Comparing forecast accuracies of machine learning and financial analyst
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Journal Title
Journal ISSN
Volume Title
School of Business |
Bachelor's thesis
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Author
Date
2022
Department
Major/Subject
Mcode
Degree programme
Rahoitus
Language
en
Pages
17
Series
Abstract
In this study, I compare financial analysts’ stock price growth prediction accuracy to neural networks prediction accuracy. Predictions are made for the same US stocks from 2019 to 2021 and the accuracies are measured in R-squared as well as accuracy metric. Findings show that the neural networks outperform financial analysts for this period in both metrics. Additionally, the prediction statistics show that neural networks make more pessimistic predictions with lower variance. Analysts on the other hand make more optimistic predictions with a higher variance which seems to be the reason why the neural networks score better values in these metrics.Description
Thesis advisor
Vihriälä, ErkkiKeywords
neural networks, analysts, stock growth predictions, accuracy
