ARK Invest is turning to Kalshi’s prediction-market data to sharpen its investment research, marking a notable step for how institutions can incorporate crowd-sourced probability signals into traditional financial workflows. The asset manager says it will use real-time market expectations from Kalshi to augment its macro and company-specific analyses, while also applying the data to risk management and hedging strategies. The move highlights a broader sector shift: prediction markets moving from niche crypto experiments toward actionable inputs for credible investment teams.

In a Kalshi statement, ARK will consume prediction-market outputs to gauge current expectations and blend them with its existing market-based research framework. Beyond tracking headline indicators, the data will inform what ARK’s researchers monitor—spanning trading activity, regulatory milestones, and notable scientific or technological breakthroughs. The goal is to obtain a more dynamic view of risk and opportunity as events unfold, rather than relying solely on lagging metrics or expert opinions.

Key takeaways

• ARK Invest will integrate Kalshi’s prediction-market data into its research and risk-management toolkit, using real-time market expectations to guide investment decisions.
• The collaboration signals growing institutional interest in prediction markets as a complementary data layer to traditional research, not just an alternative trading venue.
• Kalshi markets already cover a range of topics—such as macroeconomic indicators and corporate KPIs—and are live for a subset of subjects, according to the company’s leadership.
• Federal researchers and universities have previously highlighted Kalshi data as a potential input to macro policy and decision-making, underscoring the broader acceptance of such markets in academia and public institutions.

ARK’s use case: blending crowd wisdom with rigorous research

ARK Invest’s foray into using prediction-market data sits at the intersection of quantitative rigor and market-sentiment assessment. Cathie Wood, ARK’s founder and CEO, described the move as a natural evolution in financial research—one that brings a continuously updated measure of risk and probability into decision-making processes. Nick Grous, ARK’s research director, framed prediction markets as among the “purest expressions of risk around key economic and company-specific outcomes.”

The core value proposition for ARK, as outlined in Kalshi’s release, is to tap into high-frequency signals that reflect how participants price future events in real time. This can complement traditional indicators, which may lag or be slow to reveal shifts in expectations. For an investment team that emphasizes dynamic themes and rapid adaptation, the Kalshi feed could help identify turning points or validate the trajectory of a thesis before more conventional data points corroborate the narrative.

Kalshi notes that ARK will enlist markets on topics it is curious about—ranging from macroeconomic data to milestones in science and technology. While the company has highlighted ongoing tests and listings, ARK’s utilization underscores a broader trend: the ability to integrate structured prediction data within a research workflow that already leverages quantitative models, scenario analysis, and risk budgeting. The approach could also influence how ARK conducts portfolio hedging, potentially offering a forward-looking gauge of tail risk or event-driven catalysts that may not yet be priced into standard benchmarks.

Prediction markets in the institutional mainstream

The ARK-Kalshi collaboration arrives amid a wider institutional embrace of prediction-market data. Last year’s surge in interest highlighted these markets as a leading use case within the crypto space, with aggregate trading volumes regularly surpassing $10 billion per month. The growing attention isn’t confined to private firms; respected research bodies, including the Federal Reserve and Cornell University, have studied and employed prediction-market data to capture market sentiment and expectations with greater immediacy than traditional surveys or models can provide.

In recent research, U.S. Federal Reserve researchers argued that Kalshi data could offer a real-time, distributionally rich benchmark for macro expectations that would be difficult to obtain from conventional sources alone. They suggested such markets could augment policymakers’ understanding of the economy’s current pulse and help illuminate how participants price risks around inflation, growth, and labor trends. The sentiment within that work underscores why users like ARK view Kalshi as more than a novelty; it is a potential complement to the data stack that informs capital allocation and risk management.

Kalshi’s leadership has framed the platform as a practical testbed for institutional workflows. Tarek Mansour, Kalshi’s CEO, pointed to live markets—such as non-farm payrolls and macro-deficit indicators—as evidence that certain topics already have active, tradable signals. The company’s narrative aligns with a broader belief that prediction markets can distill diverse opinions into a quantified expectation, updated as new information arrives.

