There are a virtually endless number of ways you can find actionable stocks on the NDW platform. Today we talk through a practical use case for how to find strong stocks utilizing pre-made NDW matrices.

One of the most common questions that roll into the NDW office is something along the lines of: How can I streamline my process to make best use of time on the platform? There is no question that there are thousands of different ways/tools available to you, particularly when it comes to overall security selection. While no one pathway will work for all of you (or work in every scenario markets will throw our way…) the most common way most of us will attempt to find strong securities is by utilizing some combination of NDW’s TA scoring system or a relative strength matrix system. The TA system is a bit more straightforward… there is a saying around the NDW office in Richmond to “just buy 5’s” which is largely supported by the empirical evidence presented in our newly updated whitepaper, linked here. The matrix, however, brings with it more of the “art” than the exact science. While the easy answer is to simply buy from the top of the matrix, today’s feature will attempt to dive into the nuance of how you might put that into practice.

For those of you familiar with the intra-workings of a relative strength (RS) matrix, feel free to skip ahead. If not, we will first introduce the basic mechanics of a matrix. The end goal is to identify, through price action alone, which asset within a universe is strongest. To do so, we simply congregate a whole host of individual RS charts and count who “wins” the most matchups, quantified by who earns the most long-term RS buy signals. For example, if we wanted to systematically determine which stock within the Dow 30 was the strongest, we would count that Caterpillar (CAT) earns an RS buy signal against every other member of the universe, and is therefore in first. Conversely, a name like Sherwin Williams (SHW) earns less “wins,” resulting in less relative strength and a lower overall ranking. The “X” count works the same mechanically, but this time looks at the more sensitive column function of an RS chart. The “total” column is simply the summation of overall buys and X’s earned for that respective security. Since we are focused on long-term trends, NDW ranks assets by their buy count.

Now that we all understand the general mechanics of an RS matrix, we can jump into a practical use case for finding strong securities. To start, we will utilize the pre-made “NDW Group Matrix” to find relatively strong sectors fit for investment. This matrix takes 40 different sectors (and SPXEWI) in an attempt to identify which of the lot is the strongest. Among the top 10 you will find many of the sectors you would think based on their recent relative leadership: semiconductors, non-ferrous metals, oil, etc. That said, a quick glance at the top-ten reveals our first point of clarification: watch out for groups with strong buy counts but low X counts. For example, both representatives for oil service DWAOILS and broader oil DWAOIL sit towards the top of the matrix but have shed lots of near-term strength against other sectors. While not a guarantee of further declines, this should raise some alarm bells that these groups positions are on comparatively thin ice against other groups which maintain near-term strength.

Assuming we avoid oil/energy focused names, we have our pick of the litter for other options within the top five/ten. Semiconductors obviously stick out as a distinct leader… but assuming we wanted to find some other ideas outside of what’s probably an overcrowded semis space in your portfolio, we would like to source some individual stock ideas within these already strong sectors. To do so, we could focus on sector specific matrices which run the same RS process as our broader NDW group matrix mentioned previously, but now for a list of names spanning each individual sector. For example, the matrix for the second placed non-ferrous metals group is included below. This pits 22 different non-ferrous metal stocks against each other. In short, we would run a similar process to identify relatively strong assets here, focusing on the top of the matrix as our starting point. NEXA presented an interesting technical picture as it has broken out to near-decade level highs before pulling back to a point of previous consolidation. Many of these non-ferrous metal stocks have high RRisk scores, so keep their volatility in mind when considering suitability.

Remember, the process detailed today is evergreen. Overall matrix rankings will shift as price leadership changes, making it useful in various market leadership regimes. It is also something you can track more systematically via NDW’s custom model tool. If you have any further questions about implementing this process in your practice, feel free to email miles.clark@nasdaq.com for further assistance.