Charles Ellis, a trader and quantitative strategist at Mediolanum Asset Management, explains how data can be used to help generate alpha signals.
The first thing that Ellis points out is how trading firms can most effectively use data is dependent on their investment process and the type of research questions they are trying to use the data to answer.
For starters, he says, firms need to consider what investment time frame they are working towards.
“Then you have to ask which of these time frames can we add the most value to? What data do we have access to? And then it goes into what sort of questions can we answer using this data over these time frames?” comments Ellis.
The obvious question to ask, he points out, is whether a particular asset or security is going up or down. However, Ellis says that although such a question might be very useful for generating short-term trading signals, it is less effective when firms are trying to construct a broader “investment mosaic”.
When taking a less short-term approach, “it becomes more, can we use AI, for example, to try and pinpoint what part of the investment cycle we’re in? Or what part of the credit cycle are we in? And it just becomes an added part of the input into the final investment decision as opposed to being one signal that is going to drive immediate action”, according to Ellis.
He adds: “So I think you have to really look at what you’re good at, look at what your investment process is, and really tie your signal back to that. When you do that – I won’t say it’s easy – but you get a much clearer picture of where you are, where you need to go and how you need to get there.”
Elsewhere in the interview Ellis discusses the importance of data quality and provenance and how better data analytics can help not just asset managers, but also the end investors.
The full interview can be accessed here: