Statistical Arbitrage Trading using an unsupervised machine learning approach: is liquidity a predictor of profitability?
Master thesis
Permanent lenke
https://hdl.handle.net/11250/2825296Utgivelsesdato
2021Metadata
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- Master of Science [1684]
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