The Principle of Confirmation Can Save Your Skin (V) / Example 5: Bitcoin’s breakup that was a bull trap

 

Applying the Confirmation Principle to Bitcoin

In four earlier analyses (HERE, HERE, HERE , and HERE ), I showed how the Principle of Confirmation works across U.S. stock indexes, bonds, crypto, and precious metals. In each case, the principle proved invaluable in filtering out false moves. The idea is simple but powerful: a breakout or breakdown that is not confirmed by a related index or asset is highly suspect and prone to failure.

Today’s case study takes us back to the world of crypto.

I consider Bitcoin the main asset, and Ethereum the second asset from which I seek confirmation.

The starting point is the primary bull market, signaled on 5/8/25. Following what appeared to be a normal secondary reaction against the bull market (brown rectangles in the charts below), there was a rally (blue rectangles) that completed the setup for a potential bear-market signal. However, many potential bear signals never materialize, as there is no confirmed breakdown of the secondary reaction lows (red lines).

The rally that started on 8/29/25 for Bitcoin and 9/25/25 for Ethereum brought Bitcoin to higher highs —above its 8/13/25 all-time highs —on 10/3 and 10/6/25 (horizontal green line), thus setting new all-time highs. Many jumped the bullish bandwagon.

However, Ethereum failed to confirm the higher highs, as it did not surpass its 8/22/25 highs.

Lack of confirmation was a yellow flag: The bull market was NOT reconfirmed. So, it was not the right time to add and buy more Bitcoin. The bull market was questioned with a big question mark.

After such an unconfirmed high, both Bitcoin and Ethereum fell precipitously and jointly pierced their respective 8/29/25 (Bitcoin) and 9/25/25 lows on 10/16/25 and 10/17/25, respectively, thereby signaling a new bearish trend. Please note that the breakdown was confirmed. So, no excuses not to declare a new bear market.

The chart below displays the whole drama:

So, Bitcoin’s breakup was a bull trap. Fortunately, ETHE’s refusal to confirm proved to be our shield.

Sincerely,

Manuel Blay

Editor of thedowtheory.com

 

Gold and Silver Miners ETF at a critical juncture: Setup for a potential Bear market signal completed on 11/10/25

 

Overview: The gold and silver miners ETF reached a make-or-break moment on 11/10/25. The setup for a potential bear market is complete, and the line in the sand has been drawn. Please mind the word “potential”. Only if the two read lines I show in the chart below are jointly pierced, the trend will shift to bearish.

Gold and silver are showing such strength that they have not even entered a secondary reaction. Therefore, their primary and secondary trends remain bullish.

General Remarks:

In this post, I extensively elaborate on the rationale behind employing two alternative definitions to evaluate secondary reactions.

SIL refers to the Silver Miners ETF. More information about SIL can be found HERE.

GDX refers to the Gold Miners ETF. More information about GDX can be found HERE.

A) Market situation if one appraises secondary reactions not bound by the three weeks and 1/3 retracement dogma.  

As I explained in this post, the primary trend was signaled as bullish on 6/2/25.

Following the 10/16/25 highs for both SIL and GDX, there has been a pullback until 11/04/25. Such a pullback meets the time and extent requirement for a secondary (bearish) reaction against the still-existing primary bull market.

The rally that started at the 11/04/25 (SIL) lows and lasted until 11/10/25 set up both ETFs for a potential primary bear-market signal.

Thus, a confirmed breakdown of the 11/4/25 closing lows (SIL at 62.2 and GDX at 68.28) would signal a new primary bear market.

The table below gives you the most relevant information:

The chart below illustrates the latest price movements. The brown rectangles mark the secondary reaction against the primary bull market (Step #2). The blue rectangles indicate the rally (Step #3), positioning GDX and SIL for a potential bear market signal. The red horizontal lines show the secondary reaction lows (Step #2), where a confirmed break would signal a new primary bear market. The blue lines highlight the bull market highs (Step #1) whose confirmed breakup would re-confirm the still-existing primary bull market.

