Rules-Based Money Management – Part 5: Security Selection, Rules, and Guidelines

Note to the reader: This is the twenty-first in a series of articles I’m publishing here taken from my book, “Investing with the Trend.” Hopefully, you will find this content useful. Market myths are generally perpetuated by repetition, misleading symbolic connections, and the complete ignorance of facts. The world of finance is full of such tendencies, and here, you’ll see some examples. Please keep in mind that not all of these examples are totally misleading — they are sometimes valid — but have too many holes in them to be worthwhile as investment concepts. And not all are directly related to investing and finance. Enjoy! – Greg


Pullback Rally Analysis

The Pullback Rally Analysis is not a ranking measure, but a technique for determining the relative strength of issues by looking at the most recent rally from a previous pullback. To summarize, in pullback rally analysis, you measure the amount of the pullback in percent, then measure the current rally up to the current date in percent. The concept is fairly simple; those issues that dropped the least in the pullback, will probably outperform in the following rally.

This concept measures the percentage move during the pullback, the percentage to date of the current rally, and the percentage to date from the beginning of the pullback. This is a great method to see strength outside of the snapshot of the ranking measures. Figure 14.23 shows an example on how to determine the dates for the beginning and end of the pullback. From the chart, you can see a peak at point A with a pullback down to point B. The rally is then measured from point B to the current date.

A ratio of the percentage move of the current rally to the percentage move of the previous pullback is calculated. Another calculation is percentage the current price is from the beginning of the pullback (previous high). This data, when ranked, will help you determine strength in the rally as compared to the previous pullback. Often the stronger issues in a pullback are the leaders during the rally.

Table 14.1 shows the data for the Pullback Rally Analysis. You can see from even a quick glance at Table 14.1 that the international ETFs are outperforming, not only in the rally phase (% Rally), but also how almost all are now above where the previous high (beginning of pullback) began (% Prev. High). The iShares FTSE China 25 Index Fund also performed well during the pullback phase, with the only international ETF displaying a gain for that period of 2.95%, while the others were losses. The Ratio column shows the ratio of the percent of rally compared to the percent of pullback. The pullback is completed, so only the extent of the rally is unknown.

This ratio will show ETFs that performed in a couple of ways. One is that, if the ETF did not decline much during the pullback and rises quickly in the rally, it will have a large ratio. For example, in the Broad category, the SPDR S&P MidCap 400 ETF Trust (MDY) has a ratio of 1.40, highest in that category. This is because it was the best performer (least decline) in the pullback phase and ranked third in performance in the rally phase. This would indicate that MDY is a strong performer and a candidate to consider for buying. The last column, % Previous High, will also show you which ETFs are making new highs from the beginning of the pullback. This method of selection shows which issues are strong on a relative basis. In fact, it will also tell you which sectors and styles are strongest if you use ETFs that are tied to those strategies.

Pair Analysis

I remember following Martin Zweig years ago, and in fact used one of the techniques he described in his book, Winning on Wall Street, in the mid-1980s. In it, he described a really simple technique using his unweighted index (ZUPI) and on a weekly basis trading it whenever it moved 4% or more. If it moved up 4% in a week, he bought; if it moved down 4% in one week, he sold. Positions were held until the next opposing signal—just that simple. The problem I had back then was not only not following it, but trying to tweak it into something better. Eventually experience told me that he had already been down that road and I was the beneficiary of the results.

Anyway, I took this concept and used it on Index/ETF pairs, actually calculating the ratio of Index/ETF pairs and using the weekly movement of 4% to swap between the numerator and the denominator. It really works well with asset classes that are not correlated, such as equity vs. fixed income or equity vs. gold, and so on. Figure 14.24 shows an example of this pair strategy the S&P 600 small cap index (IJR) vs. the BarCap 7-10 Year Treasury index (IEF). The ratio line is the typical price line, with the binary signal line overlaid while the lower plot is the percent up and down moves for each weekly data point. Remember, this is a weekly chart. Whenever the ratio line moves by 4% in a week, as shown by the lower plot moving above or below the horizontal lines shown as +4% and -4%, the binary line overlaid on the price ratio changes direction. Repeated moves in the same direction are ignored.

The ratio significantly outperformed each of the individual components (IJR and IEF) and the S&P 500. Figure 14.25 shows the performance of the ratio (with the numerator and denominator swapped whenever there was a move of 4% or greater), the performance of the individual components that make up the ratio, and the S&P 500.

Table 14.2 shows the annualized performance statistics from 01/02/1998 until 12/28/2012 (weekly data). The Sharpe Ratio is slightly modified, in that the return is used as the numerator without a reduction for risk-free return. The Ratio rotation strategy outperformed in annualized return, and, when compared to the equity component, it reduced the Drawdown (DD) considerably, improved the Sharpe Ratio, and lowered the Ulcer Index.

I also found that smoothing the ratio with just a two-period moving average greatly enhanced the performance because it reduced the number of trades. Trying different percentages other than Zweig’s 4% worked well occasionally, but, overall, the 4% on weekly data yielded the most robust results time and time again.

The real advantage for a pair rotation strategy is when it is used as a core holding situation. In other words, if a strategy required a core holding percentage but that core could be actively managed, this would give an actively managed core holding that would have much lower drawdowns than a buy-and-hold core, and with considerably better returns. Table 14.3 shows the pairs used with an equal allocation of 25% each given to the four pairs. This adds up to an allocation of 100%, but, in this example, it means 100% of the core and the core percentage of total allocation is determined by the strategy, often 50%.

Figure 14.26 shows the results using the four different pairs in a core rotation strategy compared to buy-and-hold of the S&P 500. The drawdown in 2008 was limited to only 14%, and other than that was a nice ride. The average drawdown (see Table 14.4) is only 20% of the maximum drawdown. I was curious about the lack of performance in 2012 and found it was the fact that in the Gold/20-Year Treasury pair gold was the holding the entire period.

Table 14.4 shows the performance statistics for the Core Rotation Strategy (CRS) compared to the S&P 500. In this rotation strategy example, each of the pairs were smoothed by their two-period average prior to measuring the 4% rate of change. This process removes many of the signals and, while not affecting the results that much, reduces the number of trades significantly.

Figure 14.27 is the drawdown of the core rotation strategy compared to the S&P 500. You can see that the cumulative drawdown for the rotation strategy is considerably less than the drawdown of the index. The average drawdown for the rotation strategy was -3.39%, while the average drawdown for the S&P 500 was -15.88%. This would make for a very comfortable core, considering the exceptional returns and reduced risk statistics from just holding the index in a buy-and-hold situation. This core rotation strategy still meets the requirement of an always invested core while actively switching between four pairs of equity, gold, and fixed income ratios.

Ranking and Selection

Ranking and Selection is another critical component to a rules-based model. Once you have measured the market, you need to determine what to buy. This is the technical process of determining securities that meet the rules when the time to buy arrives.

Mandatory Measures

Once you have your collection of ranking measures, you need to determine which are to be used, along with the rules and guidelines as mandatory ranking measures. This means that you predefine the value range that they must be in before you can purchase that ETF. This is necessary to keep the subjectivity out of the process.

Tiebreaker Measures

Once you have determined your mandatory ranking measures, the remaining ranking measures are considered tie-breaker ranking measures. These are used to help in the selection process, especially when there are hundreds of issues that qualify based on the mandatory measures. You can further reduce these into categories if desired, such as frontline tie-breakers, those you use more often than the others.

Ranking Measures Worksheet

Table 14.5 is a partial view of the ranking measures worksheet. It only shows the top 50 to 60 issues as an example, since there are more than 1,400 ETFs in the full listing. One really important concept to grasp when looking at technical values in a spreadsheet is that you are only seeing a snapshot in time. Here is an example: let’s say that the Trend value is of primary importance and you have two ETFs, one with a Trend of 60 and one with a trend of 70. Which would you choose? Well, the quick answer is probably 70 as that is a stronger trend measure than 60. However, don’t you also need to know which direction the trend indicator is heading? If the trend that was at 60 was in an uptrend, while the one with the trend measure at 70 was in a downtrend, a completely different picture is presented. This is why all of the mandatory ranking measures also show their individual five-day rate of change, so that you can glean from the spreadsheet not only the absolute value of the ranking measure, but also the direction it is headed. It should be noted that any short-term period for rate of change will work.

Ranking Measures Are All About Momentum

Throughout this chapter it should be obvious that the ranking and selection process is centered on the concept known as momentum. Simply said, I want to buy an ETF that exhibits an upward trend that is determined by a number of different technical measures.

A final thought on momentum is that every day, in almost every newspaper’s business section, there is an excellent list of stocks to buy. It is called the 52-week new high list, or often stocks making new highs. If you were to only use this readily available tool, along with a simple stop-loss strategy, you would probably do much better at investing in the market. Sadly, many investors think about buying stocks like they think about buying something at Walmart, they look for bargains. Although this is a valid method also known as value investing, it is very difficult to put into action and seems better in theory. When you buy a stock, you buy it simply because you think you can sell it later at a higher price, I think momentum will work much better in that regard.

Rules and Guidelines

Rules and guidelines are a critical element to a good trend-following model. Once you have the weight-of-the-evidence measure telling you what the market is currently doing, the rules and guidelines provide the necessary process on how to invest based on that measure. If there was a simple answer as to why they are necessary, it is to invoke an objective approach, one that does as much as possible to remove the frail human element in the model. Rules are mandatory, while guidelines are not. That being said, if a guideline is to be ignored, one needs to ensure there is ample supporting evidence to allow it. Basically, the strategy I use is one of a conservative buyer and an aggressive seller.

After many decades in aviation and the always-increasing use of checklists, the rules and guidelines are no different for maintaining a nondiscretionary strategy than a checklist is for a pilot. In aviation, checklists grew in length over time because as accidents or incidents happened a checklist item was created to help prevent it in the future. There is an old axiom about checklists that said behind every item on a checklist, there is a story. Same philosophy goes for rules and guidelines in an investment strategy. A checklist (rules) ensures portfolio managers follow all procedures precisely and unfailingly. This overcomes the problem with experienced managers thinking they can accomplish the task and do not need any assistance. That attitude is costly.

Buy Rules

B1—If asset commitment calls for an amount greater than 50%, then only 50% will be committed, with the remainder the next day, ensuring objectives remain aligned. Forty percent can be the maximum per day if necessary for Guideline G6. This rule keeps the asset purchases to a maximum for any single day. It would not be prudent to go into the market at 100% on one day.

B2—No Buy Days are (1) FOMC announcement day, (2) First/Last day of calendar quarter, (3) days in which the market has reduced hours. FOMC announcement days are typically high-volatility days and the end/beginning of a quarter involves a lot of window dressing. Leave the noise alone.

B3—No buying unless 50 (this can also be a percentage) tradable ETFs (not counting non-correlated) have:

Weight of the Evidence = Weak: Trend>60, Intermediate: Trend>55, Strong: Trend>50

I call this the “soup on the shelf” rule. If you have been to a large grocery store lately and strolled down the aisle that has soup, you probably noticed there are thousands of cans of soup with hundreds of blends, styles, and so on to choose from. Now imagine your spouse has sent you to the store to buy soup. When you turn down the soup aisle, you notice they are essentially empty except for two cans of rhubarb turnip barley in cream sauce. You probably aren’t going to buy any soup that day. The market is similar, especially during the early stages of an uptrend, there just isn’t much to choose from. In addition, the early stages have stricter buying requirements, so the number of issues to pick from could be very small, if any. Because you never violate the rules, a rule to protect you during this period was created, hence rule B3.

B4—No buying on days when stops on current holdings are hit and assets sold. This is usually the first hint that the ensuing uptrend is faltering. It just doesn’t make sense as a trend follower to be buying on the same day as you are selling something that has hit its stop. The argument that one holding might not be correlated is weak in this example, as, with proper trading up, weak holdings should have been previously traded.

B5—No buying on days when the Nasdaq or S&P 500 is down greater than 1.0% (the indices used need to be tied you what you are using in the trend measures). Simply put, this means that if the market as determined by the S&P 500 and/or Nasdaq Composite is down more than 1% during the day, something is wrong with the uptrend and it is better to not buy that day. An argument from bargain hunters or value investors would be that one would get a better price on that day if the uptrend resumed. I can’t argue with that, but I ‘m not a value investor or a bargain hunter. It seems many investors want to buy stocks at bargain prices and I can understand that. However, we are not buying soap at a discount store; we are buying a tradable investment vehicle whose price is determined by buyers and sellers. Moreover, you only want to buy what is going up.

Sell Rules

S1—If stops are hit with End of Day data and still in place at 30 minutes (this time period is based solely on your comfort level) after the open the next day, a sell is initiated; if not in place at the 30-minute point, the issue falls under intraday monitoring (see S2).

S2—Intraday monitoring of Price and Trend (between the hours of 30 minutes after the open until 60 minutes before the close) will invoke a Sell order sent to brokers for execution. Once an issue hits its stop, then a 30-minute period is allowed before it is sold. With the constant barrage of Internet and financial media trying to be first with breaking news, often the story is presented incorrectly, and it can have an effect on a large stock, an industry, or even a sector and cause a big sell-off. Usually, if the story was reported in error or incorrectly, and then reported correctly, the issue quickly recovers. Most of this happens in a very short period of time. The 30-minute rule will help avoid most of these short-term sell-off with quick recoveries.

S3—In a broad-based sell-off and stops are hit, holdings hitting stops can begin liquidating before the 30-minute limit.

