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This paper aims to analyse the relationship between ownership structure and financial performance in the five major European football leagues from 2007-2008 to 2012-2013 and examine the impact of the financial fair play (FFP) regulation.

The sample used comprises 94 teams that participated in the major European competitions: German Bundesliga, Ligue 1 of France, Spanish Liga, English Premier League and the Italian Serie A. The estimation technique used is panel-corrected standard errors.

The results confirm an inverted U-shaped curve relationship between ownership structure and financial performance as a consequence of both monitoring and expropriation effects. Moreover, the results show that after FFP regulation, the monitoring effect disappears and only the expropriation effect remains.

The lack of transparency of the information provided by some teams has limited the sample size.

One of the main issues that the various regulating bodies of the industry should address is the introduction of a code of good practice, not only for aspects related to the transparency of financial information but also to require greater transparency in the information concerning corporate governance.

Regulating bodies could also consider other additional control instruments based on corporate governance, such as for example, corporate governance practices, corporate governance codes, greater transparency, control of the boards of directors, etc.

This study tries to provide direct evidence of the impact of large majority investors in the clubs and FFP regulation on the financial performance of football clubs.

This study investigates whether the proportion of proprietary directors (blockholders or their representatives) on the board's remuneration committee influences vertical pay inequality in Spanish listed companies and whether this relationship can be conditioned by the concentration of ownership.

The sample contains information on the individual compensation of 1048 directors of 57 Spanish listed firms during the period 2013–2018 making up an unbalanced panel with 3565 observations. Panel data regressions are used to study how the presence of proprietary directors on the remuneration committee influences the remuneration of directors, focusing not on their absolute remuneration levels, but rather on their relationship to the average remuneration of the organization's employees (as a measure of vertical pay inequality within the company). The authors also investigate whether this relationship is conditioned by firm ownership concentration.

The results indicate that the presence of proprietary directors on the remuneration committee acts as a mechanism to reduce vertical pay inequality, even in the context of high ownership concentration.

Unlike the majority of previous research dedicated to the independence of the remuneration committee, this study focuses on the role played by proprietary directors. The results help elucidate the importance of proprietary directors to properly monitor and restrain directors' compensation in contexts of high ownership concentration.

The paper analyzes the effects of financial fair play (FFP) in the competitive balance of European football industry throughout a long-term perspective.

The authors analyze the evolution of the competitive balance in the European football industry through a time-series analysis from season 1992/93 to 2018/19.

Results indicate an industry by nature dominated by a few clubs showing a general stationary behavior. FFP has had very little impact in local competitions. Just in some leagues, such as the Spanish, German, and French leagues, we can observe an increase in the imbalance in some indicators, but these results are not very robust. The improvement on the financial situation happens especially in a small group of firms that coincide with the big leagues with a strong European market orientation and strict local financial control standards.

Although the study covered 17 European Leagues, there are several leagues not accounted for and thus results should be generalized with caution.

The authors observe heterogeneity of the results of FFP in the competitive balance, associated to how the standard has been implemented in each market. This opens opportunities to study and deepen the local codes and their influence, especially in the recommendations of future financial control standards.

The authors’ main contribution to the literature is to examine the impact of the FFP rules in the competitive balance utilizing a very broad study of 17 European markets with a rich and unusual overview and long-term perspective.

Learning to rank algorithms inherently faces many challenges. The most important challenges could be listed as high-dimensionality of the training data, the dynamic nature of Web information resources and lack of click-through data. High dimensionality of the training data affects effectiveness and efficiency of learning algorithms. Besides, most of learning to rank benchmark datasets do not include click-through data as a very rich source of information about the search behavior of users while dealing with the ranked lists of search results. To deal with these limitations, this paper aims to introduce a novel learning to rank algorithm by using a set of complex click-through features in a reinforcement learning (RL) model. These features are calculated from the existing click-through information in the data set or even from data sets without any explicit click-through information.

The proposed ranking algorithm (QRC-Rank) applies RL techniques on a set of calculated click-through features. QRC-Rank is as a two-steps process. In the first step, Transformation phase, a compact benchmark data set is created which contains a set of click-through features. These feature are calculated from the original click-through information available in the data set and constitute a compact representation of click-through information. To find most effective click-through feature, a number of scenarios are investigated. The second phase is Model-Generation, in which a RL model is built to rank the documents. This model is created by applying temporal difference learning methods such as Q-Learning and SARSA.

The proposed learning to rank method, QRC-rank, is evaluated on WCL2R and LETOR4.0 data sets. Experimental results demonstrate that QRC-Rank outperforms the state-of-the-art learning to rank methods such as SVMRank, RankBoost, ListNet and AdaRank based on the precision and normalized discount cumulative gain evaluation criteria. The use of the click-through features calculated from the training data set is a major contributor to the performance of the system.

In this paper, we have demonstrated the viability of the proposed features that provide a compact representation for the click through data in a learning to rank application. These compact click-through features are calculated from the original features of the learning to rank benchmark data set. In addition, a Markov Decision Process model is proposed for the learning to rank problem using RL, including the sets of states, actions, rewarding strategy and the transition function.