2014/05/10 by Wenbin Zhang, Zhang, Wenbin, Zhen Dai +5
Economics, Econometrics and Finance · #FOS: Economics and business #Portfolio Management (q-fin.PM) #Statistical Finance (q-fin.ST) #q-fin.PM #q-fin.ST
paper · pdf · doi:10.48550/arxiv.1405.2384
16 pages
arxiv created 2014/05/10 · arxiv updated 2014/05/13
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.