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  1. ; ; ; (, International Conference on Computational Collective Intelligence)
    Nguyen, Ngoc T; Botzheim, János; Gulyás, László; Nunez, Manuel; Treur, Jan; Vossen, Gottfried; Kozierkiewicz, Adrianna" (Ed.)
    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. 
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