Funds Are Watching Prediction Markets But Not Using Them Yet, Report Finds

Institutional investors are paying closer attention to prediction markets as a potential source of alternative data, according to a report from alternative data firm Neudata.

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The study finds clear interest among hedge funds and macro investors, but no evidence of widespread adoption in investment workflows. For brokers, this creates an unusual dynamic.

Prediction markets are not yet a meaningful source of liquidity, but they are emerging as a potential input into pricing models, sentiment analysis and client-facing analytics products.

The nascent industry is led by Polymarket and Kalshi, which together accounted for over $38 billion in notional volumes in 2025. That growth has drawn attention from funds looking for new data sources, but interest has not translated into broad adoption.

A Data Market That Is Still Hard to Use

One of the main obstacles is the way the data is accessed. Funds can pull data directly from exchanges, but that requires building and maintaining internal pipelines.

Alternatively, they can rely on institutional data providers, which offer cleaner feeds but at a cost and with limited depth. Some firms also use aggregators, though questions remain around data quality and consistency.

In practice, the problem lies in infrastructure maturity. The data exists, but it is not yet easy to standardise and integrate into existing trading systems.

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Use Cases Remain Narrow

Prediction market data is used mainly in targeted strategies rather than across full portfolios. Quant firms test it in arbitrage and market-making strategies or use it as an additional signal in specific trades, while others apply it as a sentiment indicator for macro events.

The signal, however, still requires calibration and is not widely relied upon. In practice, the segment is emerging as a data layer rather than a fully institutional trading venue.

The gap between interest and adoption reflects the current state of the market. The data is gaining visibility, but it remains a specialist tool used by firms that have the resources to work with fragmented and evolving datasets. For now, it remains a niche input.

But if data standardisation improves, it could move from a specialist signal to a more widely used component in trading and risk models.