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Ensemble Models for Trend Investing
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).
2021 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Ensemble modeller för trendinvesteringar (Swedish)
Abstract [en]

Portfolio strategies focusing on following the trend, so called momentum based strategies, have been popular for a long time among investors and have had many academic studies, however with varying results. This study sets out to investigate different momentum trading signals as well as combining them in ensemble models such as Random Forest and the unique Dim Switch portfolio and then compare them to set benchmarks. Only one of the benchmarks, the 100% equity portfolio, is found to have better returns than the constructed momentum based strategies, however the momentum based strategies show a lot of potential with high risk-adjusted returns and good performance with regards to Expected Shortfall, Value at Risk and Maximum Drawdown. The most common momentum trading signal, the momentum rule with 9 months lookback, was found to have the highest risk-adjusted returns compared to both the benchmarks and the ensemble models, but it was also found to have slightly heavier left tail than the ensemble models.

Abstract [sv]

Portföljstrategier som baserar sig på att följa trenden, så kallade momentumstrategier, har varit populära länge bland investerare. Många akademiska studier har gjorts om ämnet med varierande resultat. Denna studie utreder olika trendsignaler och kombinerar dem för att forma så kallade ensemble modeller, mer specifikt Random Forest och den unika "Dim Switch"-approachen, för att sedan jämföra dessa strategier mot benchmark portföljer. Endast en av benchmark portföljerna, 100% aktier i en ''buy and hold''-portfölj hade bättre avkastning än de momentumbaserade ensemble modellerna i studien. Däremot har momentumbaserade ensemble modellerna högre riskjusterad avkastning, Expected Shortfall, Value at Risk och Maximum drawdown. Den mest återkommande trendsignalen ''Momentum rule'' med nio månaders lookback hade extremt hög riskjusterad avkastning jämfört med benchmarks och ensemble modellerna, men det kom med kostnaden av högre risker i svansen.

Place, publisher, year, edition, pages
2021. , p. 78
Series
TRITA-SCI-GRU ; 2021:209
Keywords [en]
Momentum, Machine Learning, Random Forest, Trend Investing, Dim Switch
Keywords [sv]
Momentum, Maskininlärning, Random Forest, Trendinvestering, Dim Switch
National Category
Other Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-313320OAI: oai:DiVA.org:kth-313320DiVA, id: diva2:1663269
External cooperation
Handelsbanken
Subject / course
Financial Mathematics
Educational program
Master of Science - Mathematics
Supervisors
Examiners
Available from: 2022-06-14 Created: 2022-06-02 Last updated: 2022-06-25Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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