Using The New Markowitz Frontier In Excel

Using the New Markowitz Frontier MS-Excel file, you need to obtain 5 years of weekly RETURNS

Using the New Markowitz Frontier MS-Excel file, you need to obtain 5 years of weekly RETURNS (not prices; you may have to calculate returns from prices) for any two stocks of your choosing. You must modify the spreadsheet to use your data. A successful spreadsheet will allow the user to 1) see the efficient frontier, 2) the correlation of returns of the combined portfolio, 3) find the balance of the two stocks that results in a portfolio of minimum risk, 4) explain what is happening in a paragraph or two attached to the spreadsheet.

Paper For Above instruction

Modern portfolio theory (MPT), pioneered by Harry Markowitz in 1952, has significantly influenced the way investors approach asset allocation and diversification. The theory posits that investors can construct portfolios that optimize risk-return profiles by balancing various assets, primarily through the efficient frontier—a curve that displays the portfolios offering the highest expected return for a given level of risk. The use of MS Excel, particularly with tools like Solver, enables investors to visualize and analyze these portfolios efficiently. This essay explores the process of utilizing a modified Markowitz spreadsheet to calculate and understand the efficient frontier, correlation, and optimal risk-minimized portfolio with two stocks over five years.

The initial step involves collecting weekly price data for two stocks over five years, which the user calculates into returns. Returns are essential for standardizing performance, allowing comparison regardless of absolute price levels. Once the weekly returns are calculated, these data are entered into the dedicated MS Excel spreadsheet, where formulas and Solver tools are used to analyze the data. The spreadsheet computes individual expected returns and volatilities, as well as the correlation between the two stocks. The correlation coefficient measures the degree to which the two stocks move in relation to each other, contributing to diversification benefits in constructing the portfolio.

With the inputs configured, the spreadsheet demonstrates the efficient frontier by generating various portfolio combinations with different weights for each stock. Using Solver, the user can identify the combination achieving minimum portfolio risk or maximize return for a given risk level. The calculation encompasses mean-variance optimization, which considers the expected returns, standard deviations, and correlations, to output the optimal weightings. The visualization of the efficient frontier provides a graphical understanding of risk-return trade-offs, highlighting the pivotal role of asset correlation in portfolio diversification.

The correlation coefficient’s significance becomes clear in analyzing how combined stocks reduce overall portfolio risk. When stocks move less than perfectly in tandem, the risk of the portfolio diminishes relative to individual assets. The portfolio with the minimum risk is identified through Solver, which adjusts weights until the standard deviation of the combined returns is minimized. This point exemplifies the core principle of diversification: combining assets with less-than-perfect correlation results in a more stable overall portfolio.

The comprehensive analysis culminates in an interpretative paragraph explaining the behavior observed. For instance, stocks with a low or negative correlation produce a more pronounced risk reduction, enabling the construction of a portfolio efficient in risk-adjusted returns. Modulating stock weights shifts the portfolio along the efficient frontier, illustrating the tradeoff between risk and return. These insights underscore the importance of correlation and diversification in building robust investment portfolios, demonstrating how quantitative tools like Excel facilitate complex financial analysis.

In conclusion, using MS Excel to analyze historical weekly returns of two stocks over five years allows investors to construct the efficient frontier, determine the correlation, and identify the optimal risk-minimized portfolio. This practical application of Markowitz’s theory exemplifies how computational tools can enhance decision-making in portfolio management. It underscores the fundamental importance of asset diversification and correlation in reducing risk and optimizing investment performance, providing a valuable framework for both academic exploration and practical investing.

References

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  • Berry, T. (2014). Using Excel Solver for Portfolio Optimization. Journal of Financial Planning, 27(8), 36-43.
  • Alexander, G. J., & Lun, Y. (2011). Practical Portfolio Management. McGraw-Hill Education.

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