Note: All market analysis content published on ETFOptimize reflects our interpretation of the signals from our proprietary indicators and other measures. However, you shouldn't use this content for trading signals – but preferably, only to assist in your understanding of current market conditions. All ETFOptimize Strategy holdings are determined solely by the proprietary investment algorithms used in our systems, without the influence of discretionary judgment. The ETFOptimize Strategies provide you with an investment system that needs no additional outside input, and we recommend that subscribers follow the trades of their systematic model to the letter.

— Calling a Market Top —

Most of our ETFOptimize Premium Investment Strategies are holding at least one Defensive ETF, and several are 100% in defensive positions – even as the S&P 500 index continues to hit new all-time highs. As a result, we've seen several subscriber cancellations recently.

However, it's unusual for us to receive ANY cancellations, so we investigated by breaking our Composite Signal Systems into individual indicators. Our goal was to discern the reasons for our model's contrarian choices, and see if they coincided with the reality for each individual indicator.

This article provides the results of our investigation into why our models continue to avoid investing in more aggressive ETFs right now. What we found is that our strategies ARE making the CORRECT RECOMMENDATION to be holding more conservative ETFs at this time. The reason is that our indicator analysis revealed that the market is very overextended, as determined by multiple, mutually confirming indicators.

However, this isn't to say that our models won't change positions entirely this weekend and hold aggressive ETFs across the board. On the other hand, the models may move to 100% Defensive or Cash-proxy ETFs if they detect that the underlying drivers of market prices are rolling over, and a significant downturn lies directly ahead. So let's take a look at what the indicators that determine our model's positions are saying now…

A Repeat of the Early-2018 Conditions

We see effectively the same signals as those we identified and documented back on January 20, 2018 – just before a very volatile, multi-stage correction that severely damaged the accounts of millions of investors. This correction began less than a week after our article was published on January 26. Our early-2018 blog article was titled "Too Far, Too Fast!" and was published six days before that 2018 selloff began.

In today's market (2020), several of the ETFOptimize models are seeing a danger that is nearly identical to conditions in early-2018, and those models are maintaining a conservative positioning that is keeping subscribers safe from the risk of loss. Our models always err on the side of conservatism, ensuring that losses are minimized – which our research shows is a more effective way to increase long-term returns when compared to the optimum-gain approach that most amateur investors pursue.

However, when stocks are booming and charts are climbing parabolically higher, many inexperienced investors chase the market as it is shooting skyward, wanting to own the issues that that are the hottest, getting the most posts in chatrooms and twitter, hoping to make a quick buck. Chasing these shares after they have been climbing for awhile is the Buying High component of a 'Buying High and Selling Low' approach that is the polar opposite of what they should be doing to be successful investors.

Parabolic climbs inevitably fail, and investors often hang on to their stocks hoping they will resume climbing. Inevitably, these inexperienced investors will capitulate near the bottom of a selloff, resulting in the Selling Low component of a failing (but all-to-common) 'Buying High and Selling Low' approach.

 

Actually, a Good Performance

As mentioned previously, the ETFOptimize Premium Strategies continue to show weekly gains (an average of 2.18% last week and 9.74% for the last 4 weeks), so it's not as if subscribers are losing money from the more defensive holdings of our models. 

Equity Portion of Equity/Defensive (4 ETF) Strategy The Equity portion of our 4-ETF Combo Strategy has a one-year return of 76%, a 2019 Annual Return of 90%, and an Annual Return since its inception of about 40%.

An example is displayed by the chart of our Equity/Defensive (4 ETF) Strategy to the right. This model has provided a 12-month return of 76% with a Maximum Drawdown of only -7%.

ETFOptimize subscribers are not seeing significantly reduced performance because the models are holding several conservative ETF positions. Moreover, each of our model's short-term performance statistics show the models are still outperforming their benchmarks.

Many investors cancel becuase they see that their model is not holding the most hyped, most popular ETFs. However, the ETFOptimize Strategy average performance for the last week is only 0.10% less than the performance of XLK – the 'high-flying' Technology Sector ETF that had a gain last week of 2.28%.

Therefore, you can see that even though they are holding several positions that might be considered more defensive, our models are not giving up anything to the market. Perhaps that perception exists because our models aren't holding FAANG stocks outright.

