The Price You Pay for Stocks: Why Valuations Matter More Than Ever in an Age of AI Hype and Muted Return Expectations
by Chris Brown, PhD, CFP® and Ron A. Rhoades, JD, CFP®
Executive Summary
Investors today face a perplexing landscape. Major stock market indexes, particularly in the U.S., have been propelled to historic highs by a narrow group of “Magnificent 7” technology stocks, fueled by a revolutionary narrative around Artificial Intelligence (AI). This has led to widespread fears of a market bubble akin to the dot-com era of 1999-2000. This article synthesizes decades of research from academic and industry sources to examine the ironclad relationship between current asset valuations and long-term expected returns.
Drawing on empirical work from leading researchers and asset managers like Research Affiliates, Vanguard, and J.P. Morgan, we evaluate the predictive power of various valuation metrics – including the cyclically adjusted price-to-earnings (CAPE) ratio. The evidence consistently demonstrates that while valuations are poor predictors of next year’s returns, they exhibit robust predictive power for returns over 10-, 15-, and 20-year horizons.
We then apply this framework to the two most pressing questions for investors in 2025:
Is the current stock market in a bubble?
What are the realistic, long-term expected returns for major equity asset classes?
We find that while the current market is different from 2000, U.S. large-cap equity valuations are at historic percentiles, implying sobering future returns. Conversely, these same metrics suggest that international developed markets, emerging markets, and U.S. value and small-cap stocks offer significantly more attractive long-term prospects. The article concludes with practical implications for asset allocation and setting realistic financial goals.
DISCLAIMERS. This guide is for general informational purposes only and does not constitute tax, legal, or financial advice. Please consult your financial or tax advisor or legal counsel before implementing any strategies described herein.
Scholar Financial, LLC is a practice within XY Investment Solutions, LLC, an SEC-registered investment adviser. Registration does not imply a certain level of skill or training.
FOR MORE INFORMATION: Please contact Scholar Financial at www.scholarfinancial.com or email cathy@scholarfinancial.com for additional information or to schedule a conference or call.
Introduction
The relationship between current asset prices and future returns has captivated investors and researchers for generations. At its core, this relationship reflects a fundamental economic principle: the price you pay today is the primary driver of the return you will earn tomorrow.
When assets trade at elevated valuations relative to their fundamentals (like earnings or sales), future returns tend to disappoint. Conversely, periods of depressed valuations have historically preceded periods of superior long-term performance.
Today, this discussion is not merely academic. As of late 2025, the U.S. stock market, as measured by the S&P 500, is trading at valuations that rival the peak of the 1999-2000 “dot-com” bubble. This surge has been famously led by the “Magnificent 7” stocks, a handful of mega-capitalization technology companies whose performance has become synonymous with the market itself. The excitement surrounding Artificial Intelligence (AI) has provided a powerful narrative, drawing parallels to the “new economy” enthusiasm that defined the late 1990s.
This leaves investors standing at a difficult crossroads, asking two critical questions:
- Are we in a speculative bubble driven by AI hype that is destined to burst?
- If the price for U.S. stocks is so high, what returns can I realistically expect from my portfolio over the next decade?
This article will address these questions head-on. We will first establish the theoretical and empirical link between valuations and returns. Second, we will analyze the “bubble” question by comparing the current environment to historical precedents, using data to separate the hype from the reality. Finally, we will present 10-year expected returns for key global equity asset classes, synthesized from leading capital market forecasters, to provide investors with a practical, forward-looking guide for setting expectations and building resilient portfolios.
The predictive power of valuation metrics strengthens considerably as the forecast horizon extends from one year to 10, 15, or 20 years. This makes these tools particularly valuable for the exact people who need them most: long-term investors such as pension funds, endowments, and, most importantly, individual retirement savers.
Part 1: The Theoretical Framework: Why Valuations Matter
To understand why high prices lead to low returns, we don’t need a crystal ball. The logic is rooted in simple financial mathematics and the observable, long-term behavior of markets.
The Gordon Growth Model Foundation
The theoretical foundation linking valuations to expected returns derives from dividend discount models, particularly the Gordon Growth Model. In its simplest form, this model states that the expected return on a stock is the sum of its dividend yield plus its expected future growth rate in dividends (or earnings).
