Market Strategy
by Talley Leger, Chief Market Strategist
September 25, 2026
It Would Take an Absurd Level of Rates to Break Stocks
Holding trailing 12-month (TTM) operating earnings per share (EPS) constant, what level of long-duration investment-grade (IG) corporate bond yields could produce a 20% drop in the S&P 500?
Achieving a technical bear market in the S&P 500 purely through interest rates would likely require a massive 189.4% relative increase in the Moody’s seasoned 20-year Baa corporate bond yield because my “intrinsic” valuation model operates on a logarithmic scale.*
Given that the long-duration IG corporate bond yield is currently sitting at roughly 6.3%, the yield would need to spike to an “apocalyptic” 18.4% to single-handedly drive stocks down by 20%, assuming corporate profits remained perfectly flat!
Four-Step Mathematical Breakdown
Here’s the step-by-step algebra using the -0.21 elasticity coefficient for bond yields in my valuation model equation:
1. Set the Price Target: Determine the S&P 500 future value that would be 80% of its present value (i.e., a 20% drop). In log terms, the change is:
ln (0.80) = -0.2231
2. Isolate the Yield Variable: Using my pure log-log framework:
3. Solve for the Change in Yields: Divide the log change in the S&P 500 by the bond yield coefficient:
4. Convert Back to Real Numbers: “Exponentiate” the result to find the required relative multiplier for the bond yield:
That means the new Baa yield must be 2.9 times higher than its starting point!
Economic Reality Check
In a vacuum, a 20% fundamental valuation drop in equities driven exclusively by the bond market would require an interest rate environment unseen since the severe Volcker tightening of the early 1980s.
In the real world, a spike in corporate borrowing costs from the mid-single digits to the low-double digits could also trigger a sharp “growth recession” or worse, thereby slamming the brakes on corporate profits. The resulting decline in the S&P 500 wouldn’t just be driven by the -0.21yield coefficient. Rather, it would be dragged down exponentially faster by the EPS coefficient as earnings collapsed.
Earnings Are Booming
Assuming symmetrically offsetting EPS growth of 20% year over year (Y/Y) – which would be conservative in my humble opinion – how would that soften the bond blow to stocks?
Injecting a 20% Y/Y surge in earnings creates a massive fundamental buffer that almost completely neutralizes the headwind of rising interest rates.
Because my model’s earnings coefficient (0.96) is more than four times larger than the yield coefficient (-0.21), earnings growth acts as a powerful engine pushing share prices higher, requiring a “cataclysmic” spike in corporate borrowing costs to pull the stock market down:
1. Direct Boost from Earnings
A 20% increase in TTM operating EPS translates to a log change of:
ln(1.20) = 0.1823
Applying the EPS elasticity:
EPS Impact = 0.96 × 0.1823 = +0.1750 (or a + 19.1% tailwind for stocks)
In isolation, 20% earnings growth would propel “fair value” higher by over 19%!
2. Yield Spike Needed to Keep Stocks Flat
To completely offset 20% earnings growth – just to keep the S&P 500 flat (0%) – corporate bond yields would have to experience a 130.1% relative increase:
Starting from today’s 6.3% Baa corporate bond yield, yields would have to jump to 14.6% just to erase the gains from a 20% earnings expansion and keep equities flat.
3. The Yield Spike Needed for a 20% Market Decline
To force a full 20% decline in the S&P 500 [ln(0.80) = -0.2231]despite a 20% earnings boom, the required yield increase becomes absurd:
The Moody’s Baa bond yield would need to explode 6.6 times its starting level, spiking from 6.3% to 42.2%!
This mathematical exercise highlights the core “asymmetry” of my valuation framework: Earnings dominate yields. When corporate profits are growing at a rapid clip (+20% Y/Y), discount rate movements are effectively noise which demonstrates why equity markets often rally right through Federal Reserve rate-hiking cycles. As long as underlying earnings growth remains intact, higher interest rates alone are unlikely to drag down stock prices, in my humble opinion.
