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Topological Compression of Prediction Market Belief States for Event Forecasting

Aadit Jerfy · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.25756 · cited by 0 works (OpenAlex) · open-access PDF

Korean

Topological Data Analysis (TDA)와 Persistent Homology (PH)는 단백질 모델링에서부터 금융 분석에 이르기까지 수많은 분야에서 데이터의 근본 구조를 이해하기 위해 사용되었습니다.이 논문에서는 TDA와 PH를 예측 시장 우주에 걸쳐 예측 모델에 통합하여 이러한 시장의 신념 상태가 예측 통찰력을 제공 할 수있는 근본 구조를 형성하는지 여부를 결정합니다.우리는 단일 매개 변수 PH가 선형 예측 모델의 예측 능력과 캘리브레이드 된 정확성을 향상시킬 수 있다는 결론을 내립니다.

Machine translation (Meta M2M100); the English original follows.

Abstract

Topological Data Analysis (TDA) and Persistent Homology (PH) have been utilized in numerous fields$-$from protein modeling to financial analysis$-$to understand the underlying structure of data. In this paper, we incorporate TDA and PH into forecasting models over a prediction market universe to determine whether the belief state of such markets forms an underlying structure whose shape can provide predictive insight. We conclude that single-parameter PH is able to improve predictive ability and calibrated accuracy of linear forecasting models, while two-parameter multipersistence produces similar benefits for nonlinear models.

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Topics: Nonlinear system, Computer science, Econometrics, Artificial intelligence, Machine learning, State (computer science)

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