Mean-reversion stop-loss methods

[Hugin]

Quote from dwpeters:

And how many times was there a generally steady and sustained event that reversed as intended, to recover at least some of the loss and in some cases turn a profit? The problem is that once a trade goes against you, what's the best move from that point? I find that many trades that go against me, sometimes significantly, turned around for a much smaller loss or even profits. You can use stop losses to reduce your risk on a given trade, but I haven't found a way to use them to improve the risk adjusted return of my overall strategy. I wish I could and believe me I've tried. The rare but significant moves against me are quite painful. I take solace in knowing that testing shows my strategy as is to be superior to the alternatives I could identify and knowing that most traders would not be willing to sit through such moves.

We have also seen this for mean reversion systems. We have tested a large number of stop-loss schemes and failed to find one that improves profitability (expected return increase) in walk-forward testing.

This obviously does not mean that they can't be found. Moreover, a stop-loss will truncate your S/L distribution which may be helpful even if it does not improve the expected return. Or it can be used to avoid disasters (but this is more of an insurance, and as we know insurance cost money on average).

IMO, for mean-reversion systems the problem is inherent in the underlying assumption - you enter a trade when you move away from a mean assuming it will revert back towards the mean. If it moves further away from the mean it's hard to argue, under assumption of mean-reversion, that it will be less likely to start to move towards the mean.

Hugin

 

[garchbrooks]

Quote from Hugin:


IMO, for mean-reversion systems the problem is inherent in the underlying assumption - you enter a trade when you move away from a mean assuming it will revert back towards the mean. If it moves further away from the mean it's hard to argue, under assumption of mean-reversion, that it will be less likely to start to move towards the mean.

No, it is not harder to argue that it will be less likely to move towards the mean. You're making the assumption that every draw away from the mean is sampled from some empirical distribution with the same (or similar) variance, but that is most certainly not the case. Since the variance (or volatility) is also a function of time, you cannot say that you drew from some empirical distribution that's parameterized (let's say, in terms of moments about the mean) similarly. This makes sense intuitively, because news changes things substantially. As an example, if the variance at one step into the future is 10x as large as the current variance, there is no argument for it to be more likely to revert.

What we all assume is that the probability distribution at time t+n is roughly the same as the distribution at time t, the point at which we entered the trade, and that repeated draws at each step forward in time are happening on a probably distribution that is /almost/ the same. In most cases, this is true. But in the explosive case I've outlined, the distribution at time t+1, you have no idea what the real distribution is. Condition on the previous time and attempting to forecast, you are still subject to model mis-specification risk and outliers.

 

[Hugin]

Quote from garchbrooks:

No, it is not harder to argue that it will be less likely to move towards the mean. You're making the assumption that every draw away from the mean is sampled from some empirical distribution with the same (or similar) variance, but that is most certainly not the case. Since the variance (or volatility) is also a function of time, you cannot say that you drew from some empirical distribution that's parameterized (let's say, in terms of moments about the mean) similarly. This makes sense intuitively, because news changes things substantially. As an example, if the variance at one step into the future is 10x as large as the current variance, there is no argument for it to be more likely to revert.

What we all assume is that the probability distribution at time t+n is roughly the same as the distribution at time t, the point at which we entered the trade, and that repeated draws at each step forward in time are happening on a probably distribution that is /almost/ the same. In most cases, this is true. But in the explosive case I've outlined, the distribution at time t+1, you have no idea what the real distribution is. Condition on the previous time and attempting to forecast, you are still subject to model mis-specification risk and outliers.

Actually I agree with this, but it does not give much help in creating a systematic stop-loss rule.

For example news works both ways, i.e. you may gain from them or lose. On average news will probably be close to neutral for your expected return.

Also, if volatility increases dramatically (due to news event), can you really say that it is more likely to continue inreasing than to fall back from current level?

Since such large changes in volatility (or price) are uncommon they provide very few examples to use in order to create a systematic stop-loss rule. And the impact on overall P/L will not be large if the stop-loss rule only affects a very small fraction of trades. We have had a hard time to find a systematic stop-loss rule for mean-reversion trades.

How to react to them also comes down to your position sizing, i.e. how much you risk when the trade has gone against you.

Hugin

 

[garchbrooks]

Quote from Hugin:

Actually I agree with this, but it does not give much help in creating a systematic stop-loss rule.

For example news works both ways, i.e. you may gain from them or lose. On average news will probably be close to neutral for your expected return.

Also, if volatility increases dramatically (due to news event), can you really say that it is more likely to continue inreasing than to fall back from current level?

Since such large changes in volatility (or price) are uncommon they provide very few examples to use in order to create a systematic stop-loss rule. And the impact on overall P/L will not be large if the stop-loss rule only affects a very small fraction of trades. We have had a hard time to find a systematic stop-loss rule for mean-reversion trades.

