Successful Prediction Market Strategies for Finding Information Arbitrage

Hey y'all, I've been seeing headlines about this Kalshi company and the general concept of Prediction Markets popping up constantly, especially in relation to elections, the economy, and the integration of it into news channels like CNN.

My understanding is that these platforms function like a stock market, but instead of trading company shares, you trade "event contracts" on the likelihood of a future outcome.

I guess my question is regarding the validity of them as a serious information source and how media channels now feel safe enough to use them as such?

Hey there. I'm silently making some money on prediction markets since 2021. I'll share some insights on it and I also would like to learn from your experiences. Let's Go!

1) Don't do sports betting. I know it's tempting, but I can assure you, most people lose money on sports betting and you are not any different from them.

2) If you actually do Sports betting, bet on basketball or some other sport where it's almost impossible for the outcome to be a tie. Never gamble soccer, you will lose.

3) There are only 2 kinds of markets: yes/no markets and multiple answer markets. If you hit the jackpot, multiple answer markets tends to multiply your wage way more than yes/no markets. So, keep different strategies for this two kinds of markets

3) Politics are a great category to bet. Insider trading is not impossible, but it's comparetively less dangerous than sports or entertainment.

4) Economic indicators are almost insider trading proof, múltiple answer, and monthly, so they are very good!

5) Set alerts on Google News for the markets you are invested. There are other ways of being the first to know something. The nobel prize this year is an example. If you are actually the first one to know something, don't be greedy. Place a regular bet, and keep your shady methods to yourself, for future markets.

6) Markets are size aware. If you bet big money on a small volume market your outcome will be proportionally less relevant than several small bets on good volume markets.

7) Platform matters. You can compare probabilities on different platforms and place your bet on the most favorable one. However, there is a huge diference on play money and real money bets. With all due respect, play money probabilities are totally useless.

8) The money you keep on the platform should be working for you all the time. It makes no sense to keep money on a platform and not on a market. You should also make use of safe, quickly solved and low profit markets, since small money is better than no money.

9) "small money better than no money" only when we are talking about quickly solved markets. I'm also a fan of long term markets, they are the foundation of my strategies, but requires a different approach.

10) For long term markets: 10.1) Investing on emergent country throught a simple and safe ETF can assure you sometimes a 15% profit/year. So, if your are betting on prediction markets, risks are bigger so profits MUST be at least 20%.

10.2) Now, it's not that simple to find long term market with a good margin on your prediction. I suggest using platforms API which are a wonderful way to avoid betting on a emotion, or being compelled to bet by user's interface

10.3) Once you've found a long term market which you think you know the outcome, and prices are good, you are not betting all your money on it. Let's say you have 100 Bucks. Place a inicial bet of 40 tô 60, in order to shift the probabilities on your favor. You wait. Now other bettors on the same market will probably reinforce their positions. As they places their bets, probabilities will slowly shift against you again, making it more profitable for you, bit by bit, betting the diference to tour 100 dollars inicial plan.

10.4) trust me: to wait is way more difficult than to find the right market. Once you find and place ver, if you are right, as time goes by, odds Will be on your favor. It's a long term market and nota scalping day trade. You won't sell before the market ending to realize profits earlier. You wait.

11) Bet on both sides is the best way to end with the same money you begin, less fees. It's a crap strategy.

12) For yes or no markets, first thing I check is the diference between the yes and no probability. Small diference between than implies you can double your money on the correct answer, and probably the other bettors are still confused about the right outcome. For Big yes/no diference, it means you can make small safe money betting on the favourite outcome, or you can make a small bet against them which can make a impressive ROI on a small exposition (one of my favourites). Trending news tends to distorce markets prizes and represents the best moment to bet against the majority

13) For christ sake, you are already making money without working. Read the full text for the market you are betting on. SEVERAL markets are solved from the beginning if you read carefully the small letters.

13.1) Depending on the platform and the amount of money you have on your wallet, after reading the market text carefully, you can write to support with your doubts and sometimes they really answer. Tends to work better on long term markets.

14) Choose your niche and become good on it. Read/watch the news about it everyday. Exotic markets, such as foreign country politics are my favourite. Prizes/probabilities are frequently wrong.

15) Don't buy markets, buy probabilities. Honestly, I will only read the head/title of a market after reading the outcomes and become interested by them

You won't get millionarie quickly on this strategies. You become profitable at least. Now it's your turn. Let me know your strategies!

