Investing in a World of Black Swans

Wade Slome

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. Regrettably, nobody has found the 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 JPMorgan Chase (JPM) and its CEO Jamie Dimon are already on their way to suffering more than $2 billion in losses in the quest for infinite income, due in large part to their over-reliance on pseudo-science trading models.

James Montier of Grantham Mayo van Otterloo's asset allocation team was recently a keynote speaker at the CFA Institute Annual Conference in Chicago. His prescient talk, which preceded JP Morgan's recent speculative trading loss announcement, explained why bad models were the root cause of the financial crisis. Essentially these computer algorithms under-appreciate the number and severity of Black Swans (low probability negative outcomes) and the models' inability to accurately identify predictable surprises.