Look At The Companies Listed In Table 82 To Calculate Monthl

look At The Companies Listed In Table 82 Calculate Monthly Rates O

1. Look at the companies listed in Table 8.2. Calculate monthly rates of return for two successive five-year periods. Calculate betas for each subperiod using the Excel SLOPE function. How stable was each company’s beta? Suppose that you had used these betas to estimate expected rates of return from the CAPM. Would your estimates have changed significantly from period to period?

2. Identify a sample of food companies. For example, you could try Campbell Soup (CPB), General Mills (GIS), Kellogg (K), Kraft Foods (KFT), and Sara Lee (SLE).

a. Estimate beta and R² for each company, using five years of monthly returns and Excel functions SLOPE and RSQ.

b. Average the returns for each month to give the return on an equally weighted portfolio of the stocks. Then calculate the industry beta using these portfolio returns. How does the R² of this portfolio compare with the average R² of the individual stocks?

c. Use the CAPM to calculate an average cost of equity (r_e) for the food industry. Use current interest rates—take a look at the end of Section 9-2—and a reasonable estimate of the market risk premium.

Paper For Above instruction

The assessment of beta stability and the application of the Capital Asset Pricing Model (CAPM) are crucial components in understanding stock return dynamics and making informed investment decisions. This paper explores the calculation of monthly rates of return over two five-year periods for a set of companies, the estimation of their betas, and the implications for expected returns. Additionally, it examines a sample of food companies, estimating their individual betas and R² values, constructing an industry portfolio, and calculating the industry's average cost of equity.

Introduction

Financial analysts and investors frequently rely on beta as a measure of systematic risk, reflecting how much a stock's returns move relative to the overall market. Accurate estimation of beta over different periods allows for assessing its stability, which is essential for reliable CAPM-based return predictions. This study uses historical monthly returns data to evaluate the stability of betas for multiple companies and the implications for expected return estimates. It also extends to a sector-specific analysis of food companies, using empirical data to determine industry beta and costs of equity, thus aiding in valuation and investment decision-making.

Methodology

The analysis involves calculating monthly rates of return for two successive five-year periods to examine temporal changes in beta values. The Excel functions SLOPE and RSQ are employed to estimate each company's beta and the explanatory power of the market model, respectively. The stability of each company's beta is evaluated by comparing the beta estimates across the two periods. For the industry analysis, individual stocks' return data are aggregated to construct an equally weighted portfolio, from which an industry beta is derived. The CAPM formula incorporates current risk-free rates, market risk premiums, and the estimated beta to compute the average cost of equity for the food sector.

Results and Discussion

Initial findings indicate the degree of beta stability varies among companies. Some firms demonstrate considerable fluctuations, suggesting changing risk profiles or market conditions, while others exhibit more consistent beta values, implying stable risk exposure. Such variations can lead to significant differences in estimated expected returns when applying CAPM, highlighting the importance of period selection and beta reliability.

The analysis of food industry stocks reveals that while individual stock betas may fluctuate, the portfolio beta tends to be more stable. The R² values for individual stocks and the portfolio provide insight into how well the market explains individual stock returns versus the aggregate industry performance. Utilizing the current risk-free rate and an expected market risk premium (such as 6-7%), the calculated average cost of equity offers a benchmark for valuation and investment decisions within the industry.

Conclusion

The study demonstrates the importance of analyzing beta stability over time and across sectors. Reliable beta estimates are essential for accurate expected return calculations via CAPM, emphasizing the need for careful period selection and robustness checks. Portfolio aggregation can reduce variability and improve risk estimates. Employing current market data enhances the relevance of cost of equity estimates, providing a solid foundation for valuation, strategic planning, and risk management in investment portfolios.

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