Relative Sharpe ratio
I just read Irene Aldridge blog post titled "How Profitable Are High-Frequency Strategies?".
Although no hard facts about the overall profitability of high frequency trading strategies are given, it got me thinking about something else. As Irene does in her blog post, she takes historical data and calculates the Sharpe rato of the absolute optimal, 20/20 hindsight, strategy based on historical data. Now of course, it's not possible to actually have a strategy that is this good, unless you give me a time machine and let me play with that!
But is does however give an upper range value, something you could use to compare against your own models/strategies when doing back testing. The Sharpe ratio it self only gives you a number telling you how well you're doing compared to the underlying benchmark. That is of course great when comparing two or more models against each other. But it does not tell you how much head room you have with regards to improving a specific model.
And this is where a relative Sharpe ratio would enter the picture. Take your models Sharpe rato, and divide that by the absolute maximum Sharpe ratio for the same test periode, and you have a number between zero and one. The closer that value is to the value of one, the better. I guess maybe you could also say that the more stable this value is over time, the better as well. In other words, your model can perform just as good no matter varying market conditions.
BTW Irene is the author of the brilliant book named High-Frequency Trading, a book I plan on writing a review of soon.
Decoding the psychology of trading
The Financial Times recently had an article titled "Decoding the psychology of trading" where they write about a behavioral finance based fund named MarketPsy. When I hear the words behavioral finance I often start thinking of group behavior like that shown in the video below, showing Starlings flying over Rome. We also find similar "dynamics" in groups of fish, and what I particularly associate with this type of behavior with regards to behavioral finance is how one reaction or incident in one part of the group quickly spreads across the group as a whole.
MarketPsy is using some sort of linguistics analysis on huge amonts of text data (from whatever sources they can get their hands on, I would presume), and then based on that analysis try to conclude the current (and hopefully future) investor positive/negative mood. I have on my TODO list to try something similar, using the CRM114 spam filter application as the engine needed for classifying incoming text.
I do sense however that I am a bit skeptical with regards to this approach. My reasoning is as follows:
- If you're sampling "the complete picture", ie all available sources out there, wouldn't the "mood inertia" be too big to change quickly enough?
- If you instead are sampling just a selected few sources (selected on whatever flawed reasoning used), wouldn't that be the same as doing fundamental analysis without access to the complete picture?
Would it not be better to try and do behavioral finance analysis on the actual complete picture, in other words, raw market data? For point 1, I guess maybe it's worth something as a "mother-in-law" factor as Barton Biggs refers to contrarian indicators in the book Hedgehogging, for all I know. For point 2, too risky if you'd ask me.
Well, that's just my 5 cents. Whatever the conclusion, if one exists, it's interesting stuff non the less.