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Rules-Based Money Management – Part 1: Popular Indicators and Their Uses

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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

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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
Creator, Relative Rotation Graphs
Founder, RRG Research
Host of: Sector 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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Market Research and Analysis – Part 3: Market Trend Analysis

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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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You Need To Understand NOW What Changed After The Fed Announcement

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I’ve always liked to look at certain points during a bull market or bear market where the character of the market could change based on key fundamental news. We were at one of those points on Wednesday as 2 o’clock approached. The Fed was about to deliver their latest policy statement and traders were on pins and needles. Questions were swirling about what the Fed might say, and do, given the February Core CPI and Core PPI numbers that were reported higher than expected. The Fed already has squashed the bulls once recently, when they shot down the possibility of a March 2024 rate cut after expectations were building for exactly that. There were still the 3 rate cuts supposed to occur in 2024, but the Fed told us that higher rates would remain a bit longer.

Most traders are not blessed with great patience. Things could have turned ugly this past Wednesday at 2pm ET if the Fed decided to wait even longer to lower rates, possibly cutting the expected number of rate cuts from 3 down to some lower number. And what might happen if the Fed did an “about face” and said something that might indicate they’d have to reconsider hiking again? After all, this Fed hasn’t exactly been consistent in its discussion about interest rates.

Well, a lot of that anxiety came to an end on Wednesday as the Fed stuck to its previous guidance, despite the higher inflation reports the week prior. The stock market NEVER performs well when uncertainty is rising, but it generally does quite well when that anxiety is diminished. So at the moment the Fed indicated that nothing had really changed in their view, the stock market screamed higher, with the small cap IWM quickly testing overhead price resistance:

This was the chart I sent to EB members in my Daily Market Report on Thursday. Small caps received the news it was looking for and reacted according – to the upside. But the closing breakout never occurred on Thursday and that false breakout led to some profit taking on Friday. It’ll be interesting to see where small caps head this week. Since 1987, the annualized return for the IWM over the next 7 days is 41.20%, more than 4 times its average annual return. This tells us that history suggests a strong week ahead for small caps. But nothing is more important than the combination of price and volume. Before we grow overly excited about IWM’s prospects, we need to clear candle body price resistance, currently at 208.21.

Major Index and Sector Rotation

With this new information (basically the same as the old), and with inflation fears subsiding further, where did the money go from Wednesday 2pm ET through Friday’s close? Shouldn’t we be interested in what the big Wall Street firms were doing with their money after this fundamental announcement? Well, this is what the big boys were favoring after the announcement.

Major Indices

  • NASDAQ 100 (QQQ): +1.74%
  • Russell 2000 (IWM): +1.73%
  • S&P 400 Mid Cap (MDY): +1.55%
  • S&P 500 Large Cap (SPY): +1.11%
  • Dow Jones (DIA): +0.92%

Sectors

  • Industrials (XLI): +1.49%
  • Communication Services (XLC): +1.46%
  • Technology (XLK): +1.34%
  • Consumer Discretionary (XLY): +0.84%
  • Energy (XLE): +0.74%
  • Financials (XLF): +0.73%
  • Health Care (XLV): +0.48%
  • Materials (XLB): +0.42%
  • Real Estate (XLRE): +0.16%
  • Utilities (XLU): +0.05%
  • Consumer Staples: -0.08%

Clearly, money rotated and benefited “risk on” areas of the stock market, which is secular bull market behavior. Aggressive sectors led by a wide margin over defensive sectors. Money also returned to growth as most growth vs. value ratios turned higher after Wednesday 2pm ET as well.

Industry Group Rotation

We now know that money rotated in bullish fashion and to more growth-oriented areas, though industrials’ leadership and the S&P 500’s break to yet another all-time high after the Fed announcement is further evidence of wide participation in this latest advance. And with small caps right up there with the NASDAQ 100, all those breadth arguments can be tossed right out of the window.

Here’s what we should take away from industry group performance after the Fed meeting:

  1. Semiconductors ($DJUSSC) was #1 among ALL industry groups – not too shocking
  2. The Top 10 industry group performers belonged to either technology (XLK), consumer discretionary (XLY), or industrials (XLI)
  3. Heavy construction ($DJUSHV) had broken out a few weeks ago and the Fed announcement saw momentum increase significantly within this group
  4. Trucking ($DJUSTK) bounced off 50-day SMA support and is poised to break further into all-time high territory, a very bullish development for transportation stocks ($TRAN) in general
  5. Gold mining ($DJUSPM) and mining ($DJUSMG) both saw bullish initial reactions, but then gave back most of those gains by Friday

Big Loser

In my mind, it’s once again gold ($GOLD). I think many traders believed that falling rates ahead would trigger a drop in the U.S. Dollar (UUP). Not gonna happen. Any weakness in the dollar of late has been triggered by potential erosion by inflation. The Fed essentially said that inflation isn’t a problem, despite the higher CPI and PPI readings recently. Our economy remains quite resilient and unemployment remains low, especially compared to foreign economies. That’s why the UUP is strong. Another breakout in the UUP could be at hand:

I know many keep pointing to the recent breakout in GLD, but I want to OUTPERFORM the S&P 500 and the above chart shows you that, outside of a few short-term pops to the upside (blue-dotted directional lines), the overall RELATIVE performance line is going down, down, down in a very big way. No thank you.

A Rapidly-Improving Heavy Construction Small Cap Stock

I was focusing on the heavy construction area ($DJUSHV) this weekend, because of its recent strength and then the surge after last Wednesday’s Fed meeting and policy statement. There are a number of stocks that caught my attention, but one in particular that I believe has a LOT more upside given its current technical outlook. I’ll be sending it out to our FREE EB Digest subscriber community before the market opens tomorrow morning. If you’re not already a subscriber, you can CLICK HERE to sign up with your name and email address. There is no credit card required and you may unsubscribe at any time!

Happy trading!

Tom

Tom Bowley

About the author:
Tom Bowley is the Chief Market Strategist of EarningsBeats.com, a company providing a research and educational platform for both investment professionals and individual investors. Tom writes a comprehensive Daily Market Report (DMR), providing guidance to EB.com members every day that the stock market is open. Tom has contributed technical expertise here at StockCharts.com since 2006 and has a fundamental background in public accounting as well, blending a unique skill set to approach the U.S. stock market.

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The Bond Market is Signaling a Potential Short-Term Trading Opportunity

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KEY

TAKEAWAYS

  • If treasury yields break out higher, consider selling the breakouts of bear flags and view short-term declines as selling opportunities
  • If yields break down lower, consider buying bull flags and setups.
  • There’s a chance that yields could push higher before correcting.

In our last piece, we presented a long term/secular outlook for intermediate-term Treasuries, where we concluded that the structural break above the secular downtrend from the September 1981 high, coupled with the push above the November 2018 pivot @ 3.25%, has changed the long-term secular trend from lower (a bull market) to neutral. More work is needed to move the secular trend from neutral to bearish. In this piece, we’ll assess how the weekly chart might interact with the monthly chart, and then begin to think about how investors can react to various scenarios as they are set up over the course of the next several weeks and months.