A valuation metric like the Price-to-Earnings (P/E) ratio is simply the inverse of the earnings yield (Earnings/Price). When valuations (the “P”) rise, the earnings yield (E/P) falls. If growth expectations remain constant, the only way for the equation to balance is for future returns to be lower.
Think of it this way: A stock’s price is the “present value” of all its future earnings. If the price is bid up to very high levels, it simply means that more of the future good news is already “priced in,” leaving less room for future returns for the person who buys it at that high price.
The Power of Mean Reversion
A critical feature of valuation ratios is their tendency toward mean reversion over long horizons. While valuations can deviate substantially from their historical norms for extended periods—sometimes for many years—they eventually gravitate back toward their long-term averages.
This mean reversion occurs through two channels:
- Price Changes: The market price (the “P”) can fall.
- Fundamental Growth: The earnings or book value (the “E” or “B”) can grow to “catch up” to the high price.
When valuations are elevated, returns are squeezed because the market price must, at some point, either grow more slowly than the underlying earnings or fall outright to bring the ratio back to a more sustainable level. This mean-reverting behavior is the engine that underpins the predictive power of valuation metrics, particularly at 10-year or longer horizons where short-term market “noise” and sentiment diminish.
Part 2: The Investor’s Toolkit: Major Valuation Metrics
Researchers and practitioners use several key metrics to gauge market valuations. While none are perfect, some are far more reliable than others for long-term forecasting.
Traditional Price-to-Earnings (P/E) Ratio
This is the most common metric, dividing a stock’s current price by its trailing twelve-month (TTM) earnings. While simple, the TTM P/E ratio suffers from severe limitations. During a recession, earnings can collapse, causing the P/E ratio to surge to artificially high levels precisely when stocks are often at their cheapest. Conversely, at a business cycle peak, temporarily high earnings can compress the P/E ratio, masking overvaluation. For this reason, it is a poor tool for long-term forecasting.1
Price-to-Book (P/B) and Price-to-Sales (P/S) Ratios
The P/B ratio compares a company’s market price to its book value (the accounting value of its assets). The P/S ratio compares the price to its total sales. P/S has the benefit of using sales, which are less volatile and less subject to accounting manipulation than earnings. Both metrics have moderate predictive power but are generally considered less robust than earnings-based measures that account for profitability over a full cycle.
The “Gold Standard”: Cyclically Adjusted P/E (CAPE) Ratio
The most reliable and widely cited metric for long-term forecasting is the Cyclically Adjusted Price-to-Earnings (CAPE) ratio, often called the “Shiller P/E” after its creator, Nobel laureate Robert Shiller.
The CAPE ratio divides the current market price by the average of real (inflation-adjusted) earnings over the previous ten years.
This 10-year smoothing is the metric’s superpower. By averaging earnings over a full business cycle, it smooths out the temporary distortions from recessions (low earnings) and peak-cycle booms (high earnings) that plague the simple TTM P/E ratio.
Extensive empirical evidence supports CAPE10’s predictive power. Campbell and Shiller’s (1998) original work found that the CAPE ratio explained approximately 40% of the variation in subsequent 10-year real returns for U.S. equities.2 Research Affiliates, Vanguard, and others have confirmed this finding globally, documenting that CAPE-based metrics exhibit strong predictive power across developed and emerging markets.3
The relationship is powerfully inverse: a high starting CAPE ratio predicts low 10-year returns, and a low starting CAPE ratio predicts high 10-year returns.
An Alternative Measure: Tobin’s Q
The Q ratio, commonly known as Tobin’s Q, is a valuation metric that compares the total market value of the stock market to the replacement cost of all corporate assets. The concept was first introduced by Nicholas Kaldor in 1966 and later popularized by Nobel Laureate James Tobin, who described it as “the nexus between financial markets and markets for goods and services.”4
The theoretical foundation rests on a “buy or build” arbitrage principle: if corporate assets are valued more highly in financial markets than it would cost to replace them, firms are incentivized to invest in new capital. Competition should theoretically push the ratio toward equilibrium at 1.0 over time.5 When the ratio exceeds 1.0, the market values firms above their replacement cost, suggesting potential overvaluation; when below 1.0, the market may be undervaluing assets.6
The data for computing the aggregate market Q ratio comes from the Federal Reserve’s Z.1 Financial Accounts of the United States, released quarterly. Specifically, analysts use the ratio of nonfinancial corporate equities at market value to nonfinancial corporate net worth at replacement cost.7
As of October 2025, the Q ratio stands at approximately 1.96, representing 133% above its historical arithmetic mean of approximately 0.84, and 161% above its geometric mean of 0.75. The ratio reached an all-time high of 2.09 in May 2025; for historical context, the all-time low was 0.29 in 1982—approximately 65% below replacement cost.8
The historical median value is 0.93, with a typical range between 1.33 and 1.69.9 Current readings represent extreme overvaluation by historical standards, comparable only to the tech bubble peak of 2000, which Smithers and Wright had warned about in their book Valuing Wall Street.10
The Q ratio offers little predictive power for short-term returns. Periods of over- and under-valuation can persist for many years, making the metric unsuitable for short-term investment timing.11 Research has consistently found that evidence of return predictability at horizons of a few months remains weak when using traditional valuation ratios.12
The Q ratio’s predictive power is most pronounced over long investment horizons. Harney and Tower found that “q beats all variants of the PE ratio for predicting real rates of return over alternative horizons.”13 Their research established the predictive superiority of Tobin’s Q over the price-to-earnings ratio across multiple investment horizons.
