Analysis: What-If and Financial Modeling Understanding Impacts of Variable Changes

2. Introduction to Sensitivity Analysis • Sensitivity analysis explores how different values of independent variables affect a dependent variable. • Also known as 'What-If' or simulation analysis. • Widely used in business and economics.

3. Key Takeaways • Shows how independent variables impact dependent variables. • Used for financial predictions, like share prices and bond prices. • Helps identify opportunities, risks, and communicate decisions.

4. How Sensitivity Analysis Works • A financial model determining how target variables are influenced by input changes. • Analysts predict changes by analyzing independent and dependent variables.

5. Examples of Sensitivity Analysis • Stock price prediction: Affected by earnings, shares, D/E ratios, and competition. • Bond prices: Influenced by changes in interest rates.

6. Business Applications • Understand Influencing Factors: Identifies external factors impacting outcomes. • Reduce Uncertainty: Informs what variables to monitor. • Catch Errors: Helps identify and correct mistakes in assumptions.

7. Business Applications (Cont’d) • Simplify Models: Remove non-material factors. • Communicate Results: Helps management understand diverse outcomes. • Achieve Goals: Improves long-term strategic planning.

8. Example Scenario: Sales Sensitivity • Sales Manager Example: Impact of customer traffic on sales. • Price = $1,000, Sales = 100 units, Total = $100,000. • Sensitivity analysis: How a 10%, 50%, or 100% increase in traffic affects sales.

9. Advantages of Sensitivity Analysis • In-depth Study: Provides a thorough understanding of variables. • Improvement Identification: Helps refine future strategies. • Targets Results: Helps management focus on specific outcomes.

10. Disadvantages of Sensitivity Analysis • Heavily Reliant on Assumptions: Outcomes are based on historical data. • System-Intensive: Complex models may slow systems. • Overcomplication Risk: Too many variables may distort analysis.

11. Sensitivity Analysis in NPV • Purpose: To see how changes in key variables affect project profitability. • Example: Changes in discount rates (e.g., 5%, 6%, 8%, 10%).

12. How Sensitivity Analysis is Calculated • Often performed in software like Excel. • Example: NPV formula is used to run scenarios with different discount rates.

13. Difference Between Sensitivity and Scenario Analysis • Sensitivity Analysis: Focuses on variable changes within a model (e.g., price, EPS). • Scenario Analysis: Examines larger, external events (e.g., stock market crash).

14. Summary • Sensitivity analysis shows financial impacts of variable changes. • Used to identify opportunities, mitigate risks, and aid decision-making. • A key tool for predicting financial outcomes.

15. Conclusion • Key Takeaway: A powerful decision-making tool for businesses and financial analysts. • Can provide more reliable forecasts and help in strategic planning.