Listen to the alt data album

It has been years since I picked up a CD from my shelf, let alone played one. It seems so much more convenient to use iTunes or Spotify. Any track is a few clicks away, playing from their vast archives. Despite that, I somehow miss the whole ritual involved with CDs. Flicking through CDs in the record store for hours, browsing through the cover notes, hearing the subtle click when closing the case, watching the CD spin endlessly through the glass etc.


I also tended to listen to an album all the way through. After all, it’s a hassle to continually change a CD. That of course is the whole point of an album, to hear it in it’s entirety. It’s designed to take the listener through a specific journey over an hour. Singles have always been popular of course, but you miss out on this “journey”, you just see a snapshot of what the musician intended. With iTunes or Spotify, it becomes far easier to mix and match between different artists, genres, albums etc. in the same playlist, which you could argue exposes you to many more types of music, than if you purely listened to albums alone.


In a sense, we can make the same analogy when it comes to the way we seek to understand financial markets. The key is to look at data, and increasingly alt data. It can be tempting to take the “playlist” approach, seeking to take small nuggets from many datasets, without delving into each in a huge amount of detail. If we are looking at a vast array of assets, we probably don’t have sufficient time to investigate every dataset in depth. If we’re doing systematic trading, the goal is often to try and leverage our number crunching ability, and this involves trading a large portfolio to help diversify risk. The datasets we look at need to cover all the many assets we are trading.


The alternative is to take the “album” approach. Here we’ll look at a smaller number of assets (and hence likely a smaller number of datasets). This is usually the approach of a discretionary investor. The final portfolio is likely to be a lot smaller in terms of the number of names that a systematic trader might hold. The flipside is that each asset, we’d go into a lot more detail. Let’s say a discretionary investor has positions in several large cap equity names. If we have a large amount of risk in each asset, it is likely that we would want to understand as much as possible on each. Hence, rather than going for breadth, we go for depth when it comes to datasets and also analysis. It doesn’t really matter if a dataset is very specific to only one company, if we’re holding that stock. 


Whether you use the “playlist” or “album” approach to alternative data, is a function of how you trade. Just make sure you listen to the music of alternative data, rather than the silence!