*Market Strategy Flash, S&P 500: The Tug of War Between Earnings & Interest Rates, August 21, 2026
Portfolio Strategy
by Jim Worden, CFA®, CMT®, CAIA®, Chief Investment Officer
September 25, 2026
Using the Past to Predict the Future
Distorted Mirror Maze (ChatGPT)
We’ve had a few great guests on our The Bull of Wall Street podcast recently. One guest had a Ph.D. in aeronautics before running a large asset management company. We discussed physics and finance, and we left it at the idea that finance is not really a science, but a social science. Due to the behavioral aspect of it, I think one could even argue that the markets and the economy in aggregate are more about art and understanding people than science.
Another recent guest is a technology analyst who has been covering tech stocks since the late ’90s. We discussed how we are used to looking at valuation models and cycles and how sometimes what we have always done doesn’t really fit with how we should measure the present.
Taking these two conversations into account and also understanding that the weight of the phrase “this time is different” is hard for many orthodox investors to argue with, I think it’s helpful to take a different perspective as it relates to cycles, growth, and the current race that is going on when it comes to AI and everything that AI may touch.
I was schooled in finance, thinking about present and future values, discounted cash flows, long-term growth rates, dividends, relative price ratios, and debt. I also witnessed the tech bubble burst, the Great Financial Crisis, and the pandemic up close, along with a myriad of flash crashes, frauds, and memes. I’ve spent countless hours reviewing factors, styles, and how different securities behave in different environments.
I’ve also been schooled in what prices actually do with technical analysis and how they can sometimes be completely disconnected from the fundamentals or what the macro view should be.
In my opinion, it’s healthy to reject “this time is different” by default, but we should think through what is different from time to time. If the “what” is the idea of bubbles and human behavior, one could probably argue, 100 years forward or backward, that bubbles and human behavior aren’t really that different. If the “what” is how markets work, there are some differences—in the way that people get information, the types of securities that can be easily traded, and the cost to transact. But markets haven’t changed massively over decades. Market participants who trade in the markets do change. More people participate in 401(k) accounts. More people trade online. There are more instruments or securities to trade. But this still won’t massively change markets in short periods of time, in my opinion.
The thing that feels most different, in my opinion, is how we try to understand and underwrite growth and how much disruption impacts a company, an industry, or an entire sector. Take two identical companies that have the same amount of debt and the same enterprise value. They start off at the same growth rate, let’s just say 5% top-line growth. They both have the same degree of operating leverage, and earnings growth is also the same. They even pay and grow a similar dividend.
Now, one of the companies has found a technology that will allow it to reduce its costs by 30% while also increasing its revenue by 15%, for example. It can also do this without increasing its headcount. The other company keeps doing things the way it has always done. After a few years, the two companies start diverging, with one company compounding its growth and its market share, all while increasing its profit margins and rewarding its stockholders. While the two started out the same, there’s a big difference over time.
Now examine the companies that are selling that technology, which can both reduce costs and increase revenue, not only for one company but for thousands of companies. How should these companies even be valued? We might assume exponential growth rates that eventually taper off, as well as increased competition from other companies. We can even try to use some sort of cycle analysis that overlays economic and policy cycles or commodity cycles. But it may not be very accurate in forecasting. The more assumptions we throw at it, the wider and wilder the outcomes will look. We may not even get the direction right in terms of when things start slowing.
This is how it feels with AI right now. We have those who, for a while now, have said it’s a massive bubble. We have others saying we have another 10 years of major growth, with no demand slacking anytime soon. That’s a very wide range, and most analysts are comfortable operating in small ranges with far fewer assumptions. This is partly why I believe we may continue to go through periods, even days, when the markets scream higher and, perhaps in the same week, fall precipitously. A new app that supposedly will disrupt retailers. A company CEO who says their tech is too dangerous for society another day. The headlines go on and on.
