Evaluating the Rationales of Amateur Investors

Social media's rise in popularity has demonstrated the usefulness of the wisdom of the crowd. Most previous works take into account the law of large numbers and simply average the results extracted from tasks such as opinion mining and sentiment analysis. Few attempt to identify high-quality opinions from the mined results. In this paper, we propose an approach for capturing expert-like rationales from social media platforms without the requirement of the annotated data. By leveraging stylistic and semantic features, our approach achieves an F1-score of 90.81%. The comparison between the rationales of experts and those of the crowd is done from stylistic and semantic perspectives, revealing that stylistic and semantic information provides complementary cues for professional rationales. We further show the advantage of using these superlative analysis results in the financial market, and find that top-ranked opinions identified by our approach increase potential returns by up to 90.31% and reduce downside risk by up to 71.69%, compared with opinions ranked by feedback from social media users. Moreover, the performance of our method on downside risk control is comparable with that of professional analysts.