Risk as a “Survival Variable”

I come across a lot of strategies on the blogosphere some are interesting some are a complete waste of time but most share a common feature: people developing those strategies do their homework in term of analyzing the return but much less attention is paid to the risk side its random nature. I’ve seen comment like “a 25% drawdown in 2011 but excellent return overall”. Well my bet is that no one on earth will let you experience a 25% loss with their money (unless special agreements are in place). In the hedge fund world people have very low tolerance for drawdown. Generally, as a new trader in a hedge fund, assuming that you come with no reputation, you have very little time to prove yourself. You should make money from day 1 and keep on doing so for a few months before you gain a bit of credibility.

First let’s say you have a bad start and you lose money at the beginning. With a 10% drawdown you’re most certainly out but even with a 5% drawdown the chances of seeing your allocation reduced are very high. This has significant implications on your strategies. Let’s assume that if you lose 5% your allocation is divided by 2 and you come back to your initial allocation only when you passed the high water mark again (e.g. the drawdown comes back to 0). In the chart below I simulated the experiment with one of my strategies.


You start trading in 1st June 2003 and all goes well until 23rd Jul. 2003 where your drawdown curve hits the -5% threshold (**1**). Your allocation is cut by 50% and you don’t cross back the high water mark level until 05th Dec. 2003 (**3**). If you have kept the allocation unchanged, the high water mark level would have been crossed on 28th Oct. 2003 (**2**) and by the end of the year you would have made more money.

But let’s push the reasoning a bit further. Still on the chart above, assume you get really unlucky and you start trading toward mid-June 2003. You hit the 10% drawdown limit by the beginning of August and you’re most likely out of the game. You would have started in early August your allocation would not have been cut at all and you end up doing a good year in only 4 full months of trading. In those two examples nothing has changed but your starting date….

The trading success of any individual has some form of path dependency and there is not much you can do about it. However you can control the size of a strategy’s drawdown and this should be addressed with great care.  A portfolio should be diversified in every possible dimension: asset classes, investment strategies, trading frequencies etc…. From that perspective risk is your “survival variable”. If managed properly you have a chance to stay in the game long enough to realise the potential of your strategy. Otherwise you won’t be there next month to see what happens.

As usual any comments welcome


  1. […] Risk as a “Survival Variable” [R Trader] I come across a lot of strategies on the blogosphere some are interesting some are a complete waste of time but most share a common feature: people developing those strategies do their homework in term of analy… […]

  2. Gregor Samsa says:

    Interesting idea. I am in the risk management area and we still use the definition by Knight, that risk is the deviation of the expected value.

    How would you statistically define risk in a trading strategy?(in general)

    • The R Trader says:

      Your definition of risk is fine but in practice it depends on your ojective function. In the HF world with very low drawdown tolerance I often use the average drawdown rather the max drawdown because a single point doesn’t mean very much escpecially if the recovery period is very short. I also like metrics like semi-variance as it’s a more sensible definition of real risk in my opinion.

  3. Tu Doan says:

    I want to know how to code R for this model, It is great idea

    • The R Trader says:

      This is not a model, this is just common sense approach to trading. There is no coding trick involved in it and it can be replicated with a basic piece of code. Have a look at DataCamp for introductory R course.



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