As a warning, my analysis of the shorter perspective time frame didn’t leave me with an actionable trade or even a clear expectation for a probable outcome over the next few weeks. I think the market is ready to move away from the current congestion zone, and I suspect that the direction out of the zone will provide shorter-term traders with ample opportunity for entries. This analysis has allowed me to identify the important chart points/zones around which I will pay particular attention to behaviors and market structure, and to define appropriate trading plans.

10-Year Treasury Yield: Annual Perspective

The chart below is the yearly perspective of the 10-Year Treasury note (INDX).

Chart 1: Annual Chart of the 10-Year Treasury Yield

Note the break of the secular downtrend and the push above the 3.35% pivot. It’s worth noting that the Moving Average Convergence/Divergence (MACD) oscillator has turned higher for the first time since 1985.

 Keep in mind the following points:

  • The basic definition of an uptrend is a market consistently defining higher highs and higher lows. For instance, a great example of a downtrend can be seen in the annual 10-year Treasury chart, where, over several decades, yields consistently made lower lows and lower highs, defining a very clear and obvious bull market (yields down/prices up).
  • For bonds to begin defining a secular bear (bond prices down/yields up), it will require yield to set back from a high pivot, define a higher low pivot, and subsequently make a substantive new high. From that point, you can draw tentative annual and monthly trendlines, and channel projections. You can also make Fibonacci and point-and-figure price projections. Importantly, this structure would define a secular bear and place weekly and monthly momentum harmoniously with annual momentum.  I expect this transition to occur over the next 12–18 months.
  • The biggest question in my mind is whether last October’s 4.98% high print marked the terminal point for the bearish structure that has built since the 0.40% low. I suspect that is indeed the case and that, by mid-year, yields will be falling. However, there is also a reasonable case for one final push higher into the stronger resistance zone at around 5.25%, before subsequently setting back and defining the higher low. Given this view, the evolution of the weekly chart over the next few months becomes particularly important.

10-Year Treasury Yield: Weekly Perspective

 Below is a weekly chart of the 10-Year US Treasury Yield ($TNX).

Chart 2: Weekly Chart of the 10-Year Treasury Yields Note the following points of the chart:

  • Bonds typically build reliable channels and trendlines, but the move from 0.40% is atypical in that a solid trendline or channel is difficult to find.
  • Since the move from the low doesn’t provide a solid trendline or channel, I am focused on the 2.52–3.25% (A-B) trend line. The decline from 4.98% since last October has repeatedly weakened it, and the bounce from the trendline has been very modest.
  • The inability of the trendline to generate selling (higher yields/lower prices) suggests that the pressure isn’t strong.
  • It is likely that a decline below the 3.79% pivot would likely stretch back to the 3.25% pivot, with a higher likelihood of the area around 2.65% (retracing roughly 1/2 of the 0.40% to 4.98% move).
  • The move from 3.79% has generally presented as a bull (lower yield/higher price) flag. Flags are usually corrective against the trend. Note that volume during the period has declined significantly (as would be expected), albeit from the extremely high volumes that developed during the move to last October’s high.
  • One of my favorite patterns is the “three drives to a high or low.” While this chart may technically qualify (3.48% –> 4.33% –> 4.98%) the push to 3.48% only barely qualifies, as it’s not proportional to the first two thrusts. This chart is potentially set up for a final drive higher to complete the sequence, perhaps into the strong resistance at the 5.25–5.35% area.
  • I will also be monitoring the price for a secondary test of 4.98%. A completed secondary test would set up for a significant bull (yield down/price up) market.

The balance of the structural evidence on the weekly chart favors lower yields, but it’s a close call and not particularly actionable from these levels.

Looking At Momentum

The multiple-screen momentum perspective below is a quick filtering method I use. Importantly, momentum is fractal (robust across time frames and markets). I prefer to derive the trend through the tape, so I only use the oscillators as a quick filter.

The chart below displays the annual, monthly, weekly, and daily charts of the 10-Year Treasury Yield. Note that on the chart, we move back to yield again.

Chart 3: Annual, Monthly, Weekly, and Daily Charts of the 10-Year Treasury Yield

An important point to remember: Rising yields = lower price.

  • Yearly momentum has turned toward higher yield/lower price.
  • Monthly momentum has turned toward lower yield/higher price. A slight negative divergence has formed, and the monthly is at odds with the yearly.
  • Weekly momentum is mixed/neutral, but attempting to turn to higher yield/lower price. This struggle around the zero line suggests that behaviors over the next few weeks will likely define the direction of the next 25–50 basis point movement.

I am most interested in the weekly trend (in rates, the weekly perspective is the most important), so I generally defer to the trend of one higher degree. In this case, the monthly is on a lower yield/higher price signal and is just now moving into the MACD quadrant, where significant declines (in yields) are likely to take place; Odds are better that the weekly will also turn to lower yield/higher price to be in harmony.  But, again, the evidence is mixed. Sometimes, you just need to let the price action evolve before drawing a solid conclusion.

A Weekly Perspective of TLT (Bond ETF)

Chart 4: Weekly Chart of TLT

Some important points re. volume:

  • Since we’re viewing the iShares 20+ Year Treasury Bond Fund (TLT), we’re looking at price (a downtrend is a bear market) rather than working with yield. This is because the yield indices we are using have no reported volume. The caveat here is that, in my professional capacity, I prefer to use futures volume, as they better represent institutional-rate investors, while TLT has a distinctly retail focus.
  • The evidence between futures and ETF volume is conflicting. TLT showed clear signs of short-term capitulation last October, but did not display a classic selling climax.
  • Futures are more ambiguous, with no clear surge in volume, but price behaviors are more consistent with a selling climax.
  • Since the October low, the volume in general has remained quite high, and the upward progress is relatively modest. The poor result for the effort expended suggests that the market continues to run into quality supply. The same price/volume relationship is also present in futures.
  • Note the rapid fall in volume over the last three to four weeks as the market tilted higher. This is consistent with a bear flag or pennant.
  • Finally, note the volume spike (arrow) as sellers leaned into the market a few weeks ago.  There are still strong-handed sellers willing to hit bids into strength.

I think the balance of evidence suggests that the market made a selling climax in October. That climax will likely hold for most of this year, but may be retested.

10-Year Treasury Yield Daily

Chart 5: Daily Chart of 10-Year Treasury Yield

 Note the following points:

  • Seasonal Tendency. Yields tend to set significant intermediate highs early in the year before declining into mid-year. We are near the end of the bearish (yields up/prices down) annual period. This would suggest a push lower (yield down/price up).
  • Yields have struggled to move away from the uptrend (A/B) but generally have built a bull (prices up/yields down) flag. Now, they are being squeezed between the internal resistance (gray lateral trendline) and the A-B channel bottom. From this perspective, bears (yields higher/prices lower) have an advantage.
  • If the market breaks higher from this zone, where would resistance materialize? If yields breakout higher from this zone, there isn’t much resistance between 3.50% and last year’s @ 4.89% high. Above 4.89%, 5.25–5.35% is a reasonable target.
  • If the market breaks lower from this zone, a solid support confluence exists in the 3.23–3.30% zone. But it is more likely the 0.50–0.618 retracement zone in the 2.15–2.70% zone would be in play. This would likely come as the result of an economic recession.

