“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.[1]
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[2]. 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.[3]
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.[4]
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,[5] or the group of coders that developed Paul Tudor’s “Paul in a Box”. [6] The hedge fund Point72 was also purported to test models that mimic their portfolio managers’ trades.[7]