• norsk
    • English
  • norsk 
    • norsk
    • English
  • Logg inn
Vis innførsel 
  •   Hjem
  • Handelshøyskolen BI
  • Student papers
  • Master of Science
  • Vis innførsel
  •   Hjem
  • Handelshøyskolen BI
  • Student papers
  • Master of Science
  • Vis innførsel
JavaScript is disabled for your browser. Some features of this site may not work without it.

Statistical Arbitrage Trading using an unsupervised machine learning approach: is liquidity a predictor of profitability?

Høeg, Anders; Aares, Even Kristoffer
Master thesis
Thumbnail
Åpne
2941379.pdf (6.250Mb)
Permanent lenke
https://hdl.handle.net/11250/2825296
Utgivelsesdato
2021
Metadata
Vis full innførsel
Samlinger
  • Master of Science [1823]
Sammendrag
We 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.
Beskrivelse
Masteroppgave(MSc) in Master of Business - Handelshøyskolen BI, 2021
Utgiver
Handelshøyskolen BI

Kontakt oss | Gi tilbakemelding

Personvernerklæring
DSpace software copyright © 2002-2019  DuraSpace

Levert av  Unit
 

 

Bla i

Hele arkivetDelarkiv og samlingerUtgivelsesdatoForfattereTitlerEmneordDokumenttyperTidsskrifterDenne samlingenUtgivelsesdatoForfattereTitlerEmneordDokumenttyperTidsskrifter

Min side

Logg inn

Statistikk

Besøksstatistikk

Kontakt oss | Gi tilbakemelding

Personvernerklæring
DSpace software copyright © 2002-2019  DuraSpace

Levert av  Unit