So, now we have two options:

1. If GDX and SIL jointly break down their 11/4/25 lows (SIL at 62.20 and GDX at 68.28), a new primary bear market would be signaled.
2. If GDX and SIL jointly surpass their 10/16/25 highs (SIL at 79.85 & GDX at 84.44), the primary bull market would be reconfirmed, and the secondary reaction and bearish setup would be canceled.

So, now, the primary trend is bullish, and the secondary one is bearish.

B) Market situation if one sticks to the traditional interpretation demanding more than three weeks and 1/3 confirmed retracement to declare a secondary reaction.

I explained in this post that the primary trend was signaled as bullish on 6/2/25.

The current pullback has not reached 15 confirmed days by both ETFs, so there is no secondary reaction against the bull market.

So, the primary and secondary trends are bullish under the “slower” appraisal of the Dow Theory.

Sincerely,

Manuel Blay

Editor of thedowtheory.com

The Emperor’s Clothes Again

 

In our latest conversation, Andrew and I picked up right where we left off,  back in April, when I correctly called the market bottom. In this new interview, I’m not calling a top, since I still see room for the stock market to move higher. However, I’m now a more cautious bull: the rally since the April 8th lows has been torrid. At the same time, I don’t expect a full-fledged bear market either.

I also shared the reasons behind my tempered optimism about the U.S. economy and the key factor that could still derail it.

https://thedisciplinedinvestor.com/blog/2025/11/09/tdi-podcast-the-emperors-clothes-again-946/

Sincerely,

Manuel Blay

Editor of thedowtheory.com

 

The Principle of Confirmation Can Save Your Skin (IV) / Example 4: Two Silver Breakdowns That Turned into Bear Traps

 

Applying the Confirmation Principle to precious metals

In three earlier analyses (HERE, HERE, and HERE), I showed how the Principle of Confirmation works across U.S. stock indexes, bonds, and crypto. In each case, the principle proved invaluable in filtering out false moves. The idea is simple but powerful: a breakout or breakdown that is not confirmed by a related index or asset is highly suspect and prone to failure.

Today’s case study takes us to the precious metals arena. Silver and gold—tracked through SLV and GLD—gave us two textbook examples of how confirmation can spare investors from costly mistakes. Both instances occurred within the same bull market, and in both cases a hasty reaction to silver’s weakness alone could have prematurely ended one of the most rewarding trades of the past decade.

First “fakeout” Breakdown: June 2024

The story begins with the bull market signaled on April 2, 2024 (details HERE).

After the 5/21/24 (SLV) and 5/20/24 (GLD) highs, both ETFs pulled back in what qualified as a secondary reaction. Lows were set on 6/13/24 (SLV at 26.43) and 6/7/24 (GLD at 211.6). A subsequent rally faltered, setting the stage for a possible bear market signal (full analysis HERE).

The “line in the sand” was clear: SLV had to hold 26.43, and GLD had to hold 211.6. A confirmed break of both would have killed the young bull market.

Then came the test. On 6/25/24, SLV dropped below its June low, closing at 26.40. GLD also appeared weak, but it never broke its 211.6 threshold. Without confirmation, no signal was triggered. What seemed like a breakdown turned out to be a trap, and the bull market continued.

Both metals soon proved the principle right. GLD posted new highs on 7/16/24, followed by SLV on 9/24/25.

Once again, unconfirmed breakdowns were exposed as false alarms.

This was the first “saving of our skin”, as the chart below shows. The brown rectangles highlight the secondary reaction against the bull market. The blue rectangles display the rally that set up both ETFs for a potential bear market. The red horizontal lines showcase the relevant pullback lows whose confirmed breakdown would have triggered a bear market. The grey rectangles show minor pullbacks that do not qualify as a secondary reaction.

