{"id":248447,"date":"2026-06-29T10:30:18","date_gmt":"2026-06-29T14:30:18","guid":{"rendered":"https:\/\/ibkrcampus.com\/campus\/?p=248447"},"modified":"2026-06-30T04:51:42","modified_gmt":"2026-06-30T08:51:42","slug":"the-impressive-markets-hypothesis-prices-still-know-the-future","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/the-impressive-markets-hypothesis-prices-still-know-the-future\/","title":{"rendered":"The Impressive Markets Hypothesis: Prices Still Know the Future"},"content":{"rendered":"\n

The article “The Impressive Markets Hypothesis: Prices Still Know the Future” was originally published on Alpha Architect<\/a> blog.<\/em><\/p>\n\n\n\n

Evidence-based investors have long debated the efficient market hypothesis (EMH), popularized by Gene Fama. In the new era of social media echo chambers, meme stocks, and information overload, it has become fashionable to argue that markets are growing less rational. BlackRock\u2019s William Ezratty, Gerald Garvey, Timothy McDade, and Andrew Robinson, authors of the study \u201cThe Impressive Markets Hypothesis<\/a>: Prices (Still) Forecast Fundamentals,\u201d published in the April 2026 issue of The Journal of Portfolio Management, push back on the narrative of declining market efficiency, arguing that markets remain far more efficient\u2014and thus harder to beat\u2014as stock prices remain impressive forecasters of future business performance.<\/p>\n\n\n\n

What Problem Were the Authors Trying to Solve?<\/h2>\n\n\n\n

AQR\u2019s Cliff Asness published an influential 2024 paper, \u201cThe Less Efficient Market Hypothesis<\/a>,\u201d arguing that markets have become measurably less efficient<\/em> \u2014 that prices have become detached from fundamentals, evidenced in part by historically wide \u201cvalue spreads\u201d (the gap between the cheapest and most expensive stocks by valuation metrics like book-to-price).<\/p>\n\n\n\n

The BlackRock team took this hypothesis seriously but wanted to test it directly. Rather than looking at what prices are doing relative to current book values, they asked a more pointed question: Do stock prices today actually predict future cash flows? And has that predictive power eroded over time? They examined over 3,000 US equities annually from 2004 through 2024 \u2014 a dataset spanning the Global Financial Crisis, the rise of passive investing, the COVID shock, the alternative data explosion, and the early days of large language models.<\/p>\n\n\n\n

Methodology: How Did They Test It?<\/h2>\n\n\n\n

The core approach was elegant: use a company\u2019s current valuation ratio (specifically, the book-to-price ratio \u2014 the same metric underlying the famous Fama-French \u201cvalue\u201d factor) to predict its operating cash flow<\/em> one year later. They controlled for each company\u2019s current profitability, so they could isolate what information prices add beyond what accounting statements already tell us.<\/p>\n\n\n\n

They then applied three tests.<\/p>\n\n\n\n

    \n
  1. Does the predictive power change when value spreads are wide \u2014 the condition Asness highlights as a sign of potential mispricing?<\/li>\n\n\n\n
  2. Has the power strengthened or weakened in the 2020s specifically?<\/li>\n\n\n\n
  3. Does the market\u2019s forecasting power differ between revenue generation and profit margins?<\/li>\n<\/ol>\n\n\n\n

    Key Findings<\/h2>\n\n\n\n

    The results are organized around three core findings, each with meaningful implications:<\/p>\n\n\n\n

    Prices still forecast fundamentals \u2014 robustly<\/strong><\/p>\n\n\n\n

    Higher-valued companies (those trading at a premium to book value) do go on to generate significantly stronger operating cash flows one year later. The effect is economically meaningful: a one standard-deviation shift in valuation moves the expected future profitability from roughly 10% to nearly 14% of assets. This holds across different industry definitions, time-period fixed effects, and clustering<\/a> approaches.<\/p>\n\n\n\n

    Wide value spreads don\u2019t impair the signal<\/strong><\/p>\n\n\n\n

    This is perhaps the most direct challenge to the Asness thesis. When the authors interact value spreads with their predictive measure, the coefficient is actually slightly negative \u2014 meaning prices are if anything better<\/em> forecasters when spreads are wide, not worse. The coefficient is statistically insignificant, so the more careful statement is that there is simply no evidence wide spreads reflect reduced informational content in prices.<\/p>\n\n\n\n

    Prices have gotten better at revenues, worse at margins<\/strong><\/p>\n\n\n\n

    The authors split profitability into two components:<\/p>\n\n\n\n

      \n
    1. How much revenue does the business generate from its assets?<\/li>\n\n\n\n
    2. How much profit does it keep from each dollar of revenue?<\/li>\n<\/ol>\n\n\n\n

      The former is the \u201casset turnover\u201d piece \u2014 sales divided by assets.<\/p>\n\n\n\n

