Lessons From Jim Simons

Renaissance Technologies, founded by Jim Simons, has the best track record of growing investor capital.  Better than Warren Buffett.  Better than George Soros.  The Book “The Man Who Solved The Market: How Jim Simons Launched The Quant Revolution” goes behind the scenes to explore the personal journey of Jim Simons as well as the inner workings of Renaissance.  If you want to unpack the success of a Code breaker, Mathematician, Pioneer in Quantitative Investing, and now a Philanthropist – this book is a must-read.

Here are five lessons that I took from the book:
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1)      Stay Thirsty, my friends.  Jim Simons is the real life "most interesting man in the world".  He graduated from the best institutions in the world.  He worked as a code breaker, built a great Math department at Stony Brook, and then he went on to build the best investment record.  Regardless of his success, he always took risks and aspired to reach greater heights.
 
2)      Stay Persistent.  He failed for 15 years as an investor, before tasting his first success in the early 90’s.  Very few can even imagine the self-belief and persistence it takes to keep going through 15 years of failure.  Somehow, Jim found the internal fortitude to keep going.
 
3)      Decentralized decision-making.  Jim hired the smartest people and gave them the reins.  Many say it, very few do it.  This culture of empowerment created a great team that wanted to push the boundaries of success.  He also rewarded employees generously and made them part of the team's success.
 
4)      Open Culture.  The ‘programming code’ is Renaissance's secret sauce, and it would have been easy to keep it under maximum security.  But everyone had access to it - even the secretaries.  Any employee could try to improve the code.  This level of openness and empowerment did wonders for team morale.
 
5)      Humility.  Jim Simons was always approachable and led with humility.  During tough market times, he did the unthinkable in the Quant world of overriding the programs knowing well that ‘algorithms can fail’.  

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