The Quant Frenzy: A Crude Forecast

What motivated this post?

Recently, I finished reading Gregory Zuckerman’s brilliantly researched book on the pioneering ultra-secretive quantitative investment firm Renaissance Technologies and its founder and mathematics prodigy, Jim Simons. It is an exhaustive and impressive account of a newer, data-driven, non-conservative approach to trading financial instruments. RenTec’s flagship hedge fund, Medallion, has averaged a mind-boggling 71.8% annual return, before fees, from 1994 to 2014. Medallion is considered a glaring example of a pioneering experiment that trounced skeptics and traditional investors who were smug about their intuitive abilities as traders and scoffed at the idea of letting computer programs decide the fate of the markets. Jim Simons has a net worth of 24+ billion USD today (Yes, you read that right).

Why Quant?

Jim pioneered a different way to think about the market. He shifted the focus from annual reports, economic indicators, and interviews with business employees – the whole “Get to know your company” shebang, an approach enthused by legendary “fundamental” investors like George Soros and Peter Lynch – to identifying statistically relevant patterns by diving into piles of data. He professed to treat the market as a rigid mathematical model with functions that affect variables in a complex manner. The essence of this approach is succintly captured by the following quote by Robert Mercer, a senior employee at RenTec, when he was asked by potential investors how the firm made money in 2007:

It may be noted that Chrysler was bought by a German car maker in 1998.

Leaving RenTec and its quirky activities, it’s obvious why quant is the cool kid on the block. Economic systems are driven by humans and humans are driven by deep-seated psychological beliefs. Our financial market is nothing but a cauldron of billions of belief systems colliding and coalescing.

Markets run after profit. No matter, the cost. This is the optimal way to survive in any agent-driven system. One have to be abreast with the latest technologies lest they get obliterated by their competitors. Economists have a fancy term for this – “creative destruction”.

What is happening?

Just as a fun exercise, I used to ask myself who or what would exercise power and authority in say, the 2050s? Arguably, with much limited understanding and knowledge back then, I came to the firm conclusion: “Data will be king”.

Fast-forward 2022 and I’m glad my guess is turning out to be correct. Data analytics is the basis of the future we’re building for ourselves and our progeny. Every action, every indirect decision will be captured, cleaned, processed, and analyzed (1). Corporations and firms will become hostile to each other in order to govern as much data as they can. Individual privacy will be stripped / reduced / outlawed where companies will compete and ponder to capture data in more innovative ways. It’s obvious we are already underway to such a future. The continuing frantic march of companies will continue until analytics reach their economic saturation that is, the point when everybody is access to information doesn’t make it unfair to other players (2).

Data analytics will move beyond just personalised marketing or petty targeted advertisements or customised healthcare or even newsfeed. Data will make people rich who will enjoy greater freedom to profess and/or impose their ideological and political beliefs on others. An apt example is Robert Mercer, who we have met before, a PhD in Computer Science, who helped bankrolled right Republican candidate Donald Trump’s campaign for the 2016 US Presidential elections. Imagine the political influence and power in the hands of an academic by training.

The recent Pegasus spyware uproare and the Cambridge Analytica fiasco which involved a data analysis firm infringing privacy rights by gathering personal information about Facebook users comes to memory when we think about how brutal and ruthless the system we’re a part of can degrade into.

What do people want?

Let me step back from the chaos and ask a simple question: What do people really want?

People want the ability to reliably and consistently forecast the future. This is the primary driving force for all “innovation” and schemes we encounter in any economic system, especially our financial markets.

Our economy grows when people are more productive. Economics, as I have mentioned before, is trying to figure out optimally how to dole out resources to individuals. More productive people produce more goods/services which can be, therefore, be allocated to others. The humble demand and supply principle. Platinum is expensive because there’s less of it. Cost is a human construct to decide who gets what and the token of transaction is, obviously, money – a convenient idea. Imagine if we discover a new oil field in India, that would quickly drive oil prices down because now we have more of it. People don’t (or more accurately, shouldn’t) seek money but the resources they make available.

Let’s run a fun thought experiment: Imagine in the (near?) future, a company started drilling diamond asteroids and importing them to Earth. Diamond prices will plunge. It would become a commodity rather than a scarcity. People will stop considered diamonds to be “valuable”. This is straightforward. But the interesting thing is people will find something else (preferably more scarce) to be considered valuable. The resource haven for ideal humans will be when everyone has enough of everything. But would that be the end? I say no.

What will people eventually want?

Even if we manage to get all the resources we ever need, we would need labour, assistance, and company. The actual holy grail of humanity is not to be most productive but to engineer entities that can. Truly Artifical Intelligence, a self-sustaining artificial system having cognitive abilities approaching ours is the ultimate goal of humanity. The Information Revolution laid the foundation of a society where repetitive work is delegated to computer programs. Although Simons’ money-making program, the Google assistant speech-recognition program, AlphaGo, and a thousand others are impressive in their ingenuity, none comes close to what human will eventually seek.

What is the future?

We started out talking about the so-called quant revolution and ended up touching policy-making, economy, and a bit of philosophy. But what is the future of quant firms like RenTec?

I consider quants to be a “second-order value generation scheme”. I call it second-order because they depend on the existence of other economies for their relevance. RenTec was a pioneer and like all pioneers enjoyed the first mover advantage while risking their position. At the end, the people at Medallion were confident in their models and that showed. RenTec trounced all their competitors who were still reliant on “fundamental” methods of investing. They were sharks in a tank full of guppies. That’s my justification of Medallion’s mind-numbing performance. But it’s obvious, such returns won’t be consistent as competitors up their game and shift to data-driven trading practices. The system will eventually reach equilibrium when everyone would start using quantiative approach. Eventually, someone else would have a insight missed by their contemporaries leading to another “revolution”.

This is the pattern of humanity’s progression or more accurately, economic progression. This progression would continue in kinks, approaching autonomous systems until we achieve singularity.

On May 6, 2010, the DJIA plummeted one thousand points known as a “flash crash” and consequently recovered. The reason was singled out to be a bunch of computer-programmed trading firms. Computer programs are simply rigid instructions that have no stake whatsoever to the impact they have in the real world. So, as firms shift their focused to data-driven approaches, hiring computer scientists and mathematicians, and firing “fundamental” traders, I leave you to ponder this: How safe and rational is it to delegate all of our financial systems to number-crunching programs?

Footnotes

(1): Whether that’s a good thing or a bad thing is a different and moralistic and one that is beyond the scope of this article.

(2): “Equal access to data” sounds too utopic and unrealistic to me. Hence, this middleground.