I am going to start posting a complete list of industry standings here every day. With the wealth of online financial information, I find it surprising that acquiring data regarding the performance of individual industries is so hard to come by. One of the goals of this website is to fill that void by providing an easy to use tool for assessing the performance of industry groups.
A few months ago, I developed an algorithm that compares the performance of industries versus the S&P 500. Initially, I was tracking 207, what I was calling “sub-industries”, versus the S&P. After using this methodology for a few months (you can find some of this information by looking in the archives of this blog), I found that there was often too much ‘noise’ in the data collection. Some industries have only 1 or 2 stocks of note. The fluctuations in price of these stocks would cause industry scores to vary wildly both up and down. Basically, I was tracking the performance of individual stocks and not really that of industries.
Not exactly what I was looking for. I needed more breadth and fewer outliers. I consolidated the data in the following manner:
The 207 sub-industries were grouped into 31 industries, which are part of 9 sectors. I have posted the breakdown before, but I will post it again here for reference:
Basic Materials (Chemicals, Energy, Metals and Mining)
Consumer Goods (Consumer Durables, Consumer Non-Durables, Automotive, Food and Beverage, Tobacco)
Financial (Banking, Financial Services, Insurance, Real Estate)
Healthcare (Drugs, Health Services)
Industrials (Aerospace/Defense, Manufacturing, Materials and Construction)
Services (Leisure, Media, Retail, Specialized Retail, Wholesale, Diversified Services, Transportation)
Technology (Computer Hardware, Computer Software and Services, Electronics, Telecommunications, Internet)
You can find raw performance data for these sectors and the “sub-industries” on a site like finviz.
For a long time I would try to use data from finviz, but would often find that it really wasn’t providing any sort of actionable information that I could incorporate into my analysis. Sure, for example, Basic Materials was the worst performing sector on Friday. While it is important to take that information into consideration, it is still very limited from an analytical perspective.
I wanted to know how the components of that sector are performing over a dynamic window of time, not on a static daily, weekly, monthly, quarterly, etc basis. This led me to develop an algorithm that calculates a weighted “alpha score” of each industry (listed above in italics) over the past quarter.
There are 5 different time components for which alpha scores are calculated, each weighted a bit differently, with the most emphasis placed on industry performance over the past 2 weeks and month. The S&P 500 is the baseline for this analysis and will always have a score of 0.0. If scores are above 0, then the industry is outperforming the S&P, vice versa for scores lower than 0. Pretty simple interpretation, yes? Since I incorporate a swing trading style, knowing which industries are outperforming over the past 2-4 weeks is of much greater importance to me versus performance 3 months ago, this is why I use a weighted average.
Looking at the scores from Friday (8/5), you can see that based on the weighted performance over the past quarter, Tobacco, Food and Beverage, and Utilities are the top 3 industries. Also, with the “change” columns I can see how today’s score compares to yesterday and a week ago. Is this industry on the rise compared to ‘the market’, or is it losing ground.
Ok, so how do I use this data? Well, I start by finding a list of stocks in the industries with scores over 0 and start scouring through charts to find setups that I find appealing. Here is where having a set list of stocks really can come in handy. I like large cap names, as they most closely resemble the performance of the industry they are a part of. So, boom, I go to my list of Tobacco, Food and Beverage and Utilities stocks and see what’s cracking.
Unfortunately, this information is much more difficult to incorporate into a trading strategy when the market is in free-fall, but over time it will help guide you to stocks in industries that are outperforming the market. I think that it could be especially important if/when the market makes a bottom. Finding industries that have started to recover before the broad market often leads one to finding stocks that are going to be new market leaders when things do turn around. Very useful stuff.
Here are the results from 8/5
|2||Food and Beverage||3.47||0.87||2.46|
|15||Computer Software and Services||-1.00||-0.21||0.28|
|21||Metals and Mining||-1.66||-1.29||-2.36|
|32||Materials and Construction||-5.97||-0.40||-2.65|
Like I said, I will be posting this information on a daily basis here on the blog. Follow me on Stocktwits to be informed of any and all trade/blog/industry performance updates.