Sometimes we can effectively use past market characteristics and try to extrapolate how things will be this time, but sometimes it just doesn’t work. It’s kind of like trying to say a balloon and a feather will both fall at the same time because we have only one law of gravity, even though the wind is different and the contents of the balloon are different. It’s also like trying to examine oneself, walking through a fun house of distorted mirrors – we may never see things as they really are.
In this new paradigm with AI and the AI buildout, might it be different from other technologies and other large societal shifts? And if we can admit that it may be, at least, partially different, then it also stands to reason that trying to use the past to predict the future really may not work nearly as well as it has in prior periods. Of course, when we know that we don’t know, it is usually useful to remember the wisdom of diversification and that risk management usually helps. It’s also helpful to remember the disclosure that almost always appears when we talk about performance: “Past performance is not indicative of future results.”
Definitions
S&P 500: A stock market index tracking the performance of 500 of the largest publicly traded companies in the United States. It serves as a primary benchmark for the overall health of the U.S. stock market.
Operating EPS: A company’s net profit from regular business operations divided by its outstanding shares, excluding one-time gains or losses. It shows how much profit a company generates from its core everyday business.
NBER Recession: A significant decline in economic activity spread across the economy, lasting more than a few months, as officially designated by the National Bureau of Economic Research. It is determined by analyzing factors like gross domestic product, income and employment.
Artificial intelligence (AI): Computer systems designed to perform tasks commonly associated with human intelligence, such as recognizing patterns, generating content, and making predictions.
Technical analysis: The study of historical price and trading-volume patterns to assess market trends and potential future price movements. Technical analysis does not reliably predict future performance.
Discounted cash flow (DCF): A valuation method that estimates the present value of expected future cash flows using a discount rate.
Enterprise value: A measure of a company’s total value, generally calculated as equity market value plus debt and other claims, less cash and cash equivalents.
Operating leverage: The extent to which a company’s operating profit may change in response to changes in revenue because of its mix of fixed and variable costs.
Top-line growth: Growth in a company’s revenue or sales.
Profit margin: A measure of profit as a percentage of revenue.
Market bubble: A period in which asset prices may rise substantially beyond levels supported by underlying fundamentals; identifying a bubble in real time is subjective.
Market cycle: A recurring, though not reliably predictable, pattern of expansion, contraction, or changing conditions in financial markets.
Diversification: The practice of spreading investments among different assets, securities, or investment categories to manage concentration risk.
Risk management: The process of identifying, assessing, and managing investment risks; it cannot eliminate the possibility of loss.
Disclosures
This material reflects the author’s opinions as of the date of publication and is provided for informational and educational purposes only. Opinions and forward-looking statements may change without notice and should not be regarded as forecasts or guarantees of future results.
Illustrative company examples and numerical assumptions are hypothetical and are not actual company results, projections, or representations of outcomes that investors should expect. Actual outcomes may differ materially.
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All investments involve risk, including possible loss of principal. Diversification and risk management do not guarantee a profit or protect against losses in declining markets. Past performance is not indicative of future results.
References to AI, market conditions, or potential disruption involve uncertainty. The pace, magnitude, commercial impact, and investment implications of technological change cannot be predicted reliably.
The views expressed are for informational and educational purposes only and are subject to change without notice.
This material is not intended as, and should not be interpreted as, individualized investment advice or a recommendation to buy, sell, or hold any security, sector, industry, or investment strategy.
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Investing involves risk, including the possible loss of principal. Investments in a specific industry or sector may involve greater risk and volatility than more diversified investments.
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Forward-looking statements are not guarantees of future results. They involve risks, uncertainties and assumptions; there can be no assurance that actual results will not differ materially from expectations. In addition, forward-looking statements, including index targets or market scenarios, are hypothetical in nature, reflect current views and assumptions and are subject to change based on market and economic conditions and are not guarantees of future performance. This is a hypothetical example and is not representative of any specific investment. Your results may vary. (88-LPL) Scenario outcomes are illustrative and not predictive. This does not constitute a recommendation of any investment strategy or product for a particular investor. Investors should consult a financial professional before making any investment decisions.
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Publication Date: September 25, 2026
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