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I’ve seen some work coming out of the quant-blogosphere recently related to “timing” a strategy using mean-reversion, or in other words, increasing exposure to a strategy after it performs poorly and reducing exposure after it performs well.

On paper this often tests out to be a good idea (especially in today’s very mean-reversion driven market) but I’m wary of it for two reasons: (a) evolving markets, and (b) changing the nature of the strategy. Allow me to pontificate…

First, a super-simple illustration:

20090805.01
[Growth of $10,000, logarithmically-scaled]

The graph above shows the (frictionless) results of two strategies “trading” the S&P 500 index from 2000 to present compared to buy & hold (in blue). The first (red) goes long at today’s close if the S&P 500 closes down today and short if it closes up.

The second (green) is “timing” the first strategy. Before each trade, we look at the first strategy’s returns for the previous 21-days (1-month) and compare it to all similar periods over the previous 1-year. If the most recent 21-day return is in the top 1/3, we do NOT take a position (because the strategy has been performing “too well”). Otherwise, we take the full position.

Note: This is a proof of concept; I am not suggesting anyone actually trade these strategies, only making a point. As we’ve talked about a number of times before (read more and more), daily mean-reversion is a recent phenomenon (and likely to change with a portfolio-crushing lack of notice), extremely volatile, and difficult to capitalize on alone taking trading frictions into account.

The performance improvement by “timing” the strategy becomes clearer when we look at the numbers:

20090805.02

On paper, a major improvement – improved risk-adjusted returns, significantly lower drawdowns, and a third less exposure to the market – what’s not to like?

Two Issues…

num.01 First, markets evolve (example) and without the benefit of hindsight this might be a dangerous approach.

Here we knew that the strategy would continue to perform in the future, but in the real-world (as I harp incessantly) fundamental characteristics of the market like daily mean-reversion are not static – they are constantly changing. By rewarding a strategy that was failing, we would inherently increase (or at the very least, not decrease) exposure as the market was evolving and the strategy was falling off a cliff.

Like our abnormal market filter (included in the State of the Market report, and YK and Scotty strategies), this is the “devil I don’t know” paradox. Historically our strategies have performed better without the abnormal market filter, and in a perfectly curve-fitted world, we would have no reason to use it. But as a developer I have to respect the devil that I don’t know [yet] and sometimes put logic (and the safety of the portfolio) ahead of the numbers.

num.02 Second, looked at from another perspective, we aren’t so much “timing” the strategy as changing the nature of how the strategy is trading.

That’s a little hard to wrap the noggin’ around with daily MR, so imagine the same approach applied to a trend-following strategy like 50/200-day MA crossovers.

If the strategy had performed well over the last 21-days, that’s not indicative that the strategy is working. That’s just indicative of a market that’s been bullish over the last 21-days. By then not trading the strategy (because it had performed so well) we didn’t successfully “time” the strategy, we modified the strategy to not trade when the market was overbought in the intermediate-term. We changed the nature of the strategy itself.

There’s not necessarily anything wrong with that, but it’s not our stated purpose.

Final Thought…

I’m not forever closing my doors to the concept of mean-reversion in strategies, but any approach would have to respond to the two critiques above (particularly the first).

Source: Timing a Strategy Using Mean-Reversion: A Critique