S4—If a holding has experienced a sharp run-up in price, once it reaches a 20% gain, sell 50% of the holding and invest in another holding or a new holding. This is just a prudent way of locking in exceptional gains.

S5—Any holding that is still being held after experiencing S4, once a gap open (above previous day’s high) occurs, can warrant a further reduction in the holding. Additionally, this can also anticipate a blow-off move or island reversal, while protecting most gains but still allowing for more upside, although with limited exposure. This is not a good process when trading only one issue, but is prudent when trading many issues with the ability to always find something else to trade.

Trade Up Rules

T1—With Weight of the Evidence strong: If stops are hit, but limited to single sector/industry/style, replace next day as long as the Initial Trend Measures are all indicating an uptrend.

T2—With Weight of the Evidence strong: If stops are hit on more than one sector/industry/style, reenter when Initial Trend Measures are all indicating an uptrend or Initial Trend Measures are improving, as long as there is no deterioration in the weight of the evidence.

T3—With Weight of the Evidence at an intermediate level: If stops are hit, but limited to single sector/industry group, replace next day as long as Initial Trend Measures are all indicating an uptrend.

T4—With Weight of the Evidence at an intermediate level: If stops are hit on more than a single sector/industry/style, the normal Buy rules apply.

 T5—There is no trading up when weight of the evidence or initial trend measures are deteriorating. Clearly, in this situation, there is something not good about the uptrend and it is not a time to trade up.

Guidelines

Note: Guidelines are used as reminders and offer the opportunity to be ignored, but only after considerable deliberation and examining all other possibilities. The absolute most important guideline is the first one, G1.

G1—In the event a situation arises in which there is not a rule or guideline, a conservative solution will be decided on and implemented based on immediate needs. A new guideline or rule will be developed only after the event/conflict has totally passed. This is a critically important guideline to ensure the “heat of the moment” is not used to create or change a rule. The absolute worst time to create or change a rule is when you are emotionally concerned about something that just seems to not be working correctly. In the 1970s, the Navy F-4J Phantom jet had analog instruments and, compared to today’s electronic technology, was antiquated. We had to memorize what we called initial action items for emergency procedures; these were designed to handle the quick and necessary steps to shut down an engine because of fire, no oil pressure, and so on. During simulator (talk about antiquated compared to now), many would pull the wrong lever or shut off the wrong switch during the emotional surge that comes with bright red flashing lights and loud horns. I was not excluded from that group, but found that, when something happened that required immediate action, winding the clock (they weren’t electric back then) for a few seconds to rid yourself of the adrenaline rush would allow you to perform better during the procedure. Beside the reasons given for S1 previously, this falls in line with that thinking.

G2—Try to adhere to this if possible: Weak Weight of the Evidence: SPY, MDY, DIA (ensure liquidity); Intermediate Weight of the Evidence: Styles and Sectors; Strong Weight of the Evidence: Wide Open (a pilot term meaning full throttle). The mandatory ranking measures will dominate this guideline.

G3—European ETFs need to be monitored closely after 1pm Eastern Time to ensure adequate execution time. This is because when the Europe markets close, liquidity in those issues becomes a problem.

G4—Every day when invested, trading up needs to be evaluated. Often, this involves selling the poor-performing holding and buying additional amounts of current holdings.

G5—All buy candidates should be determined by A) rising mandatory ranking components using a chart of the Ranking Measures, and B) an awareness of the issue’s price support and resistance levels.

G6—Always be aware of the Prudent Man concept. This is sort of a catchall to make one think about an action that has not been adequately covered with rules or guidelines. If deciding to do something as far as asset commitment or ETF selection, one needs to be prepared to stand in front of the boss and explain it.

There are a host of additional rules and guidelines that can be created. I would caution you on trying to develop a rule for every inconsistency or disappointment that surfaces while trading with a model. There is probably a good equilibrium about the depth and number of rules is best. I strongly suggest adding rules rationally and unemotionally.

Asset Commitment Tables

In addition to measuring what the market is doing (weight of the evidence) and a set of rules and guidelines to tell you how to invest based on what the market is doing, you then need a set of tables for each strategy to show you the asset commitment (equity exposure goal) levels to be invested to for each Weight of the Evidence scenario.

Table 14.6 is an example table showing the Initial Trend Measure Level (ITM), Weight of the Evidence (WoEv), the Points assigned to each level, and the Asset Commitment Level Percentage (Asset Commitment percent). This is merely a sample and should be based on your risk preferences and objectives. As you can see, even with the WoEv at its lowest level, as long as the much shorter-term trend measures (ITM) are all saying there is an uptrend, one can commit equity to the market.

An alternative and more conservative asset commitment table is shown in Table 14.7. It is easier and a more simple process to divide the WoEv into only three levels, with the middle or intermediate level being the transition zone.

The rules and guidelines offer a few exceptions to the above table of asset commitment, but only based on fairly rare events. Following the rules and commitment levels will lead to an objective process, which is the ultimate goal.

This article contains many measures one can use to determine which holdings should be bought. Many are only valuable in assisting in the selection process. If you consider the fact that you might only need to purchase a few holdings and there are more than 1,400 available, you need a strong set of technical measures to help you reduce the number of issues into a more manageable number. There are some that were identified as mandatory measures, which means these are the ones that have the best track record at identifying early when a holding is in an uptrend. I am positive there are many momentum indicators that are not in this chapter, but these are the ones that I have used for many years. Just keep in mind what the goal of this is: to remove human input into the selection process.


Thanks for reading this far. I intend to publish one article in this series every week. Can’t wait? The book is for sale here.

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These Three Strong Financial Stocks Look Ready To Surge Higher

KEY

TAKEAWAYS

  • XLF on strong RRG-Heading, rotating back into leading quadrant
  • XLF price approaching overhead resistance after short setback
  • Three major financial stocks ready for upward breaks to lead the sector higher

The Relative Rotation Graph for US sectors shows long tails for XLE and XLU. Both are on a strong RRG-Heading toward or into the leading quadrant. Also inside the leading quadrant are XLB and XLI, though they have rolled over and are starting to lose a bit of relative momentum.

Sectors on negative RRG-Heading and inside the lagging quadrant are XLRE, XLY, XLV, and XLK, with the S&P 500 moving higher in the last three weeks.

For this article, I want to focus on the Financials sector (XLF). The tail for XLF just completed a short rotation through the weakening quadrant and is now returning into the leading quadrant.

The Weekly Chart

The chart above, in combination with the RS-Line and the RRG-Lines, shows what is happening presently. At the dashed vertical line, both RRG-Lines had crossed above the 100-level, pushing the XLF tail into the leading quadrant on the RRG. At the start of 2024, the green JdK RS-Momentum line started to roll over and lose some strength, causing the XLF tail to roll over while still inside the leading quadrant. At the start of the red-shaded box, the RS-Momentum line dips below 100. This has pushed the XLF tail into the weakening quadrant. Note that the red JdK RS-Ratio line remains above 100. At the end of the shaded box, the RS-Momentum line crosses back above the 100-level, which pushes the tail back into the leading quadrant.

When you study the raw RS-Line, you see that it is moving inside a narrow uptrend channel. The period covered by the shaded area reflects a flat period of relative strength inside that channel, after which the rhythm of higher highs and higher lows continues. This rotation on the RRG reflects the continuation of an existing relative uptrend, making it much less risky than the turnaround from a downtrend to an uptrend, which happened at the dashed vertical line.

The Daily Chart

The recent dip to 39.50 and the subsequent rally show up in more detail on the daily chart. This week, XLF takes out its most recent high, starting a new series of higher highs and higher lows. The next resistance level is at the all-time high of 42.20 at the end of March. The setback off of that all-time high has caused relative strength to correct slightly, causing the (daily) RRG-Lines to dip below 100 and push the XLF tail into lagging on the daily RRG.

With the price chart already back on the way up, relative strength is expected to follow shortly. As soon as the daily tail starts to turn back into a 0-90 degree RRG-Heading, relative strength for XLF is expected to improve further, making it one of the leading sectors in the S&P 500.

Individual Stocks

The RRG for individual stocks inside the financials sector shows an evenly-distributed universe around the (XLF) benchmark. Going over the tails for the individual stocks, I found a few names that are definitely worth a closer look.

This RRG shows the tails at a strong heading, narrowing the search for good stocks. While checking out the individual charts, I found several promising names. The three that I want to mention here are not only at strong rotational trajectories, but also (close to) breaking out, AND they are some major names in the sector.

Morgan Stanley

MS is breaking a double resistance level this week, as the horizontal barrier over the most recent peaks and the falling resistance line coming off the 2021 peaks coincided. This unlocks fresh upward potential for MS, with intermediate resistance waiting around 100 before nearing the area around the all-time high at 105.

Subsequently breaking these barriers will push this stock further into the leading quadrant, making it one of the leaders in the sector.

Citigroup

Citigroup is still trading below its previous high. However, given the recently-formed higher low and the strong rally out of it, an upward break is likely. Such a break is supported by the recent relative rotation back into leading from weakening.

Just like MS, C is also one of the bigger names in the financials sector. Strength in big names is usually what drives a sector up.

Bank of America

BAC is also close to breaking overhead resistance, after which there is plenty of upside. Relative strength is coming out of a long downtrend that started early in 2022, making this a major reversal. Taking out the barrier at 38 opens the way for a further move toward 50, which is substantial. But unlike you may think, that area is NOT the all-time high for BAC… that was set around 55 in October 2006.

Like MS and C, BAC is also one of the more important stocks in the Financials sector. Another important name in the sector is GS, which I did not include as it is already well underway after breaking higher.

When such important names in a sector are all starting to break higher, it is good news for that sector.

#StayAlert, –Julius

Julius de Kempenaer
Senior Technical Analyst, StockCharts.com
CreatorRelative Rotation Graphs
FounderRRG Research
Host ofSector Spotlight

Please find my handles for social media channels under the Bio below.

Feedback, comments or questions are welcome at [email protected]. I cannot promise to respond to each and every message, but I will certainly read them and, where reasonably possible, use the feedback and comments or answer questions.

To discuss RRG with me on S.C.A.N., tag me using the handle Julius_RRG.

RRG, Relative Rotation Graphs, JdK RS-Ratio, and JdK RS-Momentum are registered trademarks of RRG Research.

Julius de Kempenaer

About the author:
Julius de Kempenaer is the creator of Relative Rotation Graphs™. This unique method to visualize relative strength within a universe of securities was first launched on Bloomberg professional services terminals in January of 2011 and was released on StockCharts.com in July of 2014.

After graduating from the Dutch Royal Military Academy, Julius served in the Dutch Air Force in multiple officer ranks. He retired from the military as a captain in 1990 to enter the financial industry as a portfolio manager for Equity & Law (now part of AXA Investment Managers).
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Rules-Based Money Management – Part 4: Security Ranking Measures

Note to the reader: This is the twentieth in a series of articles I’m publishing here taken from my book, “Investing with the Trend.” Hopefully, you will find this content useful. Market myths are generally perpetuated by repetition, misleading symbolic connections, and the complete ignorance of facts. The world of finance is full of such tendencies, and here, you’ll see some examples. Please keep in mind that not all of these examples are totally misleading — they are sometimes valid — but have too many holes in them to be worthwhile as investment concepts. And not all are directly related to investing and finance. Enjoy! – Greg


It is not uncommon for investors to believe that the more information they have, the better their chance at choosing good investments. Financial websites offer alerts on stocks, the economy, and just about anything you think you might need. The sad part is that the investor thinks every iota of information is important and tries to draw a conclusion from it. The conclusion may turn out to be correct, but it is usually not.

The issue is that investor is trying to tie each item of news to the movement of a stock, which generally never seems to work; just a few minutes watching the financial media should tell you that it doesn’t work. Human emotions make the investor feel good about having news that supports their beliefs, but rarely do those emotions contribute to investment success. I find it amazing how many times I go into an office and find the financial television playing, sometimes muted, but probably only when they see me coming. Too much information can lead to a total disarray of investment ideas and decisions. Keep it simple, turn off the outside noise, and use a technical approach to determine which issues to buy and sell. You’ll be healthier.

Ranking Measures

Ranking measures are the technical indicators used to determine which issues to buy based on their trendiness. They can be assigned as mandatory or tie-breaker ranking measures. The mandatory ones are the ranking measures that have to meet certain requirements before an issue can be bought. The tie-breaker ranking measures are there to assist in issue selection, but are not mandatory.

Ranking measures can be used with individual stocks, Exchange Traded Funds (ETFs), mutual funds, and bonds; however, there must be a process for selecting them, if for no other reason than to reduce the number down to a usable amount. For example, in an exchange-traded fund (ETF)-only strategy, consider that there are nearly 1,400 ETFs, and a fully invested portfolio might only have positions in 20 ETFs. Ranking measures are indicators, mainly of price or price relationships that assist in the determination of whether an issue is in an uptrend.

Throughout this section, the charts show the exchange-traded fund SPY in the top plot whenever possible, the ranking measure in the bottom plot, and the ranking measure’s binary overlaid on the SPY in the top plot. Some exceptions to using SPY are when volume is needed for the ranking measure, in which case another broad-based ETF will be used. A discussion of the parameters that can be used for each ranking measure is also included. I do not go into excruciating analysis on each chart, as the concept is really simple. The binary is the signal line, and it only represents the ranking measure’s signals exactly. Not all ranking measures have a binary signal, as they are used for confirmation of a trend direction.

The discussion for each ranking measure is varied as some are fairly simple to understand and won’t involve a detailed discussion. I certainly am not the type that discusses the wiggles and waggles of each indicator.

