Trends Watch: March 22, 2018
EisnerAmper’s Trends Watch is a weekly entry to our Alternative Investments Intelligence blog, featuring the views and insights of executives from alternative investment firms. If you’re interested in being featured, please contact Elana Margulies-Snyderman.
This week, Elana talks to Craig Kaplan, CEO, iQTick Advisors.
What is your view of the outlook for big data/artificial intelligence (AI)/machine learning?
Big data is definitely a growing area. Everybody is recognizing the increasing importance of alternative data sets and very large data sets. Big data fits nicely with machine learning and artificial intelligence because with more data, there is more information to train machines to produce useful analyses. Even though the field of artificial intelligence was invented around 1956, computer processors have gotten tremendously faster since then. Moore’s Law, which states that computer processing power doubles approximately every 18 months, has held for much longer than anyone imagined it would. Things that used to take artificial intelligence programs a long time to do, they can now do almost instantaneously. Today’s neural-network machine learning algorithms, for example, are in some ways very similar to those we had in the 1980s. However, thanks to faster computer chips, they are able to produce decisions and analyses fast enough to power self-driving cars and rapid trading in the stock market. The combination of more data to train machines and faster processors to power the machine learning algorithms have lead to a real renaissance in machine learning and artificial intelligence.
It is my understanding that you currently trade on signals created by the collective intelligence of retail investors. What other ways do you see collective intelligence being used in the future?
Currently we poll millions of retail investors over the internet as a way of tapping into their collective intelligence. We ask them for their opinions about stocks and are able to process this information using sophisticated algorithms. The algorithms generate trading signals that have outperformed our benchmarks by a wide margin. For example, in 2017, a benchmark index of many other market-neutral equity strategies returned about 1.73% after fees. In 2017, one of our market-neutral equity strategies, which is powered exclusively by algorithms that tap the collective intelligence of millions of retail investors, returned 17.5% – a 10X better performance. So we know it is possible to harness the collective intelligence of millions of people to solve difficult problems like getting an edge in the stock market. We are now working on even more advanced ways to harness the power of collective intelligence. In the future, instead of having millions of people just participate in a poll, we will ask some of them to engage in more complicated problem solving – like coming up with trading strategies themselves. Tapping the brainpower of thousands of mathematically sophisticated investors, who also have access to datasets, can be extremely powerful. We are excited about the future potential of such collective intelligence systems.
As an emerging manager, what is your assessment of the current investor landscape?
I think it is good. The markets have been good and that is generally good for emerging managers. That said, the big trend that we see is more and more money moving into passive ETFs, smart beta products, and the like. To compete effectively, I think active managers must deliver alpha. They must deliver uncorrelated returns. If active equity managers are going to try to charge 2% and 20% -- or even half that amount – they have to demonstrate that they can deliver returns that investors could not get by just investing in the S&P 500 or some other index with much lower fees. Investors are getting smarter and questioning why they should pay high management fees if the returns are correlated to the market. Why shouldn’t they just invest in a market index with low fees instead? I think this is a fair point and one of the reasons that we focus on producing uncorrelated returns. Manager need to justify their fees by providing not only good performance but also uncorrelated performance. Uncorrelated positive performance can help diversify investor portfolios and improve the risk-adjusted return profile, even after fees. Emerging managers may have a better chance at coming up with new uncorrelated strategies, so I think the outlook is generally positive for them in these market conditions.