Jim Cramer recently highlighted Hendrik Bessembinder’s paper, Which U.S. Stocks Generated the Highest Long-Term Returns? The paper shows that a remarkably small number of stocks generate most long-term stock-market wealth.

Bessembinder treats that concentration as a powerful argument for diversification; Cramer treats it as an invitation to select exceptional companies.

The Bessembinder Evidence

Bessembinder analyzes 29,078 U.S. common stocks contained in the CRSP database from December 1925 through December 2023.

The analysis reveals:

· 51.6 percent of stocks produced negative returns over their listed lifetimes.

· Seventeen stocks produced cumulative returns exceeding five million percent.

· Yet the 17 most spectacular stocks produced an average annual compound return of only 13.47 percent. Their almost unimaginable final returns resulted mainly from compounding over exceptionally long periods.

· Nvidia recorded the highest annualized return among stocks with at least 20 years of data, at 33.38 percent.

These results build on Bessembinder’s earlier and more important paper, Do Stocks Outperform Treasury Bills? That study found that four out of every seven U.S. common stocks produced lifetime buy-and-hold returns below those of one-month Treasury bills. Even more strikingly, the best-performing 4 percent of listed companies accounted for the entire net wealth created by the U.S. stock market since 1926. The remaining 96 percent, taken together, merely matched Treasury bills.

Cramer’s Interpretation

Cramer accepts Bessembinder’s central empirical finding—that most market wealth is generated by a small number of stocks—but still maintains that a portion of an investment portfolio should be placed in individual stocks.

He argues that the extraordinary winners were not necessarily obscure companies discoverable only through luck. Many were familiar businesses—including Coca-Cola, IBM, Boeing, Deere, and Johnson & Johnson—with recognizable products, strong franchises, and long records of success. Exceptional companies, in Cramer’s view, are often visible to consumers and investors before all their gains have occurred.

Cramer is not recommending that investors abandon index funds. His proposed model appears to place approximately half of an investor’s savings in an index fund, with much of the remaining half allocated among roughly five individual growth stocks from different industries, together with some form of non-stock hedge. The index position provides broad diversification and protection against mistakes in the actively selected portion, while one or two “hero stocks” may generate enough appreciation to transform the performance of the overall portfolio.

This position is not wholly inconsistent with Bessembinder. Both agree that a few stocks create a remarkably large share of market wealth. The disagreement concerns whether investors can identify those companies with sufficient reliability and hold them long enough to capture their extraordinary returns.

Bessembinder sees a haystack in which failing to find a few crucial needles can be extremely expensive. Cramer responds that some of the needles are unusually large, shiny, and sitting in plain sight.

Comment One: FANG Was a Great Call, but It Is Not a Complete Test of Cramer’s Method

Cramer deserves real credit for introducing the term FANG—Facebook, Amazon, Netflix, and Google—on February 5, 2013, and repeatedly advocating those companies. He was not merely claiming after the fact that they had been obvious. He identified them publicly before most of their subsequent gains occurred.

Using his own calculation through the end of 2024, $1,000 invested in each of the four original FANG stocks grew from $4,000 to approximately $82,655. The same $4,000 invested in the S&P 500 grew to approximately $19,400. He also calculated that a separate $1,000 investment in Apple would have grown to nearly $18,000.

That was an outstanding call. But the comparison does not establish that exceptional stocks are generally easy to select.

Cramer has made hundreds or thousands of recommendations. A television program built around discussing several stocks every night will inevitably generate both spectacular winners and serious disappointments. Evaluating only FANG creates a selection problem: the winning recommendation is remembered precisely because it won.

A study by Jonathan Hartley and Matthew Olson examined the complete history of Cramer’s Action Alerts PLUS portfolio from 2001 through 2016. The authors found that it underperformed the S&P 500 total-return index both from its inception and from the 2005 launch of Mad Money. It also produced a lower Sharpe ratio, indicating weaker performance after accounting for volatility. That portfolio is not a perfect record of every televised recommendation, but it is much closer to a complete investable record than a retrospective examination of FANG alone.

FANG proves that Cramer can identify an extraordinary group. It does not prove that the ordinary investor can reproduce the result or that Cramer’s complete set of recommendations has beaten the market.

Comment Two: FAANG’s Success Required Investors to Endure Serious Declines

Cramer’s FAANG recommendation produced extraordinary long-term returns, but it has not outperformed in every period. In 2026 through July 11, an equal-weight FAANG portfolio gained about 4.0 percent, compared with about 10.7 percent for the S&P 500.

Its long-term success was also never smooth. An equal-weight FAANG portfolio lost nearly 44 percent in 2022, while Nvidia—another of Cramer’s great long-term successes—fell about 32 percent in 2018 and 53 percent in 2022.

A preset stop or stop-limit order intended to prevent a large loss in Nvidia could easily have removed the stock during one of its severe declines, thereby preventing the investor from receiving much of its extraordinary subsequent gain. The same is true of panic selling: an investor who correctly identifies a future winner but abandons it during a frightening decline will not capture the return that Cramer cites.

Successful implementation therefore required more than identifying the right companies. Investors also needed the financial capacity and psychological resilience to withstand substantial temporary losses without selling, even though they could not know at the time whether a decline was temporary or the beginning of permanent deterioration. That ability to remain invested through uncertainty is one of the most demanding—and least emphasized—parts of the strategy.

Comment Three: Profit-Taking and Retirement Withdrawals Change the Experiment

Cramer’s FANG calculation assumes that the investor reinvested distributions, made no withdrawals, and held the stocks through the end of 2024. That is appropriate for measuring accumulation, but less realistic for retirees or others who must sell assets to finance consumption.

Withdrawals, taxes, rebalancing, and the need to limit concentration can all reduce the amount left to compound in the winning stocks. These consumption-related issues change the calculus, but a full analysis is beyond the scope of this article.

Comment Four: Many Plausible Five-Stock Portfolios Would Have Failed

Cramer’s FAANG selections were excellent, but they were also highly concentrated in technology and communications companies. A more typical investor choosing five admired companies in 2013 might instead have selected names from Fortune’s list—such as Coca-Cola, IBM, Starbucks, Disney, or General Electric—several of which subsequently produced long periods of weak or market-lagging returns.

Many reasonable five-stock portfolios designed to beat the market therefore would not have succeeded. A more diversified way to implement Cramer’s underlying growth thesis would have been to buy a technology ETF such as the Vanguard Information Technology ETF (VGT), either instead of the individual stocks or alongside them, while retaining a broad-market fund such as an S&P 500 ETF for additional diversification.

Conclusion

Cramer and Bessembinder agree about the most important empirical fact: a remarkably small number of stocks produce a remarkably large share of long-term market wealth.

Cramer interprets this concentration as an invitation. Find the exceptional companies, hold them through temporary setbacks, and allow one or two hero stocks to transform the portfolio.

Bessembinder interprets it as a warning. The winners are rare, their identities are obvious mainly in retrospect, and the penalty for omitting them can be enormous. A broad, capitalization-weighted index owns many mediocre companies, but it also guarantees that the investor will own every future hero and that each hero will become a larger part of the portfolio as it succeeds.

Cramer’s FANG recommendation was excellent and should not be dismissed as luck merely because it is inconvenient for advocates of passive investing. Several of the companies were already prominent in 2013, and investors still had an opportunity to earn extraordinary subsequent returns.

But FANG is an example of what was possible, not a reliable estimate of what was probable. The proper comparison is not FANG against the S&P 500. It is the complete set of plausible portfolios that an investor using Cramer’s reasoning might have assembled against the S&P 500.

The investor must also do more than identify the eventual winners. The investor must avoid selling them too early, withstand severe drawdowns, resist stop-loss rules that remove them from the portfolio, manage growing concentration, and finance retirement consumption without liquidating too much of their future upside.

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Every investor knows the stock market feels expensive today. But how expensive? 30%? 50%? Is a correction imminent, or are we simply operating in a new structural regime of higher permanent growth?

If you turn to Wall Street for answers, you usually get trapped in a circular argument. Analysts will tell you the S&P 500 is valued fairly compared to next year’s forward earnings estimates. But those earnings estimates are based on corporate revenue projections, which are heavily influenced by current market sentiment. It’s a self-referencing echo chamber.

To find a true, uncorrupted fundamental anchor, my son and I recently sat down to solve this quantitative puzzle. We developed an independent Trend-Line Reversion Model designed to answer two precise questions:

1. When was the last time the stock market cleared at a reasonable price reflecting long-term structural equilibrium?

2. Based on that anchor, what is the gap between the current and estimate realistic stock price?

This paper uses a trend-line reversion model to obtain valuation gap estimates for the S&P 500 under different assumptions and valuation gap estimates for portfolios of large-cap value and growth stocks.

The Trend-Line Reversion Methodology:

We assume that over long horizons, the stock market compounds at a reasonable rate, perhaps 7 percent per year. If we knew the year the stock market was a sane or reasonable estimate, we could estimate over or under valuation as the difference between the actual price and a projected reasonable price equal to S(1.07)^t where S is the value in the sane year.

A random choice of a sane year could warp the analysis. An analysis anchored to the top of the Dot-Com expansion would make subsequent decades look cheap while an analysis anchored to the absolute depth of the financial crisis would exaggerate the size of the bubble.

To eliminate cognitive bias and guesswork, we designed a Scoreboard Optimization Loop using a point-by-point least-squares percentage comparison.

We isolated a 20-year historical window (2005 through 2025) and tested every single year as a candidate baseline anchor. For each candidate year, the model projects a 7% compounding trend line forward and backward through the calibration timeline. It then compiles the Sum of Squared Percentage Errors (SSPE) between that specific trend line and actual historical data:

By squaring the percentage errors, we ensure two things: overvaluations and under valuations are penalized equally, and the math strips away raw dollar scale (so a deviation in 2005 carries the same analytical weight as a deviation in 2025).

The candidate year that finishes with the lowest total error score represents the true geometric center of gravity for the modern market—the historical point where market prices sat perfectly on the sustainable long-term trend.

Scenario 1: The S&P 500 at a 7% Normal Growth Rate

When we ran the broad S&P 500 data through the optimization loop assuming a standard historical price growth rate of 7%, the scoreboard lit up with a definitive best anchor year: 2012.