The Bottom Line

The next few weeks should represent a significant juncture in the daily and potentially the weekly chart. The market has generally been consolidating over the last several months, and the pattern breakout could be meaningful. For shorter-term traders, the direction out of the consolidation will likely define the direction of travel into the fall. In other words, it is a go-with.

  • If yields break out higher, I will likely begin selling the breakouts of bear (prices down/yields higher) flags and will view short-term declines in yields as selling opportunities. If lower, I will likely be a buyer of bull flags and setups (yields down/prices higher) as they develop.
  • If the market falls away from the trendline with velocity, the first solid support there is found in the 3.79% zone.
  • I continue to see a not-trivial chance of one last push higher into the 5.25–5.50% zone, before beginning a major weekly and monthly perspective correction (yield down/price up) that eventually makes the higher low. And while I see an advantage to being generally bullish over the next few months (falling yields, rising prices), the analysis is tentative, with only a small near-term advantage to the trade. In my trading, I would consider it non-actionable without additional price/volume development or reasonable structure to trade against. 

In deference to my macro work and business cycle work, I will be a better buyer of bullish inflections in the weekly chart over the next few months, as I fully expect a significant economic slowdown to develop into the end of the year.

Disclaimer: Shared content and posted charts are intended to be used for informational and educational purposes only. The CMT Association does not offer, and this information shall not be understood or construed as, financial advice or investment recommendations. The information provided is not a substitute for advice from an investment professional. The CMT Association does not accept liability for any financial loss or damage our audience may incur.

Good Trading.

Stewart Taylor, CMT
Chartered Market Technician

Stewart Taylor

About the author:
Stewart Taylor retired from Eaton Vance Management in January 2020 after a 40-year career in US fixed income with an emphasis on technical analysis and relative value investing. He joined Eaton Vance as the Senior Trader for the Investment Grade Fixed Income team in 2005. During his tenure, he was a portfolio manager for institutional separate accounts and mutual funds, managed the team’s inflation assets, and was the team’s strategist for duration, relative value, and economic positioning. From 1992 to 2005, he provided private investing and trading consultation to institutional buy side, broker-dealers, and hedge funds.
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What’s the Downside Risk for QQQ?

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KEY

TAKEAWAYS

  • A bearish momentum divergence and declining Bullish Percent Index suggests rough waters ahead for the QQQ.
  • The 50-day moving average and Chandelier Exit system can serve as trailing stops to lock in gains from the recent uptrend.
  • If stops are broken, we can use Fibonacci Retracements to identify potential downside targets for the Nasdaq 100.

The Nasdaq 100 ETF (QQQ) is beginning to show further signs of deterioration, from bearish momentum divergences between price and RSI to weakening breadth using the Bullish Percent Index. How can we determine whether a pullback could turn into something more disastrous for stocks? Let’s look at how the 50-day moving average, Chandelier exits, and Fibonacci retracements can help anticipate downside risk for the QQQ.

To kick things off, we need to acknowledge how the QQQ has a place of distinction on the growing list of charts showing bearish momentum divergences.

This classic sign of a bull market top is when price continues to trend higher while the RSI (or some other momentum indicator) begins to slope downwards. Think of this pattern as a train running out of steam as it reaches the top of a hill. This weakened momentum usually occurs at the end of a bullish phase, when buyers are exhausted and there just isn’t enough momentum left to push the markets much higher.

But it’s not just about weakening momentum. Breadth conditions, which remain fairly constructive for the broader equity space, have really deteriorated in the past ten weeks.

Here, we’re showing the Bullish Percent Index for the Nasdaq 100. This is a market breadth indicator based on point & figure charts, and basically measures how many stocks in a specific index are currently showing a bullish point & figure signal.

Note how, in late December, this indicator was around 90%, meaning nine out of every ten Nasdaq 100 members were in a bullish point & figure phase. This week, we saw the indicator finished just below 50%. This shows that about 40% of the Nasdaq 100 members generated a sell signal on their point & figure charts in 2024.

What’s very interesting about that particular development is that point & figure charts usually have to show quite a bit of price weakness to generate a sell signal. So names like TSLA, AAPL, and others are breaking down, which suggests that further upside for the QQQ would be limited until this breadth indicator improves.

Are you prepared for further downside for the QQQ and leading growth names? The first item in my Market Top Checklist has already been triggered. Join me for my upcoming FREE webcast on Tuesday, March 19th, where I’ll share the other six items on the checklist and reflect on what signals we’ll be watching for in the coming weeks. Sign up HERE for this free event!

So what if the Nasdaq 100 does continue lower? At what point can we confirm that a corrective phase has truly begun? I like to keep things simple, so, in terms of an initial trigger for a tactical pullback, I always start with the 50-day moving average.

The 50-day moving average currently sits about $6 below Friday’s close, and also lines up pretty well with the February swing low around $425. So as long this level would hold, the short-term trend actually remains in good shape. A break below that 50-day moving average would tell me there is a much higher likelihood of further price deterioration.

But the 50-day moving average, while a simple and straightforward situation, is perhaps not the most effective way to gauge a new downtrend phase. Alexander Elder popularized the Chandelier Exit system in his books, and it represents a more nuanced version of a trailing stop because it is based on Average True Range (ATR).

Look back at the price peak in July 2023, and notice how the price remained above the Chandelier Exit through that price high. Soon after, the price violated the trailing stop to the downside, suggesting the uptrend phase was over and a corrective move had begun. Since the October 2023 low, the QQQ has consistently remained above the Chandelier Exit on pullbacks, as the price achieved higher highs and higher lows into March. After Friday’s drop, the Nasdaq 100 remains just above this effective trailing stop indicator.

So what if the Chandelier Exit is violated next week, and the QQQ begins to drop to a new swing low? What’s next for the Nasdaq 100?

Fibonacci Retracements can be so helpful in identifying assessing downside risk, because they measure how far the price may pull back in relationship to the most recent uptrend. Using the October 2023 low and the March 2024 high, that would give an initial downside target around $408. Further support could be at the 50% level ($395) and the 61.8% level ($382).

Note how well these levels line up with previous swing lows, especially the 61.8% retracement level. That last support level lines up with the swing low in December 2023, as well as the price peak in July 2023. I refer to that sort of level as a “pivot point” because it has served as both support and resistance, and these are often important levels to monitor.

A number of the mega-cap growth stocks, such as TSLA and AAPL, have broken down in recent weeks. But the latest patterns of bearish momentum divergences and declining breadth conditions tell us that there may be further downside in store for the Nasdaq 100. By keeping a watchful eye on trailing stops and potential support levels, we can perhaps navigate choppy market waters using the power of technical analysis.

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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The Hoax of Modern Finance – Part 11: Valuations, Returns, and Distributions

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Note to the reader: This is the eleventh 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

Market Valuations

Because secular markets are defined by long-term swings in valuations, let’s look at the Price Earnings (PE) ratio and study its history. Robert Shiller created a valuable measure of PE valuation that uses trailing (actual) earnings, averaged over a 10-year period. Here’s how it is calculated:

  • Use the yearly earning of the S&P 500 for each of the past 10 years.
  • Adjust these earnings for inflation, using the CPI (i.e. quote each earnings figure in current dollars).
  • Average these values (i.e., add them up and divide by 10), giving us e10.
  • Take the current Price of the S&P 500 and divide by e10.

