The Secret World of Jim Simons by Hal Lux
https://faculty.fuqua.duke.edu/~charvey/Teaching/BA453_2005/II_On_Jim_.pdf
Like all quantitative money managers, Renaissance aims to find small market anomalies and
inefficiencies that can support profitable trading on billions of dollars of capital. Though all quant
shops are alike in their dedication to models — Let the best algorithm win! — Renaissance’s
approach differs from the "convergence trading" popularized by John Meriwether’s Long-Term
Capital Management and similar arbitrage shops. Convergence traders price financial
instruments based on complex mathematical models, find two different instruments that are
cheap and expensive on a relative basis and then buy one and sell the other, betting that the
prices will, at some point, have to return to their proper level. The Renaissance approach requires
that trades pay off in a limited, specified time frame. And Renaissance traders never override the
models.
Guided by these models, Medallion’s 20 traders conduct rapid-fire buying and selling of a
multitude of U.S. and overseas futures contracts, including all major physical commodities,
financial instruments and important currencies, in addition to trading equities and mortgage
derivatives. This year Medallion made a killing in the volatile oil futures market.
"The things we are doing will not go away. We may have bad years, we may have a
terrible year sometimes. But the principles we’ve discovered are valid."
In recent
years Simons seems to be especially keen on stockpiling computational linguists who have
worked on building computers that can recognize speech. He has hired away a good part of the
speech recognition group from IBM Corp.
Why computational linguists? "Investing and speech recognition are very similar," says one
Renaissance researcher. "In both, you’re trying to guess the next thing that happens."
In his rare discussions of trading, the Renaissance president emphasizes that trading
opportunities are by their nature small and fleeting. "Efficient market theory is correct in that there
are no gross inefficiencies," Simons told the Greenwich Roundtable last year. "But we look at
anomalies that may be small in size and brief in time. We make our forecast. Then, shortly
thereafter, we reevaluate the situation and revise our forecast and our portfolio. We do this all day
long. We’re always in and out and out and in. So we’re dependent on activity to make money."
Renaissance essentially attempts to predict the future movement of financial instruments, within a
specific time frame, using statistical models. The firm searches for something that might be
producing anomalies in price movements that can be exploited. At Renaissance they’re called
"signals." The firm builds trading models that fit the data.
When the trading starts, the models run the show. Renaissance has 20 traders who execute at
the lowest cost and without moving markets, crucial requirements for quant investors trading on
narrow margins. But the models decide what to buy and sell. Only in cases of extreme volatility,
or if the signals appear to be weakening, does the firm sometimes manually cut back. Says
Simons, "We don’t override the models."
Simons explains his firm’s approach as the financial econometrics equivalent of
blocking and tackling. "We search through historical data looking for anomalous patterns that we
would not expect to occur at random. Our scheme is to analyze data and markets to test for
statistical significance and consistency over time," says Simons. "Once we find one, we test it for
statistical significance and consistency over time. After we determine its validity, we ask, ‘Does
this correspond to some aspect of behavior that seems reasonable?’"
Mathematics and science are two different notions, two different disciplines. By its nature, good
mathematics is quite intuitive. Experimental science doesn’t really work that way. Intuition is
important. Making guesses is important. Thinking about the right experiments is important. But it’s
a little more broad and a little less deep. So the mathematics we use here can be sophisticated.
But that’s not really the point. We don’t use very, very deep stuff. Certain of our statistical
approaches can be very sophisticated. I’m not suggesting it’s simple. I want a guy who knows
enough math so that he can use those tools effectively but has a curiosity about how things work
and enough imagination and tenacity to dope it out.
Many of the anomalies we initially exploited are intact, though they have weakened some. What
you need to do is pile them up. You need to build a system that is layered and layered. And with
each new idea, you have to determine, Is this really new, or is this somehow embedded in what
we’ve done already? So you use statistical tests to determine that, yes, a new discovery is really
a new discovery. Okay, now how does it fit in? What’s the right weighting to put in? And finally
you make an improvement. Then you layer in another one. And another one.
Are markets more efficient than when you started?
Considerably more efficient. There was a time when we were trading Treasury bills and we were
looking at the discount structure of the bills. We said, Something is crazy here. Far-out bills were
trading at some huge discount, but the 12-month physical bill was not exhibiting any such
discount. Something was wrong. This was certainly something that a Long-Term Capital
Management would have eliminated in a microsecond. So we just kept looking at it and saying,
Why is this? The answer was that no one was picking up that inefficiency. So we bought up a
whole bunch of Treasury bill futures, hedged the position in various ways, kept our fingers
crossed, and sure enough, it came in. It could have gone the other way, I suppose, but not for
very long, because the chickens had to come home to roost. But those kinds of opportunities
don’t exist now. The commodities markets used to trend pretty heavily — long-term trends — but
those don’t really exist anymore.
Renaissance Technologies
http://www.hedgefundletters.com/renaissance-technologies/
Like other trader money managers, Medallion aims small pricing anomalies and market inefficiencies that can support billions of dollars of trading. Though most quant managers depend on ‘convergence trading’ algorithm – a concept made famous by John Meriwether’s Long Term Capital Management, RenTech follows a different approach. While convergence traders price two different financial instruments on a relative basis, buying one and selling another on the assumption that prices will return to their proper level at some point, RenTech’s model requires that trades pay-off in a limited time-bound fashion. Traders at RenTech conduct rapid-fire buying and selling on a plethora of commodities and financial futures contracts, both US and overseas, including currencies, commodities and mortgage derivatives.
