One of the easiest things to overlook when you are developing a trading strategy is the various costs associated with implementing that strategy. These costs can include everything from individual transaction commissions to less noted costs like data feeds, software, and hardware.
You should be very careful about making sure that all of these potential costs are worked into any strategy that you are seriously considering trading. Attention to detail can be the difference between a profitable trader and a losing trader. If you want to put together a profitable quantitative strategy, you have to know all of the costs that are associated with that strategy.
CXO Advisory Group has been working their way through releasing excerpts from chapters of their upcoming books. In a recent post, they covered some of the dangers of what they call “implementation frictions.”
The post starts with an excerpt that details some possible costs that could impact a strategy:
Investment frictions (costs) include such burdens as broker transaction fee, bid-ask spread, impact of trading (for large trades), borrowing cost for shorting, cost of leverage, costs of data, software and hardware for research, fund loads, cost of advisory services and cost of an investment manager.
Then, they continue with some closer looks at different implementation frictions. The first one is something that we have all dealt with as individual traders:
Transaction fees are generally higher percentage-wise for small trades than large trades, and therefore for investors with small accounts than those with large accounts. Sophisticated traders may be able to suppress frictions via broker arrangements and order placement algorithms.
The article also points out that these costs have been greatly reduced in recent years:
Both transaction fees and bid-ask spreads were generally much higher in past decades than now due to regulatory changes (ending of fixed commissions and decimalization) and technological advances (lower cost of execution and lower barrier to entry for discount brokers). This variation is problematic for long backtests.
Of course, the types of trades your strategy uses can be a factor as well:
Frictions are generally higher percentage-wise for option trades than equity trades of similar sizes. Frictions for futures trades are comparatively low.
The bottom line is that there are loads of costs, and analyzing them can get pretty difficult:
Realistic modeling of frictions is often very difficult, especially for samples spanning long time periods. Many researchers set a goal of analyzing gross risk premiums or anomalies and therefore ignore frictions in measuring returns and alphas (returns adjusted for widely accepted risk factors).
However, research findings based on net results may differ substantially from those based on gross results, to the extent of rendering realistic implementations unprofitable.
Green says
Sorry pal but we miss Shaun’s articles so much…