R.I.P.
Ten years ago, I attended a private dinner with Jim Simons who spoke about academia & investment management. Jim, intensely private and never one to share details of Renaissance Technologies, its investment program and the parameters/weightings that defines their model, Jim spoke about core values and his passion for investing.
Jim Simons pioneered the field of quantitative investing as the founder of Renaissance Technologies. Renowned for its systematic trading approach and complex mathematical and statistical models, Simons focused on pattern recognition. Jim’s contribution to theoretical physics, notably string theory that integrated geometry, topology, and quantum field theory. Jim served as a mathematics professor and department chair at Stony Brook University in the 1960s -1970s, making his mark in the investing world from 1980’s until his passing.
When Jim passed at age 86, he left an amazing legacy, remember Jim as a visionary, the first and most successful Quant with a legendary track record (+66% IRR from 1988- 2018); an influential philanthropist contributing $6+ billion to science, health, and education.
Four of my favorite quotes from the legend JS (and my related commentary):
"We have three criteria: If it's publicly traded, liquid and amenable to modelling, we trade it." JS
Agreed.
The global public equity market is $100T+; not all public equities are ‘liquid’, yet among thousands of cusips to capture alpha.
Public fixed income also aggregates $100T+; a large portion of the fixed income world is highly liquid. With $10T in less-liquid public credit (cross-over and HY), there is significant alpha to be generated too. My firm attempts to extract alpha in Multi-Asset Credit (MACs) from relative value, credit selection, index arbitrage, and the primary markets, but machines here, machines don’t make the decision.
“I developed a view that Markets are not random, and (are) somewhat predictable.” JS
Agreed.
Over a larger sample size of individual equities, Jim and a few select others have mostly mastered the science of mathematical machine learning, using artificial intelligence and a multi-variable set of parameters, each with its own weightings.
“Success in investing is not about being right all the time, its about minimizing the losses and maximizing the gains.” JS
Agreed.
This is first principle, a rule to live by in public and private credit markets. In private credit, one can argue it is even more essential to apply this discipline, avoid the ‘losers’ since assets can’t be easily sold.
“Don’t be afraid to take risk and embrace failure. That’s where the best opportunities often lie.“ JS
Agreed.
Whether it is a new business, a well-planned strategy, or a single investment, the greatest rewards go to those who embrace risk, not cavalier in nature, but with knowledge, capital, and a coherent and comprehensive plan.
JS: April 25, 1938 -May 10, 2024
