Renaissance Technologies: Discovering the Omnitrade

“There’s a point, seven thousand RPM, where everything fades. The machine becomes weightless, just disappears. And all that’s left is a body moving through space and time. Seven thousand RPM.” – Carroll Shelby

 

In the early days of Quantlab, we suspected that there were stock trading signals in option data. Our futures program had waaaay too much slippage in it, and we needed to make a shift into a strategy with far less position concentration if we were ever going to survive. It was 1996 – or maybe it was 1997 – and the biggest challenge we faced in making such a shift was finding clean historical option data. That’s when we met Sandor Strauss, Renaissance Technologies’ first data guru…

My brother recently gave me a copy of Greg Zuckerman’s book, “The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution,” for Christmas. Not being someone who allocates much time to reading hard-bound books, it sat at the corner of my desk, beckoning for the right moment to be cracked open. That moment was manufactured on a recent visit to see my son – and his network of Carroll Shelby hot-rodding descendants – in Palm Desert, California.  I had built plenty of poolside time into the schedule to see if Mr. Zuckerman’s work could have the effect of transporting me back to a chapter when the early Quantlab team was working along a parallel stream of quant development as the Renaissance team, while simultaneously crossing paths with several key characters in (and advisors to) the book, including Jim Simons himself… (More details of this interaction can be found in our Feed post, “Jim Simons, Godfather of the Quants: Hiding in Plain Sight.”)

I also thought the effort would be useful for Alphacution’s work  – not only because the potential for such behind-the-scenes details are so rare – but based on the thesis that deeper historical perspective provides more clues for why things are the way they are today, and where they may be headed.  Mr. Zuckerman’s book did not disappoint; adding significant color to an era that gave birth to some of the most prominent players in trading history, and unleashed forces that make it increasingly difficult for many to stay in the game today…

One of the ways that Alphacution monitors the positioning and pace of these forces is by observing the relative positioning of players on our ecosystem map over time. Renaissance Technologies, LLC (RenTech) is unique for its portfolio scale, in addition to its performance and longevity. Along with firms like Bridgewater Associates and AQR, RenTech is defining the outer boundaries of assets under management (AUM) that can be successfully managed on the basis quantitative methods and workflow automation. In turn, RenTech is among a very short list of players who are re-defining the outer boundaries of the active management zone – where all hedge funds reside – of Alphacution’s asset management ecosystem map.

We teased of this strategy capacity expansion in the recent Feed post, “Puzzle: Two Sigma and the Sons of D. E. Shaw.” The chart below – which presents the average (13F long) stock position value for four of the top quant firms in the world for the overlapping 67-quarter period Q4 2002 to Q2 2019 – is the puzzle that begs the question: Which line goes with which player?

The chart below is the key to that puzzle…

Now, the reason I am assembling all of this setup is to punctuate the unprecedented – and messy – story found in the book: While so many of the headlines have been devoted to those who achieve astronomical risk-adjusted returns by trading fast, Renaissance has achieved those returns – the book mentions a Sharpe Ratio of 6.0 in 2003 (p. 223) – by trading relatively slow.

The effect of this achievement – astonishing for the array of factors that need to be incorporated into the strategy – is to allow for larger average position values (as in the chart above) and to delve deeper into the (equity) liquidity spectrum, as is implied in the chart below…

Together – as in, more positions with large average values (relative to other quant strategies) – these portfolio characteristics drive overall strategy capacity beyond what other quant strategies have been able to do, and all while delivering Sharpe Ratios that are virtually unseen by strategies of similar capacity. The chart below showcases the growth in portfolio value for the long side only of US equity exposures over the full 83-quarter history of Renaissance’s 13F filings; an all-time high of $130.1 billion, as of Q4 2019.

If this weren’t astounding enough – particularly given that so many mature quant strategies are suffering capacity constraints in the current market environment – we should expect further growth in these figures. Why?

It turns out, this strategy has had very little exposure to ETFs, where tons of additional capacity resides. (Just ask Bridgewater.) And, though the ETF component of the portfolio currently remains small by value ($1.2 billion GLMV), it is clear that Renaissance made a shift about 2 years ago to grow their ETF exposures:

As for the definition of omnitrade, I’d like to let you think on that for a bit. The best clue, however, is to understand nested alpha strategy architecture first.

Until next time…

By | 2020-03-04T23:53:26-05:00 March 4th, 2020|Alphacution Feed|

About the Author:

Paul Rowady is the Director of Research for Alphacution Research Conservatory, a research and strategic advisory platform uniquely focused on modeling and benchmarking the impacts of technology on global financial markets and the businesses of trading, asset management and banking. He is a 30-year veteran of the proprietary, quantitative and derivatives trading arenas. Contact: feedback@alphacution.com; Follow: @alphacution.