Second “fakeout” Breakdown: November 2024

The script repeated—but with a twist. After October highs (SLV on 10/22, GLD on 10/30), both metals corrected again, carving out secondary reaction lows on 11/15/24 (SLV at 27.57, GLD at 236.59). Another weak rally fizzled, creating the setup for a new bear market signal (full analysis HERE).

The new critical levels were SLV 27.57 and GLD 236.59. On 11/27/24, SLV cracked support with a close at 27.25. But this time, GLD stayed comfortably above its low. Once again, no confirmation—no signal.

Silver drifted lower for a few more sessions, keeping traders nervous, before surging into a strong rally beginning 12/31/24. The bull case was sealed when GLD broke out on 1/30/25, and SLV followed on 6/5/25. The second “fakeout” had ended just like the first: with confirmation proving its worth.

This was the second “saving of our skin”, as the chart below shows. The brown rectangles highlight the secondary reaction against the bull market. The blue rectangles display the rally that set up both ETFs for a potential bear market. The red horizontal lines showcase the relevant pullback lows whose confirmed breakdown would have triggered a bear market. The grey rectangles show minor pullbacks that do not qualify as a secondary reaction.

Lessons Learned

In both episodes, silver alone signaled danger, but gold refused to confirm. In both cases, investors who relied solely on SLV’s movements would have been forced out of a profitable trade. However, by applying the Principle of Confirmation, the bull market remained intact and continued to be highly profitable both for SLV and GLD.

As we’ve already seen with stocks, bonds, and crypto, confirmation is more than just a Dow Theory curiosity. It’s a practical safeguard that prevents costly whipsaws and helps investors stay aligned with the true primary trend.

In upcoming posts, I will shift from case studies to data, demonstrating how the confirmation principle can be quantified and how significantly it enhances performance compared to single-index signals.

Sincerely,

Manuel Blay

Editor of thedowtheory.com

 

When Market Timing Meets Quant Research: The Results Speak for Themselves

 

How world-class quant research and timing filters slash drawdowns and boost returns.

 

What happens when you combine a strong stock-picking strategy with first-class trend following?
Something remarkable happens: returns surge, drawdowns shrink, and consistency improves dramatically. That’s exactly what the two charts below reveal.

This article was originally a brief social media post, but the conclusions drawn from the data were so compelling that I expanded it into a full blog entry.

The test covers the period from January 1, 2005, to July 25, 2025, with a 0.25% slippage applied to each side of every trade.

The stock-picking component focuses on identifying true value stocks — companies that are attractively priced and supported by solid earnings profiles.

When blending stock selection and market timing, everything must begin with a solid stock-picking foundation. The strategy should already outperform the benchmark (in this case, the S&P 500) and show a respectable Sharpe ratio on its own. Market timing can enhance results, but it can also disguise flaws — making a weak stock-picking system appear better than it really is. That’s why the underlying strategy must be robust by itself before adding any timing overlay. Once that’s in place, the combination of both becomes truly powerful: higher returns, smaller drawdowns, and far better risk-adjusted performance.

The table below summarizes the key performance metrics for the Quant + Market Timing strategy compared with the Quant-only version.

Even though the Quant-only strategy already beats the market, introducing a trend filter — a refined version of our Timing Indicators tailored to stocks — transforms the results. Performance takes off, drawdowns contract sharply, correlation with the S&P 500 falls (a welcome outcome), and the Sharpe ratio leaps higher.

The charts below display the Quant-only Strategy, and the Quant+ Market Timing Strategy. The red line showcases the stock picking strategy, and the blue one is Buy and Hold for the S&P500. The results speak for themselves:

 

So, when some still claim that market timing doesn’t work — or that those who practice it are misguided — the evidence tells a different story. The combination of disciplined quantitative research and systematic timing produces results that are too strong to dismiss.

