Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Data Mining and Knowledge Discovery
The Long-Run Stock Price Performance of Firms with Effective TQM Programs
Management Science
The Wisdom of Crowds
Harnessing the wisdom of crowds in wikipedia: quality through coordination
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Textual analysis of stock market prediction using breaking financial news: The AZFin text system
ACM Transactions on Information Systems (TOIS)
International Journal of Electronic Commerce
International Journal of Electronic Commerce
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Predicting Missing Ratings in Recommender Systems: Adapted Factorization Approach
International Journal of Electronic Commerce
ExpertRank: A topic-aware expert finding algorithm for online knowledge communities
Decision Support Systems
A decision support system for stock investment recommendations using collective wisdom
Decision Support Systems
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The phrase "the wisdom of crowds" suggests that good verdicts can be achieved by averaging the opinions and insights of large, diverse groups of people who possess varied types of information. Online user-generated content enables researchers to view the opinions of large numbers of users publicly. These opinions, in the form of reviews and votes, can be used to automatically generate remarkably accurate verdicts-collective estimations of future performance-about companies, products, and people on the Web to resolve very tough problems. The wealth and richness of user-generated content may enable firms and individuals to aggregate consumer-think for better business understanding. Our main contribution, here applied to user-generated stock pick votes from a widely used online financial newsletter, is a genetic algorithm approach that can be used to identify the appropriate vote weights for users based on their prior individual voting success. Our method allows us to identify and rank "experts" within the crowd, enabling better stock pick decisions than the S&P 500. We show that the online crowd performs better, on average, than the S&P 500 for two test time periods, 2008 and 2009, in terms of both overall returns and risk-adjusted returns, as measured by the Sharpe ratio. Furthermore, we show that giving more weight to the votes of the experts in the crowds increases the accuracy of the verdicts, yielding an even greater return in the same time periods. We test our approach by utilizing more than three years of publicly available stock pick data. We compare our method to approaches derived from both the computer science and finance literature. We believe that our approach can be generalized to other domains where user opinions are publicly available early and where those opinions can be evaluated. For example, YouTube video ratings may be used to predict downloads, or online reviewer ratings on Digg may be used to predict the success or popularity of a story.