Stops, from Newton Linchen

September 4, 2011 |

I'm reading old posts on Daily Spec, specifically an interesting discussion on the topic of Stops, that took place in 2003.

At the end, Faisal mentioned a paper of Dr. Eric Berger on calculating the probability of hitting the barrier (stop or price of the knock-out option).

Could anyone share this paper?

And, any other improvements on the discussion?

P.S. What I find difficult in those answers about not using stops, "no stops required", "stops will double your probability of loss", etc, is the time horizon. We have to give profit to our clients every month, otherwise they will fly away to another firm. How does a "no-stop" policy deals with this? And… in the event of a year such as 2008, (or 2011, in Brazil), a no-stop approach isn't a sure path to greater loss?

And for those not using stops, what's the time horizon? Do they NEVER take a loss? When will the loss ever be taken? (They put that part of their portfolio in a box, never to look at it again?)

Phil McDonnell writes: 

In introductory Stat there is almost always a section on calculating the probability of a certain sized observation in a normal distribution. That calculation tells you the probability of being at or above a certain level after a fixed period of time. To calculate the probability of having touched that level at some time during the fixed period of time simply double the probability. The reason is the Reflection Principle. For every path which wound up past that level, there is an equal and opposite path that was 'reflected' back from the level.

The same thing applies to stops on the downside. Note that all of this does not assume an infinite time period, but rather a fixed period of time - kind of an investment planning window if you will. The reason for this is that the variance always grows as time is extended. Given large enough variance then almost any price will be hit eventually both up and down.

Newton Linchen replies:


This is the subject that I have read several times in your book, but still can't imagine how this would fit someone who must give monthly performance reports, instead of a well-capitalized investor who can stand some period in the red…

This approach doesn't seem to fit short-term trading (trading with less than 30 days), unless you exit the position until the end of the month, which would be your final "chance" for the position to work.

And in the time horizon of one month, one would have to ensure protection against the ineluctable downside.

Phil McDonnell adds: 

The main point is that stop losses will double the number of losing trades at your 'maximum loss' level. So you will report more losing months. Is that what you want? So if not stops then choosing a prudent position size for a trade is really the way to control risk.

Most of my comments are made on the theoretical basis based on what should happen. But in fact in my empirical tests, reality is worse. Using stops actually hurts expected returns in most cases. Theory says that should not happen. Theoretically expectation should remain unchanged if you use stocks but that is not the case.

Attached is a little table excerpted from Larry Connors and Cesar Alvarez's book Short term Strategies That Work. It shows that for a particular simple system the average P/L was .69 per trade. with 69.81% winners. But when you add a stop at the 1% level the return plummets to .19% and only 26.89% winners. For all stop levels tested up to the 50% level the returns were lower when one added stops to the strategy.

So looking at probability per trade and return per trade stops seems worse. But theory says that they may help by reducing variance. So far as I can see that is the only good thing about them.


Dave G writes: 

Hedging makes more sense than the antiquated and disgraced use of "stops".


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