The Law of Large Numbers: An Analysis of the Renaissance Fund A case study in hedge fund replication and risk management
http://www.markovprocesses.com/download/mpi_TheLawOfLargeNumbers2007Q3.pdf
Moore Capital
Management
http://www.moorecapitalllc.com/
Manifold Learning
https://quantivity.wordpress.com/2011/05/08/manifold-learning-differential-geometry-machine-learning/#more-5397
On Jim Simons, String Theory, and Quantitative Hedge Funds
Posted on
October 24, 2014 by
Alex Burns
http://www.alexburns.net/2014/10/24/on-jim-simons-string-theory-and-quantitative-hedge-funds/
Topic Title: How does Renaissance Technologies invest?
http://www.wilmott.com/messageview.cfm?catid=4&threadid=69664
The Intersection Of Information Theory, Networks, And Investing
http://blog.semilshah.com/2014/01/25/the-intersection-of-information-theory-networks-and-investing/
Information Theory
in Horseracing, the Share Markets
and in Life
http://www.hamilton.tcd.ie/events/10Nov2004/Slides.pdf
Information Theory and Stock Market
http://www.ece.uic.edu/~devroye/courses/ECE534/project/Pongsit.pdf
Gambling and Portfolio Selection using Information theory
http://www.ece.uic.edu/~devroye/courses/ECE534/project/project_Luke_Vercimak.pdf
Game Theory and
Macro Investing
The Playbook
popular media links:
https://en.wikipedia.org/wiki/Renaissance_Technologies
The Medallion Fund has traded non-stock instruments and is international. American-traded instruments include commodities futures and US Treasury bonds. Foreign-traded instruments include currency swaps, commodities futures, and foreign bonds. The Medallion Fund has its own internal trading desk, staffed by approximately 20 traders, and trades from Monday opening bell in Australia through Friday closing bell in the US. Its origins date to the late 1980s, and it is believed to have essentially subsumed the trading positions and intellectual property of James Ax's Axcom Trading Advisors after that company's dissolution in 1992.[citation needed]
James Ax
https://en.wikipedia.org/wiki/James_AxJames Ax earned his Ph.D. from the University of California, Berkeley in 1961 under the direction of Gerhard Hochschild, with a dissertation on The Intersection of Norm Groups. After one year at Stanford University, he joined the mathematics faculty at Cornell University. In 1969, he moved to the mathematics department at Stony Brook University and remained on the faculty until 1977. In the 1980s, he and James Simons founded a quantitative finance firm, Axcom Trading Advisors, which was later acquired by Renaissance Technologies and renamed the Medallion Fund.[4] The latter fund was named after the Cole Prize won by James Ax and the Veblen Prize won by James Simons.
Simons at Renaissance Cracks Code, Doubling Assets (Update1)
By Richard Teitelbaum - November 27, 2007 13:12 EST
http://www.bloomberg.com/apps/news?pid=newsarchive&sid=aq33M3X795vQ
Trade Secrets
The firm accuses Alexander Belopolsky and Pavel Volfbeyn of appropriating trade secrets. Belopolsky and Volfbeyn deny the charges. In a July decision, the two briefly described three strategies that Renaissance had explored. One involved swaps, which are contracts to exchange interest or other payments; another used an electronic order matching system that anonymously links buyers and sellers; and a third made use of
Nasdaq and
New York Stock Exchange limit order books, which are real-time records of unexecuted orders to buy or sell a stock at a particular price.
The richest hedge funds
http://www.bloomberg.com/news/articles/2015-06-16/how-an-exclusive-hedge-fund-turbocharged-retirement-plan
Peers -
AQR
https://www.aqr.com/who-we-are/leadership
AQR (Applied Quantitative Research) was founded in 1998 by Clifford S. Asness, Ph.D.; David G. Kabiller, CFA; Robert J. Krail; and John M. Liew, Ph.D.
AQR’s story begins at the University of Chicago’s Ph.D. program where Asness, Liew and Krail met, and the foundation of AQR’s investment philosophy was established.
While working on his dissertation, Asness joined Goldman Sachs, where, a year later, he was tapped to lead a new quantitative research team for Goldman Sachs Asset Management. Liew and Krail joined him, and the new team applied what they learned in academia, using value and momentum strategies to help portfolio managers make investment decisions. Eventually, the team was also managing both hedge-fund and long-only assets, utilizing their new investment process.
In 1997, after several successful years, Asness, Kabiller — who had worked closely with the team in his role overseeing relationships with some of the largest pension and investment funds — Krail and Liew chose to leave Goldman Sachs to focus solely on research and investment product development. Together they established AQR in August of 1998.
AQR was among the first hedge-fund managers to voluntarily register at its inception with the Securities and Exchange Commission. While the first AQR product was a hedge fund, the goal was always to expand into traditional portfolio management, which was accomplished in 2000. In 2009, AQR became one of the first investment managers to offer alternative strategies in a mutual fund format.
Today, AQR is a global investment management firm that has held to its original focus of rigorous research and the development of innovative, practical investment strategies.
