How Edge Appears in Prediction Markets

What’s the difference between prediction markets trading and equities trading? On Odds on Open, the world’s #1 prediction markets trader Domer explains how prediction markets work as a form of information-based trading, where news and signals can arrive at any moment, forcing continuous price discovery and repricing. Unlike stock markets, where returns often depend on long-term growth, valuation multiples, and market beta, prediction market strategy focuses on information timing, news flow, and market reaction to new data. Rather than forecasting final outcomes, traders focus on event-driven trading, short-term price movement, and probability trading, exploiting mispriced probabilities and trading event contracts instead of holding positions to resolution. This approach allows traders to generate expected value (EV) and highlights the difference between active trading vs passive investing.Domer also explains how many participants concentrate on high-volume headline markets, while traders look for prediction market edge in event contracts trading across smaller markets. On platforms like Polymarket and Kalshi, opportunities exist in alternative markets and micro events that are less crowded and prone to pricing errors. By specializing in specific market categories and focusing on liquidity, volume, and time horizon, traders can adjust position sizing and holding periods to match their edge. This approach mirrors quantitative trading and event-driven strategies, where domain knowledge and execution outperform broad speculation.Other subjects discussed...How prediction markets trading focuses on short-term price movement and active trading rather than holding event contracts to resolution.Prediction market strategy is based on exploiting mispriced probabilities to generate expected value (EV).Prediction market edge is most common in micro markets and sub-events with lower liquidity and attention.Event contracts trading rewards traders who identify information-driven repricing before markets adjust.Information-based trading in prediction markets reacts to discrete news rather than continuous market noise.Probability trading requires distinguishing mean reversion from true regime shifts after breaking news.Losses in prediction markets are often caused by crowded trades and poor position sizing, not direction.Position sizing must scale with edge and uncertainty to preserve long-term expected value (EV).Platforms like Polymarket and Kalshi allow large traders to temporarily distort prices.Capital concentration in alternative markets can create opportunity for smaller traders.Long-term success depends on repeatable decision-making rather than individual outcomes.Prediction markets exhibit less random variance than equities because prices move on information.Poker develops risk tolerance and variance management applicable to prediction markets trading.Regulation is likely to limit influenceable event contracts while allowing large markets to grow.