Title page for etd-0012117-194528


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URN etd-0012117-194528
Author Yu-Jin Lai
Author's Email Address No Public.
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Department Applied Mathematics
Year 2016
Semester 1
Degree Master
Type of Document
Language English
Title Machine Learning Pairs Trading
Date of Defense 2016-07-11
Page Count 48
Keyword
  • cointegration
  • principal component analysis
  • profit
  • sliced inverse regression
  • spread
  • support vector machine
  • Abstract Pairs trading is a comparative-value form of statistical arbitrage designed to use temporary random departures from equilibrium pricing between two stocks. In the first part, we use the spreads of cointegrated pairs and pre-chosen thresholds to perform pair trading for daily data. We investigate the effects of several selected covariates (e.g. EPS, strength of cointegration and etc.) on the pairs trading profits. We use principal component analysis, sliced inverse regression and kernel sliced inverse regression to find risky covariate zones which result in unprofitable pairs. In the second part, we conduct high-frequency pairs trading for intraday data. We use several high frequency covariates (e.g. money flow, relative strength index and etc.) as input features for support vector machine classification to set up trading signals of entering positions. We investigate the performance of the proposed pairs trading strategies for stocks in S&P 500 index.
    Advisory Committee
  • Mong-Na Lo Huang - chair
  • Shih-Feng Huang - co-chair
  • Mei-Hui Guo - advisor
  • Files
  • etd-0012117-194528.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2017-01-17

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