Prediction markets - like Polymarket and Kalshi - are platforms where people can bet on the probability of an event occurring. Here’s our guide to them and how they work

A prediction market is a platform where participants buy and sell shares representing the probability of an event occurring. Prices move in real time as new information enters the market. By the time the event resolves, the market has produced a live, crowd-sourced probability estimate that is sometimes more accurate than expert forecasts or traditional polling. This guide explains what prediction markets are, where they came from, how they work mechanically, and what the blockchain infrastructure behind the leading platforms actually looks like.

Polymarket data was superior to polling data in predicting the 2024 Presidential Elections outcome

• Prediction markets price the probability of future events, aggregating crowd beliefs through a financial mechanism that has existed in informal forms for centuries.
• Platforms like Polymarket and Kalshi are the two biggest prediction markets in 2026.
• Blockchain-based prediction markets use smart contracts, oracles, and CLOBs to handle trading and settlement without a central custodian.
• Prediction markets can cover almost anything but are commonly used for sports, politics and economics. 

A prediction market is a financial market where traders speculate on contracts representing the probability of a future event. Contracts pay out $1 if the specific outcome occurs, and $0 if it does not occur. 

So, for example, during the U.S. presidential elections, a contract for "Donald Trump wins the election" was priced at $0.62, effectively pricing a 62% probability of that outcome. If you bought a share at $0.62 and held onto it until after the election, you would have made a profit of $0.38. Traders and speculators will buy large amounts of these contracts to try and make a significant profit.

Despite feeling like a betting shop, the mechanism behind prediction markets is closer to a futures market. Participants aren't placing wagers with a house, they are actually trading against other participants, with the collective bets determining the probability of a specific outcome occurring. This structure means the market price reflects the aggregated beliefs of everyone trading on the market, weighted by how much capital each participant is willing to put behind their view.

Prediction Market analytics data from Synthesis

Prediction markets can cover almost anything: elections, economic indicators, sporting events, corporate announcements, geopolitical outcomes, weather, and more. As a result, the true constraint is often that the outcome needs to be unambiguous enough that it can definitively be determined to have happened.

How Do Prediction Markets Work

Prediction markets work by using prices to combine and aggregate people’s beliefs. When someone believes an event is more likely to happen than the current price implies, they buy the contract. When they believe an event is less likely to happen than the current price implies, they sell. As new information enters the world (usually from the news), market participants update their positions, and prices shift accordingly.

On Polymarket, markets are structured as binary outcome contracts denominated in USDC. A market like "Will the Federal Reserve cut rates in Q1 2026?" has two sides: YES and NO. Each pair of shares always adds to $1. A trader who buys YES at $0.40 is buying a contract that pays $1 if the Fed cuts, for a gain of $0.60, or zero if it does not. The same logic applies to markets covering several possible outcomes, where each outcome will have its own YES or NO options that resolve to either $1 or $0.

A market with multiple possible outcomes on Polymarket

Kalshi has the same binary logic but within a regulated U.S. framework. Kalshi is regulated to serve retail U.S. customers directly and lists markets across finance, weather, economic data releases, and politics. Kalshi’s mechanics on how a contract works are similar to Polymarket’s, though it operates with fiat settlement and is subject to CFTC oversight.

Non-blockchain platforms like PredictIt, the Iowa Electronic Markets, and various political betting exchanges in the UK (Ladbrokes, Betfair, and Paddy Power offer political betting) use conventional order books and centralized infrastructure. These platforms handle custody, settlement, and dispute resolution internally. The tradeoff is that users must trust the operator, markets are limited by regulatory constraints, and the platforms cannot operate permissionlessly across borders.

When prediction markets moved on-chain, the fundamental mechanics of how they work changed in a few meaningful ways:

Peer-to-peer settlement: Direct transfer of funds between participants without the need for intermediaries.

On platforms like Polymarket, positions are held in smart contracts on the Polygon blockchain network, with the smart contract distributing funds automatically to winning position holders upon resolution. There is no central counterparty holding trader funds. This removes an element of custodial risk that plagued predecessors to Polymarket and Kalshi.

Smart contracts: A contract embedded in code that is self-executing upon certain conditions being met.

From creation to resolution to payout, the lifecycle of a prediction market on a specific event is governed by smart contracts rather than human operators. Market rules are set on-chain and executed deterministically. This makes it harder for an operator to unilaterally alter market terms, withhold funds, or selectively settle disputes in ways that favor the platform.

Oracles: Software that connects blockchains to external, off-chain data.