Figure 8.1 shows the S&P Composite on a monthly basis adjusted for inflation, back to 1871, with a regression line so you can get a feel (visually) of where the current price is relative to the long-term trend of prices. The lower plot is the Shiller PE10 plot, with peaks and troughs identified with their values. You can see that all prior secular bears ended with PE10 as a single digit (4.8, 5.6, 9.1, and 6.6). The PE10, on March 9, 2009, only got down to 13.3, which is considerably higher than the level reached by all prior secular bear lows. Based on this simple analogy, I think we have yet to see the secular bear low for this cycle. Remember, it does not mean that the prices have to go lower than they did in 2009; it just means the PE10 should drop to single digits. Remember, PE is a ratio of Price over Earnings. To make the ratio smaller, either the price can decline, the earnings can increase, or a combination of both.

As of December 31, 2012, the PE10 is at 21.3. Referencing the small box in the lower left corner shows that this value is in the fifth quintile of all the PE data. Based on this analysis, the market is overvalued.

So when the financial news noise is constantly parading analysts by touting the PE as overvalued or undervalued, you can count on the fact that they are using the forward PE ratio. The forward ratio is the guess of all the earnings analysts. They are rarely correct. Ignore them.

Finally, Figure 8.2 shows the PE10 in 10 percent increments or deciles. It shows the extreme level reached in the late 1990s from the tech bubble, it shows the 1929 peak, and it shows that, as of December 31, 2012, we are at the 82nd percentile of PE10. This puts the PE10 overvalued on a relative basis, and also on an absolute basis, as shown in Figure 8.1. Remember, PE10 used real reported (trailing) earnings, not forward (guess) earnings. As Doug Short says on his website at dshort.com: A more cautionary observation is that when the PE10 has fallen from the top to the second quintile, it has eventually declined to the first quintile and bottomed in single digits. Based on the latest 10-year earnings average, to reach a PE10 in the high single digits would require an S&P 500 price decline below 540. Of course, a happier alternative would be for corporate earnings to continue their strong and prolonged surge. If the 2009 trough was not a PE10 bottom, when would we see it occur? These secular declines have ranged in length from more than 19 years to as few as three. As of December 31, 2012, the decline in valuations was approaching its 13th year.

Secular Bear Valuation

Figure 8.3 shows the Shiller PE10 monthly for all the past secular bear markets since 1900, with the current secular bear (as of 2013) in bold. What is really interesting about this chart is that most of the secular bears began with PE Ratios in the 20 to 30 range and ended with them in the 5 to 10 range. The current secular bear began with a PE in the mid-40s and is now only back down to the level that the previous secular bears began. That could imply that the secular bear that began in 2000 could be a long one. These charts were created using monthly data; if yearly data were used, the concept would be even more pronounced.

Secular Bear Valuation Composite

In Figure 8.4, the current secular bear market valuation is shown in bold, with the other line representing the average of the previous four secular bears. Again, this type of analysis is just an observation and for educational purposes; you cannot make investment decisions from this. Investment decisions come from actionable information and analysis.

Secular Bull Valuation

Figure 8.5 of secular bull market valuations shows that most of them begin with PE ratios in the 5 to 10 (same as where secular bears end) and they end with PE ratios in the 20 to 30 range. The excessive secular bull of 1982 to 2000 reached unbelievable high valuations. I remember everyone saying that this time was different. Wrong!

Secular Bull Valuation Composite

 The secular bull market valuation composite is shown in Figure 8.6. It is the average of all the secular bull markets since 1900. Since we are currently in a secular bear market, the average of the secular bull markets is shown by itself.

Market Sectors

I use the sector definitions provided by Standard & Poor’s, of which there are 10. The other primary source for sector analysis is Dow Jones. Either is fine, I just prefer the S&P structure because I have been using it for so long. Table 8.1 shows the 10 sectors’ annual price performance since 1990, and Table 8.2 shows the relative performance of the total returns. When viewing a table of relative returns as in Table 8.2, keep in mind that each column (year) is completely independent of the preceding year or following year. Also, the relative ranking shows that those in the top part of the column outperformed those in the lower part of the column, independent of whether the returns were positive, negative, or a combination. Another value of this type of table is to show that picking last year’s top performer is not a good strategy. Remember, you cannot retire on relative returns.

This book does not get into the various uses of sectors as investments, but the book would not be complete without the mention of sector rotation and, in particular, how various sectors rotate in and out of favor based on the phase of the business cycle and the economy. A further delineation of sectors is their propensity to fall within the broad categories of offensive and defensive. This means that when the market is performing poorly, the defensive sectors will generally outperform, and when the market is performing well, it is the offensive sectors that are the top performers.

The phases of the economy known as economic expansions and contractions are affected by many events but generally boil down to recessions and periods of expansion. It should be noted, however, that not all contractions end up being recessions. The phases can then be broken down into early cycle, mid-cycle, and late cycle segments of the full cycle. There is a lot of literature available to cover all these details, but the point of this discussion is to show the rotational movement of the various sectors through the economic cycle.

Figure 8.7 is a graphic showing the sectors and where they fall in the cycle. It shows the rotation of sectors during an average economic cycle for the past 67 years and is courtesy of Sam Stovall, chief equity strategist, S&P Capital IQ. Sam wrote one of the best books on sector rotation years ago, Standard & Poor’s Sector Investing: How to Buy the Right Stock in the Right Industry at The Right Time, but is currently out of print as of 2013.

Another excellent study I have seen on the cycles within the phases and what sectors are affected was put out by Fidelity and dated August 23, 2010 (see Table 8.3). It clearly showed that, from 1963 through 2010, the following sectors were strongest during the various phases. In each cycle, the top-performing sectors are shown, with the first being the best of the four and the last being the worst of the top four, which is still the fourth best out of the 10 sectors.

It was interesting to note in this study that during all of the three cycles, Utilities and Healthcare were the two worst-performing of all 10 of the sectors (not shown). They only ranked in the top four during actual recessions. Since recessions are usually identified by the NBER about a year after they begin and sometime not until they have ended, this is not knowledge that you can make investment decisions with.

However, you can use a momentum analysis and always be in the top four sectors and probably do well. Clearly, this is certainly better than buy-and-hold or index investing.

Figure 8.8 shows the S&P 500 in the top plot and my Offensive-Defensive Measure in the lower plot. The concept of the Offensive-Defensive Measure is simple.

The Offensive Components

  • Consumer Discretionary
  • Financials
  • Industrials
  • Information Technology

The Defensive Components

  • Consumer Staples
  • Utilities
  • Healthcare
  • Telecom

You can see that the rally from the left side of the chart to point A (February, 2011) was strong; however, based on the switch from offensive to defensive sectors that occurred at point A, the investors were clearly concerned about the market. While the market traded sideways for months (see top plot), the defensive sectors were clearly in the lead, causing the offense-defense measure to decline. The measure declined significantly, and it wasn’t until point B (July 2011) that the market finally gave up and headed south.

Sector Rotation in 3D

Julius de Kempenaer has created a novel way of visualizing sector-rotation, or, more generally, “market-rotation,” in such a way that the relative position of all elements in a universe (sectors, asset classes, individual equities, etc.) can be analyzed in one single graph instead of having to browse through all possible combinations. This graphical representation is called a Relative Rotation Graph or RRG. As of 2013, Julius is now working together with Trevor Neil to further research and implement the use of RRGs in the investment process of investment companies, funds, and individual investors. More information can be found on their website www.relativerotationgraphs.com.

