Technical attributes help investors easily choose between winners and losers on an individual stock basis.
We have a new update to our Fund Score Whitepaper, which can be accessed here. The paper can also be accessed under the Media & Education page through the Whitepapers link. Highlights from the paper are included below. As always, please reach out to us with any questions.
Over the years, Nasdaq Dorsey Wright has created many innovative technical indicators based on momentum using Point and Figure charting. One of our most popular indicators in this space is the “Technical Attribute” rating we apply to each stock. The rating ranges from 0 (lowest strength) to 5 (highest strength) with values of 3 or higher generally regarded as investable. The purpose of this study was to determine how effective technical attributes are from a portfolio management perspective. If we “buy” a portfolio of high attribute stocks, do we outperform the market? Alternatively, what happens if we buy a portfolio of low attribute stocks? We found that high attribute portfolios had a strong propensity to outperform, with the largest outperformance reserved for the highest ratings (5 attribute stocks), while low attribute portfolios had a marked tendency to underperform under most market conditions.
We also took this initial insight and progressively refined the concept to create a portfolio with good performance, manageable portfolio size, reasonable turnover, and a simple to understand portfolio construction process. The result is a portfolio which buys 5 attribute stocks ranked highly in Nasdaq Dorsey Wright’s matrix system (explained below) and waits to sell them when their attribute rankings falls to 2 or lower.
Technical Attribute Study
The bedrock of the technical attribute approach are the individual technical attributes which are made up of two attributes from a security’s relative strength against the S&P 500 Equal Weight Index, two attributes from a security’s relative strength against its peer group, and the final attribute determined by whether the security is trading in a positive trend on its traditional Point & Figure chart.
For the first test, we chose to “buy” all stocks with 3 or more attributes in one portfolio and compare them to a portfolio owning all stocks with 2 or fewer attributes. The idea is to test whether high attributes perform better than low attributes in general.
As the graph shows, the “3’s and Higher” portfolio outperforms the “2’s and Lower” portfolio by 2.44%/year. It also outperforms the S&P 500 and an equal weighted index of the top 1000 market cap stocks (Equal Weight 1000 in the graph) while the “2’s and Lower” portfolio underperforms both benchmarks during the study. Interestingly, the typical performance difference is more extreme than shown as, with the benefit of hindsight, low attribute stocks tend to do very well after major bear markets which makes their returns looks better over the entire period than what you may get by holding them for any particular year. If we remove 2003 and 2009 from the analysis (both included the initial moves up off a major bear market) and look at the difference in returns for all other years we find that the average outperformance moves up to 3.50%/year from 2.44%/year. Of course, you can’t just ignore the performance that doesn’t help you, but it is an interesting data point. We also found that the volatility of the “2’s and Lower” portfolio is much higher than the “3’s and Higher” portfolio while offering lower returns (meaning its Sharpe ratio is lower as well).
Building off the first test, the second looks at individual attribute rankings to shed light on how attribute rankings differ from an investment standpoint. To do this, we created a portfolio for each attribute ranking (0 through 5) and bought all stocks in a top 1000 market cap universe with the specified attribute every month (equal weighted).
The graph below shows that 5 attribute stocks are the dominant contributor to the “3’s and Higher” portfolio in Test #1. In fact, 5’s alone outperformed the “3’s and Higher” portfolio by 1.77%/year (13.22%/year vs. 11.45%/year). The performance gap between 5’s and 4’s at 2.16%/year is also larger than the performance gap between any other adjacent attribute rankings. Interestingly, we expected the 0’s portfolio to do the worst, but it performs better than the 2’s and 1’s. However, as mentioned before, low attribute stocks perform well in the initial move off a major bear market bottom (as in 2003 and 2009) and that effect is most pronounced in 0 attribute stocks (up 65% and 102% in 2003 and 2009 respectively). If we remove those 2 years, the average difference in return between 5’s and 0’s jumps from 4.08%/year to 6.61%/year. Volatility also gets worse as we move into the lower attributes (again with the effect most pronounced in 0 attribute stocks which had a 25.22% standard deviation).
While rebalancing a portfolio of five attribute stocks performs well in theory, it is much harder to execute in practice whether it be managing well over 100 positions on average or the large amount of turnover. For the final test, we looked at buying the top 30 5-attribute stocks in a matrix and selling when a stock’s technical attribute falls to a predetermined level. For those unfamiliar with the matrix tool, it allows for the comparison of a universe of stocks and then sorts them by relative strength against the rest of the universe. This filtering system via the matrix tool allows the test to look at buying the strongest 5-attribute names while keeping the holdings number at a more realistic level. Secondly, to further reduce turnover, the test only rebalances the portfolio back to equal weighted when a stock’s weight in the portfolio falls below 1% or rises above 10% of the portfolio’s value. In doing this, we hoped to see good performance, manageable portfolio size, and turnover reduction.