Beyond ARK, the literature and industry chatter around prediction markets have drawn attention to their use in real-world decision-making. In academic contexts, Polymarket and other platforms have been studied for how traders react to political events in real time, illustrating the potential of prediction-market data to reveal behavioral patterns during pivotal moments. While these findings are nuanced, they contribute to a growing understanding that prediction markets can function as a supplementary data feed for both private sector decision-makers and public institutions.

Ark’s collaboration also touches on a broader conversation about governance and transparency in data-driven investing. As more institutions seek to ground strategic bets in probabilistic forecasts, the need for rigorous data provenance, auditability, and methodological clarity grows. Kalshi’s publicly stated partnerships and the types of markets it lists provide a convenient case study for how such data streams could be integrated without compromising research integrity or risk controls.

What this means for readers and market participants

For investors and traders, ARK’s adoption signals a potential shift in how prediction-market inputs could become part of the evidence base that informs long-term theses and hedging decisions. If institutional usage scales, prediction-market data may gain more credibility as a complementary signal alongside earnings momentum, macro data points, and policy expectations. For builders and data scientists, the ARK-Kalshi partnership could encourage the development of standardized data pipelines, backtesting frameworks, and risk management protocols that incorporate real-time probability distributions into models and dashboards.

However, questions remain about the boundaries and reliability of such data. Real-time markets reflect the crowd’s judgment, which can be swayed by liquidity, incentives, or strategic trading. As ARK and others experiment with their own internal workflows, market observers will watch how Kalshi-data-driven signals perform in tandem with traditional analytics across different market regimes and macro scenarios. The evolving dialogue between market practitioners, researchers, and policymakers will likely shape how prediction-market data is validated, integrated, and regulated going forward.

ARK’s move also dovetails with a broader anxiety and opportunity surrounding crypto-native data ecosystems. While the Kalshi platform sits at the intersection of finance and prediction markets, its rising profile among established asset managers demonstrates how probabilistic forecasting mechanisms can transcend niche use cases and become a practical component of risk-aware investing. The next phase will hinge on the ability of institutions to operationalize these signals with transparent methodologies and auditable results, ensuring that the data remains informative rather than noisy in the face of volatility or shifting incentives.

For readers tracking adoption, the clearest takeaway is that prediction-market data is no longer a curiosity confined to speculative or retail-focused platforms. It is entering the toolbox of serious investment management, with ARK Invest’s partnership illustrating what it could look like when research, risk management, and market sentiment intersect in real time. The implications for portfolio construction, risk hedging, and scenario planning will depend on how widely institutions embrace, validate, and standardize the use of these signals in the months ahead.

ARK did not disclose a specific rollout date for the Kalshi data integration, but the collaboration underscores a growing appetite among leading investors to test how crowdsourced forecasts can inform forward-looking decisions in a disciplined, transparent way. As more institutions publish pilots and early findings, the industry will gain a clearer picture of whether prediction-market data can consistently augment, or even outperform, conventional signals in certain contexts.

Readers should watch for any formal case studies or performance benchmarks that ARK or Kalshi may publish, as such disclosures would help quantify the impact of prediction-market inputs on research timelines, risk metrics, and portfolio outcomes. The evolving narrative around these data streams is one to follow closely, given the potential to alter how investment teams think about probability, risk, and opportunity in rapidly changing markets.

As the week closes, the broader takeaway remains: prediction markets are moving from experimental corners of the crypto world into mainstream institutional workflows, where they can influence real-world decisions. The ARK-Kalshi partnership is a tangible milestone in that trajectory, inviting more questions about scalability, governance, and what investors should expect from crowd-based forecasts in the years ahead.

Readers interested in the original Kalshi announcement can explore the press release detailing ARK’s planned usage of the platform to enhance risk management and research workflows.

