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|Title||Recommender System for Portfolio Management Based on Social Media|
|Institution||University of Zurich|
|Faculty||Faculty of Business, Economics and Informatics|
|Abstract Text||In this thesis, a recommender system is built for portfolio management based on social media. With the emergence of social media and so-called influencers, people hold on to recommendations from famous financial investors. However, to what extent the social media posts and other mediums are able to explain changes in the composition of the financial actors remains unknown. This thesis is aimed at answering this question through a pipeline which consists of news scraping, content analysis, and a recommender system. The first two parts are used to create the data model inspired by a knowledge graph, consisting of various information about the financial influencer or the entity. Whereas the third part, the recommender system, proposes user-based or item-based recommendations, with the addition that various parameters can be set to create different investing strategies. Moreover, it should be included that the system allows user-specific recommendations for a certain period of time, which sets a basis for future research questions.|