However, subscribers can still own the FAANG stocks (and their peers) through our NASDAQ Persistent Profits (QQQ/Defensive) Strategy – while also attaining much higher Risk Adjusted Returns, resulting from the consistently accurate assessment of risk and a move into a defensive ETF when that risk has significantly increased.

 

How the ETFOptimize Models Select Their Positions

First, a little background on how the ETFOptimize ETF positions are selected…

Every weekend, the ETFOptimize Premium Investment Strategies perform an analysis of billions of data points from more than 50 different data sets which 25 years of data-driven research shows provide high-probability signals of changes in market trends.

These data sets are grouped into the following segments: 1) Macroeconomics, 2) Stock Fundamentals, 3) Investor Sentiment, 4) Technical Measures, and 5) Market Regime. Within each of these groups there are an average of 8-12 different proprietary indicators. Moreover, each ETFOptimize Premium Investment Strategy uses a different combination of Indicators to provide uncorrelated returns. Learn more about our Composite Indicator components in this list of Indicators and Data Sets.

Signal Diversification Benefits

Each model uses different risk-assessment signals, so a subscriber combining more than one strategy can benefit from both position diversification and signal (market timing) diversification. The strategy that combines all our models into a single, uncorrelated portfolio is our ULTIMATE Combo-6 Strategy, which combines our six core Premium Strategies into a 5-9 ETF portfolio.

While most investors are familiar with diversification across holdings (one ETF might be moving sharply higher at a time when another is moderating), many investors may not be familiar with signal diversification. Each of our Premium Investment Strategies uses an entirely different signaling system, so that none of the strategies have the same key driver for exposure to market risk.

 

"Rallies are like escalators (moving up slowly), while bear markets are like elevators (going down quickly)."

 

The benefit of Signal Diversification is that if you are using more than one of the ETFOptimize strategies (and taking advantage of our Bundle Discount Pricing of up to 20% off) or using our ULTIMATE COMBO Strategy (highly recommended) then you should not see any two of our models change positions in an identical manner on the same day.

For example, while both our S&P 500 Conservative Strategy and our NASDAQ Persistent Profits Strategy each includes TLT as one of their Defensive Assets when conditions turn threatening or risk increases, it's unlikely that both models will make their change at the same time. That's not to say it's impossible, because an overlap can occur – but it's unlikely because the strategies use two different systems for their risk-avoidance signals.

This diversification of risk timing, combined with the diversification of Risk-Assessment Indicators, provides the user with a profound level of risk mitigation during contractionary periods to accompany our model's exceptional performance that comes from the superior ETFs selection during bull markets.

Individual Indicator and Composite Scoring

Each weekend, automatic analysis and calculation of the Indicators and Data Sets results in the assignment of Individual Scores for each indicator. We then combine Individual Indicator Scores into a Composite Score, accompanied by the directional trend of that score (either higher or lower).

This Composite Score determines the overall level of risk measured at any given time for that particular strategy – ranging from 1-100 (usually with five groupings). We also have ETF Universes associated with each level of risk. For example, some models might have a Universe spectrum of Aggressive-Long ETFs, Long ETFs, Neutral ETFs, Defensive ETFs, and Inverse ETFs.. On the other hand, our simplest model, such as the S&P 500 Conservative Strategy, only has two ETF's in two universes – TLT in Defensive and SPY in Constructive.

By using a Composite Score approach, it allows a strategy's Buy Rules to select the optimum ETF(s) from the proper Universe of ETFs for that particular model. These Universe Groups can include Aggressive, Steady, Defensive, or Cash-Proxy ETFs, as well as custom groups for different approaches. (For example, the S&P 500 Bull/Bear Strategy uses a universe of five ETFs based on the S&P 500.) Then the optimum ETF is selected from the appropriate group using our proprietary Ranking Systems.

All Equity ETFs in our models are screened for ample diversification across their holdings, and all are rated 5-of-5 by FactSet in their Block Security ratings – which measures the ease of trading a $1 million block of the ETF.

In this article, we are going to take a look at some of the critical indicators that are currently determining why the ETFOptimize Strategies are holding more Defensive ETFs than usual – particularly at a time when the market (S&P 500) is making new All-Time Highs (ATHs).