Trend

Trend is the name given to a derivative of an indicator originally created by Jim Ritter of Stratagem Software. He wrote about it in the December 1992 (V. 12:12, 534–534) issue of Stocks & Commodities magazine, in the article “Create a Hybrid Indicator.” Trend is a simple concept, yet is a powerful combination of two overbought oversold indicators: Stochastics (%K) and Relative Strength Index (RSI). The indicator uses 50% of each one in combination, and while both are range-bound between zero and 100, the combination is also range-bound between zero and 100. Stochastics, normally much quicker to react to price changes, is dampened by the usually slower-to-react RSI. In combination, you have an indicator that shows strong trend measurements whenever it is above a predetermined threshold.

Parameters

The Stochastic needs to be much longer than when used by itself, while RSI can be used close to its original value. The Stochastic range of 20 to 30 should work well, with the final value determined by the length trend you want to follow. The RSI range can vary, but you don’t want to make it too long, as it is already a slower-reacting measure. Finally, the threshold used for Trend should be in the 50 to 60 range, again dependent on how soon you want the signal, remembering that early signals will also give more whipsaws.

 The examples of Trend in Figure 14.1 have the threshold drawn at 50, which is a good all-around value. The concept is simply that whenever Trend is above 50, the ETF is in an uptrend, and whenever Trend is below 50, it is not in an uptrend. The binary is overlaid on the price plot (top) so that you can see the signals better. Notice that when prices are in an uptrend, the binary is usually at the top, and when prices are not, it is at the bottom. Also note that, in the middle of the plot, there were a number of quick signals in succession; this is why one should not rely on a single indicator for analysis.

Trend Rate of Change (ROC)

This is merely the five-day rate of change of Trend. Why would you use that? When viewing a lot of data on a spreadsheet that does not contain any charts, and you see the value for Trend is 65, you also need to know if it is rising through 65 or declining through it. A snapshot of the data can be dangerous if you don’t also look at the direction the indicator is moving.

Figure 14.2 is a chart of the five-day rate of change of Trend. You can see that while Trend is still slightly positive (above the 50 line), it is declining (see Figure 14.1). Then, when you compare it with the Trend ROC in Figure 14.2, it is showing significant weakness. Of course, showing the five-day rate of change of an indicator without showing the indicator itself is foolish; it was done here so that you could see the measure being discussed.

Parameters

This can be almost any value you desire based on what you are using it for. I used it here to see the short-term trend of an indicator, so five days is just about right. If you were using rate of change as an indicator for measuring the strength of an ETF or an index, then a longer period would probably be more appropriate. I use 21 days when I use ROC by itself.

Figure 14.3 shows the Trend with the five-day rate of change of Trend overlaid (lighter). This is the way that all the mandatory ranking measures and some of the tiebreaker measures are shown. You can see from this that the Trend is above 50, but the five-day rate of change is deteriorating and is well below zero (negative).

Trend Diffusion

This is also known as Detrend, which is a technique where you subtract the value of an indicator’s moving average from the value of the indicator. It is a simple concept, actually, and not unlike the difference between two moving averages with one average being equal to 1, or MACD for that matter. Technical analysis is ripe with simple diversions from concepts and often with someone’s name attached to the front if it— don’t get me started on that one.

Figure 14.4 is the same Trend as previously discussed, except that it is the 15-day Detrend of Trend, or Trend Diffusion. The middle plot is the Trend, with the lighter line being a 15-day simple moving average of the Trend. The bottom plot is the Trend Diffusion, which is simply the difference between the Trend and its own 15-day moving average. You can see this when the Trend moves above its moving average, the Trend Diffusion moves above the zero line. Similarly, whenever the Trend moves below its 15-day moving average in the middle plot, the Trend Diffusion moves below the zero line in the bottom plot. The information from the 15-day Trend Diffusion is absolutely no different that the information in the middle plot showing the Trend and its 15-day moving average, just easier to visualize.

Parameters

The example in Figure 14.4 uses 15 days, which is three weeks. Parameters need to be chosen based on the timeframe for your analysis. A range from 10 to 30 is probably adequate for Trend Diffusion.

Price Momentum

This indicator looks back at the price today compared to X days ago. It is created by calculating the difference between the sum of all recent gains and the sum of all recent losses and then dividing the results by the sum of all price movement over the period being analyzed. This oscillator is similar to other momentum indicators, such as RSI and Stochastics, because it is rangebound, in this case from -100 to +100.

Parameters

Price Momentum is very close to being the same as rate of change; generally the only difference between the two is the scaling of the data. Momentum oscillates above and below zero and yields absolute values, while the Rate of Change moves between zero and 100 and yields relative values. The shape of the line, however, is similar. With momentum, the threshold is shown at 50, but could be higher if requiring more stringent ranking requirements.

Figure 14.5 shows the Price Momentum ranking measure (dark line) and its five-day rate of change (lighter line). You can see that the Price Momentum is weak and the ROC is negative and declining.

Price Performance

This indicator shows the recent performance based on its actual rate of change for multiple periods, added together, and then divided by the number of rates of change used. In this example, I used three rates of change of 5, 10, and 21 days, which equates to 1 week, 2 weeks, and 1 month. Simply calculate each rate of change, add them together, and then divide by three. This gives an equal weighting to rates of change over various days.

Parameters

Like many indicators, the parameters used are totally dependent on what you are trying to accomplish. Here, I am trying only to identify ETFs that are in an uptrend.

Figure 14.6 shows the Price Performance measure using the three rates of change mentioned above. There is no need to show the typical five-day rate of change of this indicator, since it is in itself a rate of change indicator.

Relationship to Stop

This is the percentage that price is below its previous 21-day highest close. This is an extremely important ranking measure, and here’s why.

If you are using a system that always uses stop loss placement (hopefully you are), then you certainly would not want to buy an ETF that was already close to its stop. This is the case when using trailing stops; if using portfolio stops, or stops based on the purchase price, this measure does not come into play. I like to use stops during periods of low risk of 5% below where the closing price had reached its highest value over the past 21 days. If you think about this, this means that, as prices decline from a new high, then the stop baseline is set at that point and the percentage decline is measured from there.

Parameters

In most cases, this is a variable parameter determined by the risk that you have assessed in the market or in the holding. I prefer very tight stops in the early stages of an uptrend, because I know there are going to be times when it does not work, and when those times happen, I want out. The setting of stop loss levels is entirely too subjective, but I would say that as risk lessens, the stops should become looser, allowing for more daily volatility in the price action.

Figure 14.7 shows the 5% trailing stop using the highest closing price over the past 21 days. The two lines are drawn at zero and -5%. When this measure is at zero, it means that the price is at its highest level in the past 21 days. The line then continuously shows where the price is relative to the moving 21-day highest closing price. When it drops below the -5% line, then the stop has been hit and the holding should be sold.

Please notice that I did not beat around the bush on that last sentence. When a stop is hit, sell the holding. Like Forrest Gump, that is all I ‘m going to say about that.

Relative Performance

This indicator shows the recent performance of an ETF relative to that of the S&P 500. Often, there is a tendency to show the performance relative to the total return version of the S&P 500. This is only advisable if you are actually measuring and using the total return version of an ETF. In addition, most measurements are of a timeframe where the total return does not come into play. However, purists may want one over the other, and the results will be satisfactory if used consistently.

Usually the data analyzed is price-based; therefore, the relative performance should be using the price only S&P 500 Index. Also, when comparing an ETF to an index, one must be careful when comparing, say the SPY with the S&P 500 Index, two issues that should track relatively close to each other. The mathematics can blow up on you, so just be cognizant of this situation. Hence, the example in the chart below has switched from using SPY to using the EFA exchange-traded fund.

Finally, you cannot simply divide the ETF by the index and plot it, or you will have a lot of noise with no clear indication as to the relative performance. I like to normalize the ratio of the two over a time period that is appropriate for my work; in this case, over 65 days. This can further be expanded, similar to the Price Performance measure covered previously, and also use another normalization period, say 21 days, then average them. Additionally, you can then smooth the results to help remove some noise. Remember, you are only trying to assess relative performance here.

Parameters

This, like many ranking measures, is based totally on personal preference, and also on the time frame you are using for analysis. In this example, I normalized the ratio with 65 and 21 days, then smoothed the result with the difference between their 15- and 50-day exponential average.

Figure 14.8 shows EFA relative to the S&P 500 Index. Whenever it is above the horizontal zero line, then EFA is outperforming the S&P 500. This would be considered an alpha-generating ranking measure if your benchmark is the S&P 500.

Power Score

This is a combination indicator that takes four indicators into account to get a composite score. Those indicators are Trend, Price Momentum, Price Performance, and Relationship to Stop. Additionally, the PowerScore also factors in the five-day rates of change of the Price Momentum and Trend measures.

Parameters

There are not really any parameters to discuss with PowerScore, as it is created by using four of the mandatory ranking measures. The concept here can be as broad or as narrow as needed. Using only the mandatory ranking measures seems reasonable; however, the PowerScore is unlimited in what components can be used.

Figure 14.9 shows the PowerScore with a horizontal line at the value of 100. Based on the calculations of the components for this indicator, whenever PowerScore is above 100, then it is saying that the components are collectively saying the ETF is in an uptrend. This could be considered a composite measure, but, unlike the ones referred to in the weight of the evidence components, this one uses all components.

Efficiency Ratio

This ratio shows how much price movement in the past 21 days was essentially noise. It is a measure of the smoothness of the 21-day rate of change, created years ago by Perry Kaufman. It is an excellent ranking measure, but you need to know that it is an absolute measure of how an ETF gets from point A to point B; in this case, from 21 days ago until today.

Figure 14.10 is an example of how to think about this. If you were interested in two funds, fund 1 (solid line) and fund B (thicker dashed line), measuring their price movements of the same period of time, then which of the two would you prefer? The one that smoothly rose from point A to point B, or the one that had erratic movements up and down but ended up at the same place? I think everyone agrees that the smoother ride, or the solid line, is preferable.

Parameters

I use 15 or 21 days, but as always, this is more dependent on your trading style and time frame of reference. The value should closely mirror what the minimum length trend you are trying to identify, independent of direction.

Figure 14.11 shows the 21-day efficiency ratio for SPY. You can see that whenever the ETF is trending, the Efficiency Ratio rises, and when the ETF is range-bound and moving sideways, the Efficiency Ratio remains low. In other words, a high efficiency ratio means the ride is more comfortable. It is moving efficiently.

Average Drawdown

If you read the section in this book on Drawdown Analysis (Chapter 11), then you know exactly what this ranking measure accomplishes. The concept of average Drawdown for analysis and using it for a ranking measure are considerably different. To utilize average drawdown as a ranking measure, you need to use a moving average drawdown, such as over the past year. This is because an issue that has been in a state of drawdown for a number of years will not give you the ranking data that is needed for a frame of reference over the past few months. A moving average of drawdown will help reset the drawdown as time moves forward.

Parameters

I like to see the average drawdown over the past year, which is, on average 252 market days. This is enough time for a measurement, but short enough to get a feel for how long it remains in a state of drawdown.

Figure 14.12 shows the average drawdown over the past 252 days. The horizontal line is drawn at -5% as a reference. The lower plot is the cumulative drawdown, with the horizontal line being the long-term average.

Relative Average Drawdown

Figure 14.13 shows the difference between the average drawdown of the issue compared to that of the S&P 500 Index. This is shown here only as an example of another type of ranking measure, and certainly would never qualify as a mandatory ranking measure.

Price x Volume

Figure 14.14 shows the 21-day simple average of the volume times the close price. The purpose here is to show if the issue has enough liquidity to be traded. The ranking measures should always give a quick view on a variety of indicators, and this one might show you immediately if there is enough trading volume to give you the liquidity you would need to trade it. Of course, the ideal solution is to have a good relationship with the trading desk that you will be using, as they can give you up-to-date information on what volume you can trade.

Adaptive Trend

Adaptive Trend is an intermediate trend measure that changes based on the volatility of the price movements. The Adaptive Trend measure incorporates the most recent 21 days of market data to compute volatility based on a true range methodology. This process always considers the previous day’s close price in the current day’s high–low range to ensure we are using days that gap either up or down to their fullest benefit. When the price is trading above the Adaptive Trend, a positive signal is generated, and when below, a negative signal is in place.

The chart in Figure 14.15 shows the Adaptive Trend as an oscillator above and below zero, so that when it is above zero, it means the price is above the Adaptive Trend, and when below zero, price is below the Adaptive Trend. The top plot shows the Adaptive Trend binary. If you prefer, the horizontal line at zero is the adaptive trend, similar to the Trend Diffusion discussed earlier.

Weighted Performance

Figure 14.16 is a weighted average of the 1-, 3-, 5-, 10-, and 21-day rates of change. One can argue that it is difficult to decide which exact period to measure for performance, and I would not disagree. The method takes a number of periods into consideration and averages them for a single result. One could carry this concept further and weight each of the measurements and have a double-weighted performance measure.

You should, however, try to keep things simple, as complexity has a greater tendency to fail.

Slow Trend

This measure, shown in Figure 14.17 , is similar to Trend, but uses a longer period for its calculation. This concept can be used on many of the ranking measures as a second line of defense or confirmation. The faster version is good for initial selection, and the slower version is good for adding to positions (trading up).

Ulcer Index

The Ulcer Index (Figure 14.18) takes into account only the downward volatility for an issue, plus uses price crossover technique with a 21-period average. This concept was first written about by Peter Martin in The Investor’s Guide to Fidelity Funds, in 1989.

