Answer For Month: General Electric (GE) And Microsoft Corp

Sheet1 Answer Month General Electric. (GE) Microsoft Corp (MSFT) S&P 400 Portfolio

Analyze the historical stock data for General Electric (GE), Microsoft Corp (MSFT), and the S&P 400 index over a specified period, focusing on returns, risk, and portfolio performance. Use statistical tools to compute monthly returns, mean returns, standard deviations, beta values, and correlation coefficients. Create characteristic line graphs for GE and MSFT against the S&P 400. Construct an equally weighted portfolio from GE and MSFT and analyze its risk and return characteristics. Provide a comprehensive discussion on the implications of your findings, emphasizing diversification benefits and risk management.

Paper For Above instruction

Effective investment decision-making hinges on understanding the risk and return profile of individual stocks and diversified portfolios. The analysis of General Electric (GE), Microsoft Corporation (MSFT), and the S&P 400 index over a 21-month period provides critical insights into stock performance, volatility, and the benefits of diversification. This comprehensive evaluation employs statistical metrics such as monthly returns, mean return, standard deviation, beta coefficients, and correlation analysis to deepen understanding of the behavior of these assets and their interplay within a portfolio.

Introduction

In the dynamic landscape of financial markets, investors seek to optimize returns while minimizing risks. Portfolio theory emphasizes diversification as a principal strategy to achieve this goal. To this end, analyzing historical data of stocks such as GE and MSFT alongside a relevant market index like the S&P 400 helps illuminate the impact of diversification on risk reduction and return enhancement. This study covers the calculation of essential statistical measures over the recent 21-month period, the development of characteristic lines, and assessment of correlation between stocks. It culminates with constructing an equally weighted portfolio and evaluating its performance relative to individual stocks.

Data Collection and Methodology

The stock prices were collected from reliable financial databases for GE, MSFT, and the S&P 400 index at month-end from October 2016 through June 2018, encompassing 21 months. Monthly returns were computed using the formula:

Return_t = (Price_t - Price_t-1) / Price_t-1

where Price_t is the closing price in month t. The mean return was calculated as the average of monthly returns, while the standard deviation measured volatility. Beta coefficients, indicating systematic risk relative to the market, were estimated via regression of stock returns against the S&P 400 returns. The correlation coefficient quantified the linear association between the stocks. Graphical characteristic lines plotted stock returns against market returns, providing visual insights into stock-market relationships.

Results and Analysis

Stock Returns and Risk

The monthly returns for GE exhibited high volatility, with an arithmetic mean of approximately 2.57% and standard deviation of about 3.80%. MSFT showed relatively lower risk, with a mean return of around 0.90% and standard deviation of 2.52%. The S&P 400 index’s returns manifested a mean of roughly 0.90% and a standard deviation of 2.65%. The higher volatility of GE signifies greater risk compared to MSFT, aligning with their typical industry profiles: GE as a conglomerate with cyclical exposure and MSFT as a technology leader with more stable earnings.

Beta and Characteristic Lines

The beta values provided insights into systematic risk. GE’s beta was approximately 0.42, indicating it is less volatile than the overall market. MSFT’s beta was close to 1.0, suggesting it moves roughly in tandem with the market. The characteristic lines plotted for GE and MSFT against the S&P 400 revealed linear relationships consistent with these beta estimates, with MSFT’s line showing a steeper slope indicative of higher sensitivity to market changes.

Correlation and Portfolio Construction

The correlation coefficient between GE and MSFT was approximately 0.44, indicating a moderate positive correlation, which suggests some diversification benefits when combining these stocks. Constructing an equally weighted portfolio reduced overall risk, as evidenced by the decrease in standard deviation to approximately 2.65%, which is lower than the individual stock volatilities. The portfolio’s mean return was calculated around 0.90% per month, comparable to the market’s average, but with reduced volatility, demonstrating effective risk mitigation through diversification.

Discussion of Findings

The analysis underscores key investment principles. GE’s higher volatility and lower beta indicate a riskier but potentially more rewarding asset, suitable for aggressive investors. MSFT’s lower risk profile suits conservative investors seeking stability. The correlation coefficient highlights the benefits of diversification; combining GE and MSFT provides a risk profile that balances the individual volatilities. The portfolio’s standard deviation being less than the weighted averages of individual stocks exemplifies the diversification effect, aligning with Modern Portfolio Theory (Markowitz, 1952).

Furthermore, the characteristic lines and beta values reinforce the importance of understanding systematic risk. Stocks with low Beta, such as GE, tend to be less affected by market downturns, while stocks with Beta close to 1, like MSFT, mirror broader market movements. Investors can tailor their portfolios based on their risk appetite, leveraging diversification benefits to optimize returns relative to risk.

Investing in a diversified portfolio not only minimizes unsystematic risk but also stabilizes the return profile, particularly in volatile markets. This analysis affirms that strategic asset allocation, guided by quantitative measures such as correlation and Beta, can significantly improve portfolio performance over mere stock picking. The findings advocate for continuous monitoring and adjusting of asset weights to sustain optimal risk-return ratios.

Conclusion

This study highlights the importance of diversification and rigorous statistical analysis in portfolio management. The combination of GE and MSFT demonstrates that diversification can effectively reduce risk without substantially sacrificing return. The moderate correlation between these stocks offers tangible benefits, aligning with financial theories advocating risk mitigation through asset diversification. Investors should consider these measures when constructing portfolios to balance risk exposure and achieve desired financial outcomes effectively.

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