      The latter is the margin piece \u2014 Profit divided by Sales.<\/p>\n\n\n\n

      Multiply the two and you get overall profitability: OPCF\/Assets (operating cash flow divided by total assets)<\/p>\n\n\n\n

      A striking divergence emerged between the two components. Market prices have become significantly more<\/em> accurate at predicting future revenue generation over time \u2014 consistent with the rise of alternative data sources like credit card transactions, web traffic, and geolocation data that are overwhelmingly revenue-focused. However, predictive power for profit margins has declined. The cost side of the income statement appears to be increasingly underpriced by the market.<\/p>\n\n\n\n

      To illustrate the revenue skew in alternative data, the authors reference an Eagle Alpha report on the 20 most popular alternative data products in 2021. Of these, 12 were clearly classified as revenue-focused (consumer transactions, app usage, web traffic, geolocation). Only a single vendor \u2014 Revelio Labs, which harvests employment data from LinkedIn \u2014 was classified as cost-focused.<\/p>\n\n\n\n

      Their findings led the authors to conclude:<\/p>\n\n\n\n

      \u201cMarkets may not be perfectly efficient, but they contain an impressive amount of forward-looking information. This is as true today as in the past.\u201d They added: \u201cPrices are no less impressive predictors of future fundamentals when spreads are wide, nor is their information content subsumed by features such as accruals, leverage, volatility, and external financing.\u201d<\/em><\/p>\n\n\n\n

      Key Investor Takeaways<\/h2>\n\n\n\n
        \n
      1. Don\u2019t abandon market prices as an information source<\/strong>.<\/em>\u00a0The notion that markets have become informationally hollow is not supported by the data. Valuation-based strategies retain genuine forecasting content.<\/li>\n\n\n\n
      2. Wide value spreads are not necessarily a sign of irrational markets<\/strong>.<\/em>\u00a0The gap between cheap and expensive stocks reflects a mixture of risk, mispricing,\u00a0and<\/em>\u00a0legitimate forward-looking information. Investors shorting \u201coverpriced\u201d stocks purely on the basis of wide spreads should be cautious \u2014 those high valuations may well be justified by future fundamentals the market can see.<\/li>\n\n\n\n
      3. The cost side of the income statement is the new frontier<\/em><\/strong>.<\/em>\u00a0If market prices have become adept at incorporating revenue signals (via alternative data) but are weakening on margins, then active managers who build proprietary insights into cost structures \u2014 supply chain efficiency, labor cost trends, input pricing \u2014 may have a genuine edge that markets aren\u2019t yet capturing.<\/li>\n\n\n\n
      4. LLMs may close the gap \u2014 eventually.<\/strong>\u00a0The authors note that their data captures the alternative data explosion but only the early innings of large language models. LLMs, which can parse earnings call transcripts, regulatory filings, and supplier disclosures, may be better suited than consumer transaction data to illuminate the cost side. This is a space worth watching.<\/li>\n\n\n\n
      5. Valuation is a better tool than spreads for assessing market efficiency.<\/strong>\u00a0Rather than using the level of value spreads as a barometer of market rationality, investors are better served by asking whether prices retain their fundamental predictive content \u2014 and on that measure, the evidence is reassuring.<\/li>\n<\/ol>\n\n\n\n

        Conclusion<\/h2>\n\n\n\n

        This paper offers a measured, empirically grounded counterpoint to the narrative of deteriorating market quality. Its title \u2014 \u201cThe Impressive Markets Hypothesis\u201d\u2014 is a deliberate nod to (and gentle rebuke of) the \u201cLess Efficient Markets Hypothesis\u201d it argues against. Markets are not perfect. Anomalies exist. But the idea that social media and information overload have fundamentally broken the relationship between prices and business fundamentals doesn\u2019t survive contact with 20 years of data.<\/p>\n\n\n\n

        What\u2019s perhaps most valuable for practitioners is not the reassurance itself, but the nuance. The market\u2019s informational advantage is not uniform. It appears sharper on revenues than on profits. And that asymmetry \u2014 driven by the lopsided nature of the alternative data industry \u2014 points to where disciplined, fundamental-minded active investors might still find durable signal.<\/p>\n\n\n\n

        In the authors\u2019 own words: \u201cThere appears to be a major opportunity for active managers to gather new information on and model the cost side.\u201d Consistent with Andrew Lo\u2019s Adaptive Markets Hypothesis<\/a>, which holds that markets grow more efficient as investors compete to exploit inefficiencies, by doing so they will make the market even more efficient.<\/p>\n\n\n\n

        Larry Swedroe is the author or co-author of 18 books on investing, including his latest <\/em>Enrich Your Future<\/em><\/a>. He is also a consultant to RIAs as an educator on investment strategies. This article is for informational and educational purposes only and should not be construed as specific investment, accounting, legal, or tax advice.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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