A Relative Rotation Graph takes two inputs that together combine into an RRG. I’ll use the S&P Sectors for this discussion. The first step is to come up with a measure of relative strength of a sector versus the S&P 500; this is done by taking a ratio between each sector and the S&P 500. Analyzing the slope and pace of these individual RS lines gives a pretty good clue about individual comparisons versus their benchmark. These raw RS lines answer “good” or “bad.” However, they do not answer “how good” or “how bad” or “best” and “worst.” The reason for this is that Raw RS values (sector/benchmark) for the various elements in the universe are like apples and oranges, as they cannot be compared based on their numerical value.

Taking the relative positions of all elements in a universe into account in a uniform way enables “ranking.” This process normalizes the various ratios in such a way that their values can be compared as apples to apples, not only against the benchmark but also against each other. The resulting numerical value is known as the JdK RS-Ratio—the higher the value, the better the relative strength. Additionally, not only the level of the ratio, but also the direction and the pace at which it is moving, affects the outcome. A concept similar to the well-known MACD indicator is used to measure the Rate of Change or Momentum of the JdK RS-Ratio line. Here also, it is important to maintain comparable values so another normalization algorithm is applied to the ROC; this line is known as the JdK RS-Momentum. The RRG now has JdK RS-Ratio for the abscissa (X axis) and the JdK RS-Momentum for the ordinate (Y axis). Graphically, the rotation looks like Figure 8.9.

In Figure 8.10, the sectors that are showing strong relative strength, which is still being pushed higher by strong momentum, will show up in the top-right quadrant. By default, the Rate of Change will start to flatten first, then begin to move down. When that happens, the sector moves into the bottom-right quadrant. Here, we find the sectors that are still showing positive relative strength, but with declining momentum. If this deterioration continues, the sector will move into the bottom-left quadrant. These are the sectors with negative relative strength, which is being pushed farther down by negative momentum. Once again, by default, the JdK RS-Momentum value will start to move up first, which will push the sector into the top-left quadrant. This where relative strength is still weak (i.e. < 100 on the JdK RS-Ratio axis) but its momentum is moving up. Finally, if the strength persists, the sector will be pushed into the top-right quadrant again, completing a full rotation.

The next step is to add the third dimension, time, to the plot to visualize the data on a periodic basis and in fact, somewhat like watching a flip chart or animation in which you can see the movement of each of the sectors around the chart as shown in Figure 8.10.

This technology, in static form, is available on the Bloomberg professional service since January 2011 as a native function (RRG<GO>) where users can set their desired universes, benchmarks, lookback periods, and so on. On their aforementioned website, Julius and Trevor maintain a number of RRGs, static and dynamic (animated rotation), on popular universes like the S&P 500 sectors (GICS I & II). Several professional as well as retail software vendors and websites are working to embed the RRG technology in their products, which should make this unique visualization tool available to a wider audience.

Asset Classes

Asset classes can be analyzed exactly the same as market sectors. The only limitation is that they are not tied as closely to economic cycles as sectors, so it is more difficult to identify those that are offensive or defensive. Table 8.4 shows the price performance of a multitude of asset classes. Remember, this table is only showing the annual performance of each asset for each year since 1990, while Table 8.5 has the asset classes ranked each year numerically. Normally, this type of table is shown with multiple colors, but somewhat difficult in a black-and-white book, so rankings are shown. Again, remember that the rankings only show the relative performance, and each year is totally independent of the preceding or following year.

The Lost Decade

Figure 8.11 shows the S&P 500 Total Return from December 31, 1998, to December 31, 2008. Two huge bear markets and two good bull markets. If you have a strategy that could capture a good portion of those bull markets and avoid a good portion of those bear markets, you would do really well. Buy and hold has lost money over this period.

I get asked all the time, “Are we going to have another bear market?” I answer that I can guarantee you that we will; I just have no idea when it will be. However, we can turn to another group of very bright people from the third-largest economy in the world (as of 2013) and look at their market. Figure 8.12 is the Japanese Nikkei from December 31, 1985, to December 31, 2011, a period of time of 26 years, over a quarter of a century.

Clearly, buy and hold was a devastating investment strategy, and the really bad news is that it still is. Figure 8.13 shows the up and down moves during this period, in which a good trend following strategy could have protected you from horrible devastation.

The percentage moves up are shown above the plot, and the percentage moves down are below the plot. These are the percentage moves for each of the up and downs you see on the chart. There were five cyclical bull moves of greater than 60 percent during this period. There were also five cyclical bear moves of greater than -40 percent. Remember, a 40 percent loss requires a gain of 66 percent just to get back to even. The small box in the lower right edge shows the decline from the market top in late December 1989 (–73.3 percent). A 73 percent decline requires a gain of 285 percent to get back even. Most people won’t live long enough for that to happen.

Finally, please notice that Figure 8.13 covers approximately 30 years of data and that the point on the right end (most recent value) is approximately equal to the starting point back in the mid-1980s; certainly the lost three decades. Buy and Hold is Buy and Hope.

Market Returns

It is always good to see how the markets have performed in the past. With the advent of the internet, globalization, minute-by-minute news, investors have a natural tendency to focus on the short term. Without a knowledge of the long-term performance of the markets, that short-term orientation can cause one to be totally out of touch with the reality that the market does not always go up. The following charts will show annualized returns for the S&P 500 price, total return, and inflation-adjusted total return over various periods. These types of charts are also known as rolling return charts. As an example, using the 10-year annualized rolling return, the data begins in 1928, so the first data point would not be until 1938 and be the 10-year annualized return from 1928 to 1938. The next data point would be for the 10-year period from 1929 to 1939, the third from 1930 to 1940, and so on.

Figure 8.14 shows the 1-year annualized return for the S&P price. It should be obvious that one-year returns are all over the place, oscillating between highs in the 40 percent to 50 percent range, and lows in the -15 percent to -25 percent range. Following Figure 8.14 are the 3-year (Figure 8.15), 5-year (Figure 8.16), 10-year (Figure 8.17), and 20-year (Figure 8.18) charts of annualized returns, with the average for all the data shown in the chart caption. Following the 20-year chart is a further analysis for the 20-year period.

The 10-year return chart now clearly shows up-and-down trends in the data (see Figure 8.17).

The 20-year rolling return chart (Figure 8.18) continues to reduce the short-term volatility in the chart, and the up-and-down trends become clear.

Since I adamantly believe that most investors have about 20 years to really put money away in a serious manner for retirement, the following two charts show returns over 20 years for total return (Figure 8.19) and inflation-adjusted total return (Figure 8.20).

For most analysis, the Price chart is more than adequate. In the world of finance, there is an almost universal demand for the Total Return chart; however, I think that if you are going to insist on Total Return, you should then also insist on Inflation-Adjusted Total Return. Using the three preceding 20-year charts and the averages shown, you can see that the average for Price is 6.97 percent, Total Return is 11.32 percent, and Inflation-Adjusted Total Return is 7.19 percent. What this says is that the effect of including dividends (Total Return) and the effect of Inflation often neutralize each other.

