nah but at least something more, kinda like When Genius Failed which had more information than merely saying "they use mathematics and computing"
Soooo if they are judging the performance of their models on a daily basis that would imply that they have some form of intraday trading model? arbitrage? spreads?
Okay, but as I recall, When Genius Failed was essentially about one really big trade that failed to revert to the mean as expected, or some such. (It has been a while since I read it.) So of course the trade that led to the undoing of LTCM, and upon which the book was based, would be detailed to some extent. I have the book, but it is third in line on my reading list. And, yeah, I wouldn't have minded if it contained a few nuggets.
It is available on Ebay (probably illegally) for $3 in electronic form, if anybody wants to read it like right now.
How did RenTech do it? – Book Notes of “The Man Who Solved the Market” 12/20/2019taovalueBook Notes,QuantamentalBook Review,Jim Simons,Renaissance Technology,RenTech,The Man Who Solved The Market Jim Simons’ shop Renaissance Technology, or as insiders call it “RenTech”, is the hallmark of quant fund managers. I’ve been longing to learn about how they achieved it and can’t wait to read this Greg Zukerman’s new book on Jim Simons. As usual, I tried to put some lessons/thoughts useful for myself, hopefully for my readers as well. [Bonus]I also dug up some RenTech job descriptions that no one ever (at least I didn’t see) found regarding what type of talents RenTech has been hiring (thus what type of new techniques they may be using) more recently. Go straight to the last point if that is your only interest. First thing first, this book willfall short if you are after RenTech’s strategy(it only talks vaguely and generally about strategies & data sets). Well, it’s totally unsurprising retrospectively, because if otherwise, Simons would do whatever it takes to get these things out of this book given his protectiveness of his secret sauce. Been said, it’s still quite insightful to learn that RenTech started as a managed futures/CTA shop & had its challenge when trying to move to equity side. This book is more about thehistory&peopleof RenTech. E.g. half of the book is dedicated to various lead PMs over the history of RenTech (e.g. Lenny Baum, Jim Ax, Elwyn Berlekamp, Henry Laufer, Peter Brown & Robert Mercer). By the way, most of them are renown mathematicians, who have their own Wikipedia pages on their academic achievements. Jim Simons seemed to never be hands on building the strategies himself (maybe except the early years). He is more of ateam organizer, cheerleader&sales personfor the better part of the three decades, which I suppose is a natural extension of his experiences as a university Math department chair. I guess these skills are hard to learn and even may be born with. One pattern consistently showed up on how hesuccessfully attracted such a swarm of talentsis he always asksfor small incremental favor. E.g. For getting Lenny Baum to quit IDA job for his one man trading shop, Simons “asked if he could spend a day at his office helping set up trading system”; To attract Elwyn Berlekamp, Simons “invited him to fly to Huntington beach a couple times a month to learn to trade for himself and see if his expertise in statistical information theory might be useful to Axcom [a Predecessor of RenTech].” Now to thekey question– How did RenTech achieve it? Based on my understanding,two main advantages: Better bullets – Early adoption of frontier techniques/datasets: e.g. they started to record (by themselves) and useintra-day tick data in 80s, then started to adopt someearly forms of machine learning in 90s, then started usingnatural language processing (after hiring Peter Brown & Robert Mercer from IBM) in the 2000s. Data and techniques are the building blocks of trading models, and all of them arewell ahead of the market by a decadeor so. Also, being early hasa cascading advantage, that is once you identify some strong signals and start trading it “to capacity” (p.274. which means you, as the arbitrager, move the market so much that others won’t be able see this inefficiency anymore). In this way, you as the first comer, can continue exploiting the full inefficiency without worrying about it to be competed away. Better guns – Modeling the reality better by incorporating all real world constraints, like trading cost, borrowing cost, etc. Simons once said (p. 271.) I’m not sure we’re the best at all aspects of trading,but we’re the best at estimating the cost of a trade. This allows them to be able to focus on only real money-making strategies and also implement them much better. Don’t quote me on this, but from my past experience, many institutional quant managers nowadays are still using very rudimentary trading cost models in backtesting & portfolio optimization (e.g. a simple % term, or linear [see this relative new CFA FAJ article on transaction cost of Factor Investing [Link], which used a simple linear model] & square root function at most). So we know what frontier data/techniques RenTech had been using, that’s not helpful if you are managers trying find new edge. I wonderwhat type of new techniques/data sets are they currently using. Obviously that won’t be an easy task given how secretive they have been. But I know there is one place where they have to disclose at least something –Department of Labor PERM filings for sponsoring green cards [link]. For someone who’s not familiar with US employment