Abstract
Reports from stock analysis are an important source of information in the quantitative investment. How to capture the valuable information from the massive research reports quickly and accurately, and make a good stock recommendation method is one of the important issues in big data quantitative investment. Based on a kind of semi-supervised topic models (M-LDA) and by setting some fundamental emotion labels along with some certain topic labels, we are able to discover the representative words of the research reports for both explicit and latent topics. And then, by calculating the frequency of words in the topics, we designed a stock recommendation strategy. Experiments were carried out the report data from 2011 and 2014 and the results show that this method is effective to find out higher winning-rate stocks.
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