One of the hottest subsets of the fintech landscape these days, and for good reason, is alternative data providers. Quantitative investment funds, which continue to outperform their discretionary peers, are in an all-out frenzy to feed their algorithms with as much valuable data as possible to find market signals. According to research from the Tabb Group, the alternative data market could be worth around $400 billion by 2020.
These new types of data are changing the investment playing field, and coming to market fast and furious. A data set that might be innovative and nice to have this month, may be a definitely must have by next month.
Quantitative investors have become quite adept at using both structured and unstructured data sets in their models to give them an edge, but a much larger chunk of investors, those from the discretionary buy side, are just starting to catch on. It’s fair to say that discretionary managers are a bit behind, but they have woken up and are now scrambling to understand what’s taking place and how they must change in relation to it. Many will not survive the shift. Others will take advantage and be better off for it.
The move to passive investing continues, although all investing is technically active. In any case, we continue to see massive flows of capital out of active long only mutual fund and long/short hedge fund strategies and into these passive funds where alternative data is being used. Investors are now aware of the asymmetric risk they were taking on with active discretionary mutual funds, hedge funds and registered investment advisors. Most portfolio managers were simply playing with beta, whether it be through momentum, mean reversion, value, growth or sector-specific strategies. Managers were leveraging these forms of beta far more often than they were actually generating alpha.
There’s simply more efficacy to what systematic managers are doing than the vast majority of the discretionary trading world, and they’ve (mostly) put up the numbers to prove it. And not just in regards to returns, these groups are producing real alpha. Their strategies are meticulously backtested in and out of sample before going live, and are scaled up over time. And while some systematic funds don’t perform well, they also don’t have the massive blow ups that are regularly seen on the discretionary side.
However, as systems improve, there is far less alpha in the market to capture. Relative value and statistical arbitrage strategies are about capturing asset mispricings associated with the irrational behavioral aspects of fear and greed. But as a data set becomes popularized, it moves from alpha to beta over time. It may take decades for this transition, but the timeline seems to be quickening. There will always be alpha available to be arbitraged, which is associated with the irrational behavior of humans in markets, but most alpha generated by systematic traders is associated with an informational advantage.
About five years ago, many of the classic stat-arb strategies stopped working due to an influx of competitors. There simply wasn’t enough alpha to go around. This caused the smartest firms to search for new data sets with predictive power or reflexivity. Fast-forward a few years and an all-out arms race is now underway for the latest and greatest in big data, and that doesn’t appear to be slowing down anytime soon, as a plethora of new entrants come to market. Even the biggest investment banks are getting in on the game, holding alternative data events to connect their clients with data vendors, while keeping their own quants on top of the changes as well.
As this absolutely massive transformation taking place within the discretionary institutional management industry continues to play out, alternative data companies will be some of the biggest winners in fintech.