In the world of modern finance, there has always been the search for the Holy Grail. Ever since the advent of computers, practitioners have looked to harness the power of computing and direct it towards the goal of producing endless profits. Today the buzz words being used  across industries include, “AI – Artificial Intelligence,” “Machine Learning,” “Neural Networks,” and “Deep Learning.” Regrettably, nobody has found a silver bullet, but that hasn’t slowed down people from trying. Wall Street has an innate desire to try to turn the ultra-complex field of finance into a science, just as they do in the field of physics. Even banking stalwart JPMorgan Chase (JPM) and its renowned CEO/Chairman Jamie Dimon suffered billions in losses in the quest for infinite income, due in large part to their over-reliance on pseudo-science trading models.

Preceding JPM’s losses, James Montier of Grantham Mayo van Otterloo’s asset allocation team gave a keynote speech at a CFA Institute Annual Conference in Chicago, where he gave a prescient talk explaining why bad models were the root cause of the financial crisis. Montier noted these computer algorithms essentially underappreciate the number and severity of Black Swan events (low probability negative outcomes) and the models’ inability to accurately identify predictable surprises.

What are predictable surprises? Here’s what Montier had to say on the topic:

“Predictable surprises are really about situations where some people are aware of the problem. The problem gets worse over time and eventually explodes into crisis.”

When Dimon was made aware of the 2012 rogue trading activities, he strenuously denied the problem before reversing course and admitting to the dilemma. Unfortunately, many of these Wall Street firms and financial institutions use value-at-risk (VaR) models that are falsely based on the belief that past results will repeat themselves, and financial market returns are normally distributed. Those suppositions are not always true.