Sortino Ratio

Figure 14.19 shows the downside risk after the return of the issue falls below that of the 13-week T-bill yield. It is a risk-adjusted return like the Sharpe Ratio, but only penalizes downward volatility, whereas the Sharpe Ratio uses sigma (standard deviation). This is also similar to the Treynor ratio, which uses beta as the denominator and expected return for the numerator.

Beta

Figure 14.20 is the issue’s beta based on the past 126 days (6 months). The same issue exists here as with the Relative Performance earlier. You cannot measure beta unless it is measured against something, in this case the S&P 500. Therefore, be careful when comparing a large-cap ETF to a large-cap benchmark, small-cap ETF to a small-cap benchmark, and so on.

Relationship to Moving Average

Figure 14.21 shows the percent above or below the simple 65-day exponential moving average. This is similar to detrend or diffusion. I think here the value is that one should always pick a moving average period to use and stick with it, so that you get a feel for its action during certain market movements. In other words, you become accustomed to how this moving average works over time. I equate this to using only one wedge in golf instead of multiple ones. Most of us cannot devote the time to practice with multiple wedges, so learn one and stick with it.

Correlation

Correlation is an attempt to find a close relationship with an index such as the S&P 500. This is another one of those ranking measures you need to be careful not to compare a like ETF to a similar index. For example, the mathematics of correlation would blow up if you tried to compare the ETF SPY with the S&P 500 Index.

In Figure 14.22, whenever the line is near the top of the plot, then it is saying the correlation of the top plot is correlated to the benchmark being used. When the market is advancing, you want highly correlated holdings. When the market is declining, or you see it begin to roll over in a topping manner, you want to move into less correlated holdings. You must keep in mind that you are still a momentum player and, even though you want less correlation to the market, they still must be advancing on an individual basis.

Correlation is not always causation, but don’t try explaining that to your dog when he hears the can opener. — Tom McClellan


Thanks for reading this far. I intend to publish one article in this series every week. Can’t wait? The book is for sale here.

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Diverging Tails on This Relative Rotation Graph Unveil Trading Opportunities

KEY

TAKEAWAYS

  • Comparing equal-weighted and cap-weighted sectors on a Relative Rotation Graph can offer interesting insights
  • When the trajectory of the tails and their position on the chart differ significantly, further investigation is warranted
  • At the moment, two sectors are showing such divergences

All on the Same Track… or?

The difference between equal-weighted sectors and cap-weighted sectors is obvious. Namely, the cap-weighted variant is much heavier and is impacted by the changes in some heavy-weight, often mega-cap, stocks. Nevertheless, when you plot these sectors on Relative Rotation Graphs, you will often find that their tails generally move in the same direction and/or follow the same path.

When that does not happen, when the tails of the two versions of the same sector are on different paths or in completely different positions on the RRG, it’s time to investigate.

The RRG above shows the two universes, cap-weighted and equal-weighted, plotted on the same RRG and against SPY as the benchmark. Looking closely, you will find most sector pairs on the same trajectory. If you have a SC account, you can click on the graph, open the RRG in your own account, and do a closer inspection.

*You can save RRGs as bookmarks in your browser. By doing that, you can create your own custom RRGs and save them for later retrieval. Scroll to the bottom of the page, click “permalink,” and then copy and save this link as a bookmark in your browser.

Zooming In

To get a better handle and a clearer picture, I have removed the sectors where both tails are on similar trajectories and positions and only left the tails on the graph where they differ. As a result, two sectors remain: Consumer Discretionary and Communication Services.

Consumer Discretionary

Both tails are inside the lagging quadrant. However, that is as far as the comparison goes. XLY is moving higher on the RS-Momentum scale, indicating an improvement in relative momentum, while RSPD is moving lower and is on a negative RRG-Heading. Also, the tail on XLY is substantially longer than on RSPD, indicating the power behind the move.

Looking at the composition of the sector, it’s obvious which stocks inside Consumer Discretionary are causing the difference.

AMZN, TSLA, HD, and MCD comprise 50% of the index, while AMZN and TSLA are already 38%.

Looking at the performance over the last five weeks (tail length on the RRG), we can see how the sector’s performance has shifted to the large names. The table above shows the top 50 stocks in the discretionary sector. AMZN and TSLA are in the upper end of the range, and MCD is just above XLY, which is at position 17 out of 50. This implies that most stocks are performing worse than that sector index.’

Roughly the bottom half is at double-digit declines. While AMZN and TSLA are “only” up 2.4%, they drag the sector index up to around 1/3 of the entire universe, even with HD showing a 12.5% decline over that period.

Now, look at the same table. Instead of using XLY as the benchmark, we are now using RSPD as the benchmark.

RSPD is showing up at position 27 / 50, right where you’d expect an equal weight benchmark — in the middle of the universe, balancing out all the performances.

The bottom line is that XLY has been picking up recently only because of TSLA, AMZN, and MCD. But, under the hood, most discretionary stocks are going through a horrible correction.

From a trading perspective, such observations can offer great pair trading ideas.

Communication Services

The tails for XLC and RSPC are also far apart on the RRG. XLC is still inside the weakening quadrant and has just started to show the first signs of curling back up. RSPC is deep inside the lagging quadrant at a really low reading on the RS-Ratio scale overall, and is picking up relative momentum, but no relative trend (RS-Ratio) yet.

Over the five-week period, XLC lost 2.8%, while RSPC lost 4.3%. The composition for this sector is even more top-heavy than Consumer Discretionary.

META is listed as the top holding in XLC at 21%. But when we add up the weights for Alphabet A and B, it comes out to 26%. So together, the top two stocks in XLC are a whopping 47% of the sector.

Looking at the same table for XLC, we find Alphabet at the top of the list over the last five weeks. Meta is in the lower part at -9%. The sector (XLC) comes in at -2.8%, which means that META is UNDERperforming (-9% + 2.8% =) -6.2%. But Alphabet Class A is OUTperforming (10.4% + 2.8% = ) 13.2% and Alphabet Class C is OUTperforming (10.6% + 2.8% = ) 13.4%. This is a way stronger upward pull for the index than the drag caused by META.

Changing the benchmark to the EW version of Communication Services shows this table.

Again, we see the equal-weight benchmark (RSPC) dropping to near the middle of the list, balancing out the return more evenly.

All in all, this provides a similar pair trading opportunity.

This relative trend is much more mature than the XLY:RSPD pair, but, as long as the rhythm of higher highs and higher lows continues, buying the dips in this relative line offers opportunities.

Most of the time, the cap-weighted and equal-weighted versions of a sector will move more or less in tandem. But when they don’t, they’re worth investigating, as they may offer interesting trading opportunities.

#StayAlert and have a great weekend, –Julius


Julius de Kempenaer
Senior Technical Analyst, StockCharts.com
CreatorRelative Rotation Graphs
FounderRRG Research
Host ofSector Spotlight

Please find my handles for social media channels under the Bio below.

Feedback, comments or questions are welcome at [email protected]. I cannot promise to respond to each and every message, but I will certainly read them and, where reasonably possible, use the feedback and comments or answer questions.

To discuss RRG with me on S.C.A.N., tag me using the handle Julius_RRG.

RRG, Relative Rotation Graphs, JdK RS-Ratio, and JdK RS-Momentum are registered trademarks of RRG Research.

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S&P 500 Makes a New All-Time High By End of June?

We’ve been covering the signs of weakness for stocks, from the bearish divergences in March, to the mega-cap growth stocks breaking through their 50-day moving averages, to even the dramatic increase in volatility often associated with major market tops. While Q1 was marked by broad market strength and plenty of new 52-week highs, Q2 has so far provided a much different playbook for investors. Both bulls and bears have felt validated by the recent choppiness for the major market averages.

Over the last week, the S&P 500 managed to gain about 2.7%, despite some hotter-than-expected inflation data and a mixed bag of earnings for the Magnificent 7 stocks. Does this set us up for much further gains, and a potential break to new all-time highs, as we continue through the second quarter? Or are we currently experiencing the “dead cat bounce” phase with a countertrend move to the upside before the great bear market continues?

Psst! Check out the January 2024 edition of this exercise, and guess which scenario actually played out!

Today, we’ll lay out four potential outcomes for the S&P 500 index. As I share each of these four future paths, I’ll describe the market conditions that would likely be involved, and I’ll also share my estimated probability for each scenario. And remember, the point of this exercise is threefold:

  1. Consider all four potential future paths for the index, think about what would cause each scenario to unfold in terms of the macro drivers, and review what signals/patterns/indicators would confirm the scenario.
  2. Decide which scenario you feel is most likely, and why you think that’s the case. Don’t forget to drop me a comment and let me know your vote!
  3. Think about how each of the four scenarios would impact your current portfolio. How would you manage risk in each case? How and when would you take action to adapt to this new reality?

Let’s start with the most optimistic scenario, involving a move to new all-time highs over the next six to eight weeks.

Option 1: The Very Bullish Scenario

If you think the April pullback was just another buyable dip within a primary bullish trend, then the Very Bullish Scenario is for you. This scenario would be made possible only if the Magnificent 7 stocks returned to their former magnificent ways, with stocks like AMZN and NVDA following GOOGL in making new all-time highs.

We’d need to see economic indicators, especially inflation readings, come in much weaker, which would give the Fed confidence to begin cutting rates at the June Fed meeting. By the end of June, we’d be talking about the S&P 500 breaking above 5500, and even 6000 could be on the table.

Dave’s Vote: 10%

Option 2: The Mildly Bullish Scenario

What if the S&P manages to hold the April low around 4950, but is unable to push to new all-time highs? Scenario 2 could mean that value-oriented sectors like industrials and materials experience a resurgence, outpacing the growth leadership stocks from Q1. But since these sectors are much lower weight in the S&P 500, it’s just not enough market cap to move the needle on the major benchmarks.

Perhaps the rest of earnings season yields mixed results, and by the end of Q2 we are left with more questions than answers as the Fed is unable to commit to aggressive rate cuts. Interest rates remain elevated, which creates a major headwind for growth stocks.

Dave’s vote: 30%

Option 3: The Mildly Bearish Scenario

Now we get to two scenarios that would mean a more bearish picture emerges in the coming weeks. Scenario 3 would mean the S&P 500 is unable to hold the April low around 4950, but we remain above a 38.2% retracement level around 4820. The Fed either delays its first rate cut or uses language that exudes little confidence in multiple additional rate cuts in 2024.

The Magnificent 7 stocks would be choppy at best, and as they stall out attempting to return to new all-time highs, investors see that as a signal of limited upside. Gold and gold stocks become the trade of the day, as investors are looking for anything other than stocks to try and generate positive returns.

Dave’s vote: 45%

Option 4: The Super Bearish Scenario

You always have to include a doomsday scenario, and our final option would mean the April selloff was indeed just the beginning. May and June are marked with lower lows and lower highs, and Q2 feels very similar to September and October of 2023. The S&P 500 breaks through Fibonacci support around 4820, and even pushes below the 200-day moving average for the first time since the October 2023 low.

What could cause this last scenario? Economic data could come in way higher than expected, and the Fed could then become unwilling to cut rates while the economy shows signs of renewed strength. The market braces for “higher for longer” interest rates, growth-oriented sectors like technology and communication services begin the lead the way lower, and defensive sectors bump higher as investors ignite the “flight for safety” trade.

Dave’s vote: 15%

What probabilities would you assign to each of these four scenarios? Check out the video below, and then drop a comment with which scenario you select and why!

RR#6,

Dave

P.S. Ready to upgrade your investment process? Check out my free behavioral investing course!


David Keller, CMT

Chief Market Strategist

StockCharts.com


Disclaimer: This blog is for educational purposes only and should not be construed as financial advice. The ideas and strategies should never be used without first assessing your own personal and financial situation, or without consulting a financial professional.

The author does not have a position in mentioned securities at the time of publication. Any opinions expressed herein are solely those of the author and do not in any way represent the views or opinions of any other person or entity.

David Keller

About the author:
David Keller, CMT is Chief Market Strategist at StockCharts.com, where he helps investors minimize behavioral biases through technical analysis. He is a frequent host on StockCharts TV, and he relates mindfulness techniques to investor decision making in his blog, The Mindful Investor.

David is also President and Chief Strategist at Sierra Alpha Research LLC, a boutique investment research firm focused on managing risk through market awareness. He combines the strengths of technical analysis, behavioral finance, and data visualization to identify investment opportunities and enrich relationships between advisors and clients.
Learn More

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Breakdown in Mega-Cap Growth Confirms Bear Phase

KEY

TAKEAWAYS

  • Early breakdowns from AAPL and TSLA provided initial warnings of a late stage bull market.
  • Exponential gains in stocks like SMCI and MSTR have now turned into steep pullbacks with both stocks breaking below moving average support.
  • With AMZN and NFLX finishing the week below their 50-day moving average, the rotation away from growth leadership may now be in full force.

While our major equity benchmarks showed incredible strength in Q1 2024, breadth conditions have been deteriorating since mid-March. Despite the weakening breadth readings, and the initial breakdowns of the S&P 500 and Nasdaq 100, leading growth names, including the formerly-described Magnificent 7 stocks, had remained in clearly-defined uptrends.

This week, some of the top-performing stocks in the S&P 500 finally broke below their 50-day moving averages. While this signal on its own is not a sign of a market top, these breakdowns represent just one of the many clear signals that the bull market off the October 2023 low may be over.