How to react to them also comes down to your position sizing, i.e. how much you risk when the trade has gone against you.

Hugin

If volatility rises dramatically, you can say it's more likely to stay volatile because of volatility clustering; whether it's sustained volatility in on direction or another is another issue. I think this is why I notice longer trades: volatility is sustained (each successive draw is farther and farther from the mean.)

I mean, yeah, I acknowledge what you're saying. I just want my cake and be able to eat it too.

What sort of skewness do you see in your pnl results from a mean reversion system? I see a distribution centered above zero, with slight negative skewness [from news], but positive expectation.

 

[gaj]

part time based.
part price based.
part if it doesn't act like it (normally) should.
and sometimes level 2, or other associated reasons.

 

[tomahawk]

Quote from garchbrooks:

For mean-reversion traders, how do you guys arrive at your stop loss targets? Some multiple of the deviation from the mean, or just some empirical result that backtests while?

Does it make sense to use time-based approaches? i.e., if the reversion hasn't happen in such and such time, just cut it -and- it has moved against you regardless of the result.

Mainly backtesting. Stops for different strategies are all over the map in terms of risk/reward, but normally no more than 2x the distance from entry 1 to scale 1, if applicable. I never use time as a stop but do use it to evaluate entries. I mainly trade indices.

 

[Hugin]

Quote from garchbrooks:

If volatility rises dramatically, you can say it's more likely to stay volatile because of volatility clustering; whether it's sustained volatility in on direction or another is another issue. I think this is why I notice longer trades: volatility is sustained (each successive draw is farther and farther from the mean.)

I mean, yeah, I acknowledge what you're saying. I just want my cake and be able to eat it too.

What sort of skewness do you see in your pnl results from a mean reversion system? I see a distribution centered above zero, with slight negative skewness [from news], but positive expectation.

We don't trade volatility (yet) but for our stock trading systems we have seen small negative skew (but this is actually varying over time - negative for 2006 and 2008, positive for 2007, 2009) and quite large excess kurtosis (~3). The large kurtosis is why we put in quite some effort to try to truncate the down side of the distribution. But no luck so far...

Hugin

 

[garchbrooks]

Quote from Hugin:

We don't trade volatility (yet) but for our stock trading systems we have seen small negative skew (but this is actually varying over time - negative for 2006 and 2008, positive for 2007, 2009) and quite large excess kurtosis (~3). The large kurtosis is why we put in quite some effort to try to truncate the down side of the distribution. But no luck so far...

Hugin

For perspective, what %'age of your trades land in the green?

 

[Hugin]

Quote from garchbrooks:

For perspective, what %'age of your trades land in the green?

For our oldest running stock trading system, a long only mean reversion system that’s been running from 2006 and forward, we have a hit-rate of 61% over ~2100 trades. We define a hit as making excess market profit (since this makes comparisons easier between different trends and fits the way we hedge). The set-up has changed over the years but this is mostly without stop-loss and with time based exits.

Last year we created a new system where we tried to get a balance between profit target exits and stop-loss exits to retain a somewhat symmetrical distribution. Profit targets were easy (and increases the hit-rate) but we hit the wall on the stop-loss. So now we’re trying to figure out how to move forward. We’re a bit hesitant over a system where the returns distribution is truncated on the upside and open on the downside (affects hedging and position sizing).

Hugin

 

[phattails]

Quote from garchbrooks:

Many of them do come back around for smaller losses, but somehow I am not content with this.

My experience shows there to be two cases:

i) There's heavy volume and a sustained push against me. I can see the market accumulating and bouncing on the VWAP/TWAP, the correlation to the indexes drops dramatically, heavy moves on SPY/ES are nowhere near being mirrored, etc.

ii) The volume isn't heavy, but someone is passively distributing/accumulating and impeding the movement.

Both cases are dangerous. In case 1, you have no idea how explosive the move will be or how damaging it will be. In case 2, you have the problem that your hedge no longer works, and you're at the mercy of the market's volatility in your hedging instrument or basket.

With case 2, I think this can be mathematically resolved in that the disparity in realized volatility is something that might be a signal/detectable. You can dispatch this awareness to another strategy engine and profit from it, maybe even through another asset class.

With case 1, I don't think it's wise to let the explosive move run. The problem becomes qualifying what's explosive. I tend to think a manual override taking the loss makes more sense. I was thinking to augment the system with some sort of predictor on whether the explosiveness is really dangerous.

you could hedge with other baskets. you can test when it's optimal to get out of the basket. you could model and assume price shocks as alternative subset play. you could hedge volatility. you could model longer or shorter plays to also hedge. you could also better select markets to trade.

sounds like you should at least incorporate volume in your model.