## Optimization of Stock Portfolio

Now we take the same list of stocks as used in the prior blog post, only this time I optimized the portfolio by requesting for a maximum Return/Risk ratio.  In the first screenshot readers will see the QPP analysis.  In the second slide, note the correlation matrix and in the third I show the constraints placed on the portfolio of stocks.

QPP Analysis:  The following asset allocation gives up return, but significantly reduces risk.  Reduction in the standard deviation (10.7%) is related to the constraint of requesting a maximum Return/Risk ratio.  Note the increase in Diversification Matrix and the reduction in the Portfolio Autocorrelation.  This portfolio has a respectable yield of 3.8%.

Correlation Matrix:  Pay attention to those stocks that have a weight greater than zero.  For example, omit VTI, COP, etc. in your thinking.  They do not enter into the calculations even though there is a percentage present.  This portfolio ends up with no highly correlated holdings.  It is also important to note that the correlations change when the weight or percent allocated to a specific stock varies.

Portfolio Constraints:  The following screen shot shows the constraints placed on the portfolio when the Set Objective is to maximize the Return/Risk ratio.  Had I requested a maximum Return, Risk would have increased significantly.

## Top Core Stocks Analyzed Using QPP Plus Delta Factor Projections

Seeking top core stocks from investors who participate in the Growth Forum, the following analysis shows portfolio projections, the correlations within the portfolio, and the “Delta Stock” projections.

QPP Analysis:  Nearly equal percentages are assigned to each stock.  The analysis spans five years and the S&P 500 is projected to grow 7.0% per year over the next few years.  Readers familiar with this type of analysis see the 9.6% projected return, but one pays for that relatively high return with a rather high 17.4% projected standard deviation.  Even those we hold lots of stocks, the Diversification Metric falls short of our intended goal of 40%.  As William Bernstein points out, it now requires approximately 100 stocks to build a diversified portfolio due to the high correlations with each other.

This portfolio outperformed the S&P 500 by more than double over the last five years and accomplished this feat with lower volatility.  Not by much, but the risk was a tad lower.  That is an excellent record.

Correlation Matrix:  Now we want to witness how these stocks are correlated when placed in a portfolio.  Stocks and ETF, VTI, are highly correlated when the background is yellow.  White background indicates a modest correlation, and low correlated stocks have a blue background.  Of course it is better to have an 81% rating rather than a 97% ranking even though both fall into the high correlation classification.  Take a moment and look down over the correlations.  I did not include how each stock is correlated with each other as the screenshot is too wide to fit on the page.

Delta Factor Projections:  And now we come to the “Delta Factor” projections.  Based on the above allocations, which stocks are projected to do well over the next six to twelve months and which carry a higher risk?  I’ve found the best opportunities are when the “Delta Factor” indicates a Buy and the background color in the Delta column is green.  No investment is showing both signals, not surprising with this high market.  However, there are a number of stocks that show promise over the next few months, something hard to find within ETFs.

There is one error in the last investment.  That should be SO, not AGG.

## Maxwell Portfolio Update: 12 February 2013

I am a few days late with the Maxwell update as I was on a brief vacation.  Look for more blog action this week as there are several portfolios that need attention and there is a backlog of work.  Since the last review of the Madison, I sold off some of the highly correlated ETFs and as you can see from the Dashboard below, still have more work to do with this portfolio.