Momentum is a well-researched and documented (enter “momentum stock market” in the search window) anomaly in the financial markets. CXO Advisory also has completed some impressive work on momentum. As I have focused primarily on trading short-term mean-reversion setups, I have come to realize the importance of allocating assets into strategies that are not correlated.
Therefore, in this next series of posts, I am going to build a momentum-based rotational system, step by step.
As a place to start this testing I will be using some of the assumptions I’ve gleaned from CXO Advisory’s work as well as from this paper: Are Momentum Strategies Profitable? Evidence from Singapore. The assumptions are that momentum needs some time to develop (or one risks getting caught in mean-reversion), and that returns from momentum strategies will get smaller the longer the positions are held. Therefore, the measure of momentum I will use will be the stocks having the greatest rate-of-change over the last six months, and these stocks will be held for at least one month. The system will be long only.
The tests will incorporate the following ideas, but not necessarily in this order:
1. Test all stocks on U.S. exchanges, with no price, liquidity, or volume filter. (All tests will exclude OTCBB listings).
2. Add a price, liquidity, and volume filter.
3. Add a filter for volatility (volatile issues will be given a lower ranking, even if they have a higher six-month rate-of-change).
4. Add a bear-market filter.
5. Add a stop-loss.
6. Test system over de-listed data.
7. Investigate the effects of varying the number of positions held at one time.
8. Add in varying levels of commissions/slippage.
I will run these tests out-of-sample, over a 10 20 year slice of market history: 1986-2005. This period encompasses both bull and bear markets. The final tests will be run in-sample from 2006 to the present.
Next Post: Momentum Proof of Concept.
I like it. My initial concern however is that there will be too much noise in the testing of individual securities an that you would be better to use historical industry group data. This would also negate the survivorship bias that you are trying to overcome by also running the test through de-listed data.
Looking forward to your findings
Derry
Hi Derry,
The results of historical industry group data I think will be similar to the studies CXO has done with sector ETFs. I believe that there are enough studies of momo with individual securities showing the anomaly is present that the momo anomaly will outweigh the noise. We’ll see, won’t we!
Re survivorship bias: it is invaluable having delisted securities to test over for these types of investigations.
‘Are Momentum Strategies Profitable? Evidence from Singapore’ – http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1350193 – Is a fantastic paper, nice find – THANKS!!!
It is an excellent paper. I re-read it regularly. I’m glad you’ve found it helpful.
Dennis Kneale fondles cats.
(Nice post, Wood. Looking forward to your work)
Whatever happened to Kneale? I guess he just served as a good way to get my blog on CNBC. My 15 minutes, probably…
Can’t wait!!
Hawaii, I had you partially in mind with this, as you were asking for something to complement the Power Dip.
Wood, you are a genius
this is exactly the thing I have been looking at lately and wanted to test out but I didn’t know how
using ami broker also. I think the bear market filter is a brilliant idea
I will probably give out the AmiB code, unless I come upon something that seems novel (seems unlikely that will happen).
Looking forward to this study, Wood. Keep upthe great work!
Like where this is going. Have you looked at Charles Kirkpatrick’s book “Beat the market”. Of course there he is talking about relative strength investing.
Trading, haven’t read the book but I like Captain Kirk!
Also might be interesting to post relative results in relation to SPY/VTI/whatever to see how the strategy would perform hedged.