Summary
Prediction markets give institutional investors new tools for alpha generation, targeted hedging and portfolio risk management. But fragmented venues, inconsistent identifiers, pricing differences, and massive event-data volumes create new operational challenges. Managers need unified data infrastructure and automated reconciliation to incorporate prediction contracts into institutional portfolios confidently and at scale.
Right now, several U.S. states are facing off against federal policymakers over who can oversee prediction markets, which some people assert are a form of online gambling.i However, the future of prediction markets — the trading of event contracts whose value depends on the outcome of a future event — will bear little resemblance to the 2026 World Cup-driven, $50 billion per month betting business. Their risk management utility is vast. What was little more than a cultural curiosity at the beginning of 2026 could become the largest financial market in history.
At buy side institutions, they provide a granular diversification play and highly-targeted hedging opportunities. And they do so with precision and automation, benefiting from blockchain technology's speedier execution than TradFi derivative products.
Just as within the broader digital asset institutional adoption, the entrance into prediction markets demands a technology infrastructure reimagining. But the firms and exchanges that get the job done earlier will be positioned to engage in the most dynamic new hedging instrument ever introduced.
Prediction markets offer investment management firms a viable path to hedge risks from otherwise illiquid real-world events and add small exposures designed to alter the risk-return characteristics of a broader portfolio. “Event contracts now cover risks that no risk-management product previously reached,” per Fortune.ii Distributed Ledger Technology and blockchain have scaled to the point where creating and trading contracts for these events has finally become cost-effective.
Traditional derivatives like futures or options can build in unwanted noise or unrelated exposures, such as the exposures to movements across the rest of the index that managers assume when hedging one sector or portfolio company. Prediction market hedging zeroes in on the target. Instead of hedging the cost of nickel while still exposed to location, grade, delivery date, and basis risk, a manager will hedge against, for instance, a bad autumn typhoon season in Sulawesi, Indonesia that could disrupt the production and distribution of nickel.
Traditional asset managers will integrate prediction contracts into standard portfolios to dampen the highs and lows of daily price swings and variations. In metallurgy, a blacksmith adds 80% iron, 10% carbon, and small drips of other metals like 1% copper or 3% gallium to a smelter to adjust the properties of the steel as it is forged into a sword. A portfolio manager can drip small percentages of prediction products into a traditional fund to fine-tune its mandate and properties. Managers can execute this granular calibration to construct and defend targeted benchmarks, such as more easily maintaining a 3% guaranteed return portfolio, regardless of broader market movement.
As institutional crypto matures after passage of rules and policies, prediction markets promise to be extraordinary levers for managers for multi-dimensional hedging. But the maturation of institutional crypto depends on firms making certain data and operational infrastructure moves. Because these markets are growing so rapidly, firms cannot afford to wait to build systems.
"The modern era of prediction markets is typically dated to the Iowa Electronic Markets (1988), which demonstrated that market prices could aggregate dispersed information into probabilistic forecasts, often rivaling traditional polling. Building on this historical trajectory, prediction markets can be understood not as a fundamentally new financial innovation of the 21st century, but as an extension of longstanding risk-transfer and information-aggregation mechanisms into a broader set of uncertain events." — Prediction Markets as Event Hedges: Can They Actually Be Used in Practice?iii
Firms must get their data infrastructure and back-office reconciliation sorted out before the finalization of CFTC regulations and/or the ratification of the CLARITY Act, regardless of their immediate intent to trade event contracts.iv Best practices caution against patchwork or vibe-coded platforms, or the use of fragmented data to incorporate crypto business. Standard investment management tech stacks will be pushed beyond their limits by prediction markets’ lack of standardized identifiers and pricing opacity. Further, liquidity is highly fragmented across disparate trading venues, which, combined with systemic data latency, makes managing real-time risk and liquidity extremely difficult.
Successful reconciliation workflows require cloud or AI automation to prevent downstream operational headaches from corrupting portfolio analytics and risk management systems. Because prediction markets generate millions of daily data points and data is what makes them work, you must manage the data to manage this market.