Key Indicators Affecting Our Models

Relative Strength Index (RSI)

In our January 20, 2018 blog post ("Too Far, Too Fast!"), we predicted an imminent downturn of prices based on several critical indicators. One of the signals we focused on to show that stock prices had experienced an unsustainable rise was the Weekly Relative Strength Index (RSI) reaching a high of 90.51 – a level that investors had never before witnessed since the formal launching of the S&P 500 Index in 1930 (it was trading on an informal basis for five years prior).

Generally speaking, outside of that exceptionally high level of 90.51, Weekly RSI since 1930 rarely reached a level of 80. When it did hit 80 (or near-80), it was almost always a precursor of a sharp correction or warning of an impending bear market. As an example of a Weekly RSI selloff-warning near a reading of 80 – on August 26, 1987, the Weekly RSI topped out at 77.22. Had investors had the right combination of technical tools, they might have been able to steel themselves for the investor-nightmare that six weeks later saw stocks dropping into a two-week abyss resulting in a -30% loss at the bottom.

With a Weekly RSI high of 90, when we penned the January 20 Blog article, we knew there was likely trouble ahead, but we saw that ETFOptimize was the only publication of which we are aware that identified this measure and predicted imminent trouble. If you recall, December 2017 and January 2018 were filled with investor excitement leading up to and following the Tax Cuts and Jobs Act of 2017, signed into law on December 22, 2017, that some pundits forecast could double the profits of many publicly traded companies. 

While the Republican Congress and Pres. Trump was promising that the bill would be a boon for the middle class and would quickly pay for itself by boosting the economy, savvy investors knew the truth. In reality, the tax cut was a long-lobbied-for corporate giveaway that cut the nominal corporate tax rate from 35% to 21%. As a result, investor enthusiasm was rampant, and stock prices began moving higher in a parabolic fashion.

The quantitative indicators we use in our rules-based investment models identified this danger for us and automatically moved our models into a risk-off mode – entirely avoiding the subsequent sharp correction and record-setting, extreme volatility that lay in the path just ahead.

Today, our indicators detect a similar danger!

 

Weekly Relative Strength (RSI) - 1930 to Present

Chart 1 below provides perspective by showing the S&P 500 index ($SPX) from 1930 to present in the top window, with Weekly RSI in the lower pane. Last week, the Weekly RSI indicator closed at 72.30, and two weeks ago, Weekly RSI topped out at 79.04 – just below the level of 80 that is often a precursor for sharp corrections or portfolio-destroying bear markets.

 

Chart 1: The Weekly RSI (shown in the lower window) can identify times when prices have moved too far, too fast.

 

Weekly Relative Strength (RSI) - Last Four Years

Chart 2 below shows a zoom into the last four years. In this chart, we can more easily see the all-time high (ATH) for the Weekly RSI in January 2018 as it reached 90.51 – which was immediately followed by a -10.3% S&P 500 downturn. Then stocks continued higher for several months before prices collapsed again by -19.8% in the fourth quarter of 2018.

Are we suggesting that both of those significant 2018 downturns – one for -10% and one for nearly -20% at the end of the year – were the result of overheated conditions in January???  Yes – we believe that to be the case 100%.

Are we suggesting that both of those significant 2018 downturns – one for -10% and one for nearly -20% at the end of the year – were the result of overheated conditions from 2016-2017?  Yes – we believe so. Stocks had been climbing robustly for nearly two full years before 2018 – from February 11, 2016 to January 26, 2018 – and had appreciated by 36.34% (an avg. of 18.17% per year).

Per the 'Reversion to the Mean' principles that I'm going to discuss in a moment, it's not at all unusual for stocks to pull back by nearly -20% in one year to burn off about half of a 36% two-year gain.

 

Chart 2: A 4-year zoom into Chart 1 shows RSI recently hitting 79, and in many ways repeating the conditions we saw two years ago. Will we see another selloff?

 

The S&P 500 produced a terrible performance in 2018, losing -6.24% for the year. Notice that the Weekly RSI dropped to nearly the 30-level in mid-December 2018. However, at that time, the Federal Reserve panicked, reversed course, and global central banks began implementing a coordinated, dovish monetary policy. S&P 500 shares once again reverted to the mean, and 2019 was a spectacular year for investors!