For comparison with other valuation metrics, Vanguard found that the CAPE ratio’s R-squared (predictive ability) for 10-year returns between 1926 and 2011 was 0.43.14 More recent analysis found that between January 1995 and May 2020, CAPE explained 90% of the variance in subsequent 10-year returns.15 Campbell and Shiller’s foundational research demonstrated that valuation ratios based on long-term average earnings are powerful predictors of subsequent returns because variations in these ratios forecast future returns rather than future earnings growth.16 Additionally, Smithers and Wright demonstrated that peaks in the Q ratio are historically correlated with secular market tops.17
There are some important caveats regarding the use of Q ratio, however. Replacement cost is inherently a lagging and imprecise measure. Some assets—particularly intangibles like intellectual property – cannot be properly marked to market.18 Different economic environments significantly affect the metric. In high-inflation periods, replacement costs rise relative to low-inflation periods, potentially justifying higher ratio values.19 While the Q ratio has historically exhibited mean-reverting behavior, the timing and magnitude of reversion remain highly unpredictable. Blanchard et al. found that corporate profitability—rather than Tobin’s Q—more reliably predicts investment behavior.20
The Q ratio currently signals extreme overvaluation relative to historical norms. While it offers limited utility for short-term market timing, decades of academic research support its value as a strategic tool for setting long-term return expectations. At current levels exceeding 160% above the geometric mean, the ratio suggests investors should expect muted returns over the coming decade—though considerable uncertainty remains about when and how any reversion to mean might occur.
Part 3: Are We in an “AI Bubble”?
This brings us to our first critical question. With the CAPE ratio for U.S. large-cap stocks (S&P 500) standing at a staggering 40.42 as of November, 202521, we are at a level higher than nearly any point in history, except for the peak of the 1999-2000 dot-com bubble.
The above chart is from https://www.multpl.com/shiller-pe. Past performance is not a guarantee of future returns.
This is not a matter of opinion. According to data from Research Affiliates, this above-40 reading is in the 98.7th percentile of all historical values since 1880.22 Prices are, by this reliable measure, exceptionally high.
The narrative driving these prices is the transformative potential of Artificial Intelligence, leading to obvious and anxious comparisons to the dot-com bubble. Is history repeating itself?
Similarities to the Dot-Com Bubble
The parallels are undeniable, and several analyses highlight the classic characteristics of a technology-driven hype cycle.
- A Disruptive Technology: Both eras feature a truly revolutionary technology (the Internet in the 1990s, AI today) with the potential to reshape the entire economy.
- Hype and FOMO: The media and public interest in AI is intense, creating a “fear of missing out” (FOMO) that drives speculative investment from both retail and institutional investors.23
- New Valuation Paradigms: In the 1990s, analysts justified high prices using metrics like “website traffic” or “eyeballs” instead of profits.24 Today, some justify valuations based on the size of AI models, AI talent acquisition, or other non-financial metrics, deprioritizing traditional cash flow analysis.25
- Market Concentration: Just as the dot-com bubble was concentrated in “TMT” (Technology, Media, and Telecom) stocks, the current market rally has been famously narrow, led by the “Magnificent 7.”
