One of the more pervasive ideas in finance is that information gets incorporated into prices quickly. A company releases news, investors digest it, they act, and the stock adjusts. Maybe not instantly, but fast enough that trying to make money from yesterday’s public information seems silly.

A recent paper by Ryan Flugum, Kelley Bergsma Lovelace, and Feifei Wang suggests that this picture is missing something important. The market’s ability to process information may depend on what else is happening when the information arrives.

Their paper, “Does the Market Information Processing Context Matter? How Past Disclosure Timing Affects Future Stock Returns”, studies firms’ 8-K filings and constructs a measure the authors call information processing frictions, or IPF. The idea is to identify companies whose disclosures have tended to arrive when the market environment is particularly difficult for investors to process information. Firms with high IPF subsequently outperform firms with low IPF by about 3.52% per year.

That result is interesting. The underlying idea is even more interesting, and obvious in retrospect (as good ideas often are).

Markets don’t process information. People process information.

And people have limited bandwidth.

Suppose a company releases an important filing on an otherwise quiet Tuesday. Analysts read it and investors discuss it and update their views. There may still be disagreement about what the news means, but the information at least receives attention.

Now imagine exactly the same disclosure arriving while the market is dealing with an FOMC announcement, a CPI release, several major earnings reports and a sudden 2% move in the S&P 500.

Nothing about the company’s news has changed. What has changed is the competition for attention.

This gives us a slightly different way to think about market efficiency. The usual formulation asks whether information is public. Once information is public, an efficient market should incorporate it into prices. But public versus private is not the only relevant distinction. Information can be public and still be poorly processed. And that difference matters.

A Bloomberg terminal can display essentially unlimited information. A portfolio manager cannot absorb unlimited information. Computers help but eventually information needs to influence a forecast, position or decision. There is a bottleneck somewhere.

This isn’t a new insight in behavioral finance. There is a large literature on limited attention, earnings announcements, investor distraction and underreaction. But this paper is a little different. Instead of asking whether a particular announcement was ignored, the authors ask whether a company has a history of releasing information into difficult processing environments.

Some firms repeatedly release information when the market can absorb it easily. Others have historically released information when investors are distracted or overloaded. If difficult environments leave more uncertainty unresolved, those unresolved pieces of information can potentially work their way into prices later. This provides a plausible explanation for the subsequent return spread the authors find.

For investors, this leads to a useful distinction between information availability and information digestion. Most quantitative datasets focus heavily on the first. Did earnings beat expectations? Was guidance raised? Was there an 8-K? What was the sentiment of the text? How large was the surprise?

But perhaps another useful variable is, “What else was the market trying to understand at the same time?”

There are thousands of stocks, hundreds of macro releases, earnings reports, regulatory filings, analyst revisions and corporate announcements competing for attention every day. During calm periods the market may absorb all this remarkably well. During information-heavy periods, something has to move down the queue.

And importantly, the ignored information doesn’t need to be dramatic. If a stock is obviously mispriced by 30%, someone will probably notice regardless of what else is going on. The more interesting opportunities may involve small bits of information that should move fair value by 1% or 2%. Those are exactly the sorts of things that can plausibly be neglected when everyone is worrying about something larger.

This also comes back to the concept of micro alphas. People often search for signals in isolation. They ask whether earnings surprises predict returns, whether analyst revisions predict returns or whether insider transactions predict returns.

But everything is conditional. The same information could have different predictive value depending on the environment in which it arrives.

An earnings surprise announced on an empty calendar might be nearly completely incorporated immediately. The same surprise announced during a chaotic information day might create more subsequent drift.

There are obvious caveats. The paper is currently an SSRN working paper rather than a settled result, and a historical relationship between disclosure timing and subsequent returns does not automatically give us a clean, executable strategy. Transaction costs, implementation timing and the precise definition of a difficult information environment all matter.

There is also an identification issue. Perhaps companies that tend to disclose during difficult environments differ from other companies in ways that have nothing to do with attention? A return spread by itself cannot prove that investor distraction is the only mechanism.

Finance often talks about the market as though it were an infinitely powerful information-processing machine. It isn’t. The market is a collection of participants allocating finite attention to an overwhelming stream of information.

Sometimes the market misses information not because it is hidden.

It misses it because it is busy.

Disclaimer

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