Expert Stock Picker: The Wisdom of (Experts in) Crowds

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

Deliberated Intuition for Groups : An Explanatory Model for Crowd Predictions in the Domain of Stock-Price Forecasting

Crowd predictions in the domain of stock-price forecasting is a fascinating concept. Several special-interest online communities were founded following this idea. However, there is a limited body of literature about the domain of stock-price predictions based on such a crowdsourced approach. This paper presents an empirical study in the form of a two-phase, sequential mixed-methods experiment. Data from purposefully designed groups, consisting of lay people and professional financial analysts, were examined to inform the understanding of the prediction process. The findings led to an explanatory model, which we introduce as -˜deliberated intuition for groups’. The model of deliberated intuition for groups, which is proposed here, views prediction as a process of practice which will be different for each individual and group. The model proposes that a predictor will decide, consciously or semi-consciously, either to rely on gut-feeling or to undertake more analysis

Automated Update Tools To Augment the Wisdom of Crowds in Geopolitical Forecasting

Despite the importance of predictive judgments, individual human forecasts are frequently less accurate than those of even simple prediction algorithms. At the same time, not all forecasts are amenable to algorithmic prediction. Here, we describe the evaluation of an automated prediction tool that enabled participants to create simple rules that monitored relevant indicators (e.g., commodity prices) to automatically update forecasts. We examined these rules in both a pool of previous participants in a geopolitical forecasting tournament (Study 1) and a naïve sample recruited from Mechanical Turk (Study 2). Across the two studies, we found that automated updates tended to improve forecast accuracy relative to initial forecasts and were comparable to manual updates. Additionally, making rules improved the accuracy of manual updates. Crowd forecasts likewise benefitted from rule-based updates. However, when presented with the choice of whether to accept, reject or adjust an automatic forecast update, participants showed little ability to discriminate between automated updates that were harmful versus beneficial to forecast accuracy. Simple prospective rule-based tools are thus able to improve forecast accuracy by offering accurate and efficient updates, but ensuring forecasters make use of tools remains a challenge

The Network Dynamics Of Social Influence In The Wisdom Of Crowds

Research on the wisdom of crowds is motivated by the observation that the average belief in a large group can be accurate even when group members are individually inaccurate. A common theoretical assumption in previous research is that accurate group beliefs can emerge only when group members are statistically independent. However, network models of belief formation suggest that the effect of social influence depends on the structure of social networks. We present a theoretical overview and two experimental studies showing that, under the right conditions, social influence can improve the accuracy of both individual group members and the group as a whole. The results support the argument that interacting groups can produce collective intelligence that surpasses the collected intelligence of independent individuals