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Title: Assessing the Effects of Expanded Input Elicitation and Machine Learning-Based Priming on Crowd Stock Prediction
The stock market is affected by a seemingly infinite number of factors, making it highly uncertain yet impactful. A large determinant of stock performance is public sentiment, which can often be volatile. To integrate human inputs in a more structured and effective manner, this study explores a combination of the wisdom of crowds concept and machine learning (ML) for stock price prediction. A crowdsourcing study is developed to test three ways to elicit stock predictions from the crowd. The study also assesses the impact of priming participants with estimates provided by a Long Short Term Model (LSTM) model herein developed for this context.  more » « less
Award ID(s):
1850355
PAR ID:
10464689
Author(s) / Creator(s):
; ; ;
Editor(s):
Nguyen, Ngoc T; Botzheim, János; Gulyás, László; Nunez, Manuel; Treur, Jan; Vossen, Gottfried; Kozierkiewicz, Adrianna"
Date Published:
Journal Name:
International Conference on Computational Collective Intelligence
Page Range / eLocation ID:
3-16
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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