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June 2026

Attention, liquidity, and screener discipline in crowded books

Open prediction markets produce more contracts than any desk can watch. How attention and liquidity research inform Algomarket’s screener - and what Free vs Pro is for.

Every session, prediction markets mint more contracts than a human can meaningfully monitor. Social feeds optimize for drama; thin 1¢ markets scream; mid-probability political books with real volume often matter more for decision-relevant flow. A screener is not a prediction model - it is an attention allocator.

Economics and finance have long studied limited attention: agents cannot process all available signals, so prices and volumes reflect what gets noticed as much as what is true (see surveys in Hirshleifer & Teoh, 2003, on limited attention in capital markets). Prediction markets inherit the same constraint. Algomarket’s screener exists to impose deliberate filters before whale and live tools amplify noise.

Filters as research instruments

  • Volume floors - exclude books where a single retail ticket dominates the tape.
  • Probability bands - mid ranges often carry more contested information than near-certainty quotes.
  • Category and venue - politics, crypto, sports, and macro attract different participant mixes.
  • Horizon - distant resolution dates change who shows up and how patiently capital sits.
One job per pass

Find candidates → inspect structure → then decide whether to watch or size. Do not run screener, live, and arbs as one screaming surface.

Source: Algomarket desk playbook

“Discipline is about what you ignore as much as what you open.”Algomarket research

Where Free and Pro fit

The screener and whale dashboard ship on Free because they answer the first research question: what is worth looking at? Pro tools - live large fills and exposure rankings - answer the next question: what is happening now, and who is concentrated where? That sequencing mirrors how serious desks work: constrain the universe, then intensify monitoring (consistent with limited-attention framing in Hirshleifer & Teoh, 2003).

None of this invents hit rates. It is a methodology for allocating scarce attention across Polymarket and Kalshi books so that when you do open Pro flow tools, you are not drowning in every contract at once.

References

  1. Hirshleifer, D., & Teoh, S. H. (2003). “Limited Attention, Information Disclosure, and Financial Reporting.” Journal of Accounting and Economics, 36(1–3), 337–386.
  2. Wolfers, J., & Zitzewitz, E. (2004). “Prediction Markets.” Journal of Economic Perspectives, 18(2), 107–126.
  3. Arrow, K. J., et al. (2008). “The Promise of Prediction Markets.” Science, 320(5878), 877–878.
  4. Berg, J., Nelson, F., & Rietz, T. (2008). “Prediction Market Accuracy in the Long Run.” International Journal of Forecasting, 24(2), 285–300.

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