Octagon Answer Library

Are prediction markets accurate compared to polls or expert forecasts?

Prediction markets are often as accurate as polls and expert forecasts, especially close to resolution. Their strength comes from incentives, real-time updates, and the ability to aggregate diverse information into a single probability.

Prediction markets are often as accurate as polls and expert forecasts, especially close to resolution. Their strength comes from incentives, real-time updates, and the ability to aggregate diverse information into a single probability. — Incentive alignment: Traders have money at stake, which punishes overconfidence and rewards calibrated forecasts. — Real-time updates: Unlike polls (which may be days old) or forecasts (which update infrequently), markets react instantly to new data. — Information aggregation: Markets synthesize inputs from insiders, domain experts, quants, and casual observers into one price.

Best Fintool Alternatives in 2026

The best Fintool alternative depends on which parts of Fintool you actually used, especially filings research, transcript analysis, metric extraction, and finance-oriented AI workflows.

The best Fintool alternative depends on what you actually need to replace. Some users want a quick finance lookup tool. Others need an AI workflow that supports recurring research, memo prep, diligence, and structured analysis. For that second group, workflow quality matters more than surface similarity, because the real need is not search alone, it is reliable research production. — Fast answers to finance and company questions — Clear output structure that can feed into real decisions — Repeatable workflows for investor, analyst, and operator use cases

Best Kalshi alternatives in 2026

The best Kalshi alternatives in 2026 include Polymarket, Robinhood event contracts, PredictIt, and sports-focused apps for trading, plus research layers like Octagon that work across every venue.

If you want a different trading venue, the leading Kalshi alternatives in 2026 are Polymarket, Robinhood's event contracts, PredictIt for politics, and a set of sports-focused apps, and if you want better research rather than another venue, an intelligence layer like Octagon works across all of them.

Best prediction market tools in 2026

The best prediction market tools in 2026 fall into three layers: trading venues, data and intelligence platforms, and monitoring or alerting tools. The right pick depends on whether you want to trade, research, or automate.

The strongest prediction market tools in 2026 separate into three layers, trading venues where you place contracts, intelligence platforms that turn markets into research, and monitoring tools that watch moves for you, so the best choice depends on whether your goal is to trade, to research, or to automate.

Can I hedge real-world risk using prediction markets?

Yes—prediction markets can hedge exposure to discrete outcomes (policy changes, rate decisions, approvals, disruptions). The hedge works best when the contract outcome closely matches your real-world exposure and timing.

Yes—prediction markets can hedge exposure to discrete outcomes (policy changes, rate decisions, approvals, disruptions). The hedge works best when the contract outcome closely matches your real-world exposure and timing. — Hedging logic: If a specific event hurts you in real life, you can profit from a prediction market position that pays when that event occurs, offsetting your loss. — Discrete vs. continuous: Prediction markets are best for discrete outcomes (yes/no, above/below). Continuous exposures (like stock prices) are harder to hedge perfectly. — Basis risk: If the contract doesn't exactly match your exposure (timing, definition, magnitude), you have residual risk.

Can prediction markets be used to forecast geopolitical events?

Yes—prediction markets are especially useful for geopolitical forecasting because they aggregate fragmented, asymmetric information that is difficult to model formally.

Yes—prediction markets are especially useful for geopolitical forecasting because they aggregate fragmented, asymmetric information that is difficult to model formally. — Geopolitical events depend on dispersed signals: diplomatic cables, regional experts, satellite imagery interpretation — Prediction markets incentivize those with specialized knowledge to trade on it — Unlike economic data, geopolitical outcomes are driven by human decisions, negotiations, and unexpected events

Can prediction markets help forecast IPO timing?

Prediction markets can sharpen IPO-timing forecasts by attaching live probabilities to "files by," "prices by," or "debuts before" questions, best used alongside an IPO tracker and filing flow.

Yes, when a liquid contract exists, prediction markets give you a live probability on whether a company files, prices, or debuts within a window, which is a sharper signal than headline speculation about an IPO.

Fintool vs Octagon: Which research workflow fits better?