· 2005 — Total Error Score (SSPE): 1.84 | Highly Distorted (Pre-Crisis Bubble Baseline)

· 2008 — Total Error Score (SSPE): 1.12 | Displaced (Market Crisis / Undervalued Baseline)

· 2011 — Total Error Score (SSPE): 0.38 | Near Structural Fair Value

· 2012 — Total Error Score (SSPE): 0.31 (GLOBAL MINIMUM) | Optimized Structural Fair Value Equilibrium

· 2013 — Total Error Score (SSPE): 0.44 | Near Structural Fair Value

· 2016 — Total Error Score (SSPE): 0.89 | Moderate Structural Premium

· 2020 — Total Error Score (SSPE): 2.45 | Exceptionally Distorted (Emergency Stimulus Baseline)

Anchoring the model to the high-stimulus valuation of 2020 creates an enormous error score (2.45) because it forces the historical trend line way below where the market actually cleared in the mid-2000s.

The 2012 base anchor cuts through the cyclical noise flawlessly. It represents the quiet post-crisis stabilization period immediately before emergency monetary interventions completely altered equity multiples.

The 7% Valuation Gap Result

When we take that optimized 2012 trend line and project its trajectory out to the beginning of 2026, it indicates that the structural fair value of the S&P 500 should be $3,552.

The actual average price of the S&P 500 at the target checkpoint was $5,000.

Under these standard baseline assumptions, the broad market entered the year trading 40.76% above its long-term fundamental compounding curve.

The Robustness Check: What if Normal Growth is 8%?

A rigorous quantitative model must be stress-tested. What if a 7% growth assumption is too conservative for a modern economy? What if structural technology gains mean that 8% is the new baseline normal?

When we adjusted the core parameter to an 8% compounding rate and re-ran the entire optimization loop, the model shifted across two separate dimensions:

1. The Anchor Year Shifts to 2013: Because an 8% compounding line climbs at a steeper angle, the optimization loop mechanically pushed the winning anchor year forward to 2013 (SSPE: 0.38). The math needed a slightly higher historical starting price to prevent the steeper line from dropping entirely below the actual market data of the mid-2000s.

2. The Valuation Gap Compresses: Because an 8% trend line accumulates baseline value much faster, it catches up to current prices aggressively. Under the 8% model, the projected fair value for 2026 rises to $4,171.

This robustness check reveals the valuation gap is highly sensitive to the normal stock price growth assumption. A 100-basis point increase in return assumptions reduces the overvaluation gap from around 41 percent to around 20 percent.

However, the model is highly robust regarding the location of fair value. Whether you assume a 7% or 8% growth engine, the algorithm persistently locks onto the 2012–2013 window as the only structurally sound, uncorrupted baseline era of the last twenty years.

Valuation gaps for large-cap growth and large-cap value Portfolios

Looking at the S&P 500 as one massive, monolithic index can mask deeper structural imbalances. To discover where the overvaluation risk is truly concentrated, we split the index into its two primary sub-components: S&P 500 Growth and S&P 500 Value.

We cannot use a uniform, index-wide “average” growth rate for both. Because of annual index rebalancing, value indices regularly prune their top overperforming companies, while growth indices hoard multiple expansions. Furthermore, Value returns rely heavily on cash dividends (which mechanically drop out of the raw stock price), while Growth relies almost entirely on capital appreciation.

To adjust for this “style drift,” we applied distinct, historically accurate price growth parameters: 9.5% for Growth and 5.0% for Value.

When we ran these independent optimization loops, a stark divergence emerged:

1. S&P 500 Value (5.0% Baseline Anchors to 2012)

The Value index aligns perfectly with our broad market model, choosing 2012 as its optimal baseline (SSPE: 0.26).

• Actual Price (Jan 2026): $1,720

• Model-Derived Fair Value: $1,351

• Value Sector Overvaluation: 27.31%

2. S&P 500 Growth (9.5% Baseline Anchors to 2016)

The Growth index experienced a profound structural forward break, shifting its optimal anchor year to 2016 (SSPE: 0.42). This marks the exact dawn of the modern, ultra-large-cap technology dominance regime.

• Actual Price (Jan 2026): $3,650

• Model-Derived Fair Value: $2,411

• Growth Sector Overvaluation: 51.39%

The Substack Bottom Line

Perhaps the most startling result of this estimation process is that even the traditionally defensive Value sector is fundamentally overvalued. Market risk is deeply asymmetrical: the Growth sector sits in an acute overvaluation regime at 51.39% above trend, while the Value sector trades at a more moderate but still significant 27.31% above trend. If historical gravity is any indicator, a macro-reversion to the mean implies that both sectors can absolutely fall from current levels, though the drawdown will hit the heavily overextended growth curve far harder than the value baseline.

Disclaimer: This analysis represents a structural trend-line model executed for quantitative research purposes and does not constitute formal investment advice. All chart metrics correspond to annual averages up to January 2026.

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Introduction:

Traditional valuation statistics, especially the ubiquitous PE ratio, are undefined when earnings are negative. The PE ratio cannot be used to evaluate firms in the early stages of their growth or any firms with negative earnings. This paper applies a valuation transformation proposed by Bernstein (2025) that remains mathematically defined whether earnings are positive, zero, or negative, to examine the path of valuation of two historic tech companies, Amazon and Tesla, from their inception to maturity. The paper then applies this valuation methodology to projected earnings and valuation statistics for three companies, SpaceX, OpenAI, and Anthropic, which are currently preparing for an IPO.

Methodology:

My SSRN paper, Measuring Portfolio Valuation, describes the measurement of firm and portfolio PE statistics when some firms have negative earnings. This note on valuation measures is my most frequently downloaded article.

PE ratios behave counterintuitively when earnings are negative because an increase in price causes P/E to become more negative rather than larger.

By contrast, the statistic S=(V-E)/V will increase whenever V rises or E falls, regardless of whether E is positive or negative. At E=0 this number is 1, at E<0 this number is greater than one (indicative of a higher valuation), and for E>0 this number is less than 1.0.

For instance, a company trading at a conventional P/E ratio of 20 translates to S=0.95 while a company with a PE ratio of 15 has S=0.933.

The PE ratio does not exist when E is negative, but the S remains well defined.

The S can be used to evaluate valuation in the early growth years when a company has no earnings or when a company experiences large losses later in its product cycle.

Tesla and Amazon:

The early phase of tech companies is often characterized by a combination of astronomic valuations and substantial losses. The S statistic in the early phase of a firm’s growth is a measure of the relationship between expanding market enthusiasm and escalating market losses.

For Amazon, S at its 1997 public inception was 1.071, and it reached its lifetime maximum S at 1.256 in 2000.

For Tesla, the first-year value of S at its 2010 public inception was 1.091, and it reached its maximum value of S in 2012 with a highest value of 1.104.

SpaceX, OpenAI and Anthropic:

SpaceX, OpenAI and Anthropic are preparing their IPOs, and the released projections of V and E can be used to create the initial valuation statistic S.

· SpaceX: A projected V of 1.77 trillion and E of -4.9 billion yields an S value of 1.00276.

· OpenAI: A projected V of $852 billion and E of $14 billion yields an S value of 1.01643.

· Anthropic: A projected V of $965 billion and E of -14 billion yields an S value of 1.01450.

Conclusion

Unlike the traditional P/E ratio, which suffers from mathematical discontinuity, the S statistics is defined over all values of E.

The projected S values for these pending IPOs are lower than the initial S values observed for Amazon and Tesla, indicating that their projected valuations are not unusually aggressive relative to those historical benchmarks.

Of course, actual outcomes are TBD.

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One of the most frequently cited financial metrics is EBITDA. It appears in earnings releases, stock analyses, private equity transactions, and merger negotiations. Yet it is also one of the most misunderstood numbers in finance.

EBITDA stands for Earnings Before Interest, Taxes, Depreciation, and Amortization. In practice, analysts begin with a company’s net income and then add back interest expense, taxes, depreciation, and amortization.

The purpose of EBITDA is to answer a specific question: How profitable is the underlying business before considering how it is financed?

That distinction matters because two companies can operate essentially identical businesses while reporting very different net income. One company may have little debt, while the other may owe billions of dollars and face substantial interest payments. EBITDA attempts to strip away those financing differences and focus on the operating business itself.

For that reason, EBITDA can be a useful tool. But it also has an important limitation: shareholders do not receive EBITDA. They receive whatever remains after all the company’s obligations—including interest payments—have been satisfied.

Consider two companies with identical operations. Each generates $500 million in revenue and incurs $400 million in operating expenses. Both therefore produce $100 million of EBITDA. At first glance, the companies appear equally profitable.

Now suppose Company A carries very little debt. It pays only $5 million per year in interest expense and ultimately reports $75 million in net income.

Company B, by contrast, has accumulated a large amount of debt. Its annual interest expense is $95 million. After paying lenders and taxes, it reports only $4 million in net income.

The striking fact is that both companies report the same EBITDA: $100 million.

An analyst focused exclusively on EBITDA might conclude that the two businesses are equally attractive. In one sense, that is true. Their underlying operations generate the same amount of operating profit. But shareholders do not own the operations in isolation. They own the residual claim after everyone else has been paid.

In Company A, most of the operating profit ultimately belongs to shareholders. In Company B, most of the operating profit belongs to creditors. The business may be generating $100 million of EBITDA, but nearly all that value is being consumed by interest payments.

Defenders of EBITDA would correctly point out that high interest expense is not entirely a deadweight cost. Interest payments generally reduce taxable income, creating what finance professionals call a tax shield. That benefit is real. In this example, Company B pays only about $1 million in taxes compared with roughly $20 million for Company A. Yet the tax shield offsets only a fraction of the additional interest burden. Company B saves about $19 million in taxes but pays approximately $90 million more in interest expense. Shareholders are still substantially worse off.

This illustrates both the strength and the weakness of EBITDA.

• The strength: EBITDA helps analysts compare operating businesses without being distracted by financing choices.

• The weakness: Financing choices are not irrelevant. A company that has borrowed too much may leave very little value for shareholders even if its underlying operations appear healthy.

This is why experienced investors rarely stop at EBITDA. After seeing the EBITDA figure, they immediately ask additional questions:

• How much debt does the company have?

• How much interest must it pay each year?

• How much cash remains after those payments?

• How much profit is left for shareholders?

A useful analogy is that EBITDA measures the horsepower of the engine. It tells us something important about the vehicle’s capabilities. But it does not tell us how much weight the vehicle is towing. Two trucks may have identical engines. If one is pulling an empty trailer and the other is hauling a massive load, their performance will be very different despite having the same horsepower.

Likewise, two companies may report identical EBITDA. If one is carrying a heavy burden of debt, shareholders may receive far less benefit from that operating performance than the EBITDA figure suggests. EBITDA is therefore best viewed as a starting point rather than a conclusion. It can tell us whether a business has a strong engine. It cannot tell us how much of that power ultimately reaches shareholders.