based green card process, the PERM is an employer’s proof of no US citizen could be found to fill the position of a current foreign-born employee who is apply the green card. Thus, in these hypothetical hiring process, the employer would be very specific in job description (sometimes almost detailed description of that specific employee, so that only that employee would fit). It turned out there are indeed some detailed secret sauces. Here are a few interesting position descriptions. E.g. the 2018 case candidate is usingfluid dynamicsin modeling. One 2011 case candidate already started usingTensor technique. But let’s be fair, I don’t even understand many of these disciplines, so no way we can compete away RenTech’s edge. Case 1: 2018 – Mathematical Researcher:https://lcr-pjr.doleta.gov/index.cf...l.dspCert&doc_id=3&visa_class_id=6&id=1147030 JD: “Develop and enhance proprietary statistical models relating to risk management, cost, trade generation and price prediction in order to improve alpha generating potential of quantitative investment strategies. Develop complex mathematical models of small signals embedded in a large amount of noise as well as test these models against large data sets. Analyze models for their predictability of future price movements in the financial markets utilizing C++ and Perl programming in a Linux computing environment.“ “Related experience must include implementing large-scale numerical computations influid dynamicsand thesolution of partial differential equationsutilizing C++. Will accept any suitable combination of education, training or experience.” Citizenship: GERMANY 2013 UNIVERSITY OF CAMBRIDGE PhD in Physics Case 2: 2017 – Mathematical Researcher:https://lcr-pjr.doleta.gov/index.cf...l.dspCert&doc_id=3&visa_class_id=6&id=1145759 JD: “Maintain and enhance equities trading strategies utilizing mathematical and statistical algorithms for generating price move predictions, risk and cost estimates. Analyze, design and implement mathematical and statistical algorithms to predict future stocks price movements utilizing advancedlinear algebra,calculus of variation, and complex mathematical analysis. Analyze historical financial data, searching for new predictive patterns of equities price movements.“; Citizenship: AUSTRALIA 2009 THE UNIVERSITY OF MICHIGAN PhD in Math Case 3: 2011 – Mathematical Researcher:https://lcr-pjr.doleta.gov/index.cfm?event=ehLCJRExternal.dspCert&doc_id=3&visa_class_id=6&id=517043 JD: “Analyze financial markets behavior, identifying patterns and structural risk. Run estimations and simulations; conduct mathematical and statistical analysis and track unexpected results through complex mathematical algorithms, utilizing C++ and STL/Matlab” Citizenship: CHINA 2006 COLUMBIA UNIVERSITY PhD in PHYSICS Case 4: 2011 – Operations Research Analyst :https://lcr-pjr.doleta.gov/index.cfm?event=ehLCJRExternal.dspCert&doc_id=3&visa_class_id=6&id=532574 JD: “Formulate and apply mathematical modeling and other optimizing methods to develop and interpret data to assist management with decision making and policy formulation. Conduct mathematical analysis of complex algorithms, real-time portfolio valuation and risk models in order to monitor internal behavior of risk profiles of portfolios containing a variety of financial instruments. Conduct mathematical and statistical analysis of modeling techniques to detect anomalies and deconstruct models to validate computations. UtilizeEigen-vectors,Eigen-values, AdvancedTensortechniques, C++ and Bloomberg.“ Citizenship: CHINA 2006 MIT PhD in PHYSICS Case 5: 2010 – Mathematician:https://lcr-pjr.doleta.gov/index.cfm?event=ehLCJRExternal.dspCert&doc_id=3&visa_class_id=6&id=446164 JD: “Utilize mathematical modeling, mathematical transformations and advanced algorithms to improve computer-driven trading systems programs and support accounting systems, including equities, futures and options, research, accounting and various reporting programs and utilities utilizing C++. Develop quantitative models to detect data errors and ensure quality of mathematical data utilized by trading system for both research and production.“ Citizenship: CHINA 2005 SUNY PhD in COMPUTER SCIENCE Case 6: 2010 – Senior Computer Scientist:https://lcr-pjr.doleta.gov/index.cfm?event=ehLCJRExternal.dspCert&doc_id=3&visa_class_id=6&id=447767 JD: “Conduct fundamental algorithm-based research in order to design, develop and enhance a highly complex and sophisticated code base utilized for long-term market simulations and proprietary trading algorithms. Refine theoretical models, simulations and trading algorithms in order to determine new patterns in futures, options and equity markets. Incorporate software solutions into larger modeling and trading methodology. UtilizeStochastic Modeling,Combinatory Logic,Distributed Computing,Algorithmic Theoryand C++.“ Citizenship: NETHERLANDS 1993 UNIVERSITY OF AMSTERDAM PhD in COMPUTER SCIENCE https://taovalue.wordpress.com/2019...s-of-the-man-who-solved-the-market/#more-3585
Jim Simons Revamps Renaissance Board in Nod to New Generation (Bloomberg) Renaissance Technologiesis reshaping the group of directors who will eventually succeed founderJim Simonsin overseeing one of the world’s most lucrative hedge funds. The firm is doubling the number of members on its board and has promoted Jim’s son, Nathaniel Simons, to co-chairman, according to regulatory filings. The appointments open the way for a new generation of younger directors to guide the $75 billion money