Fintool was built around filings, transcripts, and finance-focused AI research. This comparison shows how Octagon serves teams doing the same kind of public-company research.

The most useful way to compare Fintool and Octagon is workflow-by-workflow. Fintool was associated with SEC filings, earnings calls, conference transcripts, metric extraction, quoted answers, and finance-oriented research. Octagon is a strong fit for teams that want filings, transcripts, financial data, cited outputs, and repeatable public-market workflows in one system. — SEC filing research: both are positioned around answering questions from regulatory filings. — Earnings call and transcript research: both point to transcript-based analysis as a core use case. — Cited answers: both emphasize answers tied back to source material rather than unsupported chat output.

How are contracts settled on Polymarket compared to Kalshi?

Kalshi contracts settle based on predefined, regulator-approved data sources, while Polymarket contracts rely on oracle mechanisms and platform-defined resolution processes.

How do analysts use prediction markets in company or sector research?

Analysts use prediction markets as a probability layer over fundamental research, weighting scenarios, timing catalysts, stress-testing theses, and surfacing sector read-throughs from a single event.

Analysts use prediction markets as a probability layer on top of fundamental work, weighting scenario models, timing catalysts, stress-testing a thesis against the crowd, and tracing how one event reprices an entire sector.

How do fees and platform rules affect prediction market prices?

Fees and platform rules directly affect pricing by widening spreads, discouraging arbitrage, and biasing prices away from theoretical probabilities.

Fees and platform rules directly affect pricing by widening spreads, discouraging arbitrage, and biasing prices away from theoretical probabilities. — Every trade incurs costs (maker/taker fees, withdrawal fees) — These costs are effectively subtracted from expected value — Small-edge trades become unprofitable after fees

How do I calculate expected value (EV) for a trade in a prediction market?

EV compares what you expect to win on average vs what you pay. For a $1 "Yes" contract: EV = (your probability × $1) − price − fees.

EV compares what you expect to win on average vs what you pay. For a $1 "Yes" contract: EV = (your probability × $1) − price − fees. — The EV formula: For a binary contract paying $1 on "Yes": EV = p − price − fees, where p is your estimated probability of "Yes." — What EV tells you: A positive EV means you expect to profit on average over many similar trades. Negative EV means the opposite. — All-in cost: "Price" should include any spread impact (slippage), and "fees" should include both trading fees and any withdrawal or settlement costs.

How do I handle correlated markets and avoid double-counting the same thesis?

Correlated markets move together because they share drivers (e.g., economic data affecting both rates and elections). If you hold multiple correlated positions, you may be taking much more risk than you realize.

Correlated markets move together because they share drivers (e.g., economic data affecting both rates and elections). If you hold multiple correlated positions, you may be taking much more risk than you realize. — Shared drivers: Many markets are influenced by the same underlying factors—macro data, policy decisions, legal rulings, or public sentiment. — Hidden concentration: Holding "Yes" on three different markets that all depend on the same outcome is effectively one large bet, not diversification. — Correlation spikes in stress: Markets that seem uncorrelated in calm periods can move together sharply when the shared driver is triggered.

How do I read a prediction market price as a probability?

For a standard binary contract, the price roughly equals the implied probability.

For a standard binary contract, the price roughly equals the implied probability. A $0.73 price suggests about a 73% chance, assuming normal liquidity and rules. — Fair price ≈ probability of Yes — "Yes" share pays $1 if Yes, $0 if No — Probability ≈ Price / $1 payout

How do investors use prediction markets for event-driven investing?

Event-driven investors use prediction markets to put live probabilities on catalysts like approvals, deals, and data releases, to time entries and exits, and to hedge discrete outcomes.

Event-driven investors use prediction markets to attach a live probability to the catalyst at the center of a trade, then use that probability to size positions, time entries and exits, and hedge the specific outcome they are exposed to.

How do Polymarket and Kalshi differ in regulation and legal structure?

Polymarket and Kalshi operate under fundamentally different legal structures. Polymarket is a crypto-based platform operating outside U.S. financial regulation, while Kalshi is a U.S.-regulated exchange overseen by the Commodity Futures Trading Commission (CFTC).

How do prediction markets differ from options markets?