The danger of using EBITDA as a definitive measure of health is not merely theoretical; financial history is littered with corporate collapses where analysts clung to glowing operating metrics while businesses were suffocating. During the telecom meltdown of 2002, companies like WorldCom masked enormous cash drains by classifying routine operating costs as capital expenditures. Because operating expenses lower EBITDA but capital investments do not, this trick kept EBITDA looking robust while the actual business was bleeding cash. Similarly, the Dot-Com crash of 2000–2001 popularized customized “Pro Forma” adjustments that added back marketing and customer acquisition costs, leading investors to back startups with zero path to net profitability. Even successful looking “roll-up” strategies, such as Valeant Pharmaceuticals in 2015, used heavily adjusted EBITDA figures to hide the massive, unsustainable debt loads required to acquire other firms.

Today, this exact dynamic is repeating itself in the artificial intelligence infrastructure boom. Wall Street analysts are aggressively valuing specialized AI cloud providers and enterprise software firms on massive EBITDA multiples, ignoring skyrocketing stock-based compensation (added back as a “non-cash” expense) and billions in hardware debt collateralized directly by depreciating microchips. Because training and maintaining AI requires continuous, intense computing power, treating these foundational, recurring operational outlays as ignorable capital expenditures misstates the actual profit margins. Ultimately, the heavily adjusted EBITDA metrics seen across modern AI valuations closely resemble the deceptive “Pro Forma” metrics of the Dot-Com era, promising future profitability while obscuring the heavy capital burdens required to stay alive.

This exact dynamic is playing out across the AI infrastructure ecosystem, where massive operating profits are paired with heavy capital burdens.

We can see this structural strain clearly in the current financial profiles of the market’s primary infrastructure players.

· For example, Oracle Corporation leverages its legacy profitability to generate a massive $27.4 billion in EBITDA yet carries over $123 billion in net debt to finance its intensive cloud expansion.

· Similarly, specialized private hyperscalers like CoreWeave boast phenomenal EBITDA margins above 50% but operate with gross leverage ratios near 5.8x to secure advanced hardware facilities.

· In the hardware and physical layers, semiconductor giant Broadcom utilizes its $37.2 billion in EBITDA to service a substantial debt load exceeding $70 billion.

This structural blind spot becomes uniquely dangerous in hyper-paced environments where companies must aggressively scale just to keep pace with rapid technological shifts. Frontline AI developers like OpenAI and Anthropic face an unprecedented capital treadmill: the moment a frontier model is completed, the company must immediately secure tens of billions of dollars in compute infrastructure to train the next generation, or risk total obsolescence within a year. Evaluating these foundational players on an EBITDA basis completely strips out the massive interest burdens, capital leases, and compute liabilities required to sustain their market positions. In a sector where technology degrades at an exponential rate, an analyst relying on EBITDA is evaluating a business as if its current assets will last forever, completely ignoring the reality that the cash required for technological survival is already spoken for.

When reading modern analyst reports on enterprise AI and software companies, look specifically for these red flags:

• “Rule of 40” calculations using Adjusted EBITDA: Analysts frequently add a company’s growth rate to its Adjusted EBITDA margin to see if it equals or exceeds 40%. If they used net income or free cash flow instead, many hyped AI firms would fail the test completely.

• Massive gaps between EBITDA and Free Cash Flow (FCF): If a company boasts $100 million in Adjusted EBITDA but has deeply negative Free Cash Flow, it means they are rapidly burning cash on hardware or heavy stock dilution just to keep their “profitable” operating engine running.

The Ultimate Takeaway: As billionaire investor Warren Buffett famously remarked regarding the metric: “Does management think the tooth fairy pays for capital expenditures?”

Just like the historical market cycles of the past, today’s AI valuations rely heavily on metrics that promise profitability eventually—if you agree to ignore the very real, recurring expenses required to build and sustain the technology. If a company must constantly spend cash to replace its equipment, update its technology, or service its debt, that cash is gone. EBITDA can tell you if a business has a great engine, but it won’t stop you from driving off a cliff if you ignore the balance sheet.

I found this article on the importance of cash flow and the EBITDA limitation useful.

https://www.ghjadvisors.com/ghj-insights/the-importance-of-cash-flow-and-the-ebitda-limitation

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Since the outbreak of the Iran war on March 2, the traditional correlation between gold and oil has collapsed. While oil has surged 50%, gold has plunged 18%. This memo identifies a systemic liquidity squeeze in which interest rate concerns and a resurgent dollar have become paramount much sooner than in previous cycles.

Key Findings

• Unlike the stagflation era, the Fed quickly became hawkish, and the public rapidly shifted focus to inflation expectations.

• Real interest rates have moved into positive territory (~1.0%) just weeks into the conflict, stripping gold of its competitive edge.

• With $2 trillion in private credit funds restricting withdrawals and the MAG7 somewhat sluggish, institutional investors are selling their most liquid winner: gold.

• The U.S. is a net exporter of oil. This allows the dollar to rise alongside oil, creating additional pressure on gold.

• These factors have created sustained selling pressure on gold, even though it has traditionally served as a safe haven during geopolitical and macroeconomic turmoil.

• Gold’s decline is not necessarily a short-term dislocation; elevated starting valuations and rising real rates suggest continued downside risk rather than a clear “buy the dip” opportunity.

The Four Major Oil Shocks

Until now, gold was the “safe-haven” destination. In 2026, it has become the “source of cash.”

• 1973 Arab Oil Embargo: Gold rose 65% as the unanchored dollar weakened following the end of Bretton Woods.

• 1979 Iranian Revolution: Gold peaked at $850 (Jan 1980), only falling after Volcker pushed real rates deeply positive to restore monetary credibility.

• 1990 Gulf War: Gold saw a 12% tactical “fear spike” that faded quickly as the conflict appeared limited.

• 2026 War with Iran: Oil is up 50%, but gold has fallen 22% from its January highs near $5,600.

2026 is the first time a major oil shock has triggered a bear market in gold. In the 20th century, gold was an accumulation asset. In 2026, it is a distribution asset.

Why the 2026 Oil Shock Differs

First, both the Federal Reserve and investors quickly became focused on inflation expectations and interest rates. Support for rate cuts has evaporated as policymakers recognize they cannot risk a resurgence of inflation after misjudging it in 2022. The market has reached the same conclusion. Between March 2 and March 24, the 10-year Treasury yield surged from 4.05% to 4.38%.

Second, previous oil shocks typically coincided with a weaker dollar, which made gold more attractive as a safe haven. This time, the United States is a net exporter of energy, and both the dollar and real interest rates have risen. Despite political turmoil, the U.S. and the dollar appear to be the safest investment options.

Third, gold entered this period at elevated levels. Investors facing liquidity needs—due to weak tech performance and restrictions in private credit—are realizing gains in gold, their most liquid outperformer.

Conclusions

The relationship between gold and oil in this shock differs fundamentally from previous episodes.

In 2026, gold is declining because it is the most attractive asset to sell in a crisis where interest rates, the dollar, and oil are moving in lockstep, while other parts of the portfolio—primarily tech and private credit—are under pressure.

This does not appear to be a “buy the dip” opportunity for gold. Prices remain historically elevated, while real interest rates and the U.S. dollar continue to rise. In this environment, gold is likely to remain under pressure as monetary conditions tighten, and liquidity constraints persist.

Authors Note: www.economicmemos.com is a source of information on policy, politics, personal finance and investment. If you liked this post, and want additional advice on how to navigate the current market downturn go here. This blog consistently recognizes that investment and wealth accumulation are not the only factors impacting financial security.

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Interesting Videos:

US Dollar Performance During Energy Shocks

The Oil Shock That Reshaped the Modern Economy - YouTube

Financial Historian · 6.6K views

This memo investigates why the recent surge in oil prices has failed to trigger a rally in “green” alternatives like heat pumps and solar power. While the long-term economic case for electrification strengthens as fossil fuels become more expensive, these sectors remain tethered to interest rate cycles and construction activity. We analyze the performance of key players like Carrier, Enphase, and First Solar to determine how and when these sectors will ultimately capitalize on the energy crisis.

Key Takeaways

• Most heat pump leaders are diversified industrial conglomerates, diluting their exposure to specific energy price shocks.

• Solar and HVAC stocks currently behave like long-duration growth assets, making them more sensitive to interest rates than to the price of a barrel of oil.

• Data center cooling requirements are creating a high-margin, non-discretionary “cushion” for diversified HVAC firms that residential markets lack.

• Investors should look for a stabilization in Treasury yields, which are linked to oil prices and inflation expectations, prior to entering these sectors.

Author’s Note

The analysis of the heat pump sector is available to all readers. Detailed research on the solar sector and specific investment entry points is reserved for paid subscribers. You can upgrade to a full subscription for just $48 per year (a 20% discount) using this link: https://www.economicmemos.com/56428713. The blog www.economicmemos.com has a mix of articles on policy politics, personal finance and investment opportunities. Readers will likely earn back the subscription fee from the personal finance section alone.

Geopolitical Shocks and Electrification: An Investor’s Dilemma

The escalation of the conflict in Iran has sent ripples through global energy markets, pushing Brent crude toward the $100–$120 range and causing localized spikes in diesel and LNG prices. For many retail investors, the intuitive reaction is to seek “green” alternatives—specifically heat pumps and solar power—as natural beneficiaries of expensive fossil fuels.

However, a closer analysis reveals a paradox: while high energy prices are a long-term structural tailwind, they are currently being overwhelmed by immediate macroeconomic headwinds. The following analysis explores why these sectors have struggled to act as “safe havens” during the 2026 energy shock.

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Heat Pump Companies

This memo evaluates whether higher oil and natural gas prices create a meaningful tactical opportunity in publicly traded heat pump and HVAC companies.

There is no “pure-play” publicly traded heat pump company. The leaders—Carrier Global (CARR), Trane Technologies (TT), and Lennox International (LII)—are diversified industrial conglomerates. High energy prices make heat pumps more competitive, but these firms are deeply tied to broader construction and interest rate cycles and higher oil prices do not lead to immediate gains in stock prices in this sector.

• Rising energy costs act as a tax on consumers, increasing recession risk. Households often defer high-capex purchases like heat pumps when the outlook is uncertain.

• HVAC demand tracks housing and commercial building. High interest rates raise financing costs for both installers and homeowners.

• Adoption cycles are governed by replacement needs and policy timelines, not daily oil price fluctuations.

Stock Performance (Feb 27 – March 19, 2026):

The market reaction has been “risk-off” rather than “energy-pivot”:

• Carrier (CARR): Fell ~8% (from $64.40 to $58.97).

• Lennox (LII): Saw significant drawdowns consistent with housing sensitivity.

