Can look at the statistics of past reverting cycles in the market to determine an average behavior of mean reversion? Simply characterizing cycle amplitudes and durations is not enough to make a successful strategy.
Formalizing in a SDE is a step in the right direction, but ignores additional information available. Of course, even modeling as a SDE is a challenge. Mean-reversion is surely not a constant.
When is a movement mean reverting and when is it a movement in the direction of a new price level? When has the movement exhausted its direction?
I have not fully studied this, but would look at the following:
- changes in the intensity of the buy and sell processes
- changes in order book complexion (skew, size, etc)
- lack or presence of news event
- speed of ascent / descent (how do we distinguish period aggressive execution from sustained)
I will revisit this topic soon.
I’ve done some interesting cointegration work for canadian securities and see some likely basket / spread trades.
I was thinking about the equity and FX markets and the vast number of total securities one might investigate. We can test for stable cointegration and mean-reversion style trading in a systematic manner. Why not create a “machine” to test the many combinations for viable trading strategies.
Of course we will need market data for a large number of securities to pull this off …
On the side have been working with someone who is looking for long term strategies in the fixed income space. My strategies focus on intra-day trading primarily, but have found the start of a number of very attractive longer term (low frequency) strategies.
In particular, we are building a multi-factor model to predict market movements for Canadian bonds. Alternatively, we are also looking at cointegration models that would be implemented as long/short baskets of securities.
Sometimes the simplest ideas work best. I decided to look at a function of momentum over a period as a predictor of return over the following period. Did not expect to have such strong results. Here is the average return predicted by momentums at various standard deviations from parity:
An alternate graph of this showing standard deviation bands for returns against momentum levels:
There is certainly more work to be done to understand maximum drawdown and optimal money management.
Beyond momentum, we are also looking at building a continuous economic index (much like the Aruoba-Diebold-Scotto Business Conditions Index). This provides a continuous forecast of economic variables based on a stochastic state space model. Will discuss further in the next post.
A colleague had asked if I could help develop a multi-factor cointegration model for the Canadian bond market on daily or more frequent sampling, based on a variety of market data and fundamental factors. I had not developed a model like this before and was skeptical that could produce a useful result short of some man years of research.
To my surprise, found a very high probability model with 95% R-squared values and very high significance in a variety of tests. Now have a variety of models based on it depending on all or some of the below:
- US 3m rates
- US 2y swap rates
- S&P 500
- S&P / TSE Composite
- Shanghai Composite index (SSE 300)
- CAD/USD fx rate
- CAD 5Y liquid bond
- Surprise Index
With the 2 variable cointegration, one is simply trading mean reversion on the spread between one security and another. With a multivariate cointegration, one trades a long or short basket against the cointegrating security.