ForesightFlow

Prediction markets have become a third venue for price discovery, but the research depth that built equities and crypto is missing.

We publish the methods, datasets, and benchmarks to close that gap.

Established
2026
Status
Research Department
Affiliation
Devnull FZCO · Dubai, UAE
Preprints
3
Datasets
3
Research tracks
5

Recent work

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Public infrastructure

Foresight Arena

live

Permissionless on-chain benchmark for AI forecasting agents. Polygon-deployed, commit–reveal submissions, Brier-score grading.

Open arena →Read the paper

FFIC

open

ForesightFlow Insider Cases dataset. 8 documented cases, 24 markets. CC-BY-4.0.

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Research tracks

Forecasting & AI agents

We study how AI systems — LLMs, ensembles, and multi-agent pipelines — perform as forecasters on real prediction markets, and how to evaluate them rigorously using proper scoring rules.

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Mechanism design

We study how prediction market rules — resolution typology, oracle design, and trading constraints — affect price accuracy, manipulation resistance, and the quality of the information aggregated.

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Microstructure

We adapt classical market-microstructure theory — PIN, VPIN, Kyle's λ, order imbalance, and variance-ratio diagnostics — to the discrete-outcome, on-chain CLOB structure of decentralized prediction markets.

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On-chain forensics

We develop wallet clustering, funding-flow analysis, novelty scoring, and cross-market behavior methods to attribute trades to economic agents rather than pseudonymous addresses.

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Quantitative strategies

We study alpha generation, market making, arbitrage, and execution on the hybrid CLOB structure of decentralized prediction markets — venues with unusual liquidity dynamics and binary payoff structures.

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