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Exploring the Gaussian Investor Sentiment Process

Exploring the Gaussian Investor Sentiment Process

Presented at the 2023 BayesiaLab Conference.

Abstract

Football (soccer) stocks are substantially subject to investor sentiment stemming from football fields. Evaluating sentiment functions helps us understand how investors interpret field signals and attach value to those signals in stock markets. This study develops the Gaussian investor sentiment process exploration programming (GISPEP) framework for exploring investor sentiment as a function of probabilistic field signals. The GISPEP provides an alternative event-study approach based on prospect theory and Bayesian analysis. We use the GISPEP to set the causality between match results and stock returns of the Fenerbahçe (FB), Galatasaray (GS), and Beşiktaş (BJK) football clubs in Turkey. A natural experiment also enables us to test the effect of competitive emotion that varies across two seasons. Our results indicate that competitive emotions regulate the asymmetric rise of availability and loss aversion heuristics under ambiguous field signals. In addition, loss signals increase the heterogeneity of market expectations.

Presentation Video

Presentation Slides

About the Presenter

Serdar Semih Coşkun is an assistant professor at İstanbul University Faculty of Economics, Turkey. Serdar holds a Ph.D. in Business Administration from the same university. Serdar’s research interests include Bayesian statistics, behavioral economics, and supply chain management.


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