Full metadata
Title
Improving Crowdsourcing-Based Stock Price Predictions through Expanded Input Elicitation and Machine Learning
Description
This study aims to combine the wisdom of crowds with ML to make more accurate stock price predictions for a select set of stocks. Different from prior works, this study uses different input elicitation techniques to improve crowd performance. In addition, machine learning is used to support the crowd. The influence of ML on the crowd is tested by priming participants with suggestions from an ML model. Lastly, the market conditions and stock popularity is observed to better understand crowd behavior.
Date Created
2022-12
Contributors
- Bhogaraju, Harika (Author)
- Escobedo, Adolfo R (Thesis director)
- Meuth, Ryan (Committee member)
- Barrett, The Honors College (Contributor)
- Computer Science and Engineering Program (Contributor)
Topical Subject
Resource Type
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Series
Academic Year 2022-2023
Handle
https://hdl.handle.net/2286/R.2.N.170851
System Created
- 2022-11-30 10:27:57
System Modified
- 2022-12-09 11:30:35
- 1 year 11 months ago
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