‘It's like being in a candy store’

Focusing on over 150 equity factors, Blitz uncovers steady alphas post-beta adjustment and reaffirms seasonal and momentum effects. These insights into factor behavior aid the development of more robust factor-based investment strategies. We sat down with him recently to discuss the paper: ‘The cross-section of factor returns’.

“For many years, as researchers we’ve been using the Fama-French factors which they provide on their website with a long history. These factors have been examined to death basically. They include the size factor, the value factor, the momentum factor and a couple more they added recently. There's so much research done on them that by now we understand these factors very well.”

“But in the meantime, the academic literature has suggested all kinds of factors. Now there's a whole zoo with hundreds of proposed factors. Several academics have made these factors publicly available. Instead of the classic three, four, or five factors, we now have libraries containing over 150 factors. It's like being in a candy store. For years you could only choose between five flavors, while now there are suddenly over 150 with so much variety in taste.”

“So, there’s a natural curiosity on our end. Are they really 150 different factors? Turns out, not really. For instance, there are like 20 value factors. It’s just value in different shapes and forms. At the end of the day, there’s probably one overarching value effect behind this.”

“Also, some factors in these libraries don’t seem to belong there. The number one condition for a factor is that it gives you a better return in the long run. Some don’t meet that basic condition. Other factors appear to be misclassified, so there’s also still a lot of room for improvement there.”

“Now, if you have 150 factors, you can also look at things like which factors have high alphas, which ones have low alphas, is there anything they have in common? Which periods do they deliver? With five factors, you can have the problem that in some periods they're just very weak. But if you have 150, surely there should be some which do well in every conceivable environment.”

“Having 150 factors opens up a whole new dimension. It's at a higher abstraction level. If you just look at individual factors, there's already some thinking behind it. But when you build strategies on top of factors, you’re operating at another layer.”

“Also, the classic Fama-French data library that has been used for decades might be becoming a bit obsolete or even mature. It's still a benchmark, but there's so much more at our disposal now. Sticking with just this handful of factors is way too restrictive.”

“In my research, the abstract and conclusion contain one observation that’s led to a lot of questions. Most of these factors have an implicit low risk tilt, which also explains why they tend to do better in bear markets. If you look at the raw factor returns, it seems like they get most of their returns in bear markets, which is only 20% of the time. The rest of the time, their return is much weaker.”

“This is quite shocking. So, does this mean that you should only do factor investing if you think we're heading towards a bear market? Usually, investors have a bullish outlook. If they're really pessimistic, they wouldn't be investing in equities in the first place. The findings are strong and statistically solid, but we have to be careful with the implications. We’re not saying here that you should only invest in factors if you expect a bear market.”