Exploring the Gaussian investor sentiment process

Football (soccer) stocks are substantially subject to investor sentiment stemming from football fields. Evaluating sentiment functions help 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.