One paper, “From Hype to Bubble,” examines these historical parallels, noting that such hype cycles often lead to inflated expectations, heavy investment, and eventual market corrections when the technology’s actual implementation proves slower or more challenging than promised.26
Crucial Differences from the Dot-Com Bubble
However, to say this is a repeat of 2000 is to ignore several fundamental differences. A comparative study of the two eras notes that the current AI boom is built on a much more solid foundation.
- Real Profits and Established Business Models: This is the most important distinction. In 2000, many dot-com companies were “just ideas lacking a clear path to profitability.”27 Companies like Pets.com, an iconic failure, were famous for spending millions on Super Bowl ads while losing money on every sale, a flawed business model from the start.28 Today’s leaders (Microsoft, Google, Nvidia, Apple) are among the most profitable corporations in world history. They have established, dominant business models and are using AI to enhance their existing moats, not as a speculative bet on future viability.29
- Mature Technological Infrastructure: The dot-com boom was built on a fragile, nascent infrastructure of dial-up modems. The AI boom is built on a mature ecosystem of global high-speed internet, cloud computing (AWS, Azure, Google Cloud), and massive existing datasets that were unavailable in 2000.30
- Tangible, Practical Applications: While many dot-com ideas were theoretical, AI is already generating tangible economic value in vital sectors like healthcare (medical diagnosis), industry (predictive maintenance), and law (contract analysis).31
Not a Bubble, But a Problem of Price
So, are we in a bubble? The answer is nuanced.
We are not in a dot-com-style bubble of unprofitable, speculative companies destined for bankruptcy. We are, however, in a valuation bubble, where the prices for high-quality, profitable companies have been bid up to levels that are mathematically unsustainable from a future-return perspective.
As finance professor Luciano Floridi argues, the hype is real, and the speculation is outpacing the reality of deployment, which history shows can take decades, not months. The market is pricing in decades of perfect execution and transformative growth today.32
For the long-term investor, the conclusion is the same whether you call it a “bubble” or “extreme overvaluation.” The price you must pay today for U.S. large-cap stocks is exceptionally high, and the 10-year expected returns are, as a direct consequence, exceptionally low.
For a much deeper dive into the valuations of individual stocks, please refer to Exhibit A in the PDF version of the guide.
Part 4: What to Expect: 10-Year Return Forecasts
This brings us to our second critical question. If the CAPE ratio for U.S. large caps is at the 98.7th percentile, what returns should we expect?
We have synthesized 10-year nominal (before inflation) return forecasts from three of the world’s leading asset managers who specialize in this type of modeling: Research Affiliates, Vanguard, and J.P. Morgan.
It is crucial to note that these are not 1-year guesses. They are 10-year, annualized projections based on rigorous models that start with current valuations (like CAPE), add expected earnings/dividend growth, and factor in mean reversion.
The results are stark and unanimous.
10-Year Equity Return Forecasts (2025-2035)
The table below presents the 10-year annualized nominal return forecasts for the specific asset classes as of September 30, 2025. Since then – through November 28, 2025, most stock assets classes have increased in value modestly – from about 1% to 4% (driven in part by a rebound in technology stocks during the week of Thanksgiving).
Note: Vanguard and J.P. Morgan do not publish forecasts for every style and size combination. We have included their closest proxy. See sources of data in footnotes. Past performance is not a guarantee of future returns. Note that even over 10-year or longer periods, there can be a substantial variance from expected versus actual returns, with a wide range of probabilities.
An Investor’s Glossary: Explaining the Asset Classes
- Large Cap (e.g., S&P 500): The biggest companies, like Apple, Microsoft, and ExxonMobil. Typically valued (as to market capitalization) from $10 billion to over $5 trillion, in the U.S.
- Small Cap (e.g., Russell 2000): Smaller, less-established publicly-traded companies that offer higher growth potential but also higher risk. Typically valued (as to market capitalization) at $10 billion or less.
- Growth (LCG/SCG): Companies that are expected to grow their earnings at an above-average rate. They often have high P/E ratios and pay low (or no) dividends. Think of technology and biotech.
- Value (LCV/SCV): Companies that appear “cheap” relative to their earnings, sales, or book value. They are often in more mature industries, like banking, insurance, and utilities, and typically pay higher dividends.
- EAFE (Foreign Developed): Stands for “Europe, Australasia, and the Far East.” These are the large- and mid-cap stocks in developed, industrialized countries outside of the U.S. and Canada (e.g., Japan, Germany, U.K., France).
- Emerging Markets (EM): Stocks in developing countries with faster-growing economies but also higher political and currency risk (e.g., China, India, Brazil, Taiwan).
