I recently had a pretty visceral encounter with the forces of friction. No, I didn’t fall off my bike – I’m talking about the friction inherent in trading activities. I’ve mentioned Andrew Lo‘s market-neutral long-short algorithm before and it sees service as my blogging muse once again. I’ve modified his original algorithm such that it behaves reasonably well though, as he observes, it’s a strategy in long term decline. My recollection was that one might expect 15-20% from an unlevered deployment of the strategy.
Recently, I went to play with it and to my shock and horror it had become a wretched loser. In fact, an incredible loser. What had happened? I looked throughout my code and couldn’t find changes; this was corroborated by my CVS repository – no changes had been made to the strategy in a long while. Any coder is familiar with the natural entropy of software systems known pejoratively as “code rot” but this seemed an especially extreme case.
To make a long story short(er), I eventually realized that the code hadn’t changed and the data hadn’t changed, but that my configuration defaults had changed – specifically those governing trading commissions and fees for equities. We’d recently done a client project in which the client had a pretty inefficient/expensive trading platform that made up for its cost by virtue of its facility for conveniently handling large numbers of managed accounts. In any case, in developing that client’s trading strategy I had set costs for trading equities out to a penny a share a trade to ensure that we had a system robust enough to withstand the costs inherent in their trading platform.
The difference this made on Dr. Lo’s strategy is pretty remarkable and so I’ll share it with you.
Without friction, my modified version of Dr Lo’s strategy yielded the following (back-tested) results since last May 1st:
Now, it’s a bumpy ride, no doubt, but a potentially rewarding one for those who can handle the volatility. With returns like that, one could conceivably seek to dampen the vol and then lever the strategy. Of course, this is assuming that one can actually execute the strategy profitably.
Assuming that every trade costs $.01 / share totally reverses the results of the strategy:
That looks pretty bad, right? This is what I returned to upon revisiting this strategy. In my effort to determine what had happened, I inverted the strategy to see how it’s inverse behaved. Corroborating observations I’ve made here previously, this didn’t help and so we had the degenerate case where a strategy and its inverse were both terrible losers. It’s a lot less bizarre than intuition might suggest!
Assuming somewhat more realistic friction for a sophisticated hedge fund, we get results more in line with what my recollection had suggested. It’s very possible for a fund to bear costs of somewhat less than 1/10 of a penny per share traded, but in keeping with a conservative approach, I illustrate the returns with costs pegged at 1/8 of a penny per share traded:
It’s a pretty remarkable difference and it illustrates the knife’s edge upon which one must dance to profitably execute an entire class of potentially fruitful quantitative strategies!