Hack the market

He didn’t look like much, but old Rocky Marciano put his back into every awkward punch he threw. More remarkably, he remains the only heavyweight champion to have ever been smart enough to get out of the game on top. He was that rarest of characters – a winner who knew when to engage and when to step away.

I’ve written a good deal about losers and ideas that might not yield the results one’s looking (hoping) for, but I haven’t written too much about life’s winners. This, of course, is absolutely par for the course amongst traders. People aren’t in the habit of giving away trade secrets, leaving sums of money on the sidewalk or revealing their trading strategies. When they do (or claim to) is likely a good time to keep an especially watchful eye on your possessions…

Thus, while I’m certainly not going to reveal any trade secrets or the internals of profitable trading strategies, it makes sense to address the issue of how to know when you’ve got something that’s good as opposed to just lucky or excessively curve-fitted.

We’ve recently been fortunate to partner with an experienced portfolio manager. Like all of these kinds of organizations, they keep their cards close to their chest, so I won’t reveal who they are just yet, but at some point they might be open to exposing their part in the relationship. In any case, they came to us about six months ago with an US equity sector rotation model based on a sweet kernel of an idea for creatively ranking sectors and the names within it. The essential characteristic of the strategy that made it a winner is simple. What it does really well is one of the hardest things in trading: figuring out when to let a trade go. This portfolio-management trend-following strategy has the lucrative ability to let its winners run while dropping its losers.

How can we characterize that a bit less loosely? Consider the below sets of distributions. The top one captures the overall strategy returns for 300 permutations of the strategy while the bottom one captures the returns on individual trades across all 300 instances of the strategy.

The top distribution looks pretty normal, albeit shifted rather smartly rightward of a random strategy (which should be centered somewhere around $0). The more interesting distribution, though, is that at the trade level where we can see that the strategy is letting its winners run while sharply curtailing the losers. It’s worth noting that the strategy doesn’t use stops or targets. If it had stops, say set at -20%, then we would inevitably see a clustering around that value. Instead, we see a natural sloping – but short – tail extending off to the left while on the right, we appreciate a felicitous long tail of profits.

The solidity of the strategy is underscored by the excellent performance it exhibits across all permutations, that is, it is not excessively sensitive to how it’s configured. Clearly, this is not a case where a data-mining exercise has yielded a normal distribution around $0 and we’ve slyly picked a convenient outlier. Although I don’t illustrate it here, we’ve also seen that the strategy behaves well across both different time periods and across different markets.

In its original format, the strategy was a brute which exhibited tremendous historical profitability but was perturbed by gut-wrenching volatility which left it with a sharpe in the .5-.8 range in spite of returns far above the broad market. Not bad, but not for the faint of heart and certainly not applicable in most institutional contexts.

The distributions above are from a more refined model which does away with the notion of sectors, replacing them with categories of global ETFs. The average sharpe amongst this family of strategies is above 1 and the average trade yields a profit of over 2.5% across many time frames and sets of markets. Our continued development on the model has shown it amenable to treatment with some form of 130/30, 120/20, &tc. formulation.

The keys elements which identify this strategy as a winner and not just a pretender are:

• it’s not excessively sensitive to particular parameters
• even the worst permutations of the strategy are winners (they beat relevant benchmarks by volatility-adjusted measures)
• it’s successful across a variety of time periods
• it’s successful for long time periods
• it’s successful across a wide set of markets

In a later post I hope to discuss the detailed process we went through to develop incremental improvements across iterations of the model (and possibly some of the marketing materials we’ve developed for the product), but it’s important to note that data-mining was no part of its development. Instead, we used the analytical and simulation capabilities of our platform to quantitatively identify the underlying nature of the strategy through each iteration. Between iterations, we used old fashioned creativity and – yes – (educated) guesswork to determine further enhancements and refinements we’d try.

Winners come in all different shapes and sizes, but they share important and identifiable core characteristics; I hope this post provides some insights into how you can quantitatively identify those characteristics in your own strategy development efforts.