The Hidden Price of Public Information

Introduction: An Uncomfortable Idea for Efficient Markets

For decades, financial theory has rested on a central axiom: public information is free, symmetric, and quickly incorporated into prices. Under this premise, investors shouldn’t be able to generate sustained returns using data available to everyone. However, a recent study by Ed deHaan, Chanseok Lee, Miao Liu, and Suzie Noh (Stanford and Boston College, 2025) challenges this idea with a compelling experiment: an AI that, relying solely on public data, systematically improves the portfolios of thousands of real-world mutual funds.

The Experiment: An AI «Virtual Analyst»

The authors design an “AI analyst” that operates under the same constraints faced by real fund managers (style, capitalization, number of holdings, etc.) and enhances their portfolios each quarter using only public data. The model selects stocks within the same benchmark group to maximize risk-adjusted returns.

The results are striking: the AI analyst generates an additional $17.1 million in alpha per quarter, outperforming 93% of human fund managers over their professional lifetimes. Even under more restrictive scenarios (AI-only portfolios or adjustments for risk and transaction costs), the advantage persists.

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Click below for the Interactive Web on the paper:

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Theoretical Framework: Grossman & Stiglitz and the Cost of Information Processing

This finding connects directly to the Grossman & Stiglitz (1980) framework, which argues that markets cannot be fully efficient if obtaining information is costly. Rational investors process information only until marginal cost equals expected benefit. Therefore, if managers failed to capture those $17.1M in additional returns, it’s because the cost (in time, staff, technology, analysis, etc.) of doing so was equal to or greater than that figure.

In this context, the alpha generated by the AI isn’t just a missed opportunity—it becomes an empirical estimate of the «shadow price» of processing public information in real-world settings.

Implications: Efficiency, Technology, and a Redefinition of «Skill»

This study forces a rethinking of several core beliefs:

• Public information isn’t free: Interpreting it precisely requires cognitive, technological, and structural capabilities that come at a real cost.
• Investment skill must be redefined: It’s no longer just about gut instinct or privileged access, but about building architectures that transform public data into superior decisions. The AI analyst, in this sense, represents a new kind of portfolio manager.
• The edge is temporary, but exploitable: If all managers adopt the same AI, excess returns will fade. But as long as access and adoption remain asymmetric, there’s a real and transient advantage.

Costs and Benchmarks: A Revealing Comparison

The alpha generated by the AI is more than five times the average fees charged by the funds studied ($3.6M per quarter) and also significantly exceeds their average alpha ($2.8M). The study controls for realistic benchmarks (DGTW) and adjusts for risk using Fama-French + Momentum models and mispricing factors (Stambaugh & Yuan, 2017). It also deducts estimated transaction costs following Frazzini et al. (2018), and still the AI outperformance holds.

Conclusion: Information Isn’t the Bottleneck—Processing It Is

This paper doesn’t just show that public information has real costs—it also quantifies them using a novel methodology. And it opens the door to a new strategic lens: in a world where everyone has access to the same data, advantage lies not in access, but in the ability to interpret faster, better, and more efficiently. The next generation of financial analysts won’t compete to gather more data—they’ll compete to build systems that understand it better.

It’s a wake-up call for the asset management industry. It forces us not only to rethink how we value and process information, but to recognize that AI is not a replacement, but an essential partner in the pursuit of efficiency and alpha in an increasingly complex market. Those who invest in understanding and leveraging this ‘hidden price’ will be better positioned to thrive in the future of finance.