Analysis of the Forecasts
The message from the data is overwhelming:
- U.S. Large-Cap Returns Are Projected to Be Anemic. The consensus forecast for the S&P 500 (U.S. Large Cap) is in the 3%-7% range, with Research Affiliates and Vanguard at the low end. The picture for U.S. Large-Cap Growth—the home of the “Magnificent 7″—is even more dire. Research Affiliates projects a paltry 1.50% per year, and Vanguard projects 1.3% – 3.3%. After accounting for inflation (projected at 1.6%-2.6% by Vanguard), the real return could be zero or negative for a decade.
- Valuations are the Reason. This isn’t a bet against AI or American innovation. It is a simple accounting of the starting price. As stated previously, the CAPE ratio for U.S. Large stocks is above 40 (98.7th percentile). In contrast, the CAPE for “Dev ex US Large” (EAFE) is a historically average 19.9 (51.6th percentile).36 Investors are paying twice as much for every dollar of cyclically-adjusted U.S. earnings as they are for international earnings. The forecasts simply reflect this gap.
- Value and Small-Cap Stocks Appear Far More Attractive. Within the U.S., the brightest spots are in value and small-cap stocks. Morningstar’s Q4 2025 outlook notes that while U.S. large-cap growth stocks are at a 12% premium to their fair value, small-cap stocks are trading at a 16% discount. This valuation gap is reflected in the forecasts: Research Affiliates projects 8.68% for U.S. Small-Cap Value, nearly 6x its forecast for U.S. Large-Cap Growth.
- International and Emerging Markets Show Robust Potential. The forecasts for Foreign Developed (EAFE) and Emerging Markets (EM) are consistently in the 7%-8% range across all providers. This is more than double the forecast for the S&P 500 from Research Affiliates and Vanguard. After a decade of U.S. market dominance, valuations and, therefore, future return prospects, have flipped in favor of international diversification.
Part 5: Practical Applications and Implications
This research has profound, practical implications for investors.
- Strategic Asset Allocation
The most important application of this data is in strategic asset allocation – setting the long-term, target percentages for your portfolio. Institutional investors like pension funds use these exact forecasts to determine their optimal mix of assets. When U.S. equity valuations are at historic highs, prudent risk management calls for rebalancing away from the most expensive assets (U.S. Large-Cap Growth) and allocating toward those with better long-term prospects (Value, Small-Cap, and International/EM stocks).
- Setting Realistic Return Expectations
Perhaps the most valuable application of all is in setting realistic financial goals. For the past decade, many investors have become accustomed to double-digit annual returns from simple S&P 500 index funds. The valuation data from nearly all major forecasters warns that this period is over.
An investor whose financial plan assumes a 10% annual return from U.S. stocks is setting themselves up for failure. A plan that assumes a more modest return would be far more realistic and encourages the right behavior: saving more, delaying retirement, or reducing spending goals. Communicating these sober expectations helps prevent disappointment and encourages appropriate risk-taking.
- The Fallacy of Market Timing
Those investors who are more risk-averse may also desire to lower the allocations to equities – although there is no guarantee that stock valuations will decline in the short term (and valuations may even go much higher).
Be aware that a critical limitation of this data is that it tells you nothing about when returns will occur. Valuations have virtually no predictive power for 1-year returns, which are driven by momentum, sentiment, and short-term news.
The late 1990s bubble illustrates this perfectly: the CAPE ratio signaled extreme overvaluation as early as 1996, yet the market continued to soar for four more years before the bubble burst. An investor who sold everything in 1996 would have missed substantial gains, even though their long-term thesis was correct.37
The value of these metrics lies in setting long-term strategic allocations, not in making short-term tactical trades. Again, however, those investors who are more risk-averse might choose to lower their allocations to equities, realizing that they might be sacrificing returns from equities should equity returns continue their recent trajectory.
Conclusion
The past 25 years of rigorous research have firmly established that current asset valuations contain meaningful information about future long-term returns. The evidence consistently demonstrates an inverse relationship: high prices today lead to low returns tomorrow, particularly over horizons of 10 years or longer.
Today, this evidence points to a clear and present challenge. U.S. large-cap stocks, driven by a powerful AI narrative and concentrated in a few technology giants, are trading at valuations in the 98th percentile of history. This is not a repeat of the 2000 bubble, as today’s market leaders are highly profitable and mature companies. However, for investors, the high price may have well “pulled forward” future returns, leaving little on the table for the next decade.