Today, we’ll briefly review some of the early breakdowns in the mega-cap growth space, how some of the top-ranked SCTR stocks have shown recent weakness, and why the “Fantastic Four” (current front-runner to replace the “Magnificent 7 moniker) breaking down may represent a key confirmation for a new bear phase.

The Early Breakdowns: Apple (AAPL) & Tesla (TSLA)

Tesla has been in a confirmed downtrend since July 2023, and Apple has appeared in a weak technical configuration since failing to break above the $200 level in December and January. But both charts have literally and figuratively made a new low this week.

Note how both charts have remained below downward-sloping 50-day moving averages since mid-January. Also observe how both have shown failed attempts to break above that moving average in recent months. When stocks are making lower lows and lower highs, and trending below downward-sloping moving averages, I’ve learned it’s best to avoid taking action until some of those conditions start to change. 


Ready to talk market breadth indicators? Our next free webinar, Breaking Down Breadth, will focus on breadth conditions now vs. previous market tops. Join me on Tuesday, April 23rd at 1pm ET as we review the current market environment through the lens of breadth indicators, compare them to conditions at previous market tops, and discuss the likelihood of further drawdowns for the S&P 500 and Nasdaq. Sign up HERE for this free webcast!


As these stocks broke down, diverging from most other leading growth names, the S&P 500 and Nasdaq 100 pushed much higher. So let’s see some of the stocks that served as leadership in Q1.

The Top-Ranked SCTRs: Super Micro Computer (SMCI) & MicroStrategy (MSTR)

Here, we have two names that were less well-known until they experienced exponential gains earlier this year. And while they certainly appeared overextended in March, they have now both come right down to earth.

From the end of 2023 to their peaks in March 2024, SMCI and MSTR gained 350% and 175%, respectively. They both were a far distance from moving average support, giving clear signs of overbought conditions. So far in April, both stocks have traded much lower, and they each finished this week below their 50-day moving averages.

It’s normal for stocks in strong uptrends to pull back and test moving average support. Indeed, the 50-day moving average often serves as a potential entry point for a “buy on the dips” strategy. But when top performers fail to hold this crucial short-term support level, I have found that it often implies a broader move to more risk-off positioning.

What about the best of the biggest–in other words, the most magnificent of the Magnificent 7?

The Fantastic Four Breakdowns: Netflix (NFLX) & Amazon (AMZN)

That brings us to perhaps the most concerning development this week. As I recently posted on my social media accounts, “As long as $AMZN and $NFLX remain above the 50-day moving average, you can make an argument for ‘short-term pullback’ as opposed to ‘protracted and painful decline.'” Unfortunately, this week, we finally observed this breakdown of breakdowns.

Mega-cap growth stocks wield an outsized influence on our top-heavy growth-dominated equity benchmarks. In recent weeks, bearish momentum divergences, weakening breadth conditions, and breaks of “line in the sand” support levels had us thinking market weakness over market strength. But the resilience of the Fantastic Four stocks gave us just a glimmer of hope that a pullback may be limited.

Given this week’s breakdown in the charts of previous top performers, we feel this just may be the beginning of the great bear phase of Q2 2024.

RR#6,

Dave

P.S. Ready to upgrade your investment process? Check out my free behavioral investing course!


David Keller, CMT

Chief Market Strategist

StockCharts.com


Disclaimer: This blog is for educational purposes only and should not be construed as financial advice. The ideas and strategies should never be used without first assessing your own personal and financial situation, or without consulting a financial professional.

The author does not have a position in mentioned securities at the time of publication. Any opinions expressed herein are solely those of the author and do not in any way represent the views or opinions of any other person or entity.

David Keller

About the author:
David Keller, CMT is Chief Market Strategist at StockCharts.com, where he helps investors minimize behavioral biases through technical analysis. He is a frequent host on StockCharts TV, and he relates mindfulness techniques to investor decision making in his blog, The Mindful Investor.

David is also President and Chief Strategist at Sierra Alpha Research LLC, a boutique investment research firm focused on managing risk through market awareness. He combines the strengths of technical analysis, behavioral finance, and data visualization to identify investment opportunities and enrich relationships between advisors and clients.
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Rules-Based Money Management – Part 1: Popular Indicators and Their Uses

Note to the reader: This is the seventeenth in a series of articles I’m publishing here taken from my book, “Investing with the Trend.” Hopefully, you will find this content useful. Market myths are generally perpetuated by repetition, misleading symbolic connections, and the complete ignorance of facts. The world of finance is full of such tendencies, and here, you’ll see some examples. Please keep in mind that not all of these examples are totally misleading — they are sometimes valid — but have too many holes in them to be worthwhile as investment concepts. And not all are directly related to investing and finance. Enjoy! – Greg


To begin Part III: Rules-Based Money Management, we need to review a few basic technical indicators that are referenced frequently. Their concepts are used throughout this part of the book. Remember, Part III is the creating of the weight of the evidence to identify trends in the overall market, a ranking and selection process for finding securities to buy based on their individual and relative momentum, a set of rules and guidelines to provide you with a checklist on how to trade the information, and the results of my rules-based trend following strategy, called Dance with the Trend.

Moving Averages and Smoothing

Most times, daily stock market data is too volatile to analyze properly. What’s needed is a way of removing much of this daily volatility. There is such a method, and that is the subject of this section on smoothing techniques.

Smoothing refers to the act of making the time series data smoother to remove oscillations, but keeping the general trend. It is a better adverb to use than always trying to explain that you take a moving average of it or take the exponential average of it; just say you are smoothing it. Some of the advantages of doing this are:

  • Reducing day-to-day fluctuations.
  • Making it easier to identify trends.
  • Making it easier to see changes in trend.
  • Providing initial support and resistance levels.
  • Much better for trend following.

One of the simplest market systems created, the moving average, works almost as well as the best of the complicated smoothing techniques. A moving average is exactly the same as a regular average (mean), except that it “moves” because it is continuously updated as new data become available. Each data point in a moving average is given equal weight in the computation; hence, the term arithmetic, or simple, is sometimes used when referring to a moving average.

A moving average smooths a sequence of numbers so that the effects of short-term fluctuations are reduced, while those of longer-term fluctuations remain relatively unchanged. Obviously, the time span of the moving average will alter its characteristics.

J. M. Hurst, in The Profit Magic of Stock Transaction Timing (1970), explained these alterations with three general rules:

  1. A moving average of any given time span exactly reduces the magnitude of the fluctuations of durations equal to that time span to zero.
  2. The same moving average also greatly reduces (but does not eliminate) the magnitude of all fluctuations of duration less than the time span of the moving average.
  3. All fluctuations that are greater than the time span of the average “come through,” or are present in the resulting moving average line. Those with durations just a little greater than the span of the average are greatly reduced in magnitude, but the effect lessens as periodicity duration increases. Very long duration periodicities come through nearly unscathed.

Simple or Arithmetic Moving Average

To take an average of just about any set of numbers or prices, you add up the numbers, then divide by the number of items. For example, if you have 4+6+2, the sum is 12, and the average is 12/3 = 4. A moving average does exactly this, but as a new number is added, the oldest number is removed. In the previous example, let’s say that 8 is the new number, so the new sequence would be 6+2+8. The original first number (4) was removed because we are only adding the most recent three numbers. In this case, the new average would be 16/3 = 5.33. So by adding an 8 and removing a 4, we increased the average by 1.33 in this example. For those so inclined, here’s the math: 8-4=4, and 4/3 =1.33.

Another feature of the simple moving average is that each component is treated equally — that is, it carries an equal weight in the calculation of the average. This is shown graphically in Figure 12.1. Note that it does not matter how many data points you are averaging; they each carry an equal contribution to the value of the average.

Because of the equal weighting of the data components in a simple moving average, the larger the average, the slower it will react to changes in price.

Let me share a little story about price charts and moving averages. Back in the 1980s, we had one of the original online services, called Prodigy. At one point, they started to provide some simple stock charts with a single moving average on them. I kept looking at it and knew something was wrong, because I had studied and created these types of charts for years. I finally discovered that they were using separate scales for the price and the price’s moving average. Although the values would be correct, the display was not because the average was using its isolated price scale. I wrote (yes, there was no e-mail then) them and explained. The first response was denial that they could be doing it wrong. I mailed them some charts showing their way and the proper way to display moving averages over price by sharing the same vertical scale. It took a long time and many letters before I finally convinced someone that they had it wrong. In appreciation, they sent me a small digital clock worth about $1.25 (battery not included).

Exponential Moving Average

This method of averaging was developed by scientists, such as Pete Haurlan, in an attempt to assist and improve the tracking of missile guidance systems. More weight is given to the most recent data, and it is therefore much faster to change direction and respond to changes in price. It is sometimes represented as a percentage (trend percent) instead of by the more familiar periods. For example, to calculate a 5% exponential average, you would take the last closing price and multiply it by 5%, then add this result to the value of the previous period’s exponential average value multiplied by the complement, which in this case is 1 –.05 =.95. Here is a formula that will help you convert between the two:

    K=2/(N + 1) where K is the smoothing constant (trend percent) and N is the number of periods.

    Algebraically solving for N: N =(2/K)-1.

For example, if you wanted to know the smoothing constant of a 19-period exponential average, you could do the math, K=2/(19 +1)=2/20=0.10 (smoothing constant), or 10% trend as it is many times expressed. In the example previously that used a 5% exponential average, the math is as follows:

    5% Exp Avg=(Current price x 0.05) + (Previous Exp Avg x 0.95)

Figure 12.2 shows how the weight of each component affects the average. The most recent data is represented by the far right on the graph.

Now for the really important piece of knowledge about the difference between the simple moving average and the exponential moving average. Notice in Figure 12.3 how long it takes the simple average (dashed) to reverse direction to the upside. From the time the price line climbs through the dashed line, it takes five to six days before the dashed line begins to rise in this example (upward arrow—SMA). In fact, immediately after the price goes below the dashed line, the dashed line is still falling. Both averages used the same number of periods.

Now note how quickly the darker exponential average changes direction when the price line moves through it (upward arrow—EMA). Immediately! Yes, because of the mathematics, the exponential average will always change direction as soon as the price line moves through it. That is why the exponential average is used, because it hugs the data tighter and eliminates much of the lag that is present in the simple average.

Now, when it comes to the question as to which is better, the answer is always that it depends on what you are trying to accomplish. Sometimes the simple average is better because of its lag, and sometimes not. The same goes for the exponential average; sometimes it is better, sometimes not. Personally, I have found that the exponential average is better for longer-term analysis, say, more than 65 periods (days). However, that becomes a personal preference as you build experience.

Stochastics

George Lane promoted it and Ralph Dystant probably created it; however, I know that Tim Slater, the creator of CompuTrac software in 1978, was probably the one that coined the name Stochastics. This is an odd name, as stochastic is a mathematical term that refers to the evolution of a random variable over time. Stochastics is a range-based indicator that normalizes price data over a selected period of time, usually 14 periods or days. It basically shows where the most recent price is relative to the full range of prices over the selected number of periods. This display of price location within a range of prices is scaled between 0 and 100. Usually there are two versions, one called %K, which is the raw calculation, and the other %D, which is just a three-period moving average of %K. Don’t get me started why there are two names for a calculation and its smoothed value. I met George Lane a number of times and found him to be a delightful gentleman; George passed away in 2008.

Personally, this is about my favorite price-based indicator. It seems that almost everyone uses Stochastics as an overbought/oversold indicator. While it is good in a trading range or sideways market, it does not work well in a trending market when used this way. However, it is also an excellent trend measure. This is good because many stocks and markets trend more than they go sideways.

So how does it work as a trend measure? If you think about the formula and realize that as long as prices are rising, then %K is going to remain at or near its highest level, say over 80. Therefore, as long as %K is over 80, you can assume you are in an uptrending market. Likewise, when %K is below 20 for a period of time, you are in a downtrending market. Personally, I like to use %D instead of %K for trend analysis, as it is smoother with less false signals.

Figure 12.4 shows a 14-day Stochastic with the S&P 500 Index above. The three horizontal lines on the Stochastic are at 20, 50, and 80.

If you use Stochastics as an overbought/oversold indicator, it will work better if you only take signals that are aligned with a longer-term trend. For example, if the general trend of the market is up, then only adhere to the buy signals from Stochastics. Finally, you are not restricted to the 80 and 20 levels to determine overbought and oversold, you can use any levels you feel comfortable with. In fact, if using %D for trend following, also using 30 and 70 will help eliminate whipsaws.

One of the really unique properties of this indicator is that it can be used to normalize data. Let me explain. If you wanted to see data prices that were contained within a range between 0 and 100, then this formula would do that. For example if you had a year’s worth of data, which is about 252 trading days, all you need is to merely set the number of periods for %K to 252 and you would be able to see where prices moved over the last year. This becomes especially valuable when comparing two different stocks or indices.

It should also be noted that Stochastics was designed to be used with data that contains the High, Low, and Close price. It can work with close-only data, but the formula must be adjusted accordingly.

RSI (Relative Strength Index)

RSI was one of the first truly original momentum oscillator indicators that was created prior to desktop or personal computers. Welles Wilder laid out the concept on a columnar pad. Basically, RSI takes a weighted average of the last 14 days’ (if using 14 for the number of periods) up closes and divides by the last 14 days’ down closes. It is then normalized so that the indicator always reads between 0 and 100. Parameters often associated with RSI for overbought are when RSI is over 70, and oversold when it is below 30.