Table 8.6 shows the annualized returns for the S&P 500 for price, total return, and inflation-adjusted total return for the following periods: 1-year, 2-year, 3-year, 5-year, 10-year, and 20-year.

Table 8.7 shows the minimum and maximum returns, along with the range of returns, their mean, median, and variability about their mean (Standard Deviation).

Distribution of Returns

The range of return data is very easy to calculate because it is simply the difference between the largest and the smallest values in a data set. Thus, range, including any outliers, is the actual spread of data. Range equals the difference between highest and lowest observed values. However, a great deal of information is ignored when computing the range, because only the largest and smallest data values are considered. The range value of a data set is greatly influenced by the presence of just one unusually large or small value (outlier). The disadvantage of using range is that it does not measure the spread of most of the values—it only measures the spread between highest and lowest values. As a result, other measures are required in order to give a better picture of the data spread. The monthly returns for the S&P 500 begin with December 1927, so, as of December 2012, there are 1,020 months (85 years) of data.

Additional charts show the distribution of data in various ways using the 20-year annualized returns of the S&P 500 inflation-adjusted total return data for rolling 20-year periods. Twenty-year returns from the S&P 500 with 1,020 months of data would yield 778 data points. Return distributions can be thought of like this: Each bar represents the proportion of the returns that meet a percentage division of the data, mathematical division of the data, or statistical division of the data. The following are definitions of the various distribution methods, as shown in the title of the following figures.

  • Decile. One of 10 groups containing an equal number of the items that make up a frequency distribution. The range of returns is determined by the difference between the minimum and maximum returns in the series, then divided by 10 to create 10 equal groups.
  • Quartile. The calculation is similar to decile (above), but with only four groupings.

(Note: This use of decile and quartile does not follow the standard definition or calculation method often used in statistics.)

  • Standard deviation. A statistical measure of the amount by which a set of values differs from the arithmetical mean, equal to the square root of the mean of the differences’ squares. Figure 8.21 shows the percentage of the data that is included in a standard deviation. You can see that the mean is the peak and that 68.2 percent of the data is within one standard deviation from the mean, and 95.4 percent of the data is within two standard deviations of the mean.
  • Percentage. A proportion stated in terms of one-hundredths that is calculated by multiplying a fraction by 100.

Figure 8.22 shows the 20-year rolling returns using inflation-adjusted total return data distributed by quartiles. From the chart, you can see that 13.24 percent of the returns fall into the first quartile, or lowest 25 percent, of the data, 28.15 percent in the second, 32.90 percent in the third, and 25.71 percent in the fourth quartile or highest 25 percent of the data.

Figure 8.23 shows the same data, but in a decile distribution where each bar represents 10 percent of the number of data items. For example, 8.23 percent of the data fell in the highest 10 percent of the data.

Figure 8.24 shows the distribution of the data based on variance from the mean or standard deviation. You can see that the two middle bars each represent 34.1 percent of the data (68.2 percent total) that is one standard deviation from the mean. As an example, 33.68 percent of the 20-year rolling returns data was within one standard deviation above the mean of all the data. You can also surmise that the two bars on the right represent 50 percent of all the data and 53.86 percent (33.68 + 20.18) of the returns. Oversimplifying this, one then knows that there were more returns greater than the mean. However, there is an asymmetrical distribution between the returns that are outside of one standard deviation from the mean, with the larger percentage to the downside.

Figure 8.25 shows the 20-year rolling returns of the S&P 500 inflation-adjusted total return within percentage ranges. The bar on the left shows all the returns of less than 8 percent, which accounted for more than 50 percent of all returns (51.41 percent), while the bar on the right shows returns of greater than 12 percent, accounted for only 11.31 percent of all returns. The bar in the middle is the range of returns between 8 percent and 12 percent, which accounted for 37.28 percent of all returns. Recall the discussion in Chapter 4 on the deception of average, and once again the average 8 percent to 12 percent return is not average.

When the market starts to decline significantly, it is not the same as when someone yells “fire” in a theater. In a theater, everyone is running for the exits. In a big decline in the market, you can run for the exits, but first you have to find someone to replace you—you must find a buyer. Big difference! This chapter has attempted to stick to what I believe are market facts and essential information you should understand in regard to how markets work and have worked in the past. If one does not know market history, it would be very difficult to keep a focus on what the possibilities are in the future.

This concludes the first section of this book, where I have attempted to show you the many popular beliefs about the market that are used by academia and Wall Street to help sell their products. Part I also wraps up with what I believe to be truisms about the market. Part II has an introductory chapter on technical analysis and is followed by two chapters on extensive research into trend determination and risk/drawdowns.

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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How Overextended Are You, QQQ?

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We’ve highlighted all the warning signs as this bull market phase has seemed to near an exhaustion point. We shared bearish market tells, including the dreaded Hindenburg Omen, and how leading growth stocks have been demonstrating questionable patterns. But despite all of those signs of market exhaustion, our growth-led benchmarks have been pounding even higher.

This week, Nvidia’s blowout earnings report appeared to through gasoline on the fire of market euphoria, and the AI-fueled bullish frenzy appeared to be alive and well going into the weekend. As other areas of the equity markets have shown more constructive price behavior and volatility has remained fairly low, the question remains as to when and how this relentless market advance will finally meet its peak.

I would argue that the bearish implications of weaker breadth, along with bearish divergences and overbought conditions, still remain largely unchanged even after NVDA’s earnings report. The seasonality charts for the S&P 500 confirm that March is in fact one of the weakest months in an election year. So will the Nasdaq 100 follow the normal seasonal pattern, or will the strength of the AI euphoria push this market to even further heights into Q2?

By the way, we conducted a similar exercise for the Nasdaq 100 back in November, and guess which scenario actually played out?

Today, we’ll lay out four potential outcomes for the Nasdaq 100. 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 even more all-time highs over the next six-to-eight weeks.

Option 1: The Very Bullish Scenario

The most optimistic scenario from here would mean the Nasdaq basically continues its current trajectory. That would mean another 7-10% gain into April, the QQQ would be threatening the $500 level, and leading growth stocks would continue to lead in a big way. Nvidia’s strong earnings release fuels additional buying, and the market doesn’t much care about what the Fed says at its March meeting because life is just that good.

In this very bullish scenario, value-oriented stocks, including Industrials, Energy, and Financials, would probably move higher in this scenario, but would still probably lag the growth leadership that would pound even higher.

Dave’s Vote: 15%

Option 2: The Mildly Bullish Scenario

What if the market remains elevated, but the pace slows way down? This second scenario would mean that the Magnificent 7 stocks would take a big-time breather, and more of a leadership rotation begins to take place. Value stocks outperform as Industrials and Health Care stocks improve, but since the mega-cap growth names don’t lose too much value, our benchmarks remain pretty close to current levels.

Dave’s vote: 25%

Option 3: The Mildly Bearish Scenario

Both of the bearish scenarios would involve a pullback in leading growth names, and stocks like NVDA would quickly give back some of their recent gains. Perhaps some economic data comes in way stronger than expected, or inflation signals revert back higher, and the Fed starts reiterating the “higher for longer” approach to interest rates through 2024.

