I'm new to this forum, this is the first question I posted. I have many candidate pairs and I've used ADF test to make a first selection. There are more than 800 selected. The pairs are absolutely too many. I'm thinking of other criteria to eliminate some of them. I've calculated the half-life and I want keep those who have low half-life, but all of them have a half-life less than 30 days(since all of them have passed the ADF test). Are there any other criteria which could select paris who have good mean reversion property? Thanks in advance.
In no particular order here are some ideas to get you started.
- Liquidity (ADV, # of shares, etc)
- Cost Basis (Cost to put on a trade)
- Back test / Cross validation
Cointegration as tested by ADF and other tests was developed to test the economic dependence of a time series on another. So, it would help you to think on similar lines. Bucketing your time series which can be economically dependent on each other would certainly help you eliminate more pairs.
If you have too many stocks in your bucket. The first step to filter the stocks would be to do a qualitative analysis of the stocks. You can segregate the stocks on the basis of a sector, market cap, daily traded volume, etc. Then check for correlation between the stocks in each segregated groups. After running correlation you will be left with less number of stocks. On the remaining stocks, you can run the cointegration test. Finally, select the pairs which are cointegrated.