During a conversation I had with expert Bill Dreiss, we discussed many topics, all with great value. One part in particular I found really interesting, and which I would like to share with you, was when I asked Bill how he defines risk. If you would like to l know what Bill said then read on! If you would like to listen to the full conversation just go click here.

How Bill Dreiss Defines Risk

Niels:  Let’s shift gears to a very important topic namely risk management. How do you define risk? I know that you are not a big fan of standard deviation, so how do you define risk in your own methodology? 

Bill:  I think, again, from my philosophical position I’m very suspicious of any kind of… things like VAR or any kind of standard risk metrics. I just think that risk is out there and your worst drawdowns always in the future.  

I find if you look back over time, you find again some pretty standard risk profiles. You look at anybody who has been around for say 20 years or so, or maybe even 10, almost every one of those managers has had a major drawdown, which I would characterize as 50% or more, and usually just one, right?  

"If you believe that your guy’s going to stay the course, then you should probably stay the course too. "

The people who’ve survived in this business are people who have been able to weather drawdowns without losing their nerve. That’s exactly it. So that’s where the risk comes in. The risk comes in - is your manager going to lose it? That’s where the ultimate risk is. If you believe that your guy’s going to stay the course, then you should probably stay the course too. 

Neils: I’m going to quote someone that you may be familiar with and who wrote a few years ago. It’s David Druz, and he wrote a few years ago that, “here’s an amazing thing about robust systems, the more robust a system, the more volatile it tends to be. This is because robust systems are not optimized to particular markets or market conditions. The converse is also true. You can design systems with excellent returns and low volatility on historical testing but which work only for a given period or given market. These systems tend to be curve fit and market fit and not robust.” 

This is completely probably against what most people will feel that robust systems are the ones that are more volatile. I sense from our conversation today that, that is the conclusion that you’ve come to as well. 

Bill:  Yeah, I know David and he’s right, and I think this is a conclusion that most people have been in the business for a while would come to. It’s what I call… This is in contrast to perhaps the dominant quant paradigm which is picking up nickels in front of a steamroller. In other words, you can design systems like Long Term Capital or whatever that can give you smooth returns for a while but what those systems are doing, in my opinion, are what I call warehousing risk.  

So essentially it’s like… And I think it’s the same sort of thing that Dave is talking about, you can design a system that essentially accumulates risk in some sense, and that when it finally breaks out it happens in a big way. This is, of course, what happened in the lead up in the financial crisis. You had all these people developing these securitizations and all these sorts of things and they were just setting the system up for a major break.

In the short term, while that was going on, everybody was happy because everybody was making money and the apparent risk was very low. That’s what I think a trend follower tends to do. I’m essentially taking risk - on an ongoing basis I’m realizing risk. I’m not warehousing it, I’m recognizing it and realizing it and that’s just part of the game. I think any sort of viable, long-term investment has to do that because otherwise it’s a disaster waiting to happen. 

Niels:  Sure, A lot of people say,