The Efficient Market Hypothesis (EMH) argues that asset prices reflect all available information, rendering sustained outperformance nearly impossible. While this idea has guided decades of academic research and shaped the passive investing revolution, it is often oversimplified. In practice, markets are highly efficient in processing information velocity, but not always accurate in information interpretation. This article highlights the crucial distinction between efficiency and accuracy, showing how historical misinterpretations have created persistent opportunities for disciplined active management. We argue that while passive investing is a powerful baseline strategy, the case for true active management remains compelling where markets systematically misread reality.

Since Eugene Fama introduced the Efficient Market Hypothesis in the 1970s (Fama, 1970), it has become a cornerstone of modern finance. Its three variants, the weak, semi-strong, and strong forms have shaped how academics, regulators, and practitioners think about information in markets. EMH has also been the intellectual foundation for the extraordinary rise of index funds and the tens of trillions now managed passively worldwide.

Yet EMH is often misunderstood. Many equate efficiency with accuracy, assuming that the ability of markets to rapidly integrate information implies that prices quickly converge on “fair value.” In reality, speed of adjustment does not guarantee correctness. Prices frequently reflect collective misinterpretations of information, influenced by flawed models, human biases, and herd behavior. This article examines the gap between efficiency and accuracy and considers its implications for modern investors.

Efficiency vs. Accuracy: A Framework

An efficient assembly line can process items at scale and speed but if the input is incorrect or the blueprint is misread, mistakes occur systematically. Similarly, financial markets can instantaneously incorporate new information into asset prices, but the collective interpretation of that information may remain flawed.

• Efficiency is about speed and consistency of processing.

• Accuracy is about correctness of interpretation.

Markets excel at efficiency but remain vulnerable to human judgment errors, over- or under-reactions, and prevailing narrative swings.

Historical Evidence of Misinterpretation

The history of markets provides rich evidence of periods where efficiency and accuracy diverged:

➡️Earnings Overreaction (1989, Bernard & Thomas): Prices often underreact or overreact to earnings surprises, producing systematic “drift” in post-earnings returns. Markets efficiently process the news, but behavioral biases lead to predictable mispricing.

➡️The 2008 Financial Crisis: Mortgage-backed securities traded as if they were safe credits, reflecting efficient absorption of ratings-agency assessments but ignoring catastrophic flaws in underlying loan quality.

➡️The Dot-Com Bubble (1995–2000): Technology companies with negligible earnings traded at inflated valuations. Information was efficiently reflected in prices with enthusiasm, venture flows, and analyst narratives but collectively misinterpreted the durability of business models.

These episodes underscore that efficiency ensures rapid updates to consensus beliefs, not necessarily consensus truth.

Factor Anomalies: Persistent Evidence Against EMH

Broad, systematic anomalies defy the core EMH claim that all known information is already in the price:

👉Value: Low-price-to-book sectors and markets outperform over long horizons, even when controlling for risk (deep value rallying after tech bubbles burst).

👉Momentum: Sectors or markets with strongest recent returns tend to outperform in the near term, defying simple risk-based explanations.

👉Low Volatility: Defensive or low-beta sectors such as utilities have earned superior risk-adjusted returns, contradicting CAPM norms.

Factor investing has grown into an accepted discipline as these anomalies persist, even after being widely publicized and targeted by many funds. Explanations range from behavioral biases (herding, myopia) to structural constraints (limited liquidity, regulatory frictions).

Passive, Closet-Active, and True Active Management

🟠Passive Investing: Built directly from EMH, passive strategies assume mispricings are rare, random, and unexploitable. They provide low-cost exposure to systematic market returns and remain a powerful default for most investors.

🟠Closet Indexing (“Pseudo-Active”): Many funds nominally market themselves as active but hug the benchmark with minor deviations. Research (Cremers & Petajisto, 2009) shows such managers rarely add value after fees, as their portfolios are simply costly versions of passive exposure.

🟠True Active Management: The subset of managers who begin their process independently of consensus. They leverage alternative datasets, behavioral insights, or systematic quantitative methods to target inefficiencies created by misinterpretation. Unlike “macro timing” approaches, the aim is not to outguess the entire market but to systematically exploit recurring disconnects between efficient information absorption and accurate valuation.

Implications for Investors

Recognizing the distinction between efficiency and accuracy reframes the debate over active vs. passive management.

✅Markets are efficient most of the time, but efficiency in speed does not eliminate errors in interpretation.

✅Mispricing opportunities emerge most often during periods of uncertainty, narrative dominance, or behavioral overreaction.

For individuals, the low fees and diversification of passive investing make it the natural foundation. However, dismissing the potential for alpha entirely ignores decades of documented anomalies and inefficiencies.

The challenge lies in identifying managers who are genuinely independent of consensus and disciplined in exploiting inefficiencies, rather than charging active fees for benchmark-like exposures.

💡Conclusion: Thames Bridge Perspective

At Thames Bridge we believe in starting from first principles: information is absorbed efficiently, but consensus frequently interprets it incorrectly. Our quantitative and macro frameworks are designed to separate efficiency from accuracy and systematically identify the gaps between the two.

By avoiding reliance on consensus narratives, employing rigorous risk controls, and leveraging alternative data, we seek to unlock sources of alpha that typically remain inaccessible to individual investors. In doing so, we aim to democratize hedge-fund-like strategies, making them available in cost-efficient managed accounts.

Ultimately, markets process information at lightning speed but the truth takes longer to emerge. For investors, the difference between efficiency and accuracy is exactly where opportunity lives. Opportunity to save and make money.