• International Plays: Daikin and NIBE moved in lockstep with global growth concerns rather than energy pricing.

These stocks behave like cyclical industrials with a long-duration electrification overlay. Sector-specific tailwinds have not been sufficient to overcome generalized market downward trends.

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Solar Power Companies

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Key Results

· The Structural Wall: NVIDIA’s $5 trillion valuation breaches IRS “25/50 rule” concentration limits, forcing major ETFs to automatically sell shares to maintain tax-advantaged status.

· Institutional “Firing” Risk: Active fund managers are mandated to trim positions exceeding 15-20% to manage risk, creating a ceiling for the stock regardless of fundamental strength.

· The Circular Loop: A significant portion of revenue is tied to NVIDIA’s investments in startups that then buy NVIDIA chips, creating concerns regarding the “Quality of Earnings.”

· Reduced Upside: While fundamentally strong, NVIDIA’s massive size necessitates a shift in investor expectations from doubling annually to moderate 30-40% annual growth.

· Strategic Restructuring: To break the structural ceiling, NVIDIA may need to spin off its investment arm to bypass ETF concentration limits and unlock independent valuation for its AI ecosystem.

Introduction: The Paradox of Success

Despite delivering three consecutive blockbuster financial reports—culminating in a record-shattering $68.1 billion in quarterly revenue with 73% year-over-year growth—NVIDIA’s stock price has failed to mirror this explosive financial performance. Instead of skyrocketing, the stock has frequently stalled or retreated immediately following these announcements. This frustrating lack of price appreciation is not a failure of the AI revolution; it is a symptom of “The Structural Ceiling”—a point where a company becomes too successful for the financial “containers” designed to hold it.

1. The Mechanical Sell-Wall: ETFs and the 25/50 Rule

The primary headwind isn’t sentiment; it’s math. Most Diversified ETFs operate under IRS “RIC” (Regulated Investment Company) rules, specifically the 25/50 rule. This mandates that no more than 25% of a fund’s assets can be in a single issuer, and the sum of all holdings over 5% cannot exceed 50% of the portfolio.

When NVIDIA’s stock price surges, it frequently breaches these legal thresholds. To maintain their tax-advantaged status, ETF managers are legally forced to sell NVIDIA and “recycle” that capital into smaller, often slower-growing firms.

As of February 2026, concentration levels in major ETFs are nearing critical thresholds:

· VanEck Semiconductor ETF (SMH): Currently holding ~20-25% in NVIDIA, frequently hitting the “Concentration Wall” and triggering forced selling.

· Vanguard Info Tech ETF (VGT): Positioned at ~17-19%, restricted by sector diversification rules upon further appreciation.

· Invesco QQQ Trust (QQQ): Holdings at ~13-15%, balanced by Apple/Microsoft, but facing high “overlap” risk.

· ProShares Ultra Semi (USD): Leveraged exposure exceeding 30%+, making it a high-volume “powder keg” for forced selling during spikes.

2. Institutional Mandates: The “Risk” Ceiling

It isn’t just passive funds. Giants like Vanguard, BlackRock, and State Street manage trillions. For an active fund manager, holding a 15-20% position in a single stock is often a violation of internal risk mandates.

As of December 2025 reporting, top institutional ownership remains concentrated:

· Vanguard Group: 2.27 Billion shares (9.23% ownership of NVIDIA)

· BlackRock: 1.94 Billion shares (7.98% ownership)

· State Street: 0.99 Billion shares (4.08% ownership)

When one or more of these three firms are forced or incentivized to trim their NVIDIA holdings—often because a stellar earnings report has pushed NVIDIA to a disproportionately large percentage of their total portfolio—the share price stagnates. Because these three firms alone own over 21% of the company, their synchronized need to manage concentration risk creates a massive supply of shares that effectively “mops up” any new buying pressure.

3. The “Circular Exposure” Web

Sophisticated investors are also wary of implicit exposure. NVIDIA has pioneered a “Virtuous AI Loop” where they invest in AI startups (e.g., CoreWeave, OpenAI) which then commit to buying NVIDIA chips.

Critics point to these “circular deals” as a potential risk to the Quality of Earnings. Furthermore, if you own Microsoft, Amazon, or Meta, you are indirectly betting on NVIDIA, as they are its largest customers. Total effective exposure for a tech-heavy investor often sits at 35-45%, leading to a “Sentiment Ceiling” where buyers simply feel “full.”

This massive concentration is why NVIDIA’s news has the potential to spill over into the rest of the tech sector. If the “Big Three” are forced to sell NVIDIA, the resulting liquidity shifts can shake the entire market. This is precisely why investors become so anxious around NVIDIA’s announcements – they impact the whole tech market.

4. The Valuation Paradox: Size vs. Growth

NVIDIA currently sells at a lower Forward P/E and PEG ratio than its competitors for two reasons.

First, NVIDIA’s shares are impacted by the selling pressure associated with institutional rules and the need for diversification described in this memo. Second, NVIDIA faces a “Size Discount” because the “Law of Large Numbers” suggests maintaining current growth is improbable and eventually some rival will come up an innovation that results in a loss of market share.

· NVIDIA (NVDA): Forward P/E ~25.0x | PEG Ratio 0.80

· AMD: Forward P/E ~40.0x | PEG Ratio 1.20

· Broadcom (AVGO): Forward P/E ~35.0x | PEG Ratio 1.25

· ASML: Forward P/E ~43.0x | PEG Ratio 1.80

In any other context, a PEG ratio below 1.0 would signal a massive “Strong Buy,” but here it reflects a stock that has become too large for the market to price efficiently.

Author’s Note to Subscribers: The structural dynamics described above explain why stellar earnings no longer guarantee a stock surge. To manage your portfolio effectively in 2026, you need to understand how to maneuver around these mechanical sell-walls.

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Paid subscribers can read on for a concrete investment strategy to manage NVIDIA’s reduced upside, a comparative analysis of how other companies solved this “concentration risk,” and the specific strategic recommendation for a corporate restructuring to unlock value.

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5. Managing “Reduced Upside” and Corporate Solutions

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Financial commentators on TV have been quick to point out a logical inconsistency in the current AI market narrative. They argue that critics are talking out of both sides of their mouths:

1. The ROI Skeptics: “AI is a bubble; the infrastructure spend is massive, but the returns for clients aren’t showing up.”

2. The Disruption Alarmists: “AI is too successful; it’s going to automate software incumbents into obsolescence.”

The AI defenders argue both fears cannot be true. If AI doesn’t work (No ROI), it can’t disrupt incumbents. If it disrupts incumbents, it clearly works (High ROI).

However, the market is not a single person with one opinion. It is a collection of thousands of portfolio managers, each with different mandates. The finance literature and some prior examples supports the contention that two seemingly inconsistent views can drive stock price and investment decision.

There is no inconsistency in Group A selling Nvidia because they fear a capex bubble, while Group B sells Salesforce because they fear Agentic AI will replace seat-based licenses. When both groups act on their specific fears, the entire sector moves down.

The “horse and buggy” analogy (historically attributed to a leading banker at Michigan Savings Bank in 1903 who advised Henry Ford’s lawyer not to invest) provides the perfect heuristic.

Imagine it’s 1905:

• Investor A refuses to invest in Ford because the infrastructure (paved roads/gas stations) is too expensive and the ROI is decades away.

• Investor B sells their shares in Carriage-Maker Inc. because they fear the “horseless carriage” will make the current business model obsolete.

Both have a bearish view, but for opposite reasons. Their combined selling pressure creates a market-wide fear of the auto-sector, even though their reasons are technically contradictory.

What the Literature Says

This phenomenon is well-documented in finance literature: Edward Miller, in a paper published in the Journal of Finance, in 1977 argues that when uncertainty is high, divergence of opinion leads to massive volatility. Prices don’t reflect an average view; they reflect the battle between the most optimistic and most pessimistic agents.

Brock & Hommes (1998) in their work on Heterogeneous Agent Models (HAM) proves that a market full of bounded rational agents—some following fundamentals (ROI), others following trends or disruption narratives—create nonlinear price shifts that a rational observer would find inconsistent.

Yan Gao, Connie X. Mao, and Rui Zhong (2006) in their study, “Divergence of Opinion and Long-Term Performance of IPOs,” use Miller’s framework to explain why high-uncertainty often leads to volatile price corrections in IPO markets.

The bottom line is that contradictory fears don’t cancel out they can lead to additional selling by each group. In the current situation, it appears as though AI technology is a “bust” for the people building it and a “threat” to the people competing with it leading to the current tech selloff.

Authors Note: This multi-topic blog has articles on economic policy, (health care, student debt, taxes, and Social Security), politics (very closely following the contest for the House of Representatives), personal finance (both issues affecting people entering the workforce and people entering retirement) and today investments and markets. Most of the material on the blog is free and I am committed to keeping it that way but some material (usually only a portion of an article) is available exclusively for paid subscribers.

This post on the impact of the wide divergence of opinions on AI on the current tech market is free to all.

People who liked this blog may also want to read A Statistically Well Behaved Transformation of PE for Growth Value Inference.

I appreciate your readership and support.

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· The AI Displacement: Crypto is losing “innovation capital” to AI infrastructure, which investors currently perceive as having higher social utility and productivity potential.

· The Energy Bottleneck: AI data centers are outbidding miners for electricity. As energy becomes a strategic resource, policymakers are likely to prioritize AI over crypto mining.

· Neutral Money vs. Digital Gold: While the “Neutral Money” bull case remains intact, crypto has failed to act as a safe haven during recent risk-off periods, trailing gold significantly.

· Structural Fraud Levels: Crypto’s “fraud-to-asset” ratio is significantly higher than traditional finance. While not a systemic threat to banks, it remains a primary barrier to institutional “core asset” status.

· The “Lost” Supply: Analysis suggests 15–20% of the Bitcoin supply is permanently inaccessible due to custody errors, creating a unique “digital burn” that impacts real-world liquidity.

Memo Summary

Is crypto a revolutionary financial layer, or a high-beta tech experiment currently losing its lunch to AI infrastructure (as a high-tech alternative) and Gold (as a low-tech safe haven)? This memo evaluates the “Neutral Money” thesis against a 50% market drawdown, rising energy competition, and the structural impact of industry fraud.

Authors Note: I believe in keeping the core analysis of Economic Memos open to everyone to help build a more informed financial community. However, the granular data and technical reference guides are reserved for my paid supporters.

Upgrade to a paid subscription to unlock the full Research Appendices, including:

· Appendix A: A credibility breakdown of the 5 different types of crypto (Bitcoin vs. Smart Contracts vs. Stablecoins).

