Jeff Hirsh Read: June 2021
Inspiration: Heard reference to the book in financial media and wanted to better understand the origins of algorithmic trading/investing
Summary
Written with the help of ChatGPT, below is a brief summary to understand what is covered in the book.
“The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution”, published in 2019 by author and financial journalist Gregory Zuckerman, tells the story of Jim Simons, a renowned mathematician and hedge fund manager. Simons, who is the founder of Renaissance Technologies, is known for using mathematical models and algorithms to make investment decisions. The book covers Simons’ early life and career, as well as the development and success of Renaissance Technologies. Zuckerman also explores the impact that Simons and his firm have had on the world of finance and investing, including the rise of quant trading and the use of data and technology in the financial industry. Throughout the book, Zuckerman tells the story of Simons’ innovative approach to investing and his influence on the world of finance.
Unedited Notes
Direct from my original book log, below are my unedited notes (abbreviations and misspellings included) to show how I take notes as I read.
Jim Simons big math prodigy at MIT before investor—love math, numbers, theoretical discussions, also love adventure, Simons join code breaking team in DoD that hires smart people and first used algos here and love, think can apply algos and differential equations (PDEs-partial differential eq) to market, Simons 1968 publish own equation on minimal varieties, Simons ignore basic investor signals and use macroscopic variables (predict market short term, 8 underlying states), not focus on dividends/earnings/news, use math (not worry about why things happen just model observed data), trade like poker player—deduce market state from price mvmt,, Chern-Simon invariants earn Simons highest prize in geometry—used by Msft for quantum computing, Simons built Stony Brook math dept then leave to trade, getting fired can be good thing just don’t make a habit, Monemetrics was name of firm to trade currency with math w Lenny Baum from IDA code break team, Markov chain=probability of what next depends only on current state not past—hidden Markov process=chain of events governed by unknown vars (see results but not the states to explain chain, Baum-Welch algo allows computer to teach self statss and probabilities (estimate probs and parameters with little info except outcome—key to machine learning), Simons algo good then fail then go to undervalued investing, 1982 change to Renaissance Technology Corp as invest in venture tech startups in addition, look to use stochastic equations (model dynamic processes that evolve over time with high uncertainty, markov chains are in the stochastic family), stochastic used to predict in randomness like weather forecasting—hire Rene Carmona to join team as stochastic expert, Carmona propose early machine learning method (let computer model and fill in missing data), Berlekamp shift simons to look at intraday anamolies for high frequency (not worry about why, just pretend casino and make lots of trade, win 51%), MS was first big bank to have quant unit called APT and do well but jealously and one year of loss cause shut down and miss opportunity , DE Shaw start alongisde Simons with firepower—David Shaw loves internet, Shaw was one of first to tell bezos internet will be for shopping and reviews (bezos work with shaw before go start amzn), Laufer join Simons and create 5-minute bars to analyze throughout the day (not just end of day or start but look in 5 min increments of data), Laufer build early machine learning dynamic algo to choose best bets, Simons need to transition to use algos for stocks—Bob Mercer and Peter Brown key from IBM, Renaissance build big system to execute reversion to mean based trading, Brown and Mercer equity system only work with small amounts of money, Magerman find error in code where static not dynamic and adjust and fix system, statistically valid signals without apparent logic good bc others won’t find/adopt, LTCM was big quant based hedge fund but 1998 russian default and panic crush them into bank takeover (did really well in years before, better than Simons), don’t believe models reflect reality—just aspects of it this, perform incredibly well early 2000s and able to lever up via basket options (sharpe ratio get as high as 6.0 vs sp500 1.0–no volatility to returns basically bc diversified), 2004-5 Simons consider opening new long term focused fund bc renaissance short term trades only work up to 5bn or so vs long term could manage 100bn, RIEF get 35bn by 2007 and consider another fund as well, bob mercer develop disdain for gvt from earliest internship where saw waste—told to max budget not be efficient, renaissance first really to exploit mkt inefficiences, mercer 2012 meet breitbart and bannon then after romney lose get angry and see shift, 2014 mercer not think mainstream repub can win so work w bannon to find outsider, bannon help mercer invest in cambridge analytica, mercer work with bannon and farage for brexit, mercer initally back cruz before trump, Simons huge dem donor, bannon say mercer single most impactful for trump win, mercer asked to step down by Simons as bad press 17-18 due to trump ties, renaissance predict stock moves relative to others stocks/index, only right just above 50% bc hard to beat market and now everyone can do it, simons donate lots to math teachers and autism research
