Machine learning methods have become powerful tools used in multiple industries. They have been successfully applied to problems such as image recognition, speech recognition and machine translation, among others. In this report, we investigated several machine learning methods for forecasting five different bond indexes. We have implemented and analyzed Feedforward Neural Nets, LSTMs, Q-Networks and Gradient Boosted Trees, and compared them to the Buy&Hold strategy. We performed manual feature extraction based on some popular features used in the industry. The features were extracted from several financial instruments and were used as predictor variables. The results showed that XGBoost and Feedforward Neural Networks were consistently able to beat the Buy&Hold strategy for three of five bond indexes. / Maskininlärningsmetoder har blivit kraftfulla verktyg som används i flera problemområden. De har framgångsrikt tillämpats på problem som bland annat bildigenkänning, taligenkänning och maskinöversättning. I denna rapport har vi undersökt flera maskininlärningsmetoder för att förutse fem olika obligationsindex. Vi har implementerat och analyserat Feedforward Neural Nets, LSTMs, Q-Networks och Gradient Boosted Trees, och jämfört dem med Buy\&Hold strategin. Vi har utfört manuell extraktion av features baserat på några populära funktioner som används inom industrin. Dessa features beräknades från flera finansiella instrument och användes som prediktorvariabler. Resultaten visar att XGBoost och Feedforward Neural Networks kan konsekvent slå Buy\&Hold strategin för tre av fem obligationsindex.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-231789 |
| Date | January 2018 |
| Creators | Horemuz, Michal |
| Publisher | KTH, Skolan för elektroteknik och datavetenskap (EECS) |
| 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-EECS-EX ; 2018:377 |
Page generated in 0.0205 seconds