Prediction markets pay a fixed amount on a discrete yes or no outcome, while options derive value from an underlying asset's price and pay along a continuous range. The payoff shape is the core difference.

A prediction market contract pays a fixed amount if a specific event happens and nothing if it does not, while an option's value depends on where an underlying asset's price ends up, so the payoff is continuous rather than all-or-nothing.

How do prediction markets react to Fed decisions, CPI, and jobs reports?

Prediction markets reprice scheduled macro releases like FOMC decisions, CPI, and jobs reports in seconds, often before the cash and rates markets fully digest the print.

Prediction markets reprice scheduled macro releases almost instantly, moving the probability of outcomes like a rate hold, a hot CPI print, or a payrolls beat in the seconds around the release rather than waiting for narratives to form.

How do you build alerts for major probability changes?

Build probability alerts by defining a baseline, a trigger threshold, and a delivery channel, then filtering for liquidity and a catalyst so you are notified on real moves, not noise.

You build a useful alert by pairing a clear trigger, such as a probability move past a set threshold on a liquid contract, with a delivery channel and a noise filter, so you are notified when an outcome's odds shift for a real reason.

How do you monitor prediction market moves automatically?

You monitor prediction markets automatically by pulling live probabilities through an API or MCP server, defining the contracts and thresholds you care about, and routing changes into a feed, dashboard, or agent.

You automate monitoring by connecting to a market data source through an API or MCP server, defining the specific contracts and probability thresholds you care about, and routing any change into a dashboard, feed, or agent so you do not have to watch screens.

How does settlement and resolution work, and why do rules matter more than headlines?

Prediction markets settle based on the contract's written resolution criteria, not what people "meant" or what headlines imply. A market can resolve against popular intuition if the rules are strict or the outcome is defined narrowly.

Prediction markets settle based on the contract's written resolution criteria, not what people "meant" or what headlines imply. A market can resolve against popular intuition if the rules are strict or the outcome is defined narrowly. — Resolution criteria: Every contract has explicit rules: the source of truth, the exact definition of the outcome, the timing, and edge case handling. — Source of truth: Markets specify which data source (government report, official announcement, specific website) determines the outcome. — Timing matters: A contract may specify "by December 31 at 11:59 PM ET"—an event on January 1 doesn't count, even if it's 12:01 AM.

How should investors use prediction markets alongside traditional research?

Prediction markets work best as a complement to traditional research—providing probabilistic context, timing signals, and consensus checks rather than standalone answers.

Prediction markets work best as a complement to traditional research—providing probabilistic context, timing signals, and consensus checks rather than standalone answers. — Traditional research often produces point estimates or qualitative views — Prediction markets add a probability dimension: "What does the crowd think?" — Rapid price movements indicate when new information is being priced in

How to Migrate from Fintool to Octagon

A practical migration guide for former Fintool users who want to rebuild their finance and research workflow inside Octagon with minimal friction.

If you previously relied on Fintool-style workflows, the fastest migration path is to rebuild your recurring use cases first. In practice, that usually means rebuilding filing research, transcript analysis, company Q&A, and memo-oriented workflows inside Octagon's public-market stack. — SEC filing research and question answering — Earnings call and transcript analysis — Company briefing and public-market memo prep

How to use prediction markets for earnings season research

Prediction markets give earnings-season researchers a live, probability-weighted read on outcomes like beats, guidance cuts, and price reactions, complementing estimates and call transcripts.

Prediction markets let you fold a crowd-sourced probability into earnings research, turning vague "the Street expects a beat" intuition into a tradeable number you can track into and out of the print.

What are the most common mistakes people make when using prediction markets?

The most common mistakes are overtrusting prices, ignoring liquidity and rules, and confusing probability with certainty.

The most common mistakes are overtrusting prices, ignoring liquidity and rules, and confusing probability with certainty. — Overtrusting prices: A 70% probability still means the event doesn't happen 30% of the time. Users often interpret high probabilities as near-certainties. — Ignoring liquidity: A price in a thin market may not reflect broad consensus—it might just be one trader's position. — Skipping the rules: Settlement criteria determine what you're actually betting on. Misunderstanding rules leads to unexpected outcomes.

