Technical analysis and its dependency on volatility, the report
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.
Excessive Optimism and Analyst Recommendations
This blog post taps into some of the theories outlined by behavioral finance. Specifically I’m going to look at research done on what is called overconfidence.
Overconfidence among investors can manifest itself in many ways. One of these forms is excessive optimism (or pessimism) with regards to beliefs in future outcome. Carleton, Chen, and Steiner (1998) and Jegadeesh, and Kim (2003) study the value of analyst recommendations. There are two aspects worth considering specifically in this context. The first is the quality of the recommendations and the second is the market reaction to them. The value of an analyst report can be defined as its impact on the market, thus the quality of the report and market reaction is not mutually exclusive but rather tightly linked. This poses some potential issues as there might be a feedback loop at work here, where analysts with a broader audience could potentially have a bigger impact than more unknown analysts with less exposure. However, given that the topic in question is an anomaly of the efficient market hypothesis, this potential feedback loop is of interest. In an efficient market, any new information in the form of a buy/sell recommendation must only have an immediate impact without any positive serial correlation in future abnormal returns if it represents new information. I will begin by analyzing a number of aspects related to analyst recommendations and proceed to cover market impact.
The profitability of technical analysis in a high frequency setting
UPDATE: The report has been completed, available here.
One busy month behind me, and another one up next. Currently doing exam revisions and had some rather time consuming coursework so far, which has sadly prohibited me from doing anything here on this blog. So, to make up for a month of no blog activity I'll set aside some time doing it now.
This blog post is going to be about my upcoming dissertation. I'm writing about the profitability of technical analysis in a high frequency setting. I'm also going to be looking at any possible link between this and volatility. And, I'm very much interested in any feedback, tips, information, or what ever you think you might want to contribute. Any specific papers you think I should read? Other sources of interesting information? Please post feedback as a comment here, or contact me directly.
I'm copying and pasting in some of what I've written to introduce my dissertation project. A PDF is available here with the complete content, also containing the bibliography.