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dc.contributor.authorHøeg, Anders
dc.contributor.authorAares, Even Kristoffer
dc.date.accessioned2021-10-25T10:47:01Z
dc.date.available2021-10-25T10:47:01Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/11250/2825296
dc.descriptionMasteroppgave(MSc) in Master of Business - Handelshøyskolen BI, 2021en_US
dc.description.abstractWe test a statistical arbitrage trading strategy, pairs trading, using daily closing prices covering the period 2000 – 2019. Stocks are clustered using an unsupervised machine learning approach and cointegrated stocks from each cluster are then paired. The strategy does not prove to be profitable on S&P500 stocks once adjusted for transaction costs. Conversely, the strategy appears to be profitable on the OSE obtaining annualized excess returns of 22% and a Sharpe Ratio of 0.84 after adjusting for both explicit and implicit transaction costs. We investigate whether a difference in the liquidity can explain why the strategy is more profitable on OSE, and provide evidence suggesting that pairs trading profits are closely related to the liquidity of the stocks traded.en_US
dc.language.isoengen_US
dc.publisherHandelshøyskolen BIen_US
dc.subjectbusinessen_US
dc.titleStatistical Arbitrage Trading using an unsupervised machine learning approach: is liquidity a predictor of profitability?en_US
dc.typeMaster thesisen_US


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