The Relative Strength Index (RSI) can be used a number of different ways. Probably the most common is to use it the same as Stochastics in an overbought/oversold manner. Whenever RSI rises above 70 and then reverses direction and drops below 70, it is a sign that the down closes have increased relative to the up close and the market is declining. Although this method seems to always be popular, using RSI as a trend measure and one to help spot divergences with price seems like two better uses for RSI. Figure 12.5 shows RSI with the S&P 500 Index above. The horizontal lines on RSI are at 30, 50, and 70.

RSI is probably one of the most popular indicators ever developed. I think that is because most could not generate the formula themselves if it were not a mainstay in almost every technical analysis software package. Wilder developed it using a columnar pad and had to come up with a way to do a weighted average of the up and down closes. It is not a true weighted average, but gets the job done.

One of the really big problems that I see with RSI is that in long continuous trends, it can be using some relatively old data as part of its calculation. For an example, let’s say the stock is in an uptrend and has been for a while. The denominator is the average of the down closes in the last 14 days. If the uptrend is strong, there might not be any down closes for a period of time. If there were not any in the last 14 days, without the Wilder smoothing technique, the denominator would be equal to zero, and that would render the indicator useless. Because of this situation, the calculation for RSI can use relatively old data. That is why RSI seems to work well as a divergence indicator, because of the old data. This is generally caused by the fact that the previous up trend keeps the denominator, which uses down closes, fairly inactive, but once the down closes started hitting again, it has a strong effect on RSI.

Moving Average Convergence Divergence (MACD)

MACD is a concept using two exponential averages developed by Gerald Appel. It was originally developed as the difference between the 12- and 26-day exponential averages; the same as a moving average crossover system, with the periods of the two averages being 12 and 26. The resulting difference, called the MACD line, is then smoothed with a nine-day exponential average, which is referred to as the signal line. Gerald Appel originally designed this indicator using different parameters for buy and sell signals, but that seems to have faded away and almost everyone now uses the 12–26–9 combination for both buy and sell. The movement of the MACD line is the measurement of the difference between the two moving averages. When MACD is at its highest point, it just means that the two averages are at their greatest distance apart (with short above long). And when the MACD is at its lowest level, it just means the two averages are at their greatest distance apart when the short average is below the long average. It really is a simple concept and is a wonderful example of the benefits of charting, because it is so easy to see.

MACD, and in particular, the concept behind it, is an excellent technical indicator for trend determination. Not only that, but it also shows some information that can be used to determine overbought and oversold, as well as divergence. You could say it does almost everything.

Figure 12.6 shows the MACD with the S&P 500 Index above. The solid line is the 12–26 MACD line and the dotted line is the nine period average.

Please keep this in mind: Although MACD is a valuable indicator for trend analysis, it is only the difference between two exponential moving averages. In fact, if you used price and one moving average, it would be similar in that one of the moving averages was using a period of one. This is not rocket science! Figure 12.6 is an example of MACD with its signal line.

A Word of Caution

Technical indicators generally deal with price and volume. Price involves the open, high, low, and close values. There are literally hundreds, if not thousands, of technical indicators that utilize these price components. These indicators use various parameters to make the indicator useful in analyzing the market.

Generally, the Relative Strength Index (RSI) is considered an overbought/oversold indicator, while Moving Average Convergence Divergence (MACD) is considered a trend indicator. With an intentional reworking of the parameters used in each, Figure 12.7 shows both the RSI and MACD of the S&P 500 Index.

Notice that they both look almost exactly the same. When you are working with only price or its components, you must be careful to not overanalyze or over-optimize the indicator or you will just be looking at the same information. See the section on Multicollinearity in previous articles for more evidence of this potential problem.

There are a host of money management techniques that have surfaced in the investment community. Each has its merits and each has its shortcomings. This section is provided to complement the book’s completeness, and does not dwell into the details.

The Binary Indicator

This part of the book also shows many charts of market data and indicators. Many will include what is called a binary measure. Binary means that it only gives two signals; it is either on or off, similar to a simple digital signal.

Figure 12.8 is a chart of an index in the top plot and an indicator in the bottom plot. The signals generated by the indicator are whenever it crosses the zero line shown on the lower plot. Whenever the indicator is above the line, it means the trend is up, and whenever the indicator is below the line, it means the trend is down (not up). To further simplify that concept, the tooth-like pattern, called the binary and overlaid on the indicator, gives the exact same information without all the volatility of the indicator. Notice that when the indicator is above the horizontal signal line that the binary is also above the line, and whenever the indicator is below the horizontal line, so is the binary. With that, we can then plot the binary directly on top of the index in the top plot and see the signals. In fact, with this knowledge, the entire bottom plot could be removed and no essential information would be lost.

Other conventions adapted to Part III of this book that you need to know are that, when discussing indicators or market measures, there are parameters used to give them specific values based on periods. A period can be any measure of time, hourly, daily, weekly, and so on. Here we will always stick to using daily analysis unless addressed locally. The terms issue and security are often used; I will stick to using ETFs as the investment vehicle.

When showing many measures that are in the same category, such as ranking measures, I attempt to show them individually, but over the same period of time using the same ETF, such as the SPY.

How Compound Measures Work

Before moving on, a concept needs to be explained. Figure 12.9 will help you understand how a compound measure works. First, you need to know that this is not a complex system; whenever two of the three indicators are in agreement, the compound measure moves in the same direction. This means that all three could be signaling, but it only takes two to accomplish the goal.

In Figure 12.9 the top plot is the Nasdaq Composite. The next three plots contain the binary indicators for the three components; in this example, they are called 1, 2, and 3. There are four instances of signals from those three components, labeled in the top plot as A, B, C, and D. Let’s go through them, starting with signal A. Notice that there are two vertical lines, with the first one being created by indicator 3. Then notice how indicator 3 dropped from its high position to its low position; that is a binary signal from indicator 3. The next vertical line shows up when indicator 2 drops to its low position. We now have two of the three indicators dropping to their low position, which means the compound binary indicator overlaid on the Nasdaq Composite in the top plot now drops to its low position.

The second signal, at B, occurs when both indicator 2 and 3 both drop to their low position at the same time; once again, this is a signal for the compound binary in the top plot to drop to its low position. Moving over to signal C, you can see that indicator 3 rose to its top position followed a few days later by indicator 2 rising to its top position, which in turn causes the compound binary in the top plot to rise to its top position.

Example D below shows indicator 2 dropping to its low position. This has caused the compound binary to drop because, if you will notice, indicator 3 had already dropped to its low position many days prior to that of indicator 2. In example D, notice that both indicator 2 and 3 both rose on the same day and indicated by the rightmost vertical line, which of course caused the compound binary to also rise. The concept is simple; it only takes two of the three indicators to control the compound binary in the top plot. It does not matter which two it is or in what combination. As you can hopefully see, the process could be expanded to using five indicators and using the best three of the five.

Now try to figure out the compound measure below without any visual or verbal assistance. In Figure 12.10, the top plot contains the Nasdaq Composite and the compound binary. There are binaries for three indicators below and they work just like the example above, any two that are on is a signal for the compound binary to move in the same direction. Good luck.


Thanks for reading this far. I intend to publish one article in this series every week. Can’t wait? The book is for sale here.

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RRG Indicates That non-Mega Cap Technology Stocks Are Improving

KEY

TAKEAWAYS

  • The Energy Sector Remains On a Very Strong Rotational Path
  • Completed Top Formation In Healthcare Opens Up Significant Downside Risk
  • Smaller Technology Stocks Are Taking Over From Mega-Cap Names

A Sector Rotation Summary

A quick assessment of current sector rotation on the weekly Relative Rotation Graph:

XLB: Still on a strong trajectory inside the improving quadrant and heading for leading. The upward break of overhead resistance on the price chart seems to be stalling at the moment, which could cause its relative strength compared to the S&P 500 to slow down. Overall, the trend, both in terms of price and relative, is still up.

XLC: Continues to lose relative strength and momentum inside the weakening quadrant and rotates toward lagging at a negative RRG-Heading. On the price chart, XLC is battling resistance, which causes its relative performance to slow down.

XLE: Is at the strongest rotation in this universe. Well inside the improving quadrant at the highest RS-Momentum reading and powered by the longest tail in the universe. The upward break in the price chart is holding up well, and the sector can even handle a small setback towards the former resistance area (just below ~95) without harming its uptrend.

XLF: Was on its way back to the leading quadrant after curling back up inside weakening, but this week’s dip is causing the tail to deviate from that path. This means we must watch this sector closely going into the close of this week and the beginning of next week to see if this is a temporary hiccup or a real change of direction. The nasty dip on the price chart pushes XLF back below its former resistance levels, which is usually not a strong sign. Caution!!

XLI: This is the only sector inside the leading quadrant at the moment, traveling at a strong RRG-Heading, taking the sector higher on both axes. The rally in the price chart is fully intact but seems to stall at current levels for three to four weeks. Plenty of room on the chart for a corrective move in this sector without damaging the uptrend.

XLK: The slow performance, primarily sideways, of the sector since the end of January has caused relative strength to flatten and for the sector to roll over and rotate into the weakening quadrant on the RRG. The jump today (Thursday, 4/11) caused an uptick in relative strength, but much more is needed to bring this sector back to the forefront.

XLP: Did not make it all the way up to horizontal resistance around 77.50 but set a lower high after a nasty reversal last week. The raw RS-Line continues steadily lower, causing the tail on the RRG to remain short and on the left-hand side of the graph, indicating a steady relative downtrend.

XLRE: After a rally at the end of last year, XLRE ended up in a sideways pattern that could turn out to be a double top after that rally. Such a top will be confirmed on a break below 37, which is the lowest low that was set in the week starting 2/12. When that happens, a decline all the way back to the late 2023 low becomes possible. The relative trend reversed back down after a very brief stint through the leading quadrant at the end of January.

XLU: Just moved into the improving quadrant from lagging but remains at a very low RS-Ratio level. The raw RS-Line continues to show a steady downtrend, making it hard for the tail to make it all the way to the leading quadrant. Price managed to break above a falling resistance line but shortly thereafter stalled in the area of Sept-23, Dec-23, and Jan-24 highs. Pressure remains in both price and relative terms.

XLV: After a short rotation through the improving quadrant that lasted roughly two months, XLV has now returned to the lagging quadrant and is pushing deeper into it on a negative RRG-Heading. On the price chart, XLV completed a (double) top formation and broke back below its former overhead resistance level, opening significant downside risk.

XLY: Is hesitating in a sideways pattern since mid-February, but still in a very shallow, uptrend. Relative strength continued to decline but is now nearing its late 2022 relative low, and the RRG-Lines are showing early signs of improvement.

Cap-weighted vs Equal-weighted

The RRG above shows the relative rotation of the relationships between the cap-weighted sector ETFs and their equal-weighted counterparts.

The more interesting information is coming from the tails that are far away from the benchmark. In this case, these are the Communication services sector, which is rolling over inside the leading quadrant, and Consumer Discretionary, which has just turned up inside the lagging quadrant.

This indicates that the large(er) cap communication services stocks are now starting to underperform the lower-tier market capitalizations. The opposite is true for Consumer Discretionary, where the opposite is happening, and larger market cap stocks are taking over from lower tier market caps.

A similar observation can be made for the Technology sector which is heading straight into the lagging quadrant, which suggests that large-cap tech is giving way to smaller names.

This information will be helpful when looking at RRGs for individual stocks inside the sectors.

#StayAlert: –Julius


Julius de Kempenaer
Senior Technical Analyst, StockCharts.com
CreatorRelative Rotation Graphs
FounderRRG Research
Host ofSector Spotlight

Please find my handles for social media channels under the Bio below.

Feedback, comments or questions are welcome at [email protected]. I cannot promise to respond to each and every message, but I will certainly read them and, where reasonably possible, use the feedback and comments or answer questions.

To discuss RRG with me on S.C.A.N., tag me using the handle Julius_RRG.

RRG, Relative Rotation Graphs, JdK RS-Ratio, and JdK RS-Momentum are registered trademarks of RRG Research.

Julius de Kempenaer

About the author:
Julius de Kempenaer is the creator of Relative Rotation Graphs™. This unique method to visualize relative strength within a universe of securities was first launched on Bloomberg professional services terminals in January of 2011 and was released on StockCharts.com in July of 2014.

After graduating from the Dutch Royal Military Academy, Julius served in the Dutch Air Force in multiple officer ranks. He retired from the military as a captain in 1990 to enter the financial industry as a portfolio manager for Equity & Law (now part of AXA Investment Managers).
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Trifecta of Trouble

The Markup Phase of a Bull Market is glorious to behold and participate in. But they do ebb and flow. The bullish run in the major stock indexes has been persistent in 2024. We often discuss the quarter-end effect for stock index trends and the upward trend has persisted into the end of the first quarter with diminishing momentum. Let’s turn our attention to some classic and powerful Wyckoff chart studies to determine the present position and possible future direction of the indexes as the second quarter begins.

S&P 500 Index with Wyckoff Markups. 2021-2024

This daily chart of the S&P 500 should be familiar to regular readers. It is our Wyckoffian record of market structure going back to the bull market peak of 2021. It is a real-time journal of our Wyckoff Analysis through the Distribution Structure, the Markdown, the Accumulation Phase of 2022-23 and now the Markup. The Markup finally exceeded the rangebound condition of the Accumulation with the advance that began in October of ’23. We had been making the case for the unfolding Accumulation here and in the Wyckoff Market Discussions (every Wednesday with Roman Bogomazov) throughout the Accumulation period.