The one point where on-chain prediction markets must interface with the real world is resolution. Oracles do this by supplying verified external data to the smart contract. Polymarket uses UMA Protocol's Optimistic Oracle for dispute resolution. When a market resolves, anyone can propose an outcome. If it goes unchallenged within a window, it is accepted. If challenged, UMA token holders vote on the correct resolution. This decentralized approach introduces complexity in edge cases where outcomes are genuinely ambiguous and there is still a centralization risk as large UMA token holders are able to manipulate resolution outcomes with their votes.

The UMA Oracle links Polymarket’s smart contracts to their real-world events

‍Central Limit Order Books (CLOBs): A mechanism used to connect buyers and sellers of contracts based on price and time.

Polymarket's trading engine uses a central limit order book hosted off-chain to match buy and sell orders efficiently, with settlement occurring on-chain. This hybrid model is better than a fully on-chain order book because the cost of executing each order update on a fully on-chain order book would make trading prohibitively expensive. Kalshi uses a similar CLOB structure, though its infrastructure is entirely traditional, like a regulated centralized exchange.

Collateral: Assets provided by the buyer of a contract to secure the loan. 

Prediction market contracts on Polymarket are fully collateralized in USDC. This means that if you want to mint a pair of YES and NO shares worth $1 in total, you must deposit $1 of USDC into the smart contract. This eliminates counterparty credit risk entirely. There is no leverage, no margin call, and no scenario in which a winning position fails to pay out because the counterparty defaulted.

Kalshi's design as a regulated DCM means it operates under CFTC margin rules, which govern how much collateral participants must post and how it is held. Kalshi uses a conventional clearing model rather than the smart-contract-based approach of Polymarket, but the economic function is similar: ensuring losing sides of trades can meet their obligations.

One notable structural difference between Polymarket and Kalshi is access. Polymarket is available globally and does not require identity verification for most interactions, operating through crypto wallets. Kalshi requires US-based account registration and identity verification as part of its CFTC-compliant structure. The tradeoff is that Kalshi can serve U.S. retail customers legally, while Polymarket formally restricts U.S. users despite being technically accessible to anyone with a crypto wallet.

As prediction markets like Polymarket have moved on-chain, the data they produce is now accessible to anyone. Every transaction, position, and outcome is recorded and verifiable. To help participants navigate this ecosystem, Synthesis l has built a comprehensive analytics platform on top of our existing platform that is dedicated to prediction market data.

Trader analytics: Comprehensive tracking of top prediction market participants based on Profit and Loss (PNL).

Users can view a feed of the top prediction market traders by PNL, alongside a history of their open and past positions.

Here’s a list of the specific metrics available to users:

• Active Positions: To reveal real-time convictions, allowing users to track, copy, or fade current strategies.
• Total PNL: To measure raw bottom-line success versus actual trading efficiency (leveling the playing field between whales and small accounts).
• Volume: To distinguish between highly experienced, frequent traders and casual participants.
• Total trades: Showing how much trades been done
• Synthesis score: accuracy score

The screenshot below from Synthesis shows a trader’s top closed positions, ordered by PNL

Trader activity analytics on Synthesis

Live market monitoring: A real-time feed of prediction market trades across the ecosystem.

Participants can monitor a live tape of prediction market trades as a whole, or filter down to all trades for a specific market they are currently tracking. Markets can be viewed across a range of active categories - politics, sports, and crypto - providing visibility into market movements and participant behavior as real-world events unfold.

The following screenshot shows Prediction Market analytics for ‘Will the U.S. Invade Iran Before 2027?’. The table at the bottom shows analytics on the current contract holders ordered by value.

Data visualization: Tools to map on-chain trading behavior directly onto market pricing.

Within individual markets, trades can be sorted by current and past positions based on PNL, or by the current holders of YES and NO shares. A built-in filtering mechanism allows users to select any specific address and overlay that trader's exact entry and exit points directly onto the market's price chart, providing a visual representation of their trading strategy and timing.

Visualize top Prediction Markets traders

Prediction markets have moved from academic curiosity to active financial infrastructure in just under 30 years. The 2024 election cycle demonstrated that these platforms can attract real liquidity and generate probability estimates that compete seriously with traditional polling and forecasting. The two models that have emerged: Polymarket's permissionless, on-chain approach and Kalshi's regulated, CFTC-registered structure, represent different bets on how this market develops. One prioritizes global accessibility and decentralization; the other prioritizes regulatory clarity and U.S. retail access.

U.S. regulators have historically been skeptical of event contracts that resemble gambling, and the CFTC's posture toward platforms like Polymarket remains an open question. On the technical side, oracle design continues to be a weak point, with the accuracy of on-chain prediction markets dependent on having reliable, manipulation-resistant resolution mechanisms. What is clear, however, is that the core mechanism, which uses financial markets to aggregate probabilistic beliefs, is here to stay.