It is impossible for a human to track, analyze, and reconcile 5,000+ data points daily to determine their true risk exposures using a spreadsheet. A 2026 survey report revealed that 56% of respondents believe the data generated by prediction markets will be at least somewhat valuable over the next two years and expect it will supplement existing market data feeds with additional color and context.v Every transaction includes dozens of relevant data attributes and produces a dozen or more downstream records/events. Prediction markets generate more priceable events, more contracts, and more venues, which yield more data to normalize and reconcile.
A firm’s data platform must be able to ingest disparate feeds and normalize identifiers and contract metadata specific to these markets. With a modern data foundation, managers can not only launch a prediction markets business but also expand into staking rewards, crypto lending, perpetuals, and tokenized assets.
Automation is compulsory. EY’s 2026 institutional digital assets survey found that 53% prefer a TradFi platform with crypto capabilities while 68% prefer partnering with a crypto-native firm to augment existing capabilities.vi These are sensible approaches, but only if the firm's data infrastructure can seamlessly ingest, normalize, and consolidate data in real-time from a mix of traditional asset systems and crypto-native, blockchain-native environments, so the firm operates over a single source of truth. It's the only way to ensure back-office ops will produce a clean, accurate, timely house view of positions and exposures. If a firm is running separate operations platforms for prediction markets and traditional markets, middle- and back-offices are forced to manually aggregate information from separate dashboards or bolted-on crypto modules.
For example, the system needs to use custom risk logic to support these event-driven instruments by modeling highly specific, event-related exposures, while simultaneously modeling risk logic for traditional and other alternative asset classes like private credit and OTC derivatives. A manager’s total portfolio view and firm-wide risk management are reliant on the infrastructure’s ability to integrate these new event-driven instruments around custom risk logic modeling, valuation, real-time risk aggregation, and compliant regulatory reporting.
Asset managers and hedge funds will find that participating in prediction markets is inevitable. Firms that wait until the official green light will fall behind as they spend upwards of six months getting their tech stacks up to speed. Prediction markets and wider crypto-market development cycles move materially faster than TradFi adoption cycles. An emerging concept can become widely used within months, if not weeks.
Several quantitative trading firms like Susquehanna, DRW, and Akuna Capital have begun a hiring wave to source specialized talent to drive their prediction market strategies; institutional trading firms increasingly believe prediction markets are becoming a serious asset class.vii Those adopting scalable single operating models will be on the ground floor of transforming hedging from a narrow financial exercise into a highly sophisticated form of real-world analysis.
Phillip Silitschanu
Phillip Silitschanu leads Arcesium's global digital asset commercial efforts as Senior Vice President, Digital Assets. Phillip is an expert and thought leader in the FinTech, blockchain, cryptocurrency, and digital assets space, known for his work as the research director leading IDC’s (Blackstone) global blockchain practice, and in various strategic roles within the financial services industry. He has authored and co-authored numerous whitepapers, reports, and books on these topics and is a recognized speaker and expert cited by major media outlets like the Financial Times and CNBC.
Sources:
i New York Times, August 27, 2026. https://www.nytimes.com/2026/08/27/technology/prediction-markets-states-kalshi-polymarket-lawsuits.html
ii Fortune, August 26, 2026. https://fortune.com/2026/08/26/cftc-commissioner-brian-quintenz-prediction-markets-kalshi/
iii Kirillov, Nikita, Prediction Markets as Event Hedges: Can They Actually Be Used in Practice? (July 26, 2026). Available at SSRN: https://ssrn.com/abstract=7185158 or http://dx.doi.org/10.2139/ssrn.7185158
iv U.S. Congress, June 24, 2026. https://www.congress.gov/crs-product/LSB11441
v Coalition Greenwich, January 29, 2026. https://www.greenwich.com/market-structure-technology/prediction-markets-its-all-about-data
vi EY, 2026. https://www.ey.com/en_us/financial-services/institutional-digital-assets-survey
vii CoinDesk, June 6, 2026. https://www.coindesk.com/business/2026/06/06/a-massive-hiring-wave-reveals-trading-firms-are-no-longer-viewing-polymarket-as-a-niche-betting-tool
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