This study deals with market reaction to public information. The sample studied concerns six different famous European football clubs that are regularly involved in European competitions. These clubs are AS Roma from the Italian championship Calcio Serie A, FC Porto from the Portuguese championship Super Liga, Ajax Amsterdam from the Dutch championship Holland Casino Eredivisie, Galatasaray and Besiktas Istanbul from the Turkish championship Super Lig, and Celtic Glasgow from the Scottish championship Premier League.               Palomino et al. (2009) is the main source of inspiration for this study. Most of the findings are in lines with their results. There are two main contributions in this research. Firstly, our sample is composed by clubs from 5 different European countries: Italy, Scotland, Turkey, Portugal and Netherlands. Secondly, the ten years period of the study includes the financial crisis period. The results obtained for the financial crisis period have contaminated most of our results, justifying the choice to focus mainly on the results of the period 2002-2012 without the 2007-2009 period, which is the period associated to the financial crisis.               This research is divided into four parts. We firstly find evidence that the release of public information during the on-season has more influence than the one of the off-season. Indeed, the abnormal volumes calculated during the on-season are greater than the abnormal volumes computed during the off season. Likewise, we observed similar results as for the volatility. Secondly, this study demonstrates that the games’ results have a positive or a negative impact on the shares’ clubs returns depending on the game outcome. Indeed, the abnormal returns’ results are negative for losses and positive for wins. Moreover, we demonstrate that the stock market absorbs negative events (e.g. defeats) faster than the positive events (e.g. victories). Thirdly, we found that the losses that occur at the end of the season have more impact in terms of magnitude on the abnormal returns. On the contrary, the investors do not seem to react differently regarding the wins. Then, we were unable to find relevant findings regarding the unexpected results’ impact on the clubs’ share price. Surprisingly, we found that there is a surprise effect concerning victories whereas there is no surprise effect regarding the defeats.               Most of the findings of the study prove that public information concerning game results does influence investors’ behavior and thus have a significant impact on the share price of the famous European clubs.

With the rise of social media, people have gained a platform to express opinions and discuss current subjects with others. This thesis investigates whether a simple sentiment analysis — determining how positive a tweet about a given party is — can be used to predict the results of the Swedish general election and compares the results to betting odds and opinion polls. The results show that while the idea is an interesting one, and sometimes the data can point in the right direction, it is by far a reliable source to predict election outcomes.

The thesis describing Strategy of business unit is divided into three parts. First theoretical part summarizes strategic management together with strategic analysis, consisting of external and internal part. Second part describes operations on betting market, contemporary Czech market, its development and actual services offered. Last part analyses the strategy of Tipsport Company. It contains basic characteristic of the company followed by development of individual parts of business strategy such as SWOT and PEST analysis and marketing strategy.

Thesis "Data Mining in the Field of English Football League Third Division's Betting Odds" deals with data mining referring to acquiring knowledge from data. The main objective of this work is to develop data models for prediction of match results and to compare these predictions with a chosen strategy of betting. The selected betting strategy is based on betting single bets with odds belonging to chosen intervals, which generate a profit. These odds intervals were discovered by analyzing 2006-2009 football matches in a created simulator. On the basis of these odds ranges data models were constructed. Each data model contains a hypothesis which is generated by SD4ft procedure of LispMiner based on all football matches played in seasons 2001-2008. Developed data models are tested afterwards using 2006-2009 football matches data. Results show that all derived data models are profitable in all four seasons under consideration. More than half of them successfully predicted 2009 matches as well. The analysis showed that betting agencies offer mostly odds which make it almost impossible to be profitable while betting on matches according to their odds. In spite of this fact I identified some odds intervals with which you can success while betting single bets on home-team, draw or visitor-team with odds falling within these intervals. Association rules with reasonable confidence and support can generate high profitability. It is important to realize that there are no data models which guarantee a certain profit. Most of developed data models are not applicable in the real world, some of them can actually generate a loss. Nevertheless there are data models to be found that could generate a profit in the real world.