Browsing by Subject "Textanalyse"

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    Investor sentiment in blogs : design of a classifier and validation by a portfolio simulation
    (2016) Klein, Achim; Kirn, Stefan
    How can investment recommendations available on the web significantly improve stock selection? This dissertation shows how online investment recommendations can automatically be analyzed, aggregated, and used to achieve a return above the market’s. To this respect, it is crucial to understand how investment recommendations affect returns. Therefore, the dissertation examines the effects of direct and indirect investment recommendations from blogs in the form of investor sentiments (i.e., opinions) on the expected development of stock prices. Blogs have made it possible for everyone to publish articles on the web. The studied blog platforms Seekingalpha and Blogspot host a wealth of semi-professional stock analyses, investor opinions, company rumors, and stock recommendations. The dissertation’s study uses about 77,000 articles from Seekingalpha and about 198,000 articles from Blogspot over a five-year period (2007-2011). A novel text classification method is developed for the automatic classification of blog articles in a positive vs. negative sentiment. To achieve a high classification accuracy, experiments were carried out to configure this method. The text classification method uses machine learning techniques, which learn from manually classified articles from a novel corpus. Using behavioral finance theory, hypotheses are developed about the effects of investor sentiments on a portfolios returns. To test these hypotheses, a monthly selection of stocks of the Dow Jones Industrial Average into a portfolio was simulated (i.e., backtested). The selection is made by means of the ranking of the monthly aggregated overall sentiment of all articles regarding a specific stock. The results show that a return above the market’s can be achieved with aggregated investor sentiments from the Seekingalpha platform. In most cases, the achieved return exceeds the return of a momentum portfolio based solely on past returns. For the platform Blogspot, results are weaker. Overall, it seems advisable for investors to select a small number of stocks based on the most positive and most negative monthly investor sentiments from professional blogs.