It was 2 years ago I created a profitable fade strategy. However, as my risk tolerance shrank so did my use of the strategy. The strategy was simple: find stocks that were up a huge percentage relative to themselves and the market. Most importantly, they had to have no news. No catalyst that drove them up. These stocks were more likely than not to fill their gap. However, in some cases it took up to 3 months with 100% draw down of investment capital to receive a 20% gain. It was not worth it.
I took the next best thing Earning Season Gap Data. These couple of months out of the year are packed with gaps that fill, run, or simply stagnate. Version 1.0 of the Gap Bot grabbed data from yahoo and analyzed the gap of a given stock. By the end of 2010 Q4 I had 1000+ data points. As I was using this technique to analyze earnings gaps, my data set lacked individual stock robustness. In other words, I only had two data points for Amazon, Apple, and the like. This was not sufficient (though profitable) to achieve a robust algorithmic procedure for trading earnings gaps. After 1k + lines of new code and scarping the internet I now have 50,000+ points of data dating back to 1998. With all this new data more challenges arose.
Below you will see 6 graphs. It’s obvious they provide very little value at all! Big data sets from unknown distributions will produce data in which you can not use most statistical tools (ANOVA, Regression, etc) because they are based on assumptions of a normal distribution. Looking at the histograms you can see how the outliers distort the picture, but you can’t just take them out! Take MAA on 5/6/2010 for example. It created a $0.05 downward gap (it opened below the previous days close). It ran $0.94 from that open, closing the gap by a huge margin. That margin is 1880% and an extremely important piece of information.
Luck for me data can be transformed. By transforming the data you can make non-normally distributed data normally distributed. The glory of this is that now we can go back to our arsenal of statistical tools and begin to play 🙂 Below is the data after it has been transformed. As Earnings Season 2011 Q4 approaches I will be posting most data and more results. Happy Trading.
- Equity Kickoff: Earnings, rising commodity prices fuel stocks (tradingfloor.com)
- Microsoft Q4, FY11 earnings: The charts (seattlepi.com)
- A brief history of S&P 500 beta (r-bloggers.com)
- Earnings season temporarily overshadows US debt concerns (tradingfloor.com)