Following up to my stock pair strategy, see original post here, I have did some quantitative analysis. Before I go to my analysis, here is a quick recap of the strategy:
- Short XLF, which is financial SPDR with a dividend yield of 2.99%.
- Long BofA with a dividend yield of 6.5%.
- The long and the short side of the strategy will be dollar for dollar.
- The dividend yield differential will be return on investment.
- The capital required for the strategy is none, so your return on investment will be quite high.
Here are my findings:
- The correlation of .7 is statistically significant. That means there is high degree of true underlying relationship between the two assets returns. How can't that be since BofA is the largest holding in the XLF making about 9% of the ETF value.
- The returns pf BofA and XLF do not follow a normal distribution, however they are positively skewed with BofA having fatter tails, i.e., BofA has more probability of outperforming XLF. See graph of the return distribution of both assets over 109 months. This signifies that the probability is higher that BofA will outperform the XLF.
- If I back-test the strategy I will earn 10.11% annually. the strategy had a positive monthly return of .55% on top the dividend yield deferential of 3.51% due to BofA outperforming the XLF. Take a look at the probability distribution table below; over the last 109 months the strategy expected return would have been .55% monthly.
Overall the strategy seems promising as the short will give a hedge against any losses in BofA due to the recent turmoil in baning. However my expectation is BofA will trade higher over the next two years.
2 comments:
So how did this pair work out for you? I am doing an enormous amount of research on different pairs to do spread trading with. I am curious as I found this blog 2010 how your idea panned out? Let us know if you see this post. Thanks
worked well but once BofA cut its dividends existed.
there was few basis points in the performance of Bofa to the xlf plus the dividend differential
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