Recently the S&P 500 climbed above the well defined Markup channel into a Throwover and OverBought condition. This OverBought / ThrowOver has arrived as the First Quarter was coming to a conclusion. Thus suggesting ‘Window Dressing’ shenanigans by institutional types. Often strong trends in the indexes (both upwards and downwards) can follow through and persist for a few weeks into the new quarter. We will watch for a change of character in the behavior of the indexes in the Second Quarter. An example of this would be a reversal of the S&P 500 back into the upward striding Trend Channel. A sudden and sharp break back into the channel would be labeled an Automatic Reaction (AR) and would represent an important confirmation of the upward trend exhaustion. Until such an event the upward trend must be respected. 

We expect the upward stride of a Markup phase to be broad and strong and such is the case here. Recall that Accumulation is a zone of deepening pessimism where the public and institutions become progressively more cautious. Such caution manifests as portfolio defensiveness (higher levels of cash, lower beta stocks and more bond type assets). Meanwhile the ‘Composite Operator’ types are absorbing stocks with good growth and value features for the next bull market. This puts stocks in very strong hands. This Supply to Demand imbalance can result in stock indexes launching higher following the preparation phase of Accumulation. As Accumulation concludes broad pessimism is observed in the extreme pessimism readings of various sentiment gauges (which were profiled in Power Charting and on WMD at the time). 

Point and Figure Price Objectives

S&P 500 Point & Figure Study. January 2023

This PnF legacy chart was first profiled in January of 2023. It estimated the downside potential of the bear market, which was fulfilled with a Selling Climax and a Secondary Test. The Climax initiated Accumulation (detailed in the vertical chart above) that continued throughout the second half of 2022. In January of ’23 the upside of the nearly completed Accumulation was estimated in three segments. The first two segments have now been fulfilled. Note the Count Line was 3,775 and the $SPX was at 4,000 when this projection was made and published. Pessimism at the time was such that this study was met with much disbelief, a very good sign for the higher prices yet to come as the indexes began climbing the ‘Wall of Worry’. 

Below is a PnF update with the Markup phase. Second phase price objectives are now being fulfilled. New price highs above the 2021 bull market peak have markedly swelled bullish sentiment. Analyst types are now rushing to make projections for the $SPX above 6,000 and even 7,000. This bulge of optimism is a warning sign that caution is warranted. 

S&P 500 Point & Figure Study, 50 Point Scale. April 2024

The current interpretation of the 2022-23 Accumulation shows the robust Markup following the Accumulation (50 point scaling slightly changes the price objectives). Once the downward trend channel was broken and tested from above the upward stride was dramatic. Index prices have now Upthrusted the bull peak of 2021 where a Backup to old resistance is likely.

Trifecta of Trouble (a summary)

1)        The S&P 500 is now above the upward stride of its trend channel. This OverBought condition could lead to a correction and would be confirmed by return into the channel. Old resistance from the 2021 bull peak would be a price level to expect support to develop. 

2)        Point & Figure Price Objectives generated in early 2023 are now being fulfilled. Watch for classic signs of stopping action such as an Automatic Reaction and range-bound sideways trading. This would generate PnF count potential for either ReAccumulation or Distribution. Higher price objectives remain and there is good seasonality in the second half of this election year. We will watch the tape for further indications.

3)        Sentiment has flipped from bearish to bullish. High readings from the NAAIM, AAII, CNN Fear & Greed and other important gauges are frothy. This is normal and typical in a Bull Market as the upward stride of the trend ebbs & flows. Corrections of the uptrend bring back caution and even pessimism which builds cash for higher prices in the future. 

A dynamic Markup is very exciting, important, and not to be missed. Corrections along the way are inevitable. They often represent rotation of leadership as the economy matures and changes character and that appears to be the case here (subject for future WPC blog posts). 

All the Best,

Bruce

@rdwyckoff

Disclaimer: This blog is for educational purposes only and should not be construed as financial advice. The ideas and strategies should never be used without first assessing your own personal and financial situation, or without consulting a financial professional. 

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Bruce Fraser

About the author:
Bruce Fraser, an industry-leading “Wyckoffian,” began teaching graduate-level courses at Golden Gate University (GGU) in 1987. Working closely with the late Dr. Henry (“Hank”) Pruden, he developed curriculum for and taught many courses in GGU’s Technical Market Analysis Graduate Certificate Program, including Technical Analysis of Securities, Strategy and Implementation, Business Cycle Analysis and the Wyckoff Method. For nearly three decades, he co-taught Wyckoff Method courses with Dr.
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Market Research and Analysis – Part 3: Market Trend Analysis

This article (and the next) focuses on trends in the market—an explanation as to why markets trend, reasons why it is good to know that markets trend, then finally, a large research section into how much markets trend. This analysis will initially be shown on 109 market indices that involve domestic, international, and commodity sectors. Following that the full list of all S&P GICS sectors, industry groups, and industries are shown following the same format. There is a great amount of data in these two sections. I try to slice through it with simple analysis, keeping in mind that lots of data does not equate to information.

Why Markets Trend

Trends in markets are generally caused by short-term supply-and-demand imbalances with a heavy overdose of human emotion. When you buy a stock, you know that someone had to sell it to you. If the market has been rising recently, then you know you will probably pay a higher price for it, and the seller also knows he can get a higher price for it. The buying enthusiasm is much greater than the selling enthusiasm.

I hate it when the financial media makes a comment when the market is down by saying that there are more sellers than buyers. They clearly do not understand how these markets work. Based on shares, there are always the same number of buyers and sellers; it is the buying and selling enthusiasm that changes.

Trending is a positive feedback process. Even Isaac Newton believed in trends with his first law of motion, which stated that an object at rest stays at rest, while an object in motion stays in motion, with the same speed and in the same direction unless acted on by an unbalanced force. Hey, an apple will continue to fall until it hits the ground. Positive feedback is the direct result of an investor’s confidence in the price trend. When prices rise, investors confidently buy into higher and higher prices.

Supply and Demand

A buyer of a stock, which is the demand, bids for a certain amount of stock at a certain price. A seller, which is the supply, offers a certain amount at a certain price. I think it is fair to say that one buys a stock with the anticipation that they can sell it later to someone at a higher price. Not an unreasonable desire, and probably what drives most investors. The buyer has no idea who will sell it to him, or why they would sell it to him. He may assume that he and the seller have a complete disagreement on the future value of that stock. And that might be correct; however, the buyer will never know. In fact, the buyer just might be the seller’s person who buys it from him at a higher price.

The reasons for buying and selling stock are complex and impossible to quantify. However, when they eventually agree, what is it that they agreed on? Was it the earnings of the company? Was it the products the company produces? Was it the management team? Was it the amount of the stock’s dividend? Was it the sales revenues? As it turns out, it was none of those things; the transaction was settled because they agreed on the price of the stock, and that alone determines profit or loss. Changes in supply and demand are reflected immediately in price, which is an instantaneous assessment of supply and demand.

What Do You Know about This Chart?

In Figure 10.1, I have removed the price scale, the dates, and the name of this issue; now let me ask you some questions about this issue.

  1. Is this a chart of daily prices, weekly prices, or 30-minute prices?
  2. Is this a chart of a stock, a commodity, or a market index? (Okay, I’ll give you this much, it is a daily price chart of a stock over a period of about six years.)
  3. During this period of time, there were 11 earnings announcements. Can you show me where one of those announcements occurred and, if you could, whether the earnings report was considered good or bad?
  4. Also during the period of time for this chart, there were seven Federal Open Market Committee (FOMC) announcements. Can you tell me where one of them occurred, and whether the announcement was considered good or bad?
  5. Does this stock pay a dividend?
  6. Hurricane Katrina occurred during this period displayed on this chart; can you tell me where it is?
  7. Finally, would you want to buy this stock at the beginning of the period displayed and then sell it at the end of the period (right side of chart)?

I doubt, in fact, I know you cannot answer most of the above questions with any tool other than guessing. The point of this exercise is to point out that there is always and ever noise in stock prices. This noise comes in hundreds of different colors, sizes, shapes, and media formats. The bottom line is that it is just noise. The financial media bombards us all day long with noise. I do not think they do it maliciously; they do it because they believe they are giving you valuable information to help you make investment decisions. Nothing could be further from the truth.

Of course, question number 7 is the one question that most can answer, because from the chart a buy-and-hold investment during the data displayed clearly resulted in no investment growth.

However, let me tell you what I see as shown in Figure 10.2. I see two really good uptrends and, if I had a trend-following methodology that could capture 65 percent to 75 percent of those uptrends, I would be happy. I also see two good downtrends, and if I had a methodology that could avoid about 75 percent of them, I would also be happy. If you could do that for the amount of time shown on the chart below, then you would come out considerably better off than the buy-and-hold investor. I generally only participate in the long side of the market and move to cash or cash equivalents when defensive. However, a long-short strategy could possibly derive even greater profit.

Trend vs. Mean Reversion

I prefer to use a market analysis methodology called trend following. Sometimes it should be called trend continuation. Why? Trend analysis works on the thoroughly researched concept that once a trend is identified, it has a reasonable probability to continue. I know that is the case because, most of the time, markets are trending markets, and I see no reason to adopt a different strategy during a period of mean reverting, such as is experienced in the market from time to time.

You can think of trend following as a positive feedback mechanism. Mean reverting measures are those that oscillate between predetermined parameters; oftentimes the selection of those parameters is the problem. Mean reversion strategies are clearly superior during those volatile sideways times, but the implementation of a mean reverting process requires a level of guessing that I refuse to be a part of. You can think of mean reversion as a negative feedback mechanism.

In technical analysis, there are many mean reverting measures that could be used. They are the ones where you frequently hear the terms overbought and oversold. Overbought means the measurement shows that prices have moved upward to a limit that is predefined. Oversold means the opposite—prices have moved down to a predetermined level. The problem with that type of indicator or measurement is that a parameter needs to be set beforehand to know what the overbought and oversold levels are. Also, if you believe something mean reverts, you will probably have difficulty in determining the rate of reversion. For mean reversion to be relevant, there must be a meaning tied to average (mean) and, since most market data does not adhere to normal distributions, the mean isn’t as meaningful (sic). Kind of like charting net worth and removing billionaires to make the data less skewed and therefore a more meaningful average.

Clearly, mean reverting measurements would work better in highly volatile markets, such as we witness from time to time. One might ask the question: Why don’t you incorporate both into your model? A fair question, but one that shows the inquiry is forgetting that hindsight is not an analysis tool that will serve you well. When do you switch from one strategy (trend following) to the other (mean reversion)? Therein lies the problem.

Another question that might be asked is why not use adaptive measures to help identify the two types of markets. Again, another fair question! I think the lag between the two types of markets and the fact that often there is no clear period of delineation is the issue. It is a natural instinct to want to change the strategy in order to respond more quickly from one to the other. Natural instincts are what we are trying to avoid, simply because they are generally wrong, and painfully wrong at the worst times.

The transition from trend following to mean reversion can be difficult to see except with 20/20 hindsight. For example, when you view a chart which clearly has gone from trending to reversion, from that point, if we had used a simple mean reverting measurement, we would have looked like geniuses. However, in reality, periods like that have existed many times in the past in overall trending markets. Then the next problem becomes when to move away from a mean reverting strategy back to a trend following one. Again, hindsight always gives the precise answer, but in reality it is extremely difficult to implement in real time.

The bottom line is that with markets that generally trend most of the time, keeping a set of rules and stop loss levels in place will probably always win over the long-term. Sharpshooting the process is the beginning of the end. Trend following is somewhat similar to a momentum strategy except for two significant differences: one, momentum strategies generally rank past performance for selection, and two, often they do not utilize stop-loss methods, instead moving in and out of top performers. They both rely on the persistence of price behavior.

Trend Analysis

If one is going to be a trend follower, what is the first thing that must be done (rhetorical)? In order to be a trend follower, you must first determine the minimum length trend you want to identify. You cannot follow every little up and down move in the market; you must decide what the minimum trend length is that you want to follow. Once this is done, you can then develop trend-following indicators using parameters that will help identify trends in the market based on the minimum length you have decided on.

Figure 10.3 is an example of various trend-following periods. The top plot is the Nasdaq Composite index. The second plot is a filtered wave showing the trend analysis for a fairly short-term-oriented trend system. This is for traders and those who want to try to capture every small up and down in the market; a process that is not adopted by this author. The third plot is the ideal trend system, where it is obvious that you buy at the long-term bottom and sell at the long-term top. You must realize that this trend analysis can only be done with perfect 20/20 hindsight, and is probably even more difficult than the short-term process shown in the second plot. The bottom plot is a trend analysis process that is at the heart of the concepts discussed in this book. It is a trend-following process that realizes you cannot participate in every small up and down move, but try to capture most of the up moves and avoid most of the down moves.

There is a concept developed by the late Arthur Merrill called Filtered Waves. A filtered wave is the measurement of price movements in which only the movement that exceeds a predetermined percentage is counted. The price component used in this concept needs to be decided on as to whether to use just the closing prices for the filtered wave or use a combination of high and low prices. This would mean that, while prices are rising, the high would be used, and while prices are falling, the low price would be used. I personally prefer the high and low prices, as they truly reflect the price movements, whereas the closing prices only would eliminate some of the data.