· Appendix B & C: A complete “Registry of Risk” detailing major fraud events and the $85B+ in lost assets.

· Appendix D: The technical data on “Lost Supply”—why millions of BTC will never return to the market.

Thank you for supporting independent economic research.

Introduction:

Cryptocurrencies are digital assets that use cryptography and decentralized computer networks to record and verify transactions without relying on central authorities such as banks or governments. They operate on blockchains, distributed ledgers maintained by network participants, rather than by a single institution. This structure allows peer-to-peer transfers across borders and continuous settlement outside the traditional banking system.

The original aim of cryptocurrencies was to create a new form of digital money: a medium of exchange independent of central banking infrastructure. Over time, a second narrative emerged—that certain cryptocurrencies could serve as stores of value, which woud protect investors from potential inflation caused by inflation or currency debasement.

Crypto networks and blockchain technology are emerging as a digital alternative to the traditional financial system. Many of these applications are already live: they allow for peer-to-peer payments and near-instant international transfers that bypass traditional middlemen. Beyond just moving money, this tech uses ‘smart contracts’ to automate lending and borrowing, creates digital versions of real-world assets, and builds identity systems that don’t rely on a central bank or a private corporation to verify your data.

The objectives of this memo are to describe the bull and bear cases for crypto technology and evaluate reasons behind the current decline in crypto prices.

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The Bull Case for Crypto

The bullish thesis holds that cryptocurrencies represent early-stage monetary and financial infrastructure innovation.

The bull case for crypto as a new financial layer is based on the following pillars:

· Neutral Money: A global currency that operates by math, not politics—no central bank can print more of it or devalue your savings.

· Unstoppable Access: A financial “exit ramp” for anyone facing high inflation, frozen accounts, or strict government oversight.

· Digital Scarcity: Unlike “easy money” issued by governments, these assets have a hard-coded limit that acts as a long-term shield against inflation.

· Modern Plumbing: A 24/7 financial system that replaces slow, human-managed banks with fast, automated code that never sleeps.

· Protocol Growth: The opportunity to own a piece of the world’s next financial infrastructure, similar to owning a “share” of the early internet.

Crypto is economically measurable but not systemically dominant.

The strongest real-world use case is stablecoin-based transfers, which process trillions of dollars in annual volume and account for roughly 1–3% of global cross-border payment flows, depending on measurement. In certain emerging-market corridors and capital-control environments, stablecoins are meaningfully used for remittances and business settlement.

Within the crypto ecosystem itself, daily trading often reaches hundreds of billions of dollars, and total market capitalization has ranged around $1–3 trillion in recent cycles.

Relative to the global financial system, however, crypto remains small. Global equity markets exceed $100 trillion, global bonds about $130 trillion, global banking assets over $300 trillion, and foreign exchange markets trade more than $7 trillion per day.

The most accurate characterization is that crypto operates at meaningful scale inside its own ecosystem and has achieved low-single-digit penetration in selected global payment corridors. It has not displaced traditional finance at systemic scale, but neither is it economically negligible.

Prominent advocates include -- Michael Saylor, Cathie Wood, Balaji Srinivasan, and Brian Armstrong. Saylor has described bitcoin as digital gold superior to physical gold. Wood perceives Bitcoin to be a new asset class and that an increase in uses by institutions would substantially increase its value. Srinivasan has described Bitcoin as an apolitical borderless alternative to central banks. Armstrong has argued that crypto is about economic freedom and that it can expand access to financial services globally.

The Bear Case for Crypto

The bearish thesis argues that cryptocurrencies lack the core features that anchor long-term asset valuation.

They do not generate cash flows, pay dividends, or produce goods and services. In short, crypto lacks intrinsic value.

If considered currencies, they should behave like relative prices rather than compounding investments.

If treated as stores of value, extreme volatility challenges their stabilizing function. Sustained price appreciation relies primarily on speculative inflows.

If crypto markets grow increasingly interconnected with banks, asset managers, payment systems, or leveraged financial intermediaries, instability within crypto could transmit shocks into the broader financial system.

The lesson many skeptics draw from past crises is that financial innovation can outpace supervision, allowing risk to accumulate in corners of the system that appear peripheral—until confidence breaks. A sufficiently integrated crypto ecosystem could create institutions that are not necessarily “too big” in isolation but become too interconnected to fail without broader consequences. Critics believe the expansion of crypto could lead to the next 2008.

Prominent skeptics include two famous investors, Warren Buffett and Charlie Munger, and two prominent economists, Paul Krugman and Nouriel Roubini. Buffet and Munger compared Bitcoin to rat poison. Krugman compares the Bitcoin craze to the tulip bubble and considers the concept to be motivated by libertarian philosophy, a new subprime market tied to leverage and lax regulation. Nouriel Roubini argues that Bitcoin resembles a Ponzi-like speculative structure sustained by continuous inflows rather than intrinsic value, and that crypto markets are structurally prone to manipulation, insider advantages, and weak regulatory oversight.

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Recent Sustained Price Decline

Over the past several months, major cryptocurrencies have pulled back sharply from their late-2025 peaks:

Bitcoin peaked at around $126,000 in October 2025. As of early 2026, it has declined roughly 40–50% from that peak, trading near $65,000–$70,000, its lowest level in about a year.

Ethereum peaked near approximately $4,000–$4,200 in late 2025. By early 2026, it had fallen to roughly $1,900–$2,200, representing a drawdown of about 45–55% from peak levels and placing it back near price ranges seen roughly a year earlier.

Crypto markets have historically experienced large cyclical drawdowns. In prior bear markets, Bitcoin has fallen 60–80% from peak to trough (notably in 2017–18 and 2021–22), and Ethereum has repeatedly endured declines of 60% or more across multiple cycles. These swings are extreme compared to traditional asset swings, but no extreme compared to past swings in crypto prices. Additional decreases in crypto values extending the recent downturn are plausible and would be consistent with previous crypto winters.

Factors Impacting the Current Crypto Market

Portfolio reallocations from booming AI and Gold

From a portfolio construction perspective, crypto does not exist in isolation. It sits inside a broader “innovation / long-duration / speculative technology” allocation bucket.

Under classical portfolio theory, investors allocate capital based on expected return, variance, and correlation. C

Crypto is perceived as a high-beta, long-duration innovation asset—similar to early-stage technology equities. It competes for portfolio share directly with other frontier themes such as AI infrastructure, AI-driven software, robotics, quantum computing, and prediction markets.

Portfolio managers currently appear to prefer these other high-tech speculative opportunities to crypto. The growth in value of AI linked opportunities occurred simultaneously with a decline in both the value of software and Crypto.

When AI enthusiasm lifts technology broadly, aggregate portfolio exposure to tech risk may rise beyond target levels. Risk management constraints can then trigger deleveraging or diversification across the entire technology complex, creating pressure on all high-beta assets, including crypto.

At the same time, crypto has also competed with gold for “alternative monetary asset” status. Prominent advocates such as Michael Saylor have argued that Bitcoin is superior to gold, describing it as “digital gold” and even “the hardest money ever created.” The proposition was that a scarce, portable, programmable asset would outperform physical gold as a store of value.

However, during periods of risk aversion and portfolio repositioning, gold has often behaved more like a traditional safe haven, while crypto has traded more like a high-beta technology asset. To the extent that investors treat gold as defensive and crypto as speculative, the claim that digital gold would displace or outperform real gold in stressed environments has not consistently held up across recent cycles.

Saylor’s statement that digital gold is superior to actual gold has not aged well.

Energy Constraints, AI Competition, and Crypto

There is growing concern that rapid AI expansion could strain electricity supply.

Large AI data centers require enormous and continuous power, and utility companies in several regions have warned about rising demand. Crypto mining—especially Bitcoin—also consumes substantial electricity. When two fast-growing industries compete for energy, it raises questions about prices, grid stability, and long-term supply.

Energy is not just an industrial issue but a political one. Governments face pressure to manage electricity costs, meet climate targets, and maintain grid reliability. In that environment, policymakers may prioritize data centers tied to AI and industrial policy over crypto mining, which is often viewed as more discretionary. Rising power costs or political scrutiny of energy use could weigh more heavily on energy-intensive crypto models than on other digital industries.

Investors may discount businesses that appear vulnerable to higher long-term energy costs. In a world where energy is strategically constrained, competition between AI infrastructure and crypto mining could become an additional pressure point for crypto markets. I expect that the political and economic pressure to reduce energy use will be more intense on crypto than AI because AI is the more profitable industry and has demonstrated greater benefits to society in the form of increased productivity.

Fraud, Regulation, and Institutional Confidence

Fraud and governance failures have been recurring features of the cryptocurrency ecosystem.

High-profile cases frequently cited by critics include the collapse of FTX, multi-billion-dollar enforcement actions against Binance, the conviction and pardon of Binance’s founder, and repeated token pump-and-dump schemes and stablecoin failures such as Terra/Luna.

These events have reinforced concerns about insider concentration, weak disclosure, and exchange conflicts of interest. For many institutional investors, the issue is not simply isolated misconduct but whether governance standards across the ecosystem are durable enough to justify long-term capital allocation.

Regulatory responses have diverged across major jurisdictions.

China has taken the strictest approach, effectively banned domestic trading and mining while promoting a state-controlled digital currency.

Europe has implemented a comprehensive rulebook through the Markets in Crypto-Assets (MiCA) framework, imposing licensing, disclosure, and reserve requirements while permitting regulated activity.

The United States has taken a more fragmented path. Congress passed the GENIUS Act, establishing a federal framework for stablecoins with reserve backing, disclosure standards, and supervisory oversight. A broader market-structure bill, the CLARITY Act, aimed at defining when digital assets are securities versus commodities and clarifying SEC/CFTC roles, passed the House but stalled in the Senate amid disputes over stablecoin interest payments, surveillance concerns, and industry opposition.

Supporters of Crypto want less regulation to stimulate innovation. Critics warn that if crypto becomes deeply intertwined with banks, asset managers, or leveraged intermediaries before governance and valuation questions are settled, instability could propagate into core financial institutions—raising echoes of 2008-style contagion dynamics.

Concerns about fraud, disputes over regulation, and concerns about the impact of crypto on the future safety and soundness of the financial industry may slow crypto’s growth.

Conclusion

The bull case views crypto as early-stage infrastructure: a digitally scarce monetary asset and programmable financial network with low but growing penetration. The bear case views it as lacking intrinsic value or worse a scam and a Ponzi scheme with the potential to destabilize the financial system.

The recent price decline does not appear historically unusual by crypto standards. Prior cycles have featured 60–80% drawdowns, and current declines of roughly 40–50% from late-2025 peaks fall within crypto’s historical volatility range.

