In a widely circulated investor letter which I published under the title "Turmoil in Quant Land," Neal Berger examined why some of the more 'pedestrian' quant strategies that worked beautifully for so many years have stopped working.
Maybe new technology is the reason why trades crest and fall: Whenever a new technology has come in, that has made the old trade obsolete. This has happened to trend following, statistical arbitrage, high frequency trading, and already starts happening in some big data strategies.
What was cutting edge or bleeding edge even two years ago such as utilizing satellite imagery to count the number of shopping carts at every single Walmart in the United States and comparing that to the day before and the day before, or, to get housing data out of China by measuring the slope of the shadow from satellites to determine how fast buildings are going up - believe it or not, all of that has already become pedestrian, says Berger. Big data strategies may work best when the investment manager uses data that cannot be purchased or that is not publicly available and rather creates proprietary data sets to try to find an edge in the market.
It checks all the boxes!
Every strategy that has positive expectancy has a certain half-life, unless it's truly a structural inefficiency where the edge providers dwarf all of the hedge fund money that's available to capitalize by a substantial amount.
The problem from an allocator's perspective is separating the wheat from the chaff. If you want to raise money for a fund today, the quickest way to do it is to put a marketing presentation together that references big data, machine learning, artificial intelligence and cryptocurrencies and all of sudden you've got a fund that people are interested in. It checks all the boxes!
But, what are some of the strategies that continue to work, are still robust, and in certain cases cannot be arbitraged away by the move toward AI and robots trading against robots? Berger thinks that in a world where experiments with AI bots playing games like AlphaGo, Chess or Dota 2 leave humans with not an iota of chance and believing that this isn't being applied to the markets is naïve: "You're competing against a bot that has simulated 100,000 years of equivalent human experience, I would say it's naïve to assume that you can beat that bot as a discretionary trader. That's not a business anymore in my opinion." (page 27, 29-30).
The Opalesque 2017 INVESTOR Roundtable, sponsored by Cohen & Company, took place in New York with:
Christopher Ferraro, CIO, Galaxy Investment Partners
Kieran Cavanna, Founder and CIO, Old Farm Partners
Neal Berger, Founder and CIO, Eagle's View Capital Management
Gaurav Chakravorty, CIO, qplum
Neil Datta,Director, Risk Management, Optima Fund Management
Corey McLaughlin, Co-President, Investment Industry Services, Cohen & Company
The group also discussed:
How did Gaurav Chakravorty and his team turn $10,000 to over seven hundred million in profits in just three years? What is he working on now? (page 4, 8-9, 27-29)
Who is the "sucker at the poker table" in this new investment world order? (page 9, 30, 31) Why investors should watch leverage and the money flows (page 13)
Constrained by Mandate: The downsides of handcuffing a hedge fund manager (page 10)
Which are some of the non-trivial structural and technical problems investors and managers are facing to get involved in trading crypto currencies? Will returns in cryptocurrencies get eroded as well over time? (page 12)
Which types of different strategies can cryptocurrency hedge funds actually execute? (page 16-18). ICOs: a remarkable resemblance to the IPO market pre-dotcom bubble (page 19-22). Risks and governance in ICOs (page 22)
Co-investment: Possibly the most exciting area in the hedge funds of funds space (page 5, 13, 15)
What edge or what inefficiency is being created because of the move towards passive indexation? (page 14 -16)
How to profit from the fact that hedge funds' core holdings almost always outperformed the track of the actual fund (page 25)
Learning the rules from data: Where will data science and artificial intelligence be in five years? (page 27)
AI's seminal turning point: AlphaGo Zero's 100:0 victory shows that "after thousands of years improving our tactics, computers tell us that humans are completely wrong: Not a single human has touched the edge of the truth of Go." (page 28)
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