I would think of this mildly bearish scenario as meaning the QQQ remains above the first Fibonacci support level, just over $400. That level is based on the October 2023 low and also assumes that the Nasdaq doesn’t get much higher than current levels before dropping a bit. We don’t see defensive sectors like Utilities outperforming, but it’s clear that stocks are taking a serious break from the AI mania of early 2024.

Dave’s vote: 45%

Option 4: The Super Bearish Scenario

Now we get to the really scary option, where this week’s upswing ends up being a blowoff rally, and stocks flip from bullish to bearish with a sudden and surprising strength. The QQQ drops about 10-15% from current levels and retests the price gap from November 2023, which would represent a 61.8% retracement of the recent upswing. Defensive sectors outperform and investors try to find safe havens as the market tracks its traditional seasonal pattern. Perhaps gold finally breaks above $2,000 per ounce, and investors start to talk about how a break below the October 2023 low may be just the beginning of a new bearish phase.

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 there for 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.
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With The Top 10 Picks In The Stock Market DRAFT, EarningsBeats.com Selects…

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We’re one day away from “DRAFT Day”! Every quarter, we select the 10 equal-weighted stocks that will comprise our 3 portfolios – Model, Aggressive, and Income. My background is in public accounting as I audited companies in the Washington, DC – Baltimore, MD metropolitan area for two decades. While most of my teaching generally encompasses technical analysis and how I use it, I still haven’t let go of my “roots” on the fundamental side. Earnings matter to me. I believe that management teams should develop a business plan that works to their strengths and limits the impact of their weaknesses. And the BEST management teams execute their plan to perfection, beating their own expectations and those of Wall Street.

In order to take advantage of this clear competitive advantage in management teams, we created our flagship ChartList at StockCharts.com, our Strong Earnings ChartList (SECL). I believe that management performance and integrity is so important that I won’t select ANY company for our 3 portfolios, unless it’s on our SECL. Currently, we have 390 companies on this ChartList. Roughly 7-8% of them will be “drafted” by us tomorrow afternoon during our “Top 10 Stock Picks” live virtual event. It’s completely FREE and you’re welcome to join us and witness the process that I go through to assess the current stock market environment and then select the stocks in the best position to benefit from that environment. CLICK HERE for more information and to register.

Let’s look at 3 companies that MIGHT make sense in our portfolios and that will be given considerable consideration:

Walt Disney Co (DIS)

It looks like the triple bottom on the long-term DIS chart near 80 has held and a new uptrend has begun. For the first time since 2020, DIS has made a successful 20-week EMA test and then gone on to break out to new high. We hadn’t seen this since the 20-week EMA was tested during Sep/Oct/Nov 2020. Check this out:

That bottom panel is worrisome for sure. The broadcasting & entertainment index ($DJUSBC) has been absolutely horrific vs. the S&P 500 for 3 years now. Can DIS perform well in such an awful industry environment? Will the industry group begin to reverse, with DIS providing leadership? That’s a difficult call. What we do know, however, is that DIS just posted excellent quarterly results. Revenues came in at $23.55 billion, slightly ahead of consensus estimates of $23.41 billion. Earnings were quite strong, however, at $1.22 per share. Expectations were set at just $.97.

Is DIS worthy of a first-round draft pick? We’ll talk about that tomorrow.

Meta Platforms (META)

Many of our scouts are saying that META could be the #1 overall draft pick. Hailing from the incredibly bullish internet space ($DJUSNS), which has been second only to semiconductors ($DJUSSC) in terms of best relative performance to the S&P 500 over the past year, META has had an MVP type of season, leading its industry peers. Here’s the current chart:

META is one of 8 stocks on our Model Portfolio last quarter that still resides on our SECL. There’s a good chance it gets selected in back-to-back drafts. Over the past 3 months, META gained 41.63%, only beaten by Palo Alto Networks (PANW), which gained 51.22%. Not too surprisingly, our Model Portfolio racked up a quarterly gain of 21.87%, which CRUSHED the S&P 500’s gain of 10.08%.

Sure, it’s trendy to say that META is overbought, along with most every other key technology or communication services name. But those who only look at the last year’s STRAIGHT UP move like to conveniently ignore the fact that META dropped 75% the year before during the cyclical bear market. Market makers were able to scoop up this All-Star at dirt cheap prices for their wealthy institutional clients. Maybe those institutions can give the #1 draft pick acceptance speech, thanking everyone who panicked during that manipulation-driven selloff.

What about META’s fundamentals? Well, last quarter the company produced revenues of $40.11 billion, easily surpassing its $38.99 estimate. And instead of the widely-expected profit of $4.83, META blew the doors off that number, instead coming in at $5.33. What’s not to like here?

Let’s see if META has its name called first on Tuesday! Or how about the other 7 Model Portfolio returning starters? Could they be re-drafted? What a great problem to have!

AZEK Company (AZEK)

It’s easy to talk about META, AMZN, NVDA, etc., but our scout team needs to look deeper and take a stand on potential high-flyers from time to time. Yes, their floor might not be nearly as high as a company like META, but the potential to the upside can be staggering for smaller-cap companies. AZEK isn’t part of the scorching-hot technology (XLK) or communication services (XLC) sectors. Instead, AZEK is a $6.6 billion company in the industrials (XLI) sector and designs, manufactures, and sells building products for residential, commercial, and industrial markets in North America. Technically, it’s been an exceptional performer over the past few months:

Like META, AZEK is a relative leader in a leading industry group, building materials & fixtures ($DJUSBD), which I always love to see. The DJUSBD is the 8th best-performing industry group over the past year. But AZEK is also a smaller company and we know that small caps have struggled relative to their larger cap counterparts. Still, it’s hard to ignore the numbers posted by AZEK. Their revenues were $240 million vs. their expected $234 million. And earnings doubled expectations, $.10 vs. $.05. Results like this can change the future projection of earnings, especially when guidance is raised. AZEK raised its Q2 revenue guidance significantly from $381.6 million to a range from $407-$413 million. And then what happens if AZEK beats estimates again?

Is the potential here solid enough to result in a Top 10 selection?

We have our work cut out for us tomorrow. I’ll be secluded for the next 24 hours in our EarningsBeats.com “War Room”, deciding where the stock market may go over the next 3 months and which areas and stocks are poised to benefit from it. If you’re interested, you can find out more information about this FREE event and REGISTER here.

Happy trading!

Tom

Tom Bowley

About the author:
Tom Bowley is the Chief Market Strategist of EarningsBeats.com, a company providing a research and educational platform for both investment professionals and individual investors. Tom writes a comprehensive Daily Market Report (DMR), providing guidance to EB.com members every day that the stock market is open. Tom has contributed technical expertise here at StockCharts.com since 2006 and has a fundamental background in public accounting as well, blending a unique skill set to approach the U.S. stock market.

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The EarningsBeats.com Strategy For Uncovering The New Winners

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Earnings and interest rates are always the key drivers to stock market success. There may be other short-term factors that influence price action, but, at the end of the day, rising earnings and interest rates conducive to job and economic growth is what results in secular bull markets.