For example, in Figure 10.4 , the background plot is the S&P 500 Index with both the close C and the high low H-L filtered waves overlaid on the prices. You can see that the H-L filtered wave techniques picks up more of the data; in fact, it shows a move of 5 percent in the middle of the plot that the Close only version did not show. In this particular example, the zigzag line uses a filter of 5 percent, which means that each time it changes direction, it had previously moved at least 5 percent in the opposite direction. There is one exception to this, and that is the last move of the zigzag line (there is a similar discussion in an earlier chapter). It merely moves to the most recent close regardless of the percentage moved so it must be ignored.

The bottom plot in Figure 10.5 shows the filtered wave by breaking down the up moves and down moves and then counting the number of periods that were in each move. There are three horizontal lines on that plot; the middle one is at zero, which is where the filtered wave changes direction. In this example, the top and bottom lines are at +21 and -21 periods, which mean that anytime the filtered wave exceeds those lines above or below, the trend has lasted at least 21 periods. Notice that, in this example, there was a period at the beginning (highlighted) where the market moved up and down in 5% or greater moves with high frequency, but never lasted long enough to exceed the 21 boundaries. Then, in the second half of the chart, there were two good moves that did exceed the 21 boundaries. This is a good example of a chart where there was a trendless market (first half) and a trending market (second half). I used the high-low filtered wave of 5 percent and 21 days for the minimum length because that is what I prefer to use for most trend analysis.

The following research was conducted using the high-low filtered wave using various percentages and various trend length measures. The research was conducted on a wide variety of market prices, such as most domestic indices, most foreign indices, all of the S&P sectors and industry groups; 109 issues in all. I offer commentary throughout so you can see that this was a robust process. Any indices or price series that is missing was probably because of an inadequate amount of data, as you need a few years of data to determine a series’ trendiness. The goal of this research was to determine that markets generally trend and if there are some markets that trend better than others. Following this large section, the trend analysis will be shown using the S&P GICS data on sectors, industry groups, and industries.

Table 10.1 is the complete list of indices used in this study along with the beginning date of the data.

I did multiple sets of data runs, but will explain the process by showing just one of them. Table 10.2 is the data run through all 109 indices for the 5% filtered wave and 21 days for the trend to be identified. The first column is the name of the index (they are in alphabetical order), while the next four columns are the results of the data runs for the total trend percentage, the uptrend percentage, the downtrend percentage, and the ratio of uptrends to downtrends.

The total reflects the amount of time relative to the amount of all data available that the index was in a trend mode defined by the filtered wave and trend time; in the case below, a trend had to last at least 21 days and a move of 5% or greater. The up measure is just the percentage of the uptrend relative to the amount of data. Similarly, the downtrend is the percentage of the downtrend to the amount of data. If you add the uptrend and downtrend, you will get the total trend.

The last column is the U/D Ratio, which is merely the uptrend percentage divided by the downtrend percentage. If you look at the first entry in Table 10.2, the AMEX Composite trends 71.18 percent of the time, with 56.16% of the time in an uptrend and 15.03% of the time in a downtrend. The U/D Ratio is 3.74, which means the AMEX Composite trends up almost 4 (3.74) times more than it trends down. You can verify the amount of data in the Indices Date table shown early to see if it was adequate enough for trend analysis. It is not shown, but the complement of the total would give you the amount of time the index was trendless.

At the bottom of each table is a grouping of statistical measures for the various columns. Here are the definitions of those statistics:

Mean. In statistics, this is the arithmetic average of the selected cells. In Excel, this is the Average function (go figure). It is a good measure as long as there are no large outliers in the data being analyzed.

Average deviation. This is a function that returns the average of the absolute deviations of data points from their mean. It can be thought of as a measure of the variability of the data.

Median. This function measures central tendency, which is the location of the center of a group of numbers in a statistical distribution. It is the middle number of a group of numbers; that is, half the numbers have values that are greater than the median, and half the numbers have values that are less than the median. For example, the median of 2, 3, 3, 5, 7, and 10 is 4. If there are a wide range of values that are outliers, then median is a better measure than mean or average.

Minimum. Shows the value of the minimum value of the cells that are selected.

Maximum. Shows the value of the maximum value of the cells that are selected.

Sigma. Also known as standard deviation. It is a measure of how widely values are dispersed from their mean (average).

Geometric mean. First of all, it is only good for positive numbers and can be used to measure growth rates, etc. It will always be a smaller number than the mean.

Harmonic mean. Simply the reciprocal of the arithmetic mean, or could be stated as the arithmetic mean of the reciprocals. It is a value that is always less than the geometric mean, and like the geometric mean, can only be calculated on positive numbers and generally used for rates and ratios.

Kurtosis. This function characterizes the relative peakedness or flatness of a distribution compared with the normal distribution (bell curve). If the distribution is “tall”, then it reflects positive kurtosis, while a relatively flat or short distribution (relative to normal) reflects a negative kurtosis.

Skewness. This characterizes the degree of symmetry of a distribution about its mean. Positive skewness reflects a distribution that has long tails of positive values, while negative skewness reflects a distribution with an asymmetric tail extending toward more negative values.

Trimmed mean (20 percent). This is a great function. It is the same as the Mean, but you can select any number or percentage of numbers (sample size) to be eliminated at the extremes. A great way to eliminate the outliers in a data set.

Trendiness Determination Method One

This methodology for trend determination looks at the average of multiple sets of raw data. An example of just one set of the data was shown previously in Table 10.2, which looks at a filtered wave of 5% and a minimum trend length of 21 days. Following Table 10.3 is an explanation of the column headers for Trendiness One in the analysis tables that follow.

Trendiness average. This is the simple average of all the total trending expressed as a percentage. The components that make up this average are the total trendiness of all the raw data tables, in which the total average is the average of the uptrends and downtrends as a percentage of the total data in the series.

Rank. This is just a numerical ranking of the trendiness average, with the largest total average equal to a rank of 1.

Avg. U/D. This is the average of all the raw data tables’ ratio of uptrends to downtrends. Note: If the value of the Avg. U/D is equal to 1, it means that the uptrends and downtrends were equal. If it is less than 1, then there were more downtrends.

Uptrendiness WtdAvg. This is the product of column Trendiness Average and column Avg. U/D. Here the Total Trendiness (sum of up and down) is multiplied by their ratio, which gives a weighted portion to the upside when the ratio is high. If the average of the total trendiness is high and the uptrendiness is considerably larger than the downtrendiness, then this value (WtdAvg) will be high.

Rank. This is a numerical ranking of the Up Trendiness WtdAvg, with the largest value equal to a rank of 1.

Table 10.4 shows the complete results using Trendiness One methodology.

Trendiness Determination Method Two

The second method of trend determination uses the raw data averages. For example, the up value is calculated by using the raw data up average compared to the raw data total average, which therefore means it only is using the amount of data that is trending and not the full data set of the series. This way, the results are dealing only with the trending portion of the index, and if you think about it, when the minimum trend length is high and the filtered wave is low, there might not be that much trending. Table 10.5 shows the column headers followed by their definitions.

Up. This is the average of the raw data Up Trends as a percentage of the Total Trends.

Down. This is the average of the raw data Down Trends as a percentage of the Total Trends.

Up rank. This is the numerical ranking of the Up column, with the largest value equal to a rank of 1.

Table 10.6 shows the results using Trendiness Two methodology.

Comparison of the Two Trendiness Methods

Figure 10.6 compares the rankings using both “Trendiness” methods. Keep in mind we are only using uptrends, downtrends, and a derivative of them, which is up over down ratio. The plot below is informally called a scatter plot and deals with the relationships between two sets of paired data.

The equation of the regression line is from high school geometry and follows the expression: y = mx + b, where m is the slope and b is the y-intercept (where it crosses the y axis); x is known as the independent variable or the predictor variable and y is the dependent variable or response variable. The expression that defines the regression (linear least squares) shows that the slope of the line (m) is 0.8904. The line crosses the y (vertical) axis at 6.027, which is b. R^2, which is also known as the coefficient of determination, is 0.7928. From R^2, we can easily see that the correlation R is 0.8904 (square root of R^2). We know this is a highly positive correlation because we can visually verify it simply from the orientation of the slope. We can interpret m as the value of y when x is zero and we can interpret b as the amount that y increases when x increases by one. From all of this, one can determine the amount that one variable influences the other.

Sorry, I beat this to death; you can probably find simpler explanations in a high school statistics textbook.

Trendless Analysis

 This is a rather simple but complementary (intentional spelling) method that helps to validate the other two processes. This method focuses on the lack of a trend, or the amount of trendless time that is in the data. The first two methods focused on trending, and this one is focused on nontrending, all using the same raw data. Determining markets that do not trend will serve two purposes. One is to not use conventional trend-following techniques on them, and the other is that it can be good for mean reversion analysis. Table 10.7 shows the column headers; the definitions follow.

Up. This is the Total Trend average from Trendiness One multiplied by the Up Total from Trendiness Two.

Down. This is the Total Trend average from Trendiness One multiplied by the Down Total from Trendiness Two.

Trendless. This is the complement of the sum of the Up and Down values (1 – (Up + Down)).

Rank. This is the numerical rank of the Trendless column with the largest value equal to a rank of 1.

Table 10.8 shows the results using the Trendless methodology.

Comparison of Trendiness One Rank and Trendless Rank

Although I think this was quite obvious, Figure 10.7 shows the analysis math is consistent and acceptable. These two series should essentially be inversely correlated, and they are with coefficient of determination equal to one.

The following tables take the data from the full 109 indices and subdivide it into sectors, international, domestic, and time frames to ensure there is robustness across a variety of data. There are many indices that appear in many of, if not most of, these tables, but keeping data of that sort for comparison with others that are not so widely diversified will enhance the research.

These tables show all three trend method results. This first table consists of all the index data. The remaining ones contain subsets of the All table, such as Domestic, International, Commodities, Sectors, Data > 2000, Data > 1990, and Data > 1980. The reason for the data subsets is to ensure there is a robust analysis in place across various lengths of data, which means multiple bull-and-bear cyclical markets are considered in addition to secular markets. The Data > 2000 means that the data starts sometime prior to 2000 and therefore totally contains the secular bear market that began in 2000.

All Trendiness Analysis

Table 10.9 contains data from all of the 109 indices in the analysis. The first column contains letters identifying the subcategory for each issue as follows:

I – International

S – Sector

C – Commodity

Blank – Domestic

Trend Table Selective Analysis

In this section, I will demonstrate more details on selected issues from Table 10.9 to show how the data can be utilized.

Using the Trendiness One Rank, you can see that the U.S. Dollar Index is number one. You can also see it is the worst for being Trendless (last column), which one would expect. However, if you look at the Trendiness One and Trendiness Two Up Ranks, you see that it did not rank well. This can only be interpreted that the U.S. Dollar Index is a good downtrending issue, but not a good uptrending one based on this relative analysis with 109 various indices. This is made clear from the long trendline drawn from the first data point to the last data point and is clearly in a downtrend.

Figure 10.8 shows the U.S. Dollar Index with a 5% filtered wave overlaid on it. The lower plot shows the filtered wave of 5% measuring the number of days during each up and down move. The two horizontal lines are at +21 and -21, which means that movements inside that band are not counted in the trendiness or trendless calculations. The only difference between what this chart shows and what the table data measures is the fact that the table is averaging a number of different filtered waves and trend lengths.

Let’s now look at the worst trendiness index and see what we can find out about it (Table 10.9). The Trendiness One rank and the Trendless Rank confirm that this is not a good trending index. Furthermore, the Up Trendiness in both One and Two also shows that it ranks low (109 and 81) in the Trendiness One, which is measuring the trendiness based on all the data, and that the rank in Trendiness Two is high (4). Remember that Trendiness Two only looks at the trending data, not all of the data. Therefore, you can say that this index when in a trending mode, tends to trend up well, but the problem is that it isn’t in a trending mode often (see Table 10.11).

Figure 10.9 shows the Turkey ISE National-100 index with the same format as the earlier analysis. Notice that it is generally in an uptrend based on the long-term trend line. From the bottom plot, you can see that there is very little movement of trends outside of the +21 and -21 day bands. Bottom line is that this index doesn’t trend well, and is quite volatile in its price movements; if you are trend follower; don’t waste your time with this one. A question that might arise is that it is also clear from the top plot that it is in an uptrend, so if you used a larger filtered wave and/or different trend length, it might yield different results. My response to that is simply: of course it will, you can fit the analysis to get any results you want, especially with all this wonderful hindsight. Bad approach to successful trend following.

Using the same data table, let’s look at an index that ranks high in the uptrend rankings (Table 10.9). From the table it ranks as middle of the road relatively based on Trendiness One and Trendless rank. However, the rank for Up Trendiness One and Trendiness Two Up rank is high (both are 5). This means that most of the trendiness is to the upside with only moderate downtrends (see Table 10.12).

Figure 10.10 shows the Norway Oslo Index clearly in an uptrend. The bottom plot shows that most of the spikes of trend length are above the +21 band level and very few are below the .21 band level. This confirms the data in the table.

In order to carry this analysis to fruition, let’s look at the index with the worst uptrend rank (Table 10.9). From the table, the Trendiness One and Two Up ranks are dead last (109). The Trendiness One overall rank is 104, which is almost last, and the trendless rank is 6, which confirms that data (see Table 10.13).

Figure 10.11 shows that the Hanoi SE Index is clearly in a downtrend; however, the bottom plot shows that very few trends are outside the bands. And the ones that move well outside the bands are the downtrends. As before, one can change the analysis and get desired results, but that is not how it should be done. One note, however, is that this index does not have a great deal of data compared to most of the others and this should be a consideration in the overall analysis.


Thanks for reading this far. I intend to publish one article in this series every week. Can’t wait? The book is for sale here.

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