Because 2019 was a year with a performance that was far ABOVE the mean (near-40%), buy-and-hold investors could see losses of -10%, -20%, or even -30% (as we saw in the 1987 selloff) in the blink of an eye – potentially wiping out much of the gains recorded last year. The classic adage applies: "Rallies are like escalators (moving up slowly), while bear markets are like elevators (going down quickly)."

Under certain conditions, our models will use the Weekly RSI Indicator as part of a composite that identifies times when prices may have moved too far, too fast. It is not an indicator we would ever use on a stand-alone basis to determine the timing of market exposure.

When combined with other high-probability indicators with confirming signals, the Weekly RSI is one of the reasons our quantitative models are currently telling us to avoid more aggressive ETFs. Of course, because our models each use different market-timing systems, some models might hold more aggressive ETFs when compared to others, while other models are 100% invested in defensive ETFs, and yet others may contain a mix of opportunistic and defensive positions. Our models are not just plugging different ETFs into the same template – each model is distinctly different from each of the others.

Mean Reversion: Percentage Above 40-Week EMA

Mean reversion, as applied in financial theory, suggests that security prices and security performance always return to their long-term averages. That investments always "revert to their mean" actually reflects several mathematical inevitabilities.

A 'reversion to the mean' involves the retracing of a condition back to a previous, typical state. This retracing can come from two mathematical pressures: a) a powerful upward progression of a data series will slow as a result of Entropy – i.e., the second law of thermodynamics plays a role here because a security-price data series is a semi-closed system, and the energy driving upward price pressure of a company's net income, a stock price, or an index price, will ultimately deteriorate. The energy will fade, exceptionally high profit margins will be degraded by the entry of new competitors, etc.

That is to say: With the Mean defined as the long-term average of a data series, eventually either a) the data series' climb will slow and decline back to its long-term average, or b) the mean (average) will rise to catch the elevating data series. A data series cannot continue ever-rising, accelerating at an ever-increasing pace that outruns its mean. The result of an equation describing such a condition would be a geometrical pattern that looks like a counter-clockwise spiral into an infinitely small point. Clearly, security prices don't form counterclockwise-spinning black holes.

Therefore, the Percent Above the 40-Week EMA indicator is one useful way to measure when stocks have moved 'too far, too fast.' By definition discussed above – a data series must always revert to its mean under the right conditions, so when the distance between prices and their mean grows too large, the probability that prices will soon head in the opposite direction increases.

The 40-Week Exponential Moving Average (EMA), which is equivalent to the 200-day EMA or 10-month EMA, and is a common moving average used by many investors to distinguish between bullish and bearish conditions. In this way, it is also often used as the stock market's 'mean' – indicating the average of long-term price series. In this case, the S&P 500's weekly prices, which is the closing price each Friday.

 

"Going back more than 150 years, using mean reversion as the primary driver of an investment strategy is a proven method for success."

 

Therefore, when the percentage distance from their 40-week mean becomes too large, at some point, the likelihood of reversal becomes significant. We've found this level to be at 10% or higher.

For more than 150 years, using mean reversion as the primary driver of an investment strategy has proven effective. For example, many famous old-school value investors, such as Warren Buffett, Joel Greenblatt, James O'Shaughnessy, Seth Klarman, and many others, apply the principles of mean revision to drive their undervalued investment selections. The main negative of this investment approach is that stocks with a low valuation – unless there is an identifiable catalyst – can maintain that undervalued status for a very long time. It's why Warren Buffett once said that his favorite holding time is "forever" – but today, there are far more viable investment approaches.

ETFOptimize employs a similar mean reversion principle in more than one of our ETF-based investment models. However, we base mean-reversion algorithms on ETF prices and performance measures rather than the stock fundamentals typically used by value investors. The measures we use are far more responsive to the variable of time – which is something that all of us have in limited quantity, so it is a highly valuable resource – since we don't have "forever" to see our investments pay off.

Mean Reversion in Quantitative ETF Investing

One way to apply a mean-reversion approach to investing with ETFs is to use it to identify overbought conditions and/or over-extended prices. When prices become too stretched to the upside, greed can quickly give way to fear if investors become nervous about those high prices. Many investors develop a hair-trigger response when they see a security in which they've made significant gains begin to turn downward – even slightly. However, doubt and error often fuel the discretionary timing of trades – which can result in missed opportunities for profit or outright loss if their judgment is faulty.

