Aadit Jerfy Releases Event Forecasting Paper on Topological Compression of Prediction Market Belief States
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Aadit Jerfy has released an academic paper introducing topological data analysis (TDA) and persistent homology (PH) into forecasting models for prediction markets, exploring the predictive value of the underlying structure of market belief states. The study finds that one-parameter PH improves the predictive power and calibration accuracy of linear forecasting models, while two-parameter multi-persistent homology yields similar gains for nonlinear models. The paper is 41 pages long with 19 figures, and belongs to the Trading and Market Microstructure (q-fin.TR) field.
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- arXiv q-finTopological Compression of Belief States in Prediction Markets for Event Forecasting
This study introduces topological data analysis (TDA) and persistent homology (PH) into forecasting models in the field of prediction markets, exploring whether the underlying structure of market belief states has predictive value. The research findings show that single-parameter PH can improve the predictive power and calibration accuracy of linear forecasting models, and that two-parameter multi-persistent homology can produce similar gains for nonlinear models. The paper is 41 pages long with 19 figures, and belongs to the field of Trading and Market Microstructure (q-fin.TR).
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