The downturn likely reflects a combination of capital reallocation toward AI-linked assets, competition with gold in risk-off environments, concerns about energy constraints, and persistent regulatory and fraud-related uncertainty. None of these factors alone explains price movements, but together they shape capital flows and required risk premia.

There is a substantial amount of fraud in this new sector. Fraud is not the source of the downturn but is likely to impede cryptos future growth and expanded adoption.

At present, crypto represents roughly 1–2% of global financial assets and has low but non-zero systemic penetration. Some diversified investors hold small allocations as an optionality play, inflation hedge, or high-beta innovation exposure. However, it is not yet universally treated as a required core asset class alongside equities, bonds, and real estate. Its role remains discretionary rather than foundational.

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Appendix A: Types of Cryptocurrencies and Relative Credibility

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Over the past year, artificial intelligence has moved from being a technological breakthrough to becoming one of the central financial narratives in global markets. AI demand can be strong, adoption can be accelerating, and yet the entire technology sector can still sell off sharply on fears related to AI itself. Periods of broad technology selloffs make that tension especially visible.

At the core is a growing concern not about whether AI will be used, but about how it is financed, where risk sits, and how concentrated that risk has become. AI has pulled forward capital spending, raised long-term earnings expectations, and clustered investor exposure into a relatively small number of highly visible firms. When confidence shifts, markets often react not by sorting among individual business models, but by repricing the technology sector as a whole.

Software and cybersecurity stocks can fall on days where AI falls even though underlying demand for products could even increase if AI does not materialize.

In these episodes, price action is often less about current earnings and more about future margins, future capital intensity, and uncertainty about competitive structure and preferences of portfolio managers now in and the future.

Crucially, these risks are not distributed evenly.

Some firms are exposed to AI through direct sponsorship and infrastructure commitments. Others are exposed primarily through aggregate spending cycles. Still others participate in AI while keeping economic exposure relatively optional. When AI fear turns into a market-wide factor, these distinctions are temporarily overwhelmed, but over time they become decisive.

Two companies sit at the center of this dynamic: Microsoft and NVIDIA.

Microsoft’s AI exposure is concentrated and structural. It combines capital provision, cloud infrastructure, and product integration around a single external AI lab. That creates meaningful upside, but it also means that bad news about AI execution, governance, or monetization can translate directly into economic and strategic risk.

NVIDIA’s exposure is different. NVIDIA benefits from AI broadly, but its downside risk is tied to the pace and financing of AI infrastructure growth. When confidence in AI spending weakens because of financing conditions, capacity digestion, or portfolio risk reduction, NVIDIA is often affected even if long-term AI demand remains intact.

Other major firms, including Amazon, Google, and Meta, invest heavily in AI as well. They do so in ways that distribute risk differently through internal model development, diversified cloud platforms, or long-term infrastructure strategies that are less sensitive to any single outcome.

The analysis here on AI financial risk involve answers to seven questions.

The Seven Questions

1. How is NVIDIA helping OpenAI grow, and what is NVIDIA’s exposure to OpenAI outcomes?

2. How is Microsoft helping OpenAI grow, and what is Microsoft’s exposure to OpenAI outcomes?

3. Why does Microsoft have more concentrated downside risk than NVIDIA?

4. How does bad AI or OpenAI news propagate through Microsoft, NVIDIA, and the AI ecosystem, and who is economically forced to absorb the shock?

5. What firms hedge long-term AI risk for Microsoft and NVIDIA, and what risks do those hedges not protect against?

6. How do Amazon, Google, and Meta differ structurally from Microsoft and NVIDIA in their exposure to AI outcomes?

7. Why can the entire technology sector fall even when AI demand and underlying fundamentals remain strong?

A future piece will examine competition and governance dynamics among major AI labs, including OpenAI, Gemini, and Anthropic, and explain when those rivalries matter financially and when they are primarily narrative.

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Questions and Answers:

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A short memo clarifying the structure, purpose, and implications of Waymo’s reported private financing, and why comparisons to IPOs or other AI transactions can mislead.

Recent press reports indicate that Waymo is exploring a substantial private financing round, reportedly seeking to raise approximately $16 billion at an implied valuation near $110 billion.

https://www.reuters.com/business/autos-transportation/waymo-seeking-about-16-billion-near-110-billion-valuation-bloomberg-news-reports-2026-01-31/

The transaction is structured as a private capital raise intended to fund ongoing investment and scale-up activities and does not appear to involve liquidity for existing shareholders.

The purpose of this memo is to clarify the economic intent and governance implications of the reported financing, distinguish it from superficially similar large private transactions that may be influenced by liquidity or IPO considerations, and create background material on private financing for readers less familiar with these markets.

Takeaway: The proposed Waymo financing is a private growth-capital raise, not a liquidity event, intended to fund continued investment while allowing Alphabet to retain control, impose capital-allocation discipline, and share risk with a small number of outside investors.

• The size of the reported valuation does not imply that Waymo is preparing for an IPO or offering liquidity to existing shareholders.

• Large private financings at other technology or AI companies may reflect employee liquidity, early-investor exits, or IPO positioning; available evidence suggests Waymo’s transaction is primarily oriented toward raising new operating capital.

• Comparisons based solely on valuation headlines or funding-to-valuation ratios can be misleading, as private transactions vary widely in structure, purpose, and disclosure.

1. Nature of the transaction
   The reported fundraising effort is a private financing round, not an initial public offering. Waymo remains a privately held company and does not have and will not have publicly traded shares after the new financing. The transaction would involve negotiated investments from a limited number of institutional or strategic investors, rather than a public listing or the creation of a public float.

2. Scale of the valuation relative to private financings and IPOs
   An implied valuation near $110 billion would place Waymo among the most highly valued private companies globally and far above the valuation at which most companies go public. Median U.S. IPO valuations are typically in the low single-digit billions, with even large, mature technology companies often listing in the $10–30 billion range; IPOs near or above $100 billion are rare. Valuations of this magnitude in private markets are confined to a very small set of companies. Among recent examples, only a handful of private firms—most notably OpenAI and Anthropic in large AI-focused financings, and SpaceX through a mix of primary and secondary transactions—have been discussed at comparable or higher valuation levels. Direct comparison of capital-raised-to-valuation ratios across these transactions is inherently imprecise, as many such valuations reflect secondary sales or blended deal structures, whereas Waymo’s proposed financing appears to involve primarily new capital raised at a single priced valuation.

3. Purpose of the financing
   The primary purpose of the transaction is to raise additional capital to support capital-intensive investments, including fleet expansion, compute and infrastructure, safety validation, and operating losses associated with scaling autonomous driving operations. There is no indication that the transaction is intended to provide liquidity or cash-out opportunities for existing owners.

4. Meaning of the reported valuation
   The approximately $110 billion figure represents an implied valuation derived from the terms of the private financing. It reflects the price per share agreed upon in this round, extrapolated to the full equity value of the company. This is not a market-clearing public valuation and may differ materially from the valuation that would emerge in a public market offering.

5. Interpreting the $16 billion investment size
   In simple terms, dividing the new capital by the post-money valuation gives a rough sense of potential dilution. Using headline figures, $16 billion divided by roughly $126 billion (valuation plus new capital) would imply that new investors collectively receive on the order of low-teens percentage ownership. Actual ownership outcomes depend on detailed deal terms, including preferred equity features, liquidation preferences, and any concurrent internal capital contributions.

6. Alphabet’s role and continued control
   Alphabet is expected to remain the controlling shareholder after the financing. Reporting suggests Alphabet itself may provide the majority of the new capital, which would limit dilution of its ownership stake. As a result, strategic and operational control of Waymo would remain with Alphabet following the transaction.

7. Why raise capital externally rather than fund internally
   Alphabet has the financial capacity to fund Waymo entirely through internal capital transfers. The decision to pursue a formal private financing reflects considerations of capital-allocation discipline, valuation anchoring, and risk sharing rather than financial necessity. External investors provide a market-based reference valuation, share downside risk in a capital-intensive and uncertain business line, and impose governance and reporting structures that are not required for purely internal funding.

8. Role of outside investors and financing formality
   Once non-Alphabet investors participate, the transaction must be structured as a formal private financing, with standardized investment terms and investor protections. The presence of a small number of outside institutional investors therefore necessitates the legal and governance formality of a priced private round. This formality does not reduce Alphabet’s control but reflects arm’s-length co-investment rather than informal internal funding.

9. Absence of a public cap table
   Waymo’s detailed capitalization table is not publicly disclosed. As a private company, it is not required to release precise ownership percentages across Alphabet, prior venture investors, management, and employees. Public information is limited to high-level descriptions of ownership and control.

10. Relationship to a potential future IPO
    A private financing of this scale does not, by itself, indicate an imminent IPO. Large late-stage private rounds are commonly used to fund expansion while preserving strategic flexibility. A public listing would require explicit announcements, regulatory filings, and structural decisions that have not been publicly disclosed in Waymo’s case.

Conclusion: Overall, the transaction reflects a deliberate choice to formalize additional investment at an externally validated valuation rather than a shift in ownership strategy or a step toward public listing.

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I’ve been thinking a lot about how people try to buy stocks during drawdowns — and why the mechanics often work against them. This note lays out a simple framework I’m using for software stocks, and how I’m experimenting with alerts created by CHAT GPT to provide me with information about when I should carefully analyze a sector and perhaps start selling or buying individual stocks.

This is not about predicting bottoms or automating trades, and I do eschew automatic limit orders on individual stocks. The purpose of the proposed system is to let the investor know it is now time to pay attention.

My first practical example of this approach is being applied to software stocks.

Why software is interesting right now

The software sector has gone through a broad, grinding drawdown after years of strong performance, largely because of new competition from AI startups.

In many cases, prices have fallen not because businesses broke, but because valuations compressed and sentiment shifted. That distinction matters.

High‑quality software companies tend to share a few traits: recurring revenue, high switching costs, durable customer relationships, and strong free cash flow. When prices fall across the whole sector, short‑term weakness is often cyclical rather than structural.

Those periods — when the sector is out of favor but business quality remains intact — are the environments I want to engage.

But I don’t want to put limit orders on individual stocks.

Why limit orders often fail in practice

A common strategy is to place limit orders and wait. In practice, this often leads to bad outcomes.

Static limit orders tend to fill first in the weakest names or during company‑specific negative events. Over time, this biases accumulation toward stocks that are falling for idiosyncratic reasons rather than broad market pressure.

Especially in software, where dispersion between strong and weak franchises can widen quickly, this approach increases the risk of buying “dogs” rather than leaders experiencing temporary stress.