Sincerely,

Manuel Blay

Editor of thedowtheory.com

 

Why Bitcoin’s Bull Market Isn’t Over Yet

 

On October 3rd, I had the pleasure of joining Maurizio Pedrazzoli Grazioli’s podcast for the second time. We picked up right where we left off from our previous conversation—when we called the bottom of Bitcoin on April 8th—and once again, we explored some bold bullish scenarios that, at the time, were far from obvious. Fast forward to today… and it turns out, we nailed it again. Watch it here:

https://www.youtube.com/watch?v=ORxzAytzuxk&feature=youtu.be

 

Sincerely,

Manuel Blay

Editor of thedowtheory.com

 

Why Today’s Stock Market Valuations Are Not Excessive: Liquidity, Capital Accumulation, and the Repricing of Risk

How liquidity and rising wealth explain today’s high valuations in the stock market

 

Introduction

Many market commentators warn that stock valuations are dangerously high, pointing to elevated price-to-earnings (P/E) ratios and market cap-to-GDP levels as evidence of a bubble. These warnings often rely on outdated benchmarks. What was considered expensive fifty years ago isn’t necessarily expensive today.

Two structural forces have redefined what counts as “normal” valuations. Today’s levels are not irrational but the natural outcome of two powerful trends:

1. The persistent expansion of global liquidity.
2. Raising capital stock per person has pushed down the natural rate of interest.

Together, these forces have changed the equilibrium level of valuations. A Price-to-Earnings ratio of 20 was considered expensive 50 years ago, but now it can be a good value.

Liquidity growth doesn’t just provide more money to invest; it fundamentally alters risk perception, reduces systemic risk fears, and creates a “risk-on” environment in which investors are willing to pay more for future earnings.

Furthermore, a nearly continuously growing stock of capital per person has reduced discount rates, which further encourages higher valuation multiples.

Misconceptions About Liquidity and Valuations

Many analysts argue that liquidity is “neutral” for valuations. Their reasoning typically takes two forms:

– Liquidity simply inflates earnings. When liquidity increases, prices, sales, and company earnings grow accordingly. Based on this reasoning, valuation metrics like the P/E ratio should remain unchanged because higher stock prices only indicate higher earnings. Therefore, liquidity would affect the “E” in the P/E ratio, but not the “P.”

– Stock market capitalization-to-GDP ratios can’t increase because liquidity inflates GDP. Critics also point to market capitalization versus GDP, arguing that liquidity affects both equally. If GDP is higher due to inflation caused by abundant liquidity, they contend that the market cap-to-GDP ratio should stay stable since both the numerator (market cap) and the denominator are influenced by liquidity.

While intuitive, these arguments miss a crucial point: liquidity not only fuels nominal growth but also changes how risk is priced. It drives a repricing of assets, lifting valuations independently of GDP or earnings growth.

Liquidity as a Valuation Multiplier for the stock market.

Liquidity is more than a measure of money in the system. It is a structural driver of asset pricing. When the growth of global money supply outpaces real GDP, surplus capital competes for a limited pool of assets. This competition raises prices and pushes valuation metrics higher.

The chart below shows the year-on-year percentage change of the S&P 500 compared to global M2 (money supply) over the past 10 years. We see that although the S&P 500’s annual performance might be above or below M2’s yearly growth, they tend to move in lockstep. The S&P 500 lags 6-12 months behind M2 growth, with a correlation ranging between 0.35 and 0.4. The most recent reading suggests that the stock market is neither too overextended nor too depressed. 

Illustration 1: Source https://en.macromicro.me/charts/101057/Major-Central-Bank-M2-Money-Supply-YoY-vs-S-amp-P-500-amp-NASDAQ-100-YoY  Accessed on 9/2/2025.

How does this astounding correlation between liquidity and stock market performance come about?

The key mechanism behind liquidity’s effect on valuations is its ability to alter risk perception.

