Publicação
Uncovering professional football transfer fee drivers, using hedonic regression models : evidence from the major european leagues
| dc.contributor.advisor | Arranhado, Esmeralda | |
| dc.contributor.author | Soares, Duarte dos Santos | |
| dc.date.accessioned | 2023-11-13T10:43:11Z | |
| dc.date.available | 2023-11-13T10:43:11Z | |
| dc.date.issued | 2023 | |
| dc.description | Mestrado Bolonha em Econometria Aplicada e Previsão | pt_PT |
| dc.description.abstract | Football transfer market news are each year a major topic of interest, as football clubs invest substantial sums of money to acquire new players for their teams. This study utilizes a distinctive cross-sectional database comprising information from 503 player transfers, regarding the 2022/23 football season, considering the six major European leagues. The primary goal of this study is to uncover the determinants of the transfer fee agreed by two clubs using hedonic price models, taking into consideration the set of characteristics included in the database. Many previous studies on this topic have typically used a straightforward approach to the problem by employing log-linear models, which, although not necessarily incorrect, can be a restrictive approach. This dissertation takes an alternative approach by utilizing a non-linear, the Poisson, estimated through quasi-maximum-likelihood. The aim is to uncover the transfer fees drivers, while also comparing the obtained results to the classical approach based on linear models. Furthermore, the most suitable regression model (Poisson) among the available options will be utilized for prediction exercise, using data from the 2023-24 season – using the six leagues and additionally a Saudi Arabian one. This will allow for a thorough evaluation of player valuation discrepancies, between the market value and the model's predictions, thereby illustrating the potential of the proposed model and suggesting whether the transferred player is potentially undervalued or overvalued. This approach can be a great tool for not only researchers, but also provides valuable information for team-management and investors. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Soares, Duarte dos Santos (2023). “Uncovering professional football transfer fee drivers, using hedonic regression models : evidence from the major european leagues”. Dissertação de Mestrado. Universidade de Lisboa. Instituto Superior de Economia e Gestão | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.5/29375 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | Instituto Superior de Economia e Gestão | pt_PT |
| dc.subject | Football | pt_PT |
| dc.subject | Transfer fee | pt_PT |
| dc.subject | Hedonic-model | pt_PT |
| dc.subject | Cross-sectional analysis | pt_PT |
| dc.subject | Poisson Quasi-maximum-likelihood estimation | pt_PT |
| dc.subject | Prediction | pt_PT |
| dc.title | Uncovering professional football transfer fee drivers, using hedonic regression models : evidence from the major european leagues | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |