Crowd predictions in the domain of stock-price forecasting is a fascinating concept. Several specialinterest 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 semiconsciously , either to rely on gut-feeling or to undertake more analysis.
When it comes to financial decision-making like predicting stock price movements, it would be conceivable that rational people had an advantage over intuitive people. An experiment was conducted to test this hypothesis. Participants of the experiment provided repeated estimates for different shares and it was expected that rational people would end up with more 'correct' answers than intuitive people. Additionally, all participants of the experiment (N=59) completed a PID scale questionnaire (Betsch, 2004; Schunk & Betsch, 2006) to evaluate their preferences for deliberate or intuitive decision-making. The PID scale provided four categories to group people according to their preferences. In summary, it was concluded that intuitive people were slightly, but not significantly, better with financial decisionmaking than were rational people. A higher significance was observed from a direct comparison of the four PID categories. Predictions of PID-S-plus participants were significantly more accurate.
Purpose – This paper aims to present the results of experiments with groups making online group stock price predictions and include the research process and a summary of the preliminary results. The overall objectives of the study are to assess the effect of individual and remote group decision-making approaches to stock price predictions, to assess whether a learning effect exists through the feedback loop of an e-Delphi process and to identify the underlying key mechanisms of the individual and of the group that influence the decision-making process. Design/methodology/approach – The experiments consist of a pilot and a main run. The main run was performed with three lay groups (totaling 49 participants) and two expert groups (totaling ten financial analysts and other stock market professionals). The groups were benchmarked with actual market prices as well as with each other, over ten e-Delphi cycles (ten weeks). Each participant in the experiment was asked to provide an estimation of the movement (up or down) for one-week, one-month and three-month future periods for each share, as well as to enter a stock price prediction for a three-month period. Findings – Although the pilot run has provided some indications that in certain situations and with careful group design, stock price predictions can be superior to the predictions of experts, the main experiment indicated a more differentiated picture and provided some information about the underlying decision-making process. Originality/value – The paper presents influence factors and measures the impact of the group decision-making process of Internet communities focusing on stock trading, based on predicting share prices.
Financial analysis is a topic of interest for both academic research and businesses. Financial analysts are important elements of economic interactions. Nevertheless, there are doubts about the quality of their predictions. Special crowdsourcing platforms facilitate group decisions as an alternative to traditional financial analysis. The objective of this paper is to investigate the quality of predictions by individuals and groups using this alternative approach. Various groups-consisting of laypeople but also financial professionals-were formed purposefully to generate equity forecasts. The data from the experiment suggest that some variables, in terms of participants' characteristics, have a significant impact on the quality of predictions. The results show that intuition plays an important role in the decision-making process. Also, good predictors base their intuition on several factors. The results led to an explanatory model, which we introduce as "deliberated intuition", a practice process being different for each individual. It appears that thinking about the problem in different ways and with various techniques contribute to making good predictions. The model may help in designing teams for traditional financial analysts.
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