We show that with the implementation presented in this paper, the Random Forest Classification model was able to predict whether or not a stock was going to increase in value during the coming day with an accuracy higher than 50\% for all stocks included in this study. Furthermore, we show that the active trading strategy presented in this paper generated higher returns and higher risk-adjusted returns than the passive investment in the stocks underlying the strategy. Therefore, we conclude \textit{(i)} that a Random Forest Classification model can be used to provide valuable insight on publicly traded stocks, and \textit{(ii)} that it is probably possible to create a profitable trading strategy based on a Random Forest Classifier, but that this requires a more sophisticated implementation than the one presented in this paper.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-330283 |
| Date | January 2023 |
| Creators | Nordfjell, Oscar, Ring, Gustav |
| Publisher | KTH, Skolan för teknikvetenskap (SCI) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TRITA-SCI-GRU ; 2023:113 |
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