The forecasts from Research Affiliates, Vanguard, and J.P. Morgan all confirm this, projecting low-single-digit returns for U.S. large-cap growth stocks.
But this is not a counsel of despair. It is a counsel of diversification. The very same metrics that paint a bleak picture for the S&P 500 tell a far more optimistic story for U.S. small-cap stocks, U.S. value stocks, and international stocks in both developed and emerging markets. In these unloved corners of the market, valuations are reasonable, and 10-year expected returns are two to three times higher.
For long-term investors, the core message remains clear: valuations matter. While they cannot predict the timing of market movements, valuation metrics provide our best window into long-term expected returns. In an uncertain world, this information is invaluable for asset allocation, risk management, and setting the realistic expectations that are the foundation of all successful financial plans.
About the Authors
Ron A. Rhoades, JD, CFP®
Ron Rhoades is an Associate Professor of Finance at the Gordon Ford College of Business, Western Kentucky University. He also serves as a financial advisor at Scholar Financial, a practice within XY Investment Solutions LLC. With a background as both an attorney and a CERTIFIED FINANCIAL PLANNER™ professional, Ron is a nationally recognized authority on the fiduciary duties of financial advisors.
Chris Brown, Ph.D., CFP®
Chris Brown is a faculty member in the Department of Finance at the Gordon Ford College of Business, Western Kentucky University, and a financial advisor at Scholar Financial, a practice within XY Investment Solutions, LLC. He holds the CERTIFIED FINANCIAL PLANNER™ designation and a Ph.D. in Personal Financial Planning. His research and teaching focus is on behavioral finance, retirement planning, and evidence-based investment strategies.
Disclosure
This guide is for educational purposes only. It should not be construed as financial, legal, tax, or investment advice, nor as a recommendation to implement any specific strategy, product, or investment. Consult with a qualified financial professional before making investment decisions.
References
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21 Source: https://www.multpl.com/shiller-pe
22 See Arnott, R. D., Beck, N., & Kalesnik, V. (2016). Shiller’s CAPE: Market timing and risk. Research Affiliates Publications.
23 Taherdoost, H. (2025). From hype to bubble: a historical analysis of technology trends and the case for artificial intelligence. Future Digital Technologies and Artificial Intelligence, 1(1), 1–7. https://doi.org/10.55670/fpll.fdtai.1.1.1
24 Al-Maani, I. (2025). The internet bubble of 2000 and the potential artificial intelligence bubble: A comparative study of economic and legal dimensions. Legal Studies and Advanced Technology Unit.
25 Floridi, L. (forthcoming). Why the AI hype is another tech bubble. Digital Ethics Center, Yale University. See also Taherdoost, supra n.6.
26 Taherdoost, supra n.6.
27 Al-Maani, supra n.7.
28 Id.
29 Id.
30 See, e.g., Taherdoost, supra n.6, and Al-Maani, supra n.7. See also, e.g., Investing.com. (2025, October 6). Why the AI boom may defy history: 4 reasons this time could be different.
31 Floridi, L. (forthcoming). Why the AI Hype is another Tech Bubble. Digital Ethics Center, Yale University.
32 Id.
33 Research Affiliates, LLC. (2025). Asset allocation interactive data (As of September 30, 2025) [Data file].
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36 Research Affiliates, LLC. (2025). Asset allocation interactive data (As of September 30, 2025) [Data file].
37 This value of 25.2 was already considered quite high compared to the historical average (which was around 16-17 at that time). The ratio continued to climb rapidly throughout 1996 and 1997, reaching approximately 28 by January 1997. The CAPE ratio finally peaked at around 44 during the Dot-com bubble in 2000.
38 YCharts. (2025). S&P 500 P/E Ratio (Quarterly) – United States – Historical… https://ycharts.com/indicators/sp_500_pe_ratio (Provides 27.88x as of June 2025, reflecting a more recent S&P 500 P/E than the original source’s 21.8x forward P/E).