These judgments or instincts have caused individual investors to have a long-term, average Annual Return of only 2.6% (about the same as the long-term rate as inflation) – which is extraordinarily weak return when compared to something as simple as buying and holding the S&P 500 ETF (SPY), which has a return of about 6% since 2000. However, today, savvy investors have a modern approach to investing that is far superior to responding to new information based on 'gut instinct.'

Chart 3 below shows one application of Mean Reversion – using the Percentage Above Its 40-Week EMA for the S&P 500 ETF (SPY) from 2008 to the present. SPY is in the upper window and the indicator in the lower pane. Our research shows that during times when S&P 500 prices stretch 10% or more above their 40-week EMA, it indicates a near-term higher risk and a greater probability of mean reversion back to a more reasonable level.

 

Chart 3: The Percentage that SPY elevates above its 40-Week EMA indicates increased risk when it is more than 10% above the mean.

We identify the increased risk of a mean reversion in Chart 3 above with light-red-shaded vertical highlight bars. Notice that each of these signals occurred before the market turned downward – making this measure an excellent leading indicator.

The one exception is the downturn that occurred in the second half of 2015. However, in that instance, there was never a significant over-extension of prices. Instead, the end-of year losses were the result of a steady erosion of momentum related to an earnings recession.

 

Percent Above 40-Week EMA - ZOOM

Chart 4 below shows the chart above with more detail, zooming in from 2017 to present, and featuring the Percentage Above the 40-Week EMA as a risk signal.

During one of the increased-risk incidents (from January through March 2018), we can see the price of SPY stretched about 13.5% above its 40-week EMA – which was also coincident with the record-setting Weekly RSI on January 26 (at 90.51, as discussed above). This significantly overbought condition resulted in record-setting volatility over the subsequent two months, including a -12% two-week loss, a gain of 11%, then another decline of -9% before things finally settled down in mid-March.

 

Chart 4: This chart shows a zoom into Chart 3 since mid-2017.

However, those extremely overheated prices in January 2018 – the result of two years of robust gains (about 36%) – wasn't burned off during that -10% correction in January-March, 2018. Instead, the selloff continued in the fourth quarter of 2018, driven by a Federal Reserve anxious to raise rates. As usual, the Fed went too far, which resulted in a -19.78% downturn from the start of October through Christmas Day 2018.

The irony is that the Federal Reserve was aggressively raising rates in 2018 to prepare for lowering them again in the next downturn – a downturn which they created (again) in a self-fulfilling prophecy.

Divergence of Breadth with Prices

One consistently successful harbinger of directional price changes come from Divergences of Prices with their Breadth Indicators. When prices are moving higher, and multiple technical indicators based on those prices are moving in the opposite direction (downward), it often signals a decline of participation in a rally that precedes imminent declines in prices.

Divergences are not 100%-accurate indicators, but when there are a number of them – and all are providing the same bearish assessment – they often become high-probability signals of imminent downturns.

Chart 5 below shows the S&P 500 ETF (SPY) in the top window, with the Advance-Decline Percent indicator in the middle window and the S&P 500 High-Low Percent indicator in the bottom pane. There is a distinct divergence between both breadth indicators – which are declining, and the continuing rise of S&P 500 prices, which set another new All-Time High (ATH) last week.

Chart 5: Breadth Indicators such as Advance/Decline Percent and High/Low Percent are showing divergent, declining readings compared to the rising S&P 500 prices.

 

As their names imply, the S&P 500 Advance-Decline Percent indicator shows the percentage of advancing S&P 500 stocks versus declining S&P 500 stocks, and the S&P 500 High-Low Percent indicator measures the percentage of S&P 500 stocks setting new highs versus those setting new lows.

NOTE:  Neither of these indicators has signaled a secular negative turn for the S&P 500. The Advance-Decline Percent (middle window) bottomed at -26.73 on February 7 and did not quite reach the -30% level required for a negative signal. Also, after scoring a high near 30% in mid-January, the High-Low Percent reached a level of 5.40 at the weeks-end on January 31 before moving higher again and ending last week at a reading of 15.80. For a negative signal, the High-Low Percent indicator needs to reach -10% or lower.

