“A computer will never tell you to buy one stock and sell another… (there is) no substitute …for flair in judgement, and a sense of timing.” - Wall Street Journal 1962.
After digging through archives in Cambridge while working on a project to write a revisionist history of machine learning in finance, I came across early ‘Cybernetics’ articles that have been lost to modern researchers and developers in quantitative finance and portfolio management.
As far back as 1962, GPE Clarkson, a researcher from Carnegie Tech, showed how bank investment officers’ portfolio selection decisions could be automated using discriminator nets, i.e., a sequential branching computer program. I didn’t know that around the same time, a system developed by a New-York-based brokerage firm called Jesup & Lamont, not only routinised investment decisions based on decision heuristics like that of Clarkson, but also learned new patterns for future refinement.
This system might well have been the world’s first self-learning financial robot. Sadly, Jesup & Lamont’s innovation was never put into production, and the 133-year-old brokerage firm filed for bankruptcy in 2010.
Since the 1960s the word heuristic has been given a problem-solving connotation. Heuristic models were one of the business world’s first inroads to learn from data. A heuristic or rule can be as easy as buy-when-the-price-is-low for stock trading, first-in-first-out for accounting, or first-come-first-serve for job scheduling.
In portfolio management, heuristic programming is not unlike the 20-person team that was said to translate Ray Dalio’s unique financial worldview into algorithms, or the group of coders that developed Paul Tudor’s “Paul in a Box”.  The hedge fund Point72 was also purported to test models that mimic their portfolio managers’ trades.