Trade on Synthesis

Something I keep noticing and want to check against this sub's experience.

The most liquid markets on Polymarket and Kalshi are almost always the ones with the least edge available. Elections, Fed decisions, big sports. Thousands of participants, tight spreads, and the price basically tracks the polling averages and the futures curves everyone can already see. If you're trading the presidential market you are competing against every quant, journalist, and poll aggregator on earth for maybe two points of edge.

Meanwhile the markets where real edge exists sit at a few thousand dollars of volume. Niche entertainment outcomes. Weird tech milestones. Cultural questions where the resolution is clean but the attention isn't there. The people I know who consistently make money are all camped in these, quietly, specifically because nobody else is looking.

So the liquidity is pooled exactly where the edge isn't, and the edge is pooled exactly where the liquidity isn't. You can be right in a thin market and barely get paid because you can't size up. You can size up in a thick market where being right is nearly impossible.

Three possible explanations and I don't know which I believe:

1. Volume follows attention, not opportunity. People trade what's on the news. The big markets are entertainment products and the pricing accuracy is a side effect.
2. Thin markets are thin for a reason. The resolution risk and the ambiguity in niche markets is real, and the crowd is correctly pricing in "I don't trust this market to settle fairly" rather than missing an opportunity.
3. It's a bootstrap problem. Edge-rich markets stay thin because sizing is capped, so sharps extract what's there and leave, so the market never deepens, forever.

If it's 3, that's a structural flaw in how these platforms list and seed markets, not a trader behavior problem. And it would mean the category's real growth isn't more volume on elections, it's solving whatever keeps the long tail thin.

Where do you land? And for anyone actually trading the thin stuff, is the edge as real as it looks from outside, or does the resolution risk eat it?

I've been researching prediction markets recently and am curious about the market-making side of things.

For those who have experience providing liquidity on prediction markets, sports exchanges, or similar binary-event markets:

• How did you get started?
• What level of capital is typically needed to make market making worthwhile?
• Are most market makers operating manually, semi-automated, or fully automated strategies?
• What kind of edge is required to be profitable after fees and adverse selection?
• What infrastructure (APIs, bots, pricing models, risk systems) is considered essential?
• Are there opportunities for smaller independent operators, or is the space dominated by firms?

I'd appreciate hearing about both the technical and business side of becoming a liquidity provider. Any resources, lessons learned, or common mistakes to avoid would be helpful.

Thanks!

I’ve been learning more about prediction markets and I was wondering what actually keeps people using prediction markets?

Are people really trying to make money when everyone is likely to lose? Or is this really just for entertainment? I feel like it is framed as investing but isn't it just gambling. How can you beat this? or should I only do this for fun?

So I am a superforecaster and I do this professionally. So I can maybe help with answering some of these questions.

1. People trade on topics they are interested in mostly. The World Cup is going to have more volume than say a women’s volleyball game at TCU.

2. I don’t think that’s that big of an issue, and I think it tied back to 1. But resolution risk is a problem and something to be accounted for.

3. I think the average trade for human traders is probably around 150 bucks (but it’s a funky distribution) so not enough capital isn’t that big of a problem.

Most people do this for fun or to make (and inevitably lose) some extra cash. So they bet on things they care about. Money is often a reflection of what you value.

both. the people who stick around treat it like information arbitrage, not betting. you're not predicting outcomes, you're finding markets where the public price diverges from what you can actually verify. the gambling crowd loses to the rake, but narrow informational edges compound if you're disciplined about size and source

I only do elections, generally in thinner markets. It’s a built in edge because partisans are the dumbest people on the planet and will try to wishcast with their positions, they take their side’s propaganda at face value, and are slow learners because they’re very invested in the outcome itself even if they weren’t in a prediction market.

You're thinking about it backwards. Markets automatically get sharper as the volume in them increases. If everybody thought they knew something about

CNBC recently interviewed me about an economic-event bot I run on Kalshi. The interesting part was not the coverage. It was how little a headline win rate says about the system:

Their description included this line:

“At the time of the interview, Farmer’s bot had about a 70% win rate in trading on economic-related event contracts on Kalshi,”

That was a snapshot of what I told CNBC at the time. CNBC did not audit the bot, and a 70% win rate is not the same as a 70% return. It is not even proof that a strategy is profitable.

The part I wish the short article had room to explain is what “AI bot” means in this case.

The live trade-decision path does not ask an LLM which contract to buy. It is deterministic Python and statistical logic. I use AI around the system for code review, log analysis, and troubleshooting. The code that risks money follows explicit rules.

