Some time ago I posted about the topic of my dissertation. There were some interest in getting access to the report when it was completed, and I’m glad to say that it’s finally done.
So if you want to go straight to the details, here’s the link: The profitability of technical analysis in a high frequency setting and its dependency on volatility.
If on the other hand you don’t feel like reading 11000 words or so, here’s the 30000 ft summary:
There is a strong (significant) positive relationship between returns from these strategies and volatility. If you’re a prop trader you might be doing a face palm right now. But remember that academia have spent at least 30 years or so building a framework around the assumption that markets are efficient. Technical analysis shouldn’t in that context be able to produce any abnormal returns, so we need to play at that field when testing these things. I am however delighted to see an increase in fields like behavioral and emotional finance as that surely is the only decent way forward in distancing academia from its extreme views.
So what does that relationship imply? Well, since volatility is fairly easy to “predict” as it is rather persistent, this also allows us to know when you should deploy or not to deploy technical analysis. That allows us to vastly improve our trading results compared to either always using technical analysis, or worse, purely rely on a long only buy-and-hold based strategy.
To produce these results one minute OHLC bars was used, analyzing the 15 biggest (in terms of trading volume) companies currently part of the S&P 100. If you’re interested in how this was analyzed feel free to contact me. Most of the software was Java based and custom made by me, deployed across a cluster of Linux servers. With over 12 years of data doing daily parameter optimization and back testing, that amounts to about 30 TB of analyzed data.