Use the sector to gate attention, not individual stocks

Instead of anchoring decisions to individual price moves, I prefer to use sector conditions to decide when to engage.

For software, that means watching the sector itself and asking a simple question:

Is weakness broad and sentiment‑driven, or is this just noise in a single name?

When the sector is under pressure, individual stock declines are more likely to reflect risk‑off behavior rather than deteriorating fundamentals. That’s when it makes sense to review high‑quality names and consider action.

This flips the usual logic: sector stress creates opportunity selectively, not mechanically.

Where alerts actually help

The real value of alerts isn’t execution — it’s attention.

Most people don’t want (or need) to stare at markets all day. The goal is to be notified when conditions might justify scrutiny, not to be told what to do.

Alerts are rare and scheduled, not constant • They provide context, not instructions • Silence is the default state. In other words, alerts exist to say “this might be worth a look”, not “act now.”

The current alert framework

Right now, the framework is intentionally simple.

Morning sector context Shortly after the market opens, I get a brief snapshot of the software sector: how it’s trading versus the prior close, where it sits in its longer‑term range, and whether the day appears routine or notable.

This answers one question: Can I ignore the sector today, or should I keep it on my radar?

Pre‑close status check About 30 minutes before the close, I review where the sector and a small set of tracked stocks finished the day. This helps frame overnight exposure and whether broader conditions are improving, worsening, or unchanged.

There is currently only one alert example in use. Additional variations may be added later, but the emphasis will remain on restraint.

It may be desirable to create a midday alert or an alert that goes once and only once if a certain level of volatility is reached. I am thinking about this approach but don’t want to spam myself.

What this is — and what it isn’t

This system does not automate trades. It does not chase volatility. And it does not attempt to time exact bottoms.

It does impose structure:

Sector conditions determine when to look • Stock quality determines what to buy • Alerts reduce emotional and ad‑hoc decision‑making

Volatility becomes an input, not a trigger.

A note on delivery

A practical note before closing.

ChatGPT today functions best as a reasoning and context layer, not as a brokerage-style notification system. Alerts appear in-app and can generate email notifications that link back to the analysis stream. This is sufficient to prompt review and attention, but it is not the same as a real-time SMS or trading-platform alert.

In time, the logic behind these alerts can become more nuanced without becoming more frequent. Rather than responding to singleday moves, triggers could incorporate patterns such as several consecutive down days, cumulative weakness over a one- to two-week window, or broader measures of stress like proximity to recent lows. The intent would remain the same: not to prompt action, but to indicate that conditions are sufficiently unusual to justify a deeper review.

As AI tools mature and gain access to richer market and fundamental data, their usefulness in this role should improve. Better inputs can help distinguish routine volatility from more meaningful dislocations. That said, even with imperfect data and basic triggers, the value of this framework comes less from precision than from discipline — creating a structured reason to engage, rather than relying on constant monitoring or ad-hoc reactions.

Why I’m sharing this

I think many investors intuitively understand these ideas but struggle to operationalize them. Alerts are usually either noisy or useless. Limit orders are mechanical but blind to context.

Combining a sector‑driven framework with disciplined alerts is one way to bridge that gap.

I’m still refining this approach, and there’s more to explore — additional alert types, better delivery mechanisms, and different sector applications. For now, the goal is modest but important: turn market stress into a reasoned opportunity rather than a reactive moment.

More to come.

Next: why this same alert logic applies even more cleanly to bank stocks — and how sector-level stress can matter more than individual price moves in financials.

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Over the past year, the valuation spread between Target and Walmart has widened dramatically, reflecting diverging market sentiment, fundamentals, and expectations for the two retailers. Looking at four core metrics—market cap, trailing P/E, forward P/E, and price-to-sales—the contrast today is far sharper than it was in late 2024.

Current Snapshot

• Market Cap: Walmart now sits at 917 billion dollars, while Target has fallen to 42 billion dollars, a gap of 876 billion.

• Trailing P/E: Walmart trades at 40 times earnings, compared to Target at 11 times, giving Walmart a multiple roughly 3.6 times higher.

• Forward P/E: Forward valuations show a similar spread, 34 times earnings for Walmart versus 11 times for Target, a 3x premium.

• Price/Sales: Walmart’s 1.32x revenue multiple stands more than three times above Target’s 0.40x.

Across every valuation measure, Walmart commands a premium multiple while Target trades near recession-level ratios despite stabilizing operations.

How the Gap Has Changed Since October 2024

The direction of change tells an even clearer story than the levels:

• Market Cap: Target has shed 27 billion dollars in value while Walmart has added 259 billion, creating a performance spread of roughly 286 billion.

• Trailing P/E: Both companies saw some compression, but Target’s drop was larger, 4.3 turns versus 2.4 for Walmart.

• Forward P/E: This is where the divergence is most dramatic. Target’s forward multiple fell by 3.3 turns while Walmart’s rose nearly 4 turns, a seven-point swing.

• Price/Sales: Target’s price-to-sales ratio contracted by 0.25 while Walmart’s expanded by 0.32, widening the spread by about 0.57.

The valuation gap didn’t just exist; it expanded, with Walmart re-rating higher while Target de-rated lower across almost every metric.

Summary Insight: Two Retailers, Two Narratives

The market has effectively priced Walmart as a secular compounder benefiting from scale, omnichannel dominance, and accelerating earnings momentum. Target, by contrast, is priced like a company facing margin pressure, inconsistent traffic trends, and uncertain earnings recovery.

Walmart is experiencing multiple expansion, market-cap acceleration, and sustained investor enthusiasm.
Target is undergoing multiple compression and market-cap erosion despite improving execution.

The result is the largest valuation divergence between the two companies in years, and one that continues to widen.

Premium Section: How Investors Think About Momentum, Mean Reversion, and Why Takeover Speculation Sometimes Emerges

Note: This is not investment advice. This section outlines analytical frameworks investors commonly use, plus a factual summary of recent media speculation.

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Introduction

In my recent analysis, The Reemergence of Volatility in the Tech Sector, I highlighted a series of warning signals suggesting that the technology market may be transitioning into a more turbulent phase.

The Reemergence of Volatility in the Tech Sector
https://bernsteinbook1958.substack.com/p/the-reemergence-of-volatility-in

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From Michael Burry’s bearish posture—rooted in concerns about accounting distortions and inflated AI optimism—to OpenAI’s surprising suggestion that governments might one day serve as financial “backstops” for its capital-intensive operations, the sector is flashing signs reminiscent of prior late-cycle periods. These developments, combined with extreme index concentration in the “Magnificent Seven,” suggest that investors relying too heavily on mega-cap AI names may face a level of volatility they are no longer compensated for.

In that earlier piece, I argued that investors should not assume the Magnificent Seven will continue delivering the same low-volatility compounding they have exhibited for most of the last decade. High expectations, rising capex, valuation stretch, and narrow leadership all raise systemic risk.

I concluded by suggesting that it may be time to begin rotating—carefully and selectively—into the second tier of technology innovators. This paper is the next step.

Here, I propose a structured, theme-based 8-Bucket Second-Tier Mag Framework designed to reduce concentration risk and provide exposure to the next big tech winner. The greater diversification outlined here—achieved through the purchase of buckets of similar stocks—is essential because these companies are smaller, more volatile, and more sensitive to product cycles than the Mag 7. This approach increases the chance of capturing an early-stage compounding engine that becomes a market-defining company.

Before we dive into the details, here are the eight buckets:

1. AI Networking, Infrastructure & Architecture Hardware

2. Semiconductor Equipment (EUV/DUV)

3. AI Compute & Edge AI Chips

4. Cybersecurity + Secure Communications

5. Enterprise Software & Digital Productivity

6. Consumer Digital Media

7. Autonomous Systems, Drones & Defense AI

8. Mobility Networks

In total, this framework reviews 29 individual stocks across the eight buckets, including both core positions and optional names.

Below the paywall, readers receive a full breakdown of every stock in all eight buckets, including buy-now and limit-order guidance, along with preliminary percentage weights for each bucket and company. The section also explains why certain names are prioritized over others by providing valuation notes such as P/E ratios, price-to-sales metrics, and margin considerations, as well as clear reasoning for why specific stocks were excluded. For investors seeking exposure while waiting for limit orders, optional ETF alternatives such as SMH are discussed. Finally, the section offers practical guidance on risk management, portfolio balance, and volatility control to help readers apply the framework effectively.

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Key Findings:

· Recent events are raising red flags for many tech investors and increasing volatility for many tech stocks.

· First, Michael Burry, famous for his decision to short the market in 2008, is bearish partially because of concern over accuracy of accounting statistics. He has closed his fund to outside investors—an approach he took in 2008, illustrated in The Big Short—and he has purchased puts for several firms he believes are overvalued, including NVDA and PLTR.

· OpenAI’s CFO suggested the governments might serve as a financial “backstop,” implying public guarantees could support the company’s massive infrastructure spending if private financing fell short. This alarmed investors because it signaled potential solvency issues.

· Given high valuations in AI-linked stocks and extreme concentration in the “Magnificent 7,” investors may benefit from diversifying away from mega-cap AI names toward smaller, non-AI tech firms with more balanced risk profiles.

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Motivation. This study is motivated by three objectives:

1. Current market context. Several large, mature food companies have recently traded at or near fresh 52‑week lows, including TSN and GIS which hit new lows today. Are these new 52-week lows a sign that it is time to buy?

2. Signal design problem. Many stocks do not make a single clean bottom; they may produce clusters of consecutive 52‑week lows while continuing to drift downward. We therefore test alternative cool‑down rules that attempt to create more meaningful, non‑redundant entry signals.

3. Trend regime risk. A 52‑week low behaves differently depending on whether the stock is in a temporary dislocation (mean‑reverting regime) or in a multi‑year structural drawdown. We explicitly examine how a long down‑trend regime degrades the effectiveness of a 52‑week‑low entry strategy.

Background — Food stocks at fresh lows

At the time of writing, multiple widely‑followed food manufacturers and protein processors — including names such as TSN (Tyson Foods), GIS (General Mills), CAG (Conagra), CPB (Campbell Soup), and KHC (Kraft Heinz) — have traded at or near 52‑week lows. TSN and GIS, the companies studied in this memo, hit new 52-week lows today.

All five of these companies are seasoned with long operating histories, stable brands, and established dividends. Intuition might suggest that such lows present attractive entry points for long‑horizon investors seeking defensive exposure at a discount.

However, price alone does not reveal whether the low represents an exhausted washout or merely one waypoint in a continuing deterioration process.

This study tests entry rules – purchase at 52-week lows regardless of long-term trends.