What does a prediction market actually measure: belief, probability, or truth?

Prediction markets measure tradeable belief, not objective truth. Prices reflect what participants are willing to risk capital on under current information and constraints.

Prediction markets measure tradeable belief, not objective truth. Prices reflect what participants are willing to risk capital on under current information and constraints. — Prices represent the intersection of buyers and sellers willing to put capital at risk — This is "skin-in-the-game" probability, not polled opinion — Markets can be wrong—they aggregate available information, not perfect foresight

What does market manipulation look like in prediction markets, and how can I spot it?

Manipulation typically looks like pushing the price with aggressive trades to shape perception, then reversing later. You can often spot it through sudden price jumps without new info, thin order books, and quick mean reversion.

Manipulation typically looks like pushing the price with aggressive trades to shape perception, then reversing later. You can often spot it through sudden price jumps without new info, thin order books, and quick mean reversion. — Why manipulation happens: Prediction market prices are public signals—moving them can influence perception, news narratives, or other traders' behavior. — Common tactics: A manipulator may place large aggressive orders to move the price, attract attention or copycat trades, then exit at better prices when the market reverts. — Spoofing: Large orders posted at a price level then canceled before execution, creating a false impression of demand or supply.

What happened to Fintool? Options for former users

Fintool's homepage now states that Microsoft has acquired Fintool, which changes the product story from independent startup to acquisition and transition.

If you visit Fintool today, the homepage does not present the product the way it used to. It now states plainly that Microsoft has acquired Fintool. That means the right explanation is not that the company simply disappeared, but that the standalone product has entered an acquisition and transition phase. — A clear understanding that the standalone Fintool experience is changing because of the Microsoft acquisition — A fast way to replicate their most important workflows — A tool that produces answers clean enough to use in memos, notes, or team discussions

What is a prediction market, and how is it different from betting?

A prediction market is a marketplace where prices represent the crowd's implied probability of a future event.

A prediction market is a marketplace where prices represent the crowd's implied probability of a future event. Unlike traditional betting (fixed odds set by a bookmaker), prediction market prices move dynamically based on supply and demand. — Why this exists: Markets aggregate dispersed information—many small signals combine into a single, tradable probability estimate. — Market-driven odds: Participants trade with each other (often via an order book), so the "odds" update continuously as new information arrives. — Price as probability: If a "Yes" share trades at $0.62, the market is implying roughly a 62% chance of "Yes."

What is arbitrage in prediction markets, and when is it actually possible?

Arbitrage is a low-risk profit from inconsistent prices (e.g., "Yes" + "No" priced below $1 combined). In practice, true arbitrage is rare because fees, slippage, limits, and timing risk often eliminate it.

Arbitrage is a low-risk profit from inconsistent prices (e.g., "Yes" + "No" priced below $1 combined). In practice, true arbitrage is rare because fees, slippage, limits, and timing risk often eliminate it. — Definition: Arbitrage exploits price inconsistencies that guarantee a profit regardless of outcome—buying underpriced contracts and selling overpriced ones. — Binary market example: If "Yes" is $0.45 and "No" is $0.50, buying both costs $0.95 and pays $1.00, locking in $0.05 profit. — Multi-outcome example: If all candidates in a race sum to less than $1 (or more than $1), an arbitrage exists.

What is liquidity, and why does it matter so much in prediction markets?

Liquidity determines how easily you can trade without moving the price. Low liquidity increases spreads, slippage, and exit risk.

Liquidity determines how easily you can trade without moving the price. Low liquidity increases spreads, slippage, and exit risk. — What liquidity means: Liquidity is the ability to buy or sell a contract quickly at a price close to the current market price. High liquidity = easy trading; low liquidity = costly trading. — How to measure it: Look at the bid/ask spread (tighter is better), the depth of the order book (more shares near the mid-price is better), and consistent volume over time. — Why it matters: Your "paper edge" can vanish once you factor in spread, slippage, and fees. A 5-cent edge means nothing if you pay 6 cents in execution costs.

What is the difference between prediction markets and futures markets?