Abundant liquidity acts as a cushion against systemic risk. Crashes and severe bear markets often emerge from liquidity traps or credit shortages, where even solvent entities cannot secure funding. These crises create downward spirals, forcing investors to demand steep risk premiums. In contrast, liquidity-rich environments do the opposite:

1. They reduce systemic risk fears. Investors recognize that abundant liquidity minimizes the likelihood of financial meltdowns, thereby lowering the perceived risk of holding equities and other risk assets.
2. They compress volatility expectations. High liquidity levels reduce the probability of market dislocations. Lower expected volatility drives higher valuations because investors are willing to pay more for future cash flows when risks feel manageable.
3. They improve market functioning. Deep liquidity allows distressed assets to be absorbed quickly, reducing contagion risk. This stability encourages long-term investors to remain fully invested, adding further upward pressure on prices.
4. They encourage a search for yield. With abundant liquidity and low interest rates, investors shift from cash or bonds into equities, boosting demand and driving multiples higher.

This is why liquidity expansions have an asymmetric effect: they push valuations higher much faster than they push GDP or earnings. They change investor psychology and pricing models, making equities and other risk assets appear safer relative to their historical risk profile.

The chart below plots M2 (x-axis top) vs. the PER (y-axis). We observe that M2 tends to grow exponentially as every doubling of M2 occurs in less time. The linear regression shows that the higher M2, the higher the PER.

 Illustration 2: Correlation M2 and the S&P500 performance.

And what happens to the stock market when liquidity increases so much that its annual growth rate exceeds that of the real GDP?

When the U.S. M2 annual growth exceeds annual real GDP growth, equities deliver much stronger returns. When GDP growth outpaces M2 growth, the stock S&P500 stagnates or declines. This shows that liquidity does not merely raise nominal stock market earnings or GDP; it reshapes the market’s willingness to pay for those earnings.

Jim Paulsen’s research captures this dynamic as he shows in this chart:

Illustration 3: Correlation between excess liquidity and the S&P500 performance.

I quote Jim Paulsen:

“[s]ince 1960, for all quarters when real excess annual liquidity growth was positive, the forward 1-quarter average annualized S&P 500 total return was a robust 15.97% compared to a forward 1-quarter average annualized S&P 500 loss of -6.08% for quarters when real excess liquidity was negative. Moreover, during quarters when excess liquidity was negative, the S&P 500 index suffered forward 1-quarter total return losses nearly 50% of the time compared to only 22.6% of the time when real excess liquidity was positive.”

As an aside, as of this writing (early September 2025), Jim Paulsen’s observation is bullish for the US stock market.

Historical Perspective: Liquidity Traps and Crashes

Market history shows that major financial crises, such as the Great Depression, the 2008 Global Financial Crisis, and the 2020 COVID-19 crash, were triggered or amplified by liquidity shortages. When liquidity dries up, credit channels seize, systemic risk skyrockets, and investors demand steep discounts for risk assets.

Conversely, periods of abundant liquidity not only support valuations but also act as a stabilizing force. In the post-2008 era, unprecedented central bank liquidity injections not only prevented systemic collapse but also ushered in a decade-long bull market. This underscores that liquidity doesn’t just follow market cycles; it shapes them.

But there’s a catch. Markets can become “hooked” on easy liquidity, making them vulnerable to sharp pullbacks if it suddenly dries up. That’s why timing indicators matter: they help us ride the wave when liquidity flows freely and protect us when the tide turns.

Rising Capital per Capita and the Decline in Natural Rates

While abundant liquidity helps push higher values, another crucial factor is the growth of capital per person.

As economies become wealthier, capital becomes more plentiful, leading to lower natural interest rates. By “natural” interest rate, I mean the interest rate free of inflation that would typically prevail in a given economy without central bank interference. The Austrian School of Economics has excellent insights into the natural rate of interest.

Interest is the cost of capital. If capital is scarce, the natural interest rate will rise. If it becomes abundant, like the price of everything, its rate will decline. This is why wealthy countries have lower interest rates than poorer countries. Lower interest rates result in higher valuations for stocks (PER).

