Firstly, apologies. It’s been a long time since I last posted but it’s been a hectic few months. This summer saw me moving house, changing jobs, writing up my PhD thesis and getting burgled so it’s been an interesting one to say the least.
I can’t deny that getting the thesis submitted was a struggle. Consolidating four years of research and experimentation that spanned a veritable plethora of academic disciplines* into a single book that no one will read was a mind mangling task that required hard-to-muster motivation. Chaos aside, I came across a great number of things that I plan to post about in the coming weeks and that’s exactly the subject of this post.
The next series of posts will concern the use of machine learning methods for stock picking. I know, I know, you don’t have tell me that’s a bad idea! You may have even read one of my previous posts that discouraged exactly this sort of tomfoolery. However, the objective of this work was not to build a Skynet type device that provides magical insight into the equity markets. As you will see, this research uses equity price data to explore the best ways to produce stable predictions in non-stationary time-series using only simple modifications to well-documented machine learning methodologies.
After exploring prediction of non-stationary processes we’ll get a little more application specific and explore the use of these techniques for forecasting price impact of large equity orders using depth-of-book data but more on this when the time comes. For now, thanks for bearing with me and I hope you enjoy what’s to come.
* Artificial intelligence, machine learning, mathematical finance, agent-based modelling and complexity theory were the major players.