Who wins and who loses in prediction markets? Evidence from Polymarket

Original Article ↗ Hacker News Discussion ↗

Research on Polymarket, a large crypto-based prediction market, finds that trading profits are extremely concentrated, with the top 1% of users capturing over three-quarters of gains, largely by patiently providing liquidity via limit orders rather than impulsively taking it with market orders. Commenters debate whether this edge reflects genuine forecasting skill, structural advantages like better technology and capital recycling, or simply the inevitability of power-law outcomes in zero-sum markets. The thread also highlights concerns about insider trading, opaque event resolution, and parallels to broader economic inequality and gambling-style products where most participants statistically lose money.

Profit concentration & inequality

  • Commenters highlight that top-1%-capture-~75%-of-profits matches broader power-law patterns (OnlyFans, economy, wealth models).
  • Yard-sale / Boltzmann-style models are cited as analogies: repeated random exchanges tend to extreme concentration.
  • Debate over whether we should deliberately “fight” such power laws via policy; some label that as akin to communism, others argue for progressive/“sigmoid” taxation.
  • Several argue real-world payoffs reward capital and decision-making more than “hard work,” reinforcing inequality.

Sources of edge and trading behavior

  • The paper’s result that winning traders mostly provide liquidity via favorable limit orders matches many readers’ prior: markets transfer wealth from impatient, less-informed takers to patient makers.
  • Some suspect many top accounts are essentially arbitrage or liquidity bots rather than “forecasters from first principles.”
  • There’s discussion of cross-venue arbitrage: some see it as a real skill and primary profit source; others say basic API connectivity is easy and real edge is still informational.
  • Authors note they have not yet studied cross-venue matching or capital locked in positions; incorporating capital reuse would likely make liquidity providers look even better.

Insider trading and information

  • Several think insiders must exist, especially for events under direct human control (press conference length, taped shows, etc.).
  • Authors state insiders likely trade but don’t account for a large share of total profits, and are hard to identify because opportunities are one-off and accounts can be rotated.

Market structure, resolution, and comparisons

  • Prediction markets are emphasized as zero-sum (before fees), unlike the stock market which many see as having real growth, dividends, and capital formation; others counter that even equity markets are ultimately redistributive within a fixed money supply.
  • Polymarket is described as user-vs-user with the platform taking fees, unlike sportsbooks where the house bears risk and bans sharp winners.
  • Sports markets appear especially profitable for sophisticated traders, possibly because many users bet with identity/loyalty, pushing prices away from “reality.”
  • Some long-horizon markets lack explicit time discounting, which may systematically hurt traders willing to overpay for far-dated outcomes.

Resolution, gray areas, and governance

  • Resolution rules can be ambiguous; examples are given where mispronunciation or fine-print criteria led to controversial rulings.
  • UMA-based “independent” resolution is viewed by some as largely cosmetic, with accusations that platforms still effectively choose outcomes.
  • Questions arise about famous disputed markets and whether corruption in oracles is an overblown concern versus a real structural risk.

Baselines, skill vs luck, and persistence

  • Readers ask what profit distribution would look like if everyone bet randomly; authors say many shapes are possible depending on assumptions but their simulations confirm that high concentration is unsurprising even without extreme skill.
  • Monthly performance shows weak persistence; some interpret this as sample selection rather than robust trader skill, echoing classic “coin-flipping contest” analogies.

User losses, warnings, and regulation

  • Many stress that most participants lose money, comparing prediction markets to lotteries, CFDs, Vegas, and sportsbooks’ “vig.”
  • Suggestions include mandatory risk disclosures similar to EU CFD warnings or cigarette labels (“you will lose money on this app”).
  • Some worry about broader societal damage if political or economic insiders can profitably manipulate or hedge via these platforms; calls for tighter regulation or bans appear alongside more neutral/curious takes.

Meta: AI-generated comments and community norms

  • A substantial subthread debates whether some comments are LLM-generated “slop,” how to detect them (stylistic tells), and whether banning AI-written posts is good policy.
  • Some argue AI comments should be quietly downvoted; others defend explicit calling-out and strict enforcement, citing time-wasting and low-quality content concerns.