Methods

A basic “52-week low” rule says: buy when the stock hits the lowest price seen in the past year. The problem is that when a stock is in a steady decline, it can keep setting new 52-week lows day after day. Those repeated lows are not fresh opportunities — they are just the same drop continuing. Counting each of them as a new buy signal would give a false picture of how well the rule works.

The evaluation of whether purchase at 52-week lows can lead to long term profitability should account for different trading rules which restrict purchases during steady downturns. Three rules are considered.

1. Rule 1 — No cool‑down (naïve baseline). Every 52‑week low counts as a distinct entry, even if the stock is falling in a straight line.

2. Rule 2 — Fixed calendar cool‑down. After a low signal, we require a minimum number of trading days (e.g. 20 or 63) before another low can register. This suppresses dense clusters but remains agnostic to price behavior.

3. Rule 3 — Logical (price‑based) cool‑down. After a low signal, no new low is recorded until the price first rebounds sufficiently (e.g. +10%) — meaning the prior down‑cycle is likely “completed” before evaluating a fresh low as a new episode.

This structure allows us to separate (i) the mechanical fact that the price printed a new low, from (ii) the interpretive question of whether the new low represents a new information regime or just repetition inside an ongoing decline.

Results

Where stocks sit in a 5-year time frame

Using adjusted prices over the most recent five years:

• TSN is at roughly the 7th percentile
  → lower than about 93% of all closes in five years
  → a deep multi-year low, not just a local dip

• GIS is at roughly the 1st percentile
  → lower than about 99% of all closes in five years
  → an extreme long-horizon low, effectively a worst-case zone

Neither stock is making a one-off 52-week low — they are sitting in the bottom decile or worse of a five-year distribution. That is the statistical fingerprint of a persistent down-trend regime, not a short-lived mean-reversion setup.

Profits for the three purchase at 52-week low rules

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Introduction / Overview

This study examines whether earnings per share and expected revenue growth jointly explain cross-sectional variation in equity prices. The sample consists of the 25 largest reported holdings of the Vanguard large-cap growth ETF (VOOG) and the Vanguard large-cap value ETF (VOOV).

Five securities present in both lists were included once, yielding 45 unique firms. For each firm, price per share and earnings per share were taken from Yahoo Finance, and expected sales growth was taken from the “Next Year” estimate under the Revenue section of the Analysis tab.

The objective is to test whether the pricing of earnings varies with expected growth, and whether interaction terms between earnings and expected growth supply explanatory power beyond their main effects.

Methods

Price per share was regressed on centered earnings, centered expected sales growth, and their interaction. The primary specification used a robust (Huber) linear estimator to reduce the impact of high-influence observations without removing them from the sample.

Results

Table 1 reports the robust linear specification including the interaction. Earnings and expected sales growth are individually significant, and the interaction term is also statistically significant in the robust linear model. This indicates that the pricing of earnings is not uniform across growth levels: the market appears to pay more per dollar of earnings when expected growth is above average.

Including the interaction materially improves fit relative to a main-effects-only model in both OLS and robust estimation. In OLS with robust errors, the interaction is not statistically significant; however, once outlier influence is downweighted the interaction becomes significant and reduces prediction error.

Discussion

Conceptually, the specification parallels the intuition behind PEG ratios, which also embed the idea that the market capitalizes a dollar of earnings more richly when expected growth is higher. The interaction term operationalizes this intuition in a linear pricing model. In the linear specification, both earnings and expected sales growth explain cross-sectional price variation, and allowing their interaction improves explanatory performance.

In the linear specification, both earnings and expected sales growth explain cross-sectional price variation, and allowing their interaction improves explanatory performance. The fact that the interaction is detected under robust estimation and absent under unadjusted OLS indicates that high-influence observations obscure the effect in standard regression but do not generate it.

In alternative log-price specifications estimated as a robustness check, the interaction term does not achieve significance, and a nested F-test confirms that its contribution is not statistically supported in the log specification.

Conclusion

The evidence supports an earnings–growth interaction in linear price space under robust estimation: the pricing of earnings rises with expected sales growth. This effect is not statistically supported in log-price models and is not detectable in ordinary least squares without accounting for outliers. Thus, the existence of the interaction is conditional on linear specification and robust estimation but materially improves fit when those conditions hold.

APPENDIX

Methods

The sample consists of the top reported holdings of VOOG and VOOV. Five overlapping names were included once, yielding 45 unique firms. Price and earnings per share were taken from Yahoo Finance; expected sales growth was taken from the “Next Year” estimate under the Revenue section of the Analysis tab and expressed as a decimal. Regressors entering interactions were mean-centered. The dependent variable is price in levels.

The primary specification is a robust (Huber) linear model with price regressed on centered earnings, centered expected sales growth, and their interaction. The interaction tests whether the price–earnings slope varies with growth. Ordinary least squares with HC3-robust errors was estimated for comparison.

Notes on Sample and Methodological Limits

The analysis is cross-sectional and does not imply causality or predictive validity. The interaction is statistically supported only in the linear specification with robust estimation. The models exclude risk controls, sector structure, leverage, profitability measures beyond EPS, and macro conditions; results are conditional on this specification.\

Previous work considering differences between growth and value stocks can be found here

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Before you model style drift, you need to know whether the “overlap” names in VOOG and VOOV actually behave like growth or like value. This is the first statistical pass: we compare expected sales growth across growth-only, value-only, and overlap stocks to see where the overlap cohort actually belongs. If you care about how real fundamentals line up with ETF labels — this is the ground truth you need before building anything on top of it.

MEMORANDUM

Subject: Statistical comparison of growth, value, and overlapping stocks using normalized sales growth
Date: (Date)

Overview

This memorandum evaluates differences in expected sales growth across three equity cohorts derived from the top holdings of two style-defined Vanguard ETFs. The growth cohort was taken from the top 25 constituents of VOOG (Vanguard S&P 500 Growth ETF), and the value cohort from the top 25 constituents of VOOV (Vanguard S&P 500 Value ETF). Five securities — MSFT, AAPL, AMZN, JPM, and BRK.B — appeared in both ETFs. Those were assigned to a separate “both” category, while the remaining securities were retained in mutually exclusive growth-only or value-only groups.

For each security, expected next-year sales growth was collected from the Revenue Estimate section of the Yahoo Finance “Analysis” tab, using the “Next Year” column of the Sales Growth (year/est) row. To express this expected growth on a proportional scale, the percentages were normalized by dividing the sales growth percentage by 100 to make it a ratio and adding 1.0

For example, a 14% expected growth rate is represented as 1.14. This is a linear transformation and does not alter statistical inference.

All subsequent statistical comparisons were conducted on this normalized sales-growth measure across the three mutually exclusive groups: growth, value, and both. Separating the five overlapping names into their own category does not widen or manufacture differences between growth and value; it simply prevents double-counting and preserves the validity of the comparison.

Findings

1) Group positioning

The mean normalized sales-growth value for the “both” cohort lies closer to the value cohort than to the growth cohort.

2) Statistical differences

A one-way ANOVA confirms a statistically significant difference across the three cohorts (p < 0.001). Tukey post-hoc comparisons show:

• A statistically significant difference between growth and value,

• No significant difference between both vs. value, and

• No significant difference between both vs. growth at the 5% threshold.

Thus, while growth and value differ significantly, the overlap group is not statistically distinct.

Interpretation & Portfolio Implications

The overlap cohort — securities appearing simultaneously in both VOOG and VOOV — does not differ statistically from either the growth or the value cohorts in forward revenue growth. However, its average growth level is numerically closer to the value cohort, indicating that overlap names lean toward value-like fundamentals on this dimension even though they do not form a statistically distinct group.

The clear and statistically significant gap between growth and value further indicates that expected sales expansion is an economically relevant discriminator across style classifications, supporting its use in style diagnostics or screening.

Appendix — Statistical Tables

Previous work on valuation of stocks in growth and value ETFs found here.

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This paper analyzes the relationship between stock prices, earnings per share (EPS), and sales growth for the 24 largest constituents of the S&P 500 Growth ETF (VOOG). Both linear and log-linear regressions are used to assess how well these fundamentals explain price variation across firms. The log-linear model provides a stronger fit, indicating that proportional changes in EPS and sales growth are better predictors of valuation levels. Residual analysis identifies the five most overvalued and five most undervalued firms relative to model-implied prices. The findings suggest that while EPS remains a key driver of valuation, sales growth acts as a less noisy, forward-looking determinant in growth-oriented equities.

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1. Introduction and Objective

This analysis examines the valuation patterns of the 24 largest firms in the S&P 500 Growth ETF (VOOG).

The objective is to understand how stock prices relate to two key fundamentals: earnings per share (EPS), representing current profitability, and sales growth, representing forward expansion potential.

By modeling the logarithm of stock prices as a function of the logarithms of EPS and sales growth, the analysis captures elastic relationships — showing how proportional changes in fundamentals correspond to proportional changes in market value.

The goal is to identify which firms appear overvalued or undervalued relative to these model-implied fundamentals.

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2. Analysis

A regression model where stock price is a function of earnings per share and revenue growth is estimated and used to identify stocks, which are likely over and undervalued. Two specifications of the model are consider a linear version and a log-log version where all variables are logarithmic are considered.

The log–log form expresses how percentage changes in EPS or sales growth affect percentage changes in price.

This approach is particularly useful for cross-sectional equity analysis because it:

• Reduces the impact of scale differences among firms (EPS and growth vary widely).

• Allows for interpretation in elasticities — directly comparable across firms.

• Reflects how investors typically think in percentage, not absolute, terms.

Sales growth was used instead of income growth, the traditional variable used in a PEG ratio, because earning growth is usually a noisy more volatile statistic.

The estimates for the linear and log-log models are as follows.

• Linear model:

P=57.4+25.6(EPS)+7.5(SalesGrowth)   R² = 0.43

• Log–log model:

ln(P)=3.83+0.51ln(EPS)+0.42ln(SalesGrowth)ln(P)  R² = 0.49

• Comparison:

The log–log model provides a modestly better fit and more stable residuals, suggesting proportional (percentage-based) relationships capture valuation behavior more effectively.

Interpretation:

• A 1% increase in EPS is associated with a 0.51% increase in price.

• A 1% increase in sales growth corresponds to a 0.42% increase in price.

Both effects are statistically significant, indicating that investors reward profitability slightly more than top-line expansion — but that growth still plays a major role in explaining valuation differences within VOOG’s universe

4. Relative Mispricing

Residuals from the log-linear model show which firms trade above or below their fair-value estimate.

Most Overvalued

Most Undervalued

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