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News3 min read·2026-10-20DisconfirmAI Editorial

Identifying Stock Bubbles: A New Method Separates Fundamentals from Speculation, Explaining 61% of Valuation Ratio Variation

Andreasen and Bro's study in the Review of Finance decomposes stock prices into expected dividends, expected returns, and a rational bubble, using investor survey data to distinguish fundamentals from speculation. Based on U.S. monthly data from 1971 to 2024, the model estimates that the bubble component explains 61% of variation in the S&P 500 price-dividend ratio, identifying multiple historical speculative episodes including a potential AI-related phase. The paper also finds that positive bubble shocks are followed by lower unemployment and higher monetary policy rates.

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Original publication time 2026-10-20 11:17 GMT+8

Stock market bubbles are easy to identify in hindsight—the dot-com boom, the run-up to the global financial crisis, and the surge in technology stocks during the pandemic are all familiar examples. But identifying a bubble while it is forming remains one of the longstanding challenges in financial economics. Rising stock prices do not necessarily indicate speculation: investors may rationally expect stronger earnings growth, lower future returns, or improving economic conditions. Distinguishing these fundamental explanations from a speculative bubble is precisely the problem that Andreasen and Bro address in their 2026 paper published in the Review of Finance.

They develop a present-value model that decomposes stock prices into three components: expected future dividends, expected future returns, and a rational bubble. The key innovation is incorporating survey-based expectations about dividend growth and stock prices alongside traditional market data. This matters because a high price-dividend ratio alone does not establish that a bubble exists: when investors anticipate stronger dividend growth or lower required returns, high valuations can be rational. By incorporating information about what market participants actually expect, the model attempts to identify the portion of valuations that cannot be explained by these fundamental components. In this framework, a rational bubble is a non-fundamental component of prices consistent with the model's rational-expectations assumptions.

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Using U.S. monthly data from January 1971 through December 2024, the authors find that the estimated bubble accounts for 61% of the variation in the S&P 500 price-dividend ratio. Expected returns account for another 42%, while expected dividend growth contributes little to explaining variation in the ratio over the full sample. These variance contributions are not additive shares of a fixed total and should not be interpreted as percentages summing to 100%. The findings suggest that speculative forces may play a much larger role in explaining stock market valuation fluctuations than traditional models without bubbles imply. The authors also show that removing the estimated bubble substantially reduces the excess volatility of stock valuations and eliminates the evidence of explosive dynamics detected in the unadjusted price-dividend ratio.

The model identifies several recognizable historical bubble episodes. During the early-1970s Nifty Fifty boom, the estimated bubble represents approximately 10% of the price-dividend ratio before declining to around 2% during the 1980s. It then rises sharply during the dot-com era, reaching 27% in January 2001. Another increase occurs around the global financial crisis, followed by a COVID-era bubble that peaks at approximately 19% in November 2021. Most notably, the estimated bubble reaches nearly 25% of the price-dividend ratio in December 2024, potentially reflecting investor enthusiasm surrounding artificial intelligence. This is a model-based historical estimate, not evidence that AI-related stocks were necessarily overvalued or that a subsequent market correction was inevitable.

Sources

  • 原始出处alphaarchitect.com↗
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