Collect The Stock Price Data For Any Two Companies Of Your C

Collect the stock price data for any 2 companies of your choice and the S&P 400 index

You will need to collect the stock price data for any 2 companies of your choice and the S&P 400 index. Choose companies that are in different industries. Collect the end-of-month prices for the last 21 months of your chosen stocks and the S&P 400 and set them on the spreadsheet. Taking this raw data, compute the monthly returns for all (there should be 20 returns). Make sure that I am able to see the cell formulas for each computation.

Next, compute the mean rate of return and the standard deviation of returns for both your stocks and the stock market. Compute the beta value for each stock and plot the characteristic lines of the 2 companies on 2 graphs. Compute the correlation coefficient for the 2 stocks. Plot the returns of your stocks against each other on a graph within the spreadsheet. Next, assume that you construct a portfolio where you put equal money in each of your 2 stocks.

Compute the 20 monthly rates of return for the portfolio and the standard deviation of returns of the portfolio and its beta value. Finally, within the spreadsheet discuss in DETAIL your findings regarding risk/return of your portfolio as compared to the risk/return characteristics of the 2 individual stocks. These comments must be detailed with a complete analysis.

Paper For Above instruction

Collect the stock price data for any 2 companies of your choice and the S P 400 index

Collect the stock price data for any 2 companies of your choice and the S&P 400 index

The process of analyzing risk and return characteristics of stocks involves systematic data collection, computation of preliminary statistics such as returns, and advanced statistical measures such as beta and correlation coefficients. This study demonstrates the application of these methodologies using real market data from two selected companies across different industries, along with the S&P 400 index as a benchmark. The objective is to understand how individual stocks and their combined portfolios behave in terms of risk and return, facilitating informed investment decisions.

The initial step involves selecting two companies from different economic sectors to diversify the analysis. For example, one might choose a technology company like Apple Inc. (AAPL) and a retail company such as Walmart Inc. (WMT). These companies are representatives of distinct industries with different growth dynamics and risk factors. The next step involves collecting end-of-month stock prices over the most recent 21 months from reliable financial sources such as Yahoo Finance or Bloomberg. The data should be organized carefully in a spreadsheet, with each month's closing price for each company and the S&P 400 index clearly documented.

Calculating Monthly Returns

Once the raw data is assembled, monthly returns are calculated for each stock and the index. The monthly return is computed as the percentage change in the end-of-month price from one month to the next, using the formula:

Monthly Return = [(Price at month t) - (Price at month t-1)] / (Price at month t-1)

These calculations should be performed for all 20 intervals, with the formulas visible in the spreadsheet. Achieving transparency in formulas allows for validation and replication of the analysis.

Statistical Analysis of Returns

The mean monthly return and the standard deviation of the returns are fundamental metrics that summarize the stocks’ average performance and variability, respectively. The mean is derived by averaging the 20 monthly returns, while the standard deviation measures the volatility of returns.

To measure systematic risk, the beta of each stock is calculated—an indicator of the stock’s sensitivity to overall market movements. Beta is obtained through regression analysis of each stock’s returns against the S&P 400 index returns. The slope of the characteristic line is the beta coefficient, illustrating how much the stock tends to move relative to the market.

Graphical Representation and Correlation

Plotting the characteristic lines of both stocks against the index visually demonstrates their relationships. The characteristic line graph shows regression lines with the beta coefficient, highlighting the systematic risk component.

The correlation coefficient between the two stocks’ returns quantifies their co-movement. Values close to 1 indicate strong positive correlation, while values near 0 suggest independence.

A scatter plot of the two stock returns further visualizes their relationship, helping interpret diversification benefits.

Portfolio Analysis

Constructing an equal-weighted portfolio involves averaging the monthly returns of the two stocks. The portfolio’s mean return is simply the average of the individual stocks’ mean returns. To determine the portfolio’s risk, the standard deviation of its returns is computed, considering the correlation between stocks. The formula accounts for the variances of individual stocks and their covariance.

The beta of the portfolio measures its systematic sensitivity, calculated as a weighted sum of the individual betas. This value indicates the portfolio’s overall market risk.

Discussion and Interpretation

The final step involves a comprehensive analysis comparing the risk and return characteristics of the individual stocks and the portfolio. Typically, diversification reduces portfolio risk, evidenced by a lower standard deviation compared to individual stocks, especially if the stocks are not perfectly correlated. The relationship between the beta values reflects the overall market risk exposure. Higher beta indicates greater sensitivity to market fluctuations, which influences risk-return tradeoffs.

Returns alone are insufficient to gauge investment quality; understanding volatility and systematic risk provides a more complete picture. For example, a stock with high returns but also high volatility and beta might be riskier than a stock with moderate returns and lower betas.

In conclusion, this analysis offers insights into how combining stocks from different industries can optimize risk and return profiles, emphasizing the importance of diversification in portfolio management.

References

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