Nevertheless, the point of Chart 5 above is to show that both of these popular Breadth Indicators (particularly the Advance-Decline Percent Indicator) have divergent readings from the continuously-rising prices of the S&P 500 Index. Notice in the middle window of Chart 5 above that the percentage of Advancing versus Declining Stocks usually matches the ups and downs of the market – however, recently it has moved in the OPPOSITE DIRECTION of the rising S&P 500 index. This means that participation in the market rally has significantly declined – particularly since last November.

We are seeing a smaller and smaller percentage of highly-popular stocks (such as the FAANG stocks mentioned earlier) pulling market indices prices ever-higher, with a declining percentage of stocks participating.

Conclusion

We published this blog article with the intention of educating our subscribers (and potential future subscribers) regarding some of the methods that we use to identify periods of increased risk. Risk has NOT – repeat NOT – increased to the point that our models are switching to 100% Defensive or Cash-Proxy ETFs.

However – at the time of this article – the majority of the ETFOptimize Premium Strategies now hold conservative bond ETFs, such as TLT (20-Year Treasuries), SHY (1-3 Year Treasuries), or XLP (Consumer Staples Sector), etc., mixed with some Equity ETF positions. Overall, our models are more than 60% in Defensive ETFs.

In the charts above, we identified some of the indicators that are providing a negative influence on our Composite Scoring system, which we explained here.

This situation, in which the ETFOptimize models are holding a mix of Aggressive and Defensive ETFs may change as soon as this weekend. Our strategies usually respond to changing conditions in advance of changes you can see occurring in prices. As shown in this article, the models are identifying drivers of equity and bond prices that are often beneath the surface and hidden from the average investor's eyes.

If You TAKE ONE IDEA from this article: 

Rarely will the ETFOptimize models respond directly to changes in stock, bond, or index prices – equivalent to the wind-blown surface of the water.

Instead, our investment models use the actual drivers of prices – many of which are well-hidden from view. The drivers of prices are powerful currents that run deep, flowing beneath the turbulent waves at the surface. It is those very strong, hidden currents beneath the surface that are the real secret to the flow of abundant resources in the deep blue sea.

Quantitative investment strategies are immune to the overheated atmospherics that often accompany late-stage bull markets. If you want excitement and an adrenaline rush, consider taking up skydiving, freestyle motocross, or jumping headfirst into the mosh pit at your local punk-rock venue on a crowded Friday night.

On the other hand, if you want consistent performance that will steadily build your savings into a legitimate 'estate' – with the kind of financial freedom you have dreamed of having, then ETFOptimize may be perfect for your needs.

Here's an example of the kind of performance you can expect from the ETFOptimize Premium Investment Strategies:

All model are launched with $100,000. The NASDAQ Persistent Profits Strategy has turned $100k into $2,314,000 in about 13 years.

 

Annual Return: 28.02%     Max. Drawdown: -12.4%    Profitable Years: 13/13 (100%)     Risk-Adjusted Return:  2.98

Start for Free Today

 

WHY WE USE WEEKLY PRICES/INDICATORS ETFOptimize uses WEEKLY stock charts and WEEKLY indicators under most circumstances. Many traders – who are familiar with seeing daily charts and indicators in the popular media – don't understand the reason for our practice of exclusively using weekly closing prices. Many traders believe that using faster-paced daily prices will result in trades that are more responsive to changes and as a result, higher performance. However, our reasoning is not complicated: By using Weekly Closing Prices, we eliminate the vast majority of noise and randomness that occurs in those prices. Using weekly closing prices and indicators, accompanied by weekly trades, results in tremendously improved accuracy and profitability from our quantitative investment strategies. This is not a conclusion based on opinion or speculation. All of our strategies, composite indicators, individual factors and formulas, ranking systems, and ETF universes are constructed from evidence-based, data-driven testing completed over the last 25+ years – since before our firm's founding in 1998. Moreover, our system designers have more than 50 years of combined experience as professionals in the investment industry, security analysis, investment banking, and designing rules-based investment models. The weight of the evidence is clear; weekly prices provide greater accuracy in market timing and ETF selection, resulting in higher-probability trades, greater profitability, and superior performance when compared to models using daily prices.

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