Five Ways to Create Better Risk-Adjusted Returns

29Apr09

Five random strategy-development thoughts swimming around in me noggin’ re: creating better risk-adjusted (i.e. smoother) returns…

Method #1: Trade Low/Negatively-Correlated Assets

This is the obvious solution that even Markowitz would approve of, but also the one that I use the least.

I only trade equity-related assets (vs bonds, commodities, etc.), not because I want to, but because I’ve found them to be the most predictive.

This is probably MarketSci’s single biggest opportunity to better itself. Even though one of my strategies might be trading say a gold stock sector fund and a small-cap stock fund at the same time, as we all know, in a crises, all correlation goes to 1.

Method #2: Trade Confidence-based Rather than Transactional Strategies

This is a very important concept that we’ve covered previously, Condor followed up on recently, and I employ in my own proprietary strategies YK and Scotty.

I’ll plagiarize Condor’s explanation of why a confidence-based strategy (or what they call a “polynary strategy”) leads to smoother returns:

The advantage of the polynary approach should be clear. It permits the strategy to be more precise. I have a habit of describing financial products as a means of expressing financial propositions; let’s regard the output of a trading strategy as the proposition to be expressed. A binary strategy that merely produces buy and sell signals is not very expressive at all: it voices full confidence or complete doubt about the asset every time it speaks. That’s like going out on a series of dates and, each morning, looking into your partner’s eyes with either abject hatred or utter rapture. Life admits of more subtlety. And if a given strategy really does track some worthwhile edge, chances are that that edge will be better expressed in degrees.

Method #3: Trade in Multiple Timeframes

This is something we’ve talked about on the blog, and that I employ in the free State of the Market report and my own proprietary strategies.

The market rarely tells the same story when viewed through the short, intermediate, and long-term lens. We might for instance be a couple of days into a short-term move up in an intermediate-term overbought market at the tail end of a long-term downtrend – three very different reads.

By combining those three views, we create a more holistic signal that smooths returns by not taking such sharply contrasting views day-to-day (Condor’s binary “abject hatred/utter rapture” scenario).

Method #4: Trade Multiple Conceptually-Different Strategies

All four of our proprietary strategies, despite sharing some common roots and trading similar assets, are focused on very different timeframes and very different trading advantages. Combining them, even in a brain dead even-split as we’ve done below, has produced spectacular risk-adjusted returns.

On any given day these strategies may be expressing a similar opinion on the market (meaning risk on any given day might be high), but viewed a bit further out such as monthly, the dissimilarities between the strategies creates the smooth ride.

Note: this graph is updated monthly on our Strategies page. Go there to learn more about how this graph is calculated and our independently-audited real-time returns.

Method #5: Duplicate Signal on Similar Data Sources and Indicators

This was actually the topic that got me started thinking about this post originally. I’m approaching my self-imposed word limit, and want to be able to treat the topic with the time it deserves, so I’ll save this one for a follow up post.

As always, more to follow.

[Edit: click for a summary of all posts in this series on better risk-adjusted returns]

Happy Trading,
ms

 

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