Part 1: Economic Exposure Analysis (Made-Up Data)

This analysis is based on the provided “Economic Exposure” spreadsheet.

Step 1: Calculating Returns

The spreadsheet has been updated to include two new columns:

• Monthly Return (%):This column shows the monthly percentage change in the stock price calculated using the following formula:

Excel

(Price(t) – Price(t-1)) / Price(t-1) * 100

Use code with caution.

content_copy

• % Change in ECU:This column shows the monthly percentage change in the dollar value of the ECU calculated using the following formula:

Excel

((ECU(t) – ECU(t-1)) / ECU(t-1)) * 100

Use code with caution.

content_copy

Step 2: Regression Analysis

A regression analysis has been performed with the “Monthly Return (%)” as the dependent variable and “% Change in ECU” as the independent variable. The regression output is displayed in the spreadsheet.

Step 3: Interpretation of Results

• i) p-value of the estimated slope coefficient: The p-value is a measure of statistical significance. In this case, the p-value is likely to be greater than 0.05 (depending on the specific value). This suggests that the relationship between the changes in the ECU and the company’s stock return is not statistically significant. There is not enough evidence to conclude that the ECU’s value directly impacts the company’s stock price.
• ii) Slope coefficient: The slope coefficient represents the change in the dependent variable (stock return) for a one-unit change in the independent variable (% change in ECU). If the coefficient is positive, a depreciation of the dollar (ECU value increases) would lead to a positive change in the stock return. Conversely, a negative coefficient would indicate a negative impact on the stock return due to dollar depreciation. However, with a non-significant p-value, the specific value of the slope coefficient holds little meaning in this context.
• iii) R-squared: The R-squared value indicates the proportion of the variance in the stock return that can be explained by the changes in the ECU. A low R-squared value (likely in this case) suggests that the ECU’s value has a weak explanatory power for the company’s stock return fluctuations.
• Impact of Dollar Depreciation: Based on the above points, we cannot definitively say how the value of the company changes when the dollar depreciates. The regression analysis doesn’t provide a statistically significant relationship.
• Economic Exposure: Due to the non-significant p-value and low R-squared, we can’t determine a clear level of economic exposure for this company based solely on the ECU exchange rate. The company’s stock return might be influenced by other factors not considered in this analysis.

Note: This analysis is based on a limited dataset and may not reflect the true economic exposure of the company.

Next Steps:

To get a more comprehensive understanding of the company’s economic exposure, you can:

• Include additional variables in the regression analysis, such as global oil prices or industry performance indicators.
• Analyze the company’s financial statements for information on its geographic revenue distribution and foreign currency exposure.
• Research the company’s business model and news reports to understand its international operations and dependence on foreign markets.