A scan cycle works roughly like this:

1. Reconcile fills, stale orders, settlements, and database state against the live Kalshi account.
2. Fetch the Cleveland Fed inflation nowcast and supporting FRED/BLS-derived data.
3. Scan eligible CPI and Core PCE contracts.
4. Parse each strike, direction, close time, volume, bid, and ask.
5. Convert the nowcast into a contract probability using an explicit uncertainty model.
6. Compare that probability with the price the bot can actually execute, not just a displayed midpoint.
7. Reject markets that fail the edge, volume, price, budget, duplication, position, or consistency checks.
8. Size the top surviving candidate, submit a limit order, and track the actual fill state.
9. Log the model probability, market price, edge, score, order state, and every skip reason.

The refusal logic matters as much as the forecast. The bot can say no because the nowcast is missing, the portfolio snapshot is incomplete, the daily-loss control is active, an order already exists, related strikes would create a contradiction, the event already has enough exposure, or the remaining budget is too small.

A recent live scan found valid candidates and placed nothing because matching orders were already open. That is not inactivity I need to “fix.” It is the system doing its job.

The hardest production problems have usually been less glamorous than forecasting. An order submission is not necessarily a fill. Partial fills exist. Resting orders can become stale. API fields change. Database rows can drift from the exchange. If those details are wrong, a good forecast can still produce bad risk accounting.

The 70% example is also a useful warning. Buy ten contracts for 90 cents each and win seven. You spent $9 and received $7. That is a 70% win rate and a $2 loss before fees.

The code CNBC discussed is my Econ/Inflation Bot. It is separate from the Weather Bot I developed later.

This is not financial advice or a claim that anyone will profit. Prediction-market trading involves real risk.

If you run bots on Kalshi, which production failure has caused you more trouble: forecasting, execution, or state reconciliation? The last two have been harder than the first for me.

Both, but the mindset changes fast if you're trying to make money.

I treat it less like “can I predict the future?” and more like “is the market price worse than the public data sitting in front of me?”

Weather markets are the cleanest example I’ve found. If a Kalshi temp market is pricing something at 40c, but multiple forecast ensembles are clustering closer to 65-70%, that’s at least a real thesis. Still doesn’t mean auto-buy. You have to check fees, spread, liquidity, timing, and resolution rules.

Most of my bot scans place zero trades, which is honestly the point. The edge is not just being right. It’s refusing to trade when the setup is messy.

The refusal logic observation resonates — the system saying no is often the most important decision. Curious about your sigma assumption for converting the Cleveland Fed point estimate to a contract probability. That seems like the key undisclosed step in the chain.

I’ve been working on a prediction market product and I’m at that awkward stage where the product exists… but users doesn’t 😅

Curious to hear from people who’ve built or marketed similar things. Things like How do you get early users to actually care about predicting outcomes? What channels worked best for you (communities, content, partnerships, etc.)?

Any creative growth loops or hooks that made it click?

Honestly I found my way with doing weather predictions!

since the thread is being weird, dropping this here. on polymarket i mostly watch wallet flow on niche markets, the same wallets that profited on early geopolitical resolutions tend to show up early on adjacent markets too, so being downstream of those wallets is the cleanest edge. on kalshi i look at sports same-day, lines move slow on injury news so live longshots in the teens or 20s give you minutes of mispricing if you're watching the source. crowdintel is good for the polymarket wallet side

interesting read, i run a small bot on kalshi too and the state reconciliation part is where everything falls apart for me

the forecasting is fun to work on but tracking partial fills and making sure my local db match what exchange actually have is a nightmare, spent 3 hours last week debugging a position that was off by 0.2 contracts

your point about win rate meaning nothing is so true, people see 70% and think printing money but the edge on each trade matter way more

The submitted, filled, settled split you described pays off twice. Second time is at filing. Kalshi does not issue a 1099-B for event contracts, so your database is the only record of what happened. Collapse those three states into one and the year total is wrong, with nothing to catch it until April. Settled date, cost basis, proceeds per lot is also exactly what a Form 8949 row needs, so your schema is most of the way there already. Reconciliation is the one that keeps costing after the year ends.

I run a small prediction markets research firm. Here's what three months of tracking Brier scores vs. Kalshi's implied probabilities actually looks like: wins & losses.

If you want free prediction markets research three times a week, top three calls with edge scores, I publish them on Substack:

• Total scored forecasts: 5
• Correct: 4
• Incorrect: 1
• AFG Brier: 0.111 vs Market Brier Score: 0.132
• Accuracy: 80%