39 Edgewater, The NASDAQ hit an all-time high in April (2017), Edgewater Insights.
40 Stotz, A. (2017). An empirical study of financial analysts earnings forecast accuracy. Social Science Research Network. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2943146
41 Source: https://www.multpl.com/s-p-500-pe-ratio (retrieved Nov. 29, 2025).
42 The Motley Fool; LSEG. (2025). The Magnificent Seven: What you need to know; ‘Magnificent-7’ Q4 2024 earnings review…
43 LSEG. (2025, March 7). ‘Magnificent-7’ Q4 2024 earnings review: Growth holds, but rotation awaits. Lipper Alpha Insight. https://lipperalpha.refinitiv.com/2025/03/magnificent-7-q4-2024-earnings-review-growth-holds-but-rotation-awaits/
44 Butters, J. (2025, November 24). “’Magnificent 7′ Companies Reported Lowest Earnings Growth Since Q1 2023.” FactSet; Reuters Breakingviews. (2025, November 21). “Humble 493 hang tough with the Magnificent 7.” Reuters / Breakingviews; LPL Research / RTI Wealth. (2025, November 20). “Weekly Market Commentary – November 24, 2025.” LPL Research; Moneycontrol. (2025). “Magnificent Seven’s sharp losses test US stock market’s strength.” moneycontrol.com.
45 Alaric Securities. (2025, September). Magnificent 7 earnings: Summer 2025 update. https://alaricsecurities.com/magnificent-7-earnings-summer-2025-update/
46 Stotz, A., & Lu, W. (2015). Financial analysts were only wrong by 25% (working paper). SSRN. https://doi.org/10.2139/ssrn.2695216
47 Id. See also Dechow, P. M., Hutton, A. P., & Sloan, R. G. (2000) — Over-optimism around equity offerings / long-term forecasts; see also Karamanou, I., & Vafeas, N. (2012) — Emerging markets / value relevance of analyst forecasts.
48 Bradshaw, M. T., Drake, M. S., Myers, J. N., & Myers, L. A. (2012). A re-examination of analysts’ superiority over time-series forecasts of annual earnings. Review of Accounting Studies, 17(4), 944–968. See also So, E. C. (2013). A new approach to predicting analyst forecast errors. Journal of Banking & Finance (see publisher for full citation). https://doi.org/10.1016/j.jbankfin.2013.03.002
49 Bank for International Settlements. (2024, September). The valuations of tech stocks: dotcom redux? BIS Quarterly Review. Retrieved from https://www.bis.org/publ/qtrpdf/r_qt2409.pdf.
50 See, e.g., Barmpoutis, V. (2014). The naive extrapolation hypothesis and the rosy-gloomy forecasts (Working paper). Retrieved from https://arxiv.org/abs/1406.1733. See also, e.g., Guo, H. (2021). Earnings Extrapolation and Predictable Stock Returns. Retrieved from https://fnce.wharton.upenn.edu/wp-content/uploads/2022/07/Paper4_Guo.pdf.
51 Fortune. (2025b, September 28). Everyone’s wondering if, and when, the AI bubble will pop: Here’s what went down 25 years ago that ultimately burst the dot-com boom.
52 Massachusetts Institute of Technology. (2025, August). The GenAI Divide: State of AI in Business 2025 (NANDA Initiative Report). MIT Media Lab Connected AI. Retrieved from https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf
53 Hecht, J. (2016, October). Boom, bubble, bust: The fiber optic mania. Optics & Photonics News, 27(10), 46-51. https://internethistory.org/wp-content/uploads/2020/01/OSA_Boom.Bubble.Bust_Fiber.Optic_.Mania_.pdf
54 Investopedia. (2025, March 14). Mean reversion. Investopedia.
55 Shiller, R. J. (2015). Irrational exuberance (3rd ed.). Princeton University Press.
56 Multpl.com.
57 Buffett, W. E. (1999, November 22). Mr. Buffett on the stock market. Fortune Magazine. Retrieved from https://archive.fortune.com/magazines/fortune/fortune_archive/1999/11/22/269071/index.htm
58 Computed from data retrieved from Robert Shiller’s web site, using data through October 1, 2025. According to Professor Shiller: “As documented in Bunn & Shiller (2014) and Jivraj and Shiller (2017), changes in corporate payout policy (i. e. share repurchases rather than dividends) have now become a dominant approach in the United States for cash distribution to shareholders). This may affect the level of the CAPE ratio through changing the growth rate of earnings per share. This subsequently may affect the average of the real earnings per share used in the CAPE ratio. A total return CAPE corrects for this bias through reinvesting dividends into the price index and appropriately scaling of earnings per share.” https://shillerdata.com/
