This is easy to compute by creating a matrix with a column for each stock and a row for each weight of portfolio A used in the data table, then multiplying the weight of portfolio A by the respective weight of stock A in portfolio A and adding this to the weight of portfolio B (1 - weightA) multiplied by the respective weight of Stock A in portfolio B, and completing the entire matrix with such a formula. This efficient frontier is computed based on two efficient portfolios, and if we were constructing a portfolio based on these findings we would be curious to see what weight to apply to each stock in this portfolio at any given point. Below is a screenshot of the closing price of the stocks each month over the past five years, along with an index of the S&P 500 to include in our model. Let’s say that we choose to construct a risky portfolio with all 30 stocks in the Dow Jones Industrial Average. I will demonstrate how the efficient frontier and capital market line can be modeled in Excel in order to produce efficient portfolios. By levering up the capital market line or combining the optimized portfolio with a risk-free asset, we can achieve expected returns higher than the efficient frontier at respective standard deviations. The capital market line will lie tangent to the efficient frontier at the portfolio with the highest sharpe ratio and represent the highest expected return per unit of risk this represents the market portfolio.
#Graph efficient frontier in excel for mac free
We can then combine our efficient portfolio with a risk free asset to create a portfolio of portfolios which we can graph as the capital market line. We can plot every possible combination of risky assets in a portfolio to find the best possible return at each level of risk (and lowest possible risk for each level of expected return), and the resulting curve would have a higher return for any given level of risk than any individual asset.
Imagine a graph with risk on the X axis (measured as standard deviation of the asset’s historical returns) and dividend-adjusted return on the Y axis (measured as an average of historical return).
Assuming that markets are efficient and that the assets in a portfolio aren’t perfectly correlated, we can reduce the total variance of a portfolio at any given expected return by combining assets in various weights. The theory is that by diversifying through a portfolio of assets we can get a higher return per unit of risk than we can by holding an individual asset, and that by adjusting the weights of each asset in a portfolio we can create an optimal portfolio for each investor’s level of risk aversion. Modern portfolio theory attempts to maximize the expected return of a portfolio for a certain level of risk.
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