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

Endress, Tobias and Gear, Tony (2018) "Deliberated Intuition for Groups”: An Explanatory Model for Crowd Predictions in the Domain of Stock-Price Forecasting. In: Proceedings of the 51st Hawaii International Conference on System Sciences 2018. Hawaii International Conference on System Sciences, Manoa, Hawaii, pp. 4094-4101. ISBN 9780998133119

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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.

Item Type: Book Section
Additional Information: This paper was presented by Tobias Endress at the 51st Hawaii International Conference on System Sciences in Manoa on 3-6 January 2018. It was nominated for the Best Paper Award.
Uncontrolled Keywords: Stock price forecasting; Crowdsourcing; Intuition
Related URLs:
Subjects: H Social Sciences > HG Finance > HG4501 Investment, capital formation, speculation
Divisions: Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas: Applied Business & Technology
Depositing User: Susan Turner
Date Deposited: 05 Jan 2018 11:46
Last Modified: 04 Aug 2023 20:31
URI: https://eprints.glos.ac.uk/id/eprint/5281

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