Mary J Becker
Datos Biográficos
| ID | 5935330 |
|---|---|
| NOMBRE | Mary J Becker |
| NOMBRES | Mary J |
| APELLIDO | Becker |
| FIRMA | BECKER M J |
| AFILIACIONES | University of Akron |
| ORCID | 0000-0001-8452-5709 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 2 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2025 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 0 |
Political Uncertainty and Credit Risk
This paper examines whether prediction market data can forecast sovereign credit risk during periods of geopolitical conflict. Using prices from 152 political event contracts traded on Polymarket, we construct a market‐based measure of geopolitical uncertainty and aggregate information using a neural network. We evaluate its ability to predict daily changes in Ukrainian sovereign bond spreads from September 2024 to September 2025. Models incorpor…
Partisan Sentiment and Returns From Online Political Betting Markets in the 2020 US Presidential Election
In this study, we estimate the role of daily partisan sentiment in predicting the returns from political betting markets on the PredictIt platform for 10 of the most competitive states in the 2020 US presidential election. We utilize a textual analysis approach (multinomial inverse regression method) to measure partisan sentiment for market participants through message board posts on each market's web page. Our results suggest that estimated part…
Sin obras prominentes en esta página.
Partisan Sentiment and Returns From Online Political Betting Markets in the 2020 US Presidential Election
In this study, we estimate the role of daily partisan sentiment in predicting the returns from political betting markets on the PredictIt platform for 10 of the most competitive states in the 2020 US presidential election. We utilize a textual analysis approach (multinomial inverse regression method) to measure partisan sentiment for market participants through message board posts on each market's web page. Our results suggest that estimated part…
Political Uncertainty and Credit Risk
This paper examines whether prediction market data can forecast sovereign credit risk during periods of geopolitical conflict. Using prices from 152 political event contracts traded on Polymarket, we construct a market‐based measure of geopolitical uncertainty and aggregate information using a neural network. We evaluate its ability to predict daily changes in Ukrainian sovereign bond spreads from September 2024 to September 2025. Models incorpor…
Bond (1 obras) · Bond market (1 obras) · Cinema and Media Studies (1 obras) · Construct (python library) (1 obras) · Credit risk (1 obras) · Credit Risk and Financial Regulations (1 obras) · Economics (1 obras) · Electoral Systems and Political Participation (1 obras) · Event (particle physics) (1 obras) · Event study (1 obras)