Organize Your Trading Candidates With ChartLists

While I follow interest rates very closely and consider them when evaluating likely future market direction, it’s really the earnings reports that we follow most closely at EarningsBeats.com. Q4 earnings are not yet complete, but most of the very influential companies in the Dow Jones, S&P 500, and NASDAQ have reported. Our research, including earnings research, is organized into many ChartLists, which I briefly describe below:

  • Strong Earnings (SECL): companies beating both revenue and EPS estimates and meeting other liquidity and performance filters. I view it as a list of companies demonstrating high quality technicals and fundamentals. It’s the ChartList that I trade from most frequently.
  • Strong Future Earnings (SFECL): companies that show excellent relative strength (high SCTR scores) and adequate liquidity that are not already on the SECL. I think of it as a list of excellent companies that simply weren’t able to beat estimates in their prior quarter, but who are trading as though they may do so in the quarter ahead.
  • Strong AD (SADCL): companies showing excellent relative strength (high SCTR scores), adequate liquidity, and rising AD (accumulation/distribution, not advance/decline) lines. The AD lines IGNORES opening gaps and focuses only on price action during the day, with volume being the multiplier. Companies on this ChartList are companies that tend to trade higher into the close, suggesting morning weakness might be bought.
  • Raised Guidance (RGCL): companies that, as the name would suggest, raise guidance – either revenues, EPS, or both. I like management teams that feel confident in their business and raise guidance throughout the quarter.
  • Bullish Trifecta (BTCL): companies that are common to the SECL, SADCL, and RGCL. These companies have produced strong quarterly results, have raised guidance, and show possible accumulation by big Wall Street firms.
  • Earnings AD (EADCL): companies that gain AT LEAST 5% from the opening bell to the closing bell on the day after earnings are reported. I then review every one of these companies and provide my Top 30 – companies that I really want to consider trading in the days and weeks ahead.
  • Short Squeeze (SSCL): companies whose float is heavily shorted. We track those companies with short percentage of float in excess of 20%. High short interest can trigger massive short squeeze rallies.
  • Seasonality (SEASCL): companies that have a history of performing well during certain calendar months.
  • Portfolio ChartLists: every quarter, we provide a list of companies that we “draft” into our 4 portfolios – Model Portfolio, Aggressive Portfolio, Income Portfolio, and Model ETF Portfolio.
  • Relative Strength Industry Groups (RSICL): This is an exclusive ChartList for our annual members that tracks the relative strength of every industry group over the past few years. Trading leading stocks in leading industry groups is how you beat the S&P 500 and this ChartList provides us those leading industry groups.

There are other ChartLists that we create from time to time, but you can see from the above that our research is broad and provides a TON of great information for our members on a regular basis. But before trading anything, it makes sense to evaluate the current state of the market. Is the current rally sustainable?

S&P 500: Is the Current Rally Sustainable?

I say yes. Sure, we’ll have some pullbacks along the way, but right now money is flowing into aggressive areas of the market and that “risk on” environment bodes well for higher prices ahead. Check out this S&P 500 chart with several key “sustainability” ratios in the panels below the S&P 500 price chart:

Is this not obvious? Money continues to POUR INTO aggressive areas. The 6 sustainability ratios above can be summarized as follows:

  • QQQ:SPY – NASDAQ 100 performance vs. S&P 500 performance. The NASDAQ 100 is a much more aggressive index, focusing almost solely on high growth large cap stocks.
  • XLY:XLP – consumer discretionary vs. consumer staples. Two-thirds of our GDP is consumer spending. It just makes sense to see which area of consumer spending, aggressive discretionary vs. defensive staples, Wall Street is favoring. That tells us what the big Wall Street firms are expecting in the months ahead.
  • IWF:IWD – large cap growth vs. large cap value.
  • $DJUSGL:$DJUSVL – another measure of large cap growth vs. large cap value
  • $DJUSGM:$DJUSVM – mid cap growth vs. mid cap value
  • $DJUSGS:$DJUSVS – small cap growth vs. small cap value

Every one of my aggressive vs. defensive ratios is climbing. Personally, I love all the pessimists out there constantly trying to tear apart this bull market. The problem is that many analysts are trying to handpick one or two SECONDARY indicators to determine market direction, which is absolutely wrong in my opinion. We remain extremely bullish if we look at the primary indicator, which is price and volume. Sentiment does a great job of marking market tops and bottoms and my favorite sentiment signal is the equity only put call ratio ($CPCE).

Sentiment Paving The Path To Higher Prices….For Now

Despite the nearly straight-up move that we’ve seen on our major indices since late-October, there is little complacency in the options world. Over the past 11 years, or approximately the duration of this entire secular bull market, the average daily CPCE reading has been in the .60-.65 range. Readings higher than this show an unusually heavy dose of equity put buyers (which coincides with market bottoms or approaching market bottoms), while lower readings suggest an unusually heavy dose of equity call buyers (which coincides with market tops or approaching market tops). While action has been mostly bullish in 2024, the average CPCE reading in 2024 has been .65 – a far cry from the 5-day average readings of .55 and below that typically mark market tops. Check this out:

Those red arrows highlight the very low 5-day CPCE readings and show you where the S&P 500 was at roughly the same time. After reviewing this chart, I’d quickly conclude that this rally may continue until we see options traders start pouring into equity calls. Friday’s CPCE reading was 0.48. If the S&P 500 continues higher through much of next week, it’s possible we could finally get a 5-day CPCE reading below .55 to mark a top. Friday’s 0.48 reading was a good start. Keep an eye on this throughout next week.

What Stocks Are Likely To Lead The Next Market Surge

Well, I believe our Earnings AD ChartList (EADCL) will hold the key. Again, this ChartList comprises 30 names that performed exceptionally well the day after its earnings were released as new fundamental information started to be priced in. I expect many of them to perform very well in the weeks ahead. Most of the companies on this ChartList are leaders among their peers. But others might just be getting started. Let me give you 1 of the 30 stocks featured, and one that might fit this description of just getting started – Allegro Microsystems (ALGM), a $6.1 billion semiconductor company:

ALGM’s relative strength vs. its semiconductors peers has been awful. But is it just starting to reverse higher? The AD line began strengthening a few months ago at the initial bottom and, on Friday, ALGM finally broke above a triple top. Notice that volume that accompanied the post-earnings run. We never have any guarantees of future price direction, but I’d certainly say that ALGM has my attention and is a stock that I’ll be watching as this could be the start of a very powerful advance.

In tomorrow’s EB Digest, our FREE newsletter, I’ll be providing everyone a link to our ENTIRE Earnings AD ChartList. If you’re a StockCharts.com Extra or Pro member, you can download this ChartList right into your SC account. Otherwise, you can view all 30 charts to see which stocks could be our leaders in 2024. If you’re not already a FREE EB Digest subscriber, it’s easy to get started. Simply CLICK HERE and provide us your name and email address and we’ll be happy to send you that Earnings AD ChartList in our Monday EB Digest newsletter. There is no credit card required and you can unsubscribe at any time.

Happy trading!

Tom

Tom Bowley

About the author:
Tom Bowley is the Chief Market Strategist of EarningsBeats.com, a company providing a research and educational platform for both investment professionals and individual investors. Tom writes a comprehensive Daily Market Report (DMR), providing guidance to EB.com members every day that the stock market is open. Tom has contributed technical expertise here at StockCharts.com since 2006 and has a fundamental background in public accounting as well, blending a unique skill set to approach the U.S. stock market.

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