A proxy for the capital stock per capita is GDP per capita, which I will use for the rest of this article.

I created the chart below that shows the relationship between GDP per capita and PER. I used the ETF representing each country as a proxy for its stock market. The x-axis displays GDP per capita, and the y-axis displays PER. The linear regression line clearly indicates that the higher the country’s GDP per capita, the higher the PER.

The correlation between a country’s wealth and its PER is noteworthy:

Illustration 4: Correlation Between Wealth per Capita in several countries and Valuation Multiples

The chart above has a Pearson correlation coefficient of 0.64, which is high, implying that a 2x richer country tends to have a 1.4 P/E points higher valuation (based on the fitted slope).

But things get even more interesting when we focus on the USA. The USA has continually grown richer in inflation-adjusted terms, which means a higher real GDP per capita.

Illustration 5: Correlation Between US inflation-adjusted GDP per Capita and Valuation Multiples

Higher capital, measured by GDP per capita, also contributes to higher valuations in the USA. GDP inflation-adjusted has risen from the 1960s to today, as has the PER.

A lower natural interest rate resulting from increasing GDP directly boosts valuation multiples: when future earnings are discounted at a lower rate, their present value rises. Comparing current valuation levels to those of the 1970s or 1980s ignores this structural change. In a world with abundant capital, it is rational—not speculative—for investors to pay higher prices for each unit of earnings.

How much of the PER expansion is due to liquidity and how much to growing GDP per capita?

So far, we have examined how both liquidity and rising GDP per capita influence stock market valuations. An attentive reader might wonder whether we are conflating these two variables, as both affect the price-to-earnings (P/E) ratio. It is essential to separate and isolate the impact of each factor on P/E expansion.

Using AI, I conducted an in-depth analysis to separate the effects of liquidity growth from those of increasing GDP per capita. How much has each factor contributed to expanding PERs?

Since 1960, there has been a rise in the trailing P/E of about +12.7 points in total.

• Of that, roughly +4.1 points came from GDP per capita growth.
• Roughly +8.6 points came from more liquidity (real M2).

Therefore, for Trailing P/E, liquidity accounts for about twice as much of the increase as GDP does, but both are important.

Roughly, an increase of +1% in GDP per capita results in an increase of about +0.032 PER points.

And an increase of +1% in real M2 brings about a rise of +0.046 PER points.

Conclusion: Implications for Investors.

For investors, this analysis changes how valuations should be read. High P/E ratios or market cap to GDP levels are not automatic warning signs. They reflect powerful forces: abundant liquidity, low natural interest rates, reduced systemic risk, and a growing capital base. Valuations must be measured against these realities rather than clinging to outdated rules that label a P/E of 10 as cheap and 22 as expensive.

In this vein, just as a P/E ratio of 22 is often seen as expensive, many investors claim that another indicator points to extreme overvaluation: the Buffett Indicator, which compares the total stock market value to the country’s GDP. At first glance, its current reading (red line in the chart) appears extreme and seems to confirm that a crash is inevitable. That conclusion is wrong. Adjusting the Buffett Indicator for M2 gives a very different picture. The blue line at the bottom of the chart shows the ratio has hardly moved, from 0.13 in 1978 to 0.14 today.

During the dotcom bubble, the liquidity-adjusted indicator reached 0.296 in March 2000, more than twice today’s level. Seen through the lens of liquidity, current valuations are elevated but nowhere near the excesses of that era.

 

In today’s global economy—wealthier, more liquid, and better-capitalized than at any time in history—high valuations are not signs of irrational exuberance but reflections of structural forces that have permanently shifted the baseline for pricing risk.

However, should liquidity suddenly contract and/or GDP per capita shrink (due to misguided economic policies, war, etc.), then we should brace ourselves for a market crash, which is precisely what happened in the 1930s depression: Shrinking liquidity and real GDP. Market timing will be the lifeboat in such a case.

Sincerely,

Manuel Blay

Editor of thedowtheory.com