  This paper examines the implementation of a statistical arbitrage trading
strategy based on co-integration relationships where we discover candidate
portfolios using multiple factors rather than just price data. The portfolio
selection methodologies include K-means clustering, graphical lasso and a
combination of the two. Our results show that clustering appears to yield
better candidate portfolios on average than naively using graphical lasso over
the entire equity pool. A hybrid approach of using the combination of graphical
lasso and clustering yields better results still. We also examine the effects
of an adaptive approach during the trading period, by re-computing potential
portfolios once to account for change in relationships with passage of time.
However, the adaptive approach does not produce better results than the one
without re-learning. Our results managed to pass the test for the presence of
statistical arbitrage test at a statistically significant level. Additionally
we were able to validate our findings over a separate dataset for formation and
trading periods.
