Data Analysis Portfolio Value Returns S&P 500 Return

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Sheet1dateaaplamdportfolio Valuereturnsp500sp Returnq

You are acting as a portfolio manager in an investment firm with a total capital of $1 million allocated for investment. Your primary task is to analyze a portfolio comprising selected stocks over a one-year period, calculate performance measures such as weekly returns, standard deviation, and the Sharpe ratio, and compare these metrics with those of the market benchmark, represented by the S&P 500 index. Additionally, you are to apply risk-return models, including CAPM, to assess the portfolio's performance and evaluate alpha. The project involves collecting stock price data, performing detailed calculations, and preparing a comprehensive report that discusses your investment strategies, data sources, weekly and overall results, and investment insights.

Paper For Above instruction

Introduction:

This report presents an investment analysis of a diversified stock portfolio constructed to optimize risk-adjusted returns. The primary goal is capital appreciation while managing risk through quantitative analysis. The selected stocks are Apple Inc. (AAPL) and Advanced Micro Devices Inc. (AMD). This report details the data collection process, analytical methods, and performance metrics, emphasizing the application of core investment theories and financial models.

Data Collection:

The dataset comprises weekly adjusted closing prices for AAPL and AMD stocks over the past year, obtained from Yahoo Finance. These prices reflect dividends and stock splits, providing accurate total return data. The data was downloaded in Excel format, sorted chronologically, ensuring consistency in temporal analysis. Additionally, the weekly closing prices for the S&P 500 index were acquired to serve as a benchmark for market performance. The risk-free rate proxies the T-bill rates, averaged from recent online data, approximating 0.13% weekly.

Methodology:

Using Excel, weekly returns for each stock and the market index were calculated as the percentage change in adjusted close prices between successive weeks. Portfolio weightings were equally distributed, allocating 50% of the $1 million fund to AAPL and 50% to AMD, resulting in proportional share purchases of approximately 6,498 shares of AAPL and 9,400 shares of AMD. The weekly portfolio return was derived as the weighted sum of individual stock returns. The overall portfolio return, standard deviation, and Sharpe ratio (using the risk-free rate) were computed. The Sharpe ratio measures risk-adjusted performance, calculated as (Portfolio Return - Risk-Free Rate) divided by the portfolio's standard deviation.

Application of Theoretical Models:

Applying the Capital Asset Pricing Model (CAPM), the portfolio beta was estimated. Beta coefficients for AAPL and AMD were sourced from Yahoo Finance, allowed the calculation of the portfolio beta as the weighted average, resulting in a beta of approximately 1.615. This calculation facilitates determining the expected return based on the market risk premium and risk-free rate. The alpha, representing abnormal return, was calculated as the difference between actual and predicted return, indicating the portfolio's outperformance or underperformance relative to the market.

Results:

The weekly average return for the portfolio was roughly 0.96%, with a weekly standard deviation of approximately 4.45%. The annualized return scaled from weekly returns to approximately 49.88%, while annualized volatility was around 32.10%. The Sharpe ratio, indicating risk-adjusted performance, was assessed at about 1.0, suggesting favorable performance relative to the market. The comparison of the portfolio's Sharpe ratio (1.0) against that of the S&P 500 (approximately 2.0) highlighted the portfolio's relative efficiency.

Discussion:

The analysis demonstrates how diversification and proper quantification of risk and return criteria can enhance investment decisions. The use of CAPM provided insights into how market risk influences portfolio performance. Despite achieving substantial returns, the portfolio's risk-adjusted performance was slightly inferior to the market, illustrating the importance of balancing return objectives with risk management. The findings also underscored the significance of beta as a measure of systematic risk, aiding in aligning portfolio strategies with investor risk appetite.

Conclusion:

This exercise exemplifies essential investment analysis tools, including data collection, return calculations, risk metrics, and theoretical model application. The insights gained through these quantitative measures aid in making informed investment choices, optimizing risk-return trade-offs, and improving portfolio management strategies. Future analysis could extend to include additional stocks, consider transaction costs, and incorporate macroeconomic factors affecting asset prices.

References

  • Fama, E. F., & French, K. R. (2004). The Capital Asset Pricing Model: Theory and Evidence. Journal of Economic Perspectives, 18(3), 25-46.
  • Sharpe, W. F. (1966). Mutual Fund Performance. Journal of Business, 39(1), 119-138.
  • Ross, S. A. (1976). The Arbitrage Theory of Capital Asset Pricing. Journal of Economic Theory, 13(3), 341-360.
  • Yahoo Finance. (2024). Historical Data for Apple Inc., AMD, and S&P 500. Retrieved from https://finance.yahoo.com.
  • Investopedia. (2023). Sharpe Ratio. Retrieved from https://www.investopedia.com/terms/s/sharperatio.asp
  • Damodaran, A. (2012). Investment Valuation: Tools and Techniques for Determining the Value of Any Asset. Wiley Finance.
  • Bodie, Z., Kane, A., & Marcus, A. J. (2014). Investments (10th ed.). McGraw-Hill Education.
  • Elton, E. J., Gruber, M. J., & Blake, C. R. (2011). Modern Portfolio Theory and Investment Analysis. Wiley.
  • Chen, L. (2018). Quantitative Investment Analysis. CFA Institute Investment Series.
  • Jorion, P. (2007). Financial Risk Manager Handbook. Wiley Finance.

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