Prediction markets settle on whether a discrete event happens, while futures settle on the future price or level of an asset or index. One prices an outcome, the other prices a value.

A prediction market settles on whether a defined event happens, paying a fixed amount on a yes or no outcome, while a futures contract settles on the future price or level of an underlying asset or index, so it tracks a continuous value rather than a binary result.

What makes a prediction market signal trustworthy versus noisy?

A trustworthy prediction market signal usually has deep liquidity, clear resolution rules, and a move backed by real information. Noise comes from thin books, vague rules, and unexplained spikes.

A signal is trustworthy when liquidity is deep, the resolution rules are clear, and a price move lines up with real new information. It is noisy when the book is thin, the rules are vague, or the price jumps with no news behind it.

What types of markets are listed on Polymarket versus Kalshi?

Polymarket lists a wide range of global markets, including politics, geopolitics, and cultural events, while Kalshi focuses on regulated, event-based markets such as U.S. economic data, policy decisions, and legally permissible political outcomes.

What's the difference between Polymarket and Kalshi?

Polymarket and Kalshi are both prediction market platforms, but they differ fundamentally in regulation, user access, and market structure. Polymarket operates globally using crypto infrastructure, while Kalshi is a U.S.-regulated exchange designed to operate within traditional regulatory frameworks.

Who can legally use Polymarket and Kalshi by geography?

Kalshi is legally accessible to eligible users within the United States, while Polymarket primarily serves non-U.S. users due to regulatory restrictions affecting U.S. participation.

Why do prediction market probabilities change so quickly after news events?

Prediction markets react quickly because they reprice probabilities, not narratives. A single credible data point can materially shift expected outcomes, causing sharp price moves even when headlines seem incremental.

Prediction markets react quickly because they reprice probabilities, not narratives. A single credible data point can materially shift expected outcomes, causing sharp price moves even when headlines seem incremental. — Probability, not sentiment: Markets price expected outcomes, not how important a story "feels." A minor headline can have major probability impact if it changes the expected path. — Threshold effects: Many outcomes are binary or have discrete triggers. Crossing a threshold (e.g., an endorsement, a vote count, a data release) can flip the probability quickly. — Real-time aggregation: Traders incorporate new information immediately. Unlike polls or forecasts with delays, markets update in seconds.

Why do prediction markets sometimes disagree with financial markets?

Prediction markets and financial markets price different things: discrete outcomes versus continuous economic impact. Disagreement often reflects different assumptions, time horizons, or risk premia.

Prediction markets and financial markets price different things: discrete outcomes versus continuous economic impact. Disagreement often reflects different assumptions, time horizons, or risk premia. — Prediction markets price the probability of a discrete event (Yes/No) — Financial markets price expected value of continuous cash flows or assets — Stock prices reflect long-term discounted earnings

Why do prediction markets sometimes look wrong, even when they're popular?

Markets can be "wrong" due to new information not yet absorbed, biased participation, or structural frictions like low liquidity and fees. The displayed price is a tradeable consensus—not a guarantee of accuracy.

Markets can be "wrong" due to new information not yet absorbed, biased participation, or structural frictions like low liquidity and fees. The displayed price is a tradeable consensus—not a guarantee of accuracy. — Information absorption delay: News takes time to spread and be processed by traders. A price may lag reality by minutes, hours, or even days. — Biased participation: If the market attracts mostly one type of participant (e.g., fans of a candidate, crypto enthusiasts), the price reflects that group's beliefs—not the broader population. — Structural frictions: Fees, withdrawal limits, jurisdictional restrictions, and capital lockup reduce the incentive to correct mispricing.

Why do some prediction markets stay mispriced for long periods?

Mispricings persist due to low liquidity, unclear resolution rules, capital constraints, and lack of arbitrage incentives—especially in niche or long-dated markets.

Mispricings persist due to low liquidity, unclear resolution rules, capital constraints, and lack of arbitrage incentives—especially in niche or long-dated markets. — Thin markets mean few participants actively correcting prices — Large orders move prices significantly, discouraging correction — Informed traders may not have sufficient capital to fully correct mispricings