Ukraine has complicated President Donald Trump’s efforts to stabilize oil markets amid the Iran war, amplifying risks for financial markets, including cryptocurrencies.

For nearly a month, markets have been gripped by a single concern: the Iran war. Disruptions in the Strait of Hormuz – a critical oil chokepoint – have driven prices sharply higher, stoking fears of sticky inflation, a risk-off shift, and renewed Fed rate hikes.

To cool things down, the Trump administration quickly lifted sanctions on Russian crude for the short term, opening the tap to compensate for oil supply disruptions caused by the Iran war.

It came across as a solid plan to stabilize energy markets until Ukraine blew it up.
This week, Ukraine launched drone strikes on ports and refiners in Russia’s Leningrad, leading to what one observer described as “the most serious threat” to the country’s oil exports since Putin’s full-scale invasion of Ukraine in 2022.

The damage is significant, with roughly 40% of Russia’s oil export capacity offline. Oilprice.com editor Michael Kern described it as “a logistics problem first – and a supply problem second,” underscoring that moving oil to buyers is now as difficult as producing it.

“In conjunction with the war in the Middle East and de facto closure of the Strait of Hormuz and subsequent oil/LNG production outages, the Russian disruption adds a fresh element to already sky-high oil prices,” Kern noted.

In other words, oil prices may remain elevated longer than initially expected. For risk assets, including bitcoin and other cryptocurrencies, that’s an issue because higher sticky energy prices could lead to sticky inflation, potentially putting pressure on global central banks to raise borrowing costs and drain liquidity.

Traders are already prepping for a potential Fed rate hike in the short term. According to Bloomberg, flows in the options market tied to overnight interest rates indicate traders are wagering on a rate increase within two weeks.

Taken together, these factors suggest bitcoin’s recent resilience may face tests, with the $65,000–$75,000 range vulnerable to a downside break.

At press time, bitcoin traded near $68,500, down nearly 2% over the past 24 hours, according to CoinDesk data. WTI oil, which slipped nearly 10% to $83.95 per barrel on Monday, has since bounced back to $93.50. Brent crude is once again trading above the $100 mark.

US lawmakers have introduced a second bill this week aimed at curbing prediction market insider trading by government officials, amid growing concerns over such activity on major platforms such as Kalshi and Polymarket.

In an announcement on Thursday, US lawmakers Todd Young, Elissa Slotkin, John Curtis and Adam Schiff unveiled the bipartisan Public Integrity in Financial Prediction Markets Act of 2026.

“No one should be profiting off the information and knowledge gained as a public servant, period,” Slotkin said, adding: “This bill is an important first step in placing common sense rules around prediction markets, and it has real teeth to ensure those who break these rules face real consequences.”

The bill underscores growing unease that prediction markets could become a new frontier for insider trading, as bets tied to real-world events blur the line between wagering and financial activity. 

Bill aims to stop insider profiteering

The latest bill, which has been introduced in the second session of the 119th Congress, aims to prohibit government executives from using “insider information to bet on a prediction market contract.”

If enacted, the Public Integrity in Financial Prediction Markets Act of 2026 would cover the president, vice president and politicians across Congress, the House of Representatives and the Senate. 

It would also cover political appointees and “employees of an Executive agency or independent regulatory agency.”

The bill defines insider information as anything that a “reasonable investor would consider important in making a decision related to a prediction market contract and is not publicly available.”

It also outlines reporting requirements under which a government official must report any contract wagers over $250 within 30 days to the supervising ethics office. The individual must include “the number of contracts purchased, price of contract, date and time of transaction, name of contract, position taken on contract, name of trading platform used, profit or loss made on transaction.”

The penalties will see individuals charged the greater of $500 or double the amount of profit made from the prediction market contract.

Related: SEC is no longer a ‘cop on the beat‘ on crypto, says US lawmaker

The bills come amid an increasing number of state and federal lawmakers taking aim at prediction markets.