@inproceedings{chen-etal-2026-prediction,
title = "Prediction-Market Seed Capital Recovery from Noise-Dominant Flow",
author = "Chen, Yankai and
He, Bowei and
Xie, Zhuohan and
Liu, Zhiwei and
Zhang, Fan and
Peng, Xueqing and
Wang, Yan and
Qian, Lingfei and
Liu, Xue",
editor = "Huang, Jimin and
Lee, Yongjae and
Ananiadou, Sophia and
Chen, Hsin-Hsi and
Takamura, Hiroya and
Chen, Chung-Chi",
booktitle = "Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.finnlp-1.47/",
pages = "574--589",
ISBN = "979-8-89176-466-8",
abstract = "Automated prediction markets require sponsors to prefund liquidity before observing order flow, creating a financing challenge at launch. We study whether nonnegative charges conditioned on observable payoff direction can improve recovery of this prefunded capital while limiting their effect on informed participation. We develop Seed Capital Flow (SCF), a direction-conditioned levy, in a stylized binary cost- function market with informed and liquidity-motivated traders. When order composition differs across directions, SCF concentrates the permitted fee burden on the direction with relatively more liquidity-motivated flow, whereas a uniform fee spreads it across both directions. Under a common retention constraint, this allocation yields higher expected recovery capacity and can make additional liquidity choices financially viable. Synthetic numerical audits examine robustness to alternative flow patterns, stochastic arrivals, and label misspecification. The results characterize a mechanism-design tradeoff rather than an empirical prediction: the market remains prefunded, recovery is expected rather than guaranteed, and the analysis is limited to an opening-cohort setting."
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<abstract>Automated prediction markets require sponsors to prefund liquidity before observing order flow, creating a financing challenge at launch. We study whether nonnegative charges conditioned on observable payoff direction can improve recovery of this prefunded capital while limiting their effect on informed participation. We develop Seed Capital Flow (SCF), a direction-conditioned levy, in a stylized binary cost- function market with informed and liquidity-motivated traders. When order composition differs across directions, SCF concentrates the permitted fee burden on the direction with relatively more liquidity-motivated flow, whereas a uniform fee spreads it across both directions. Under a common retention constraint, this allocation yields higher expected recovery capacity and can make additional liquidity choices financially viable. Synthetic numerical audits examine robustness to alternative flow patterns, stochastic arrivals, and label misspecification. The results characterize a mechanism-design tradeoff rather than an empirical prediction: the market remains prefunded, recovery is expected rather than guaranteed, and the analysis is limited to an opening-cohort setting.</abstract>
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%0 Conference Proceedings
%T Prediction-Market Seed Capital Recovery from Noise-Dominant Flow
%A Chen, Yankai
%A He, Bowei
%A Xie, Zhuohan
%A Liu, Zhiwei
%A Zhang, Fan
%A Peng, Xueqing
%A Wang, Yan
%A Qian, Lingfei
%A Liu, Xue
%Y Huang, Jimin
%Y Lee, Yongjae
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%Y Takamura, Hiroya
%Y Chen, Chung-Chi
%S Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-466-8
%F chen-etal-2026-prediction
%X Automated prediction markets require sponsors to prefund liquidity before observing order flow, creating a financing challenge at launch. We study whether nonnegative charges conditioned on observable payoff direction can improve recovery of this prefunded capital while limiting their effect on informed participation. We develop Seed Capital Flow (SCF), a direction-conditioned levy, in a stylized binary cost- function market with informed and liquidity-motivated traders. When order composition differs across directions, SCF concentrates the permitted fee burden on the direction with relatively more liquidity-motivated flow, whereas a uniform fee spreads it across both directions. Under a common retention constraint, this allocation yields higher expected recovery capacity and can make additional liquidity choices financially viable. Synthetic numerical audits examine robustness to alternative flow patterns, stochastic arrivals, and label misspecification. The results characterize a mechanism-design tradeoff rather than an empirical prediction: the market remains prefunded, recovery is expected rather than guaranteed, and the analysis is limited to an opening-cohort setting.
%U https://aclanthology.org/2026.finnlp-1.47/
%P 574-589
Markdown (Informal)
[Prediction-Market Seed Capital Recovery from Noise-Dominant Flow](https://aclanthology.org/2026.finnlp-1.47/) (Chen et al., FinNLP 2026)
ACL
- Yankai Chen, Bowei He, Zhuohan Xie, Zhiwei Liu, Fan Zhang, Xueqing Peng, Yan Wang, Lingfei Qian, and Xue Liu. 2026. Prediction-Market Seed Capital Recovery from Noise-Dominant Flow. In Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing, pages 574–589, Budapest, Hungary. Association for Computational Linguistics.