• The Quant Approach of Jeopardy Champ James Holzhauer  

    Recently, I was doing one of my favorite weekend activities, walking my dog and listening to podcasts (I like a simple life). A recent episode of an NPR podcast came on and it was a very charming piece about James (uncle Jamie) Holzhauer and his incredible Jeopardy! run. I was listening to this story unfold and was surprised when a few topics connected the story to the investment world—which I suppose I should have expected given the title of the podcast is “Planet Money”!

  • How to Judge a Quantitative Model  

    Recently, FiveThirtyEight posted a review of how accurate their models have been. The blog talks about calibration, which “measures whether, over the long run, events occur about as often as you say they’re going to occur.” As you could probably guess, their models are fairly well calibrated. This is not surprising given they are in the business of making predictions; and if they were bad at making predictions they probably wouldn’t still be around or be posting about it…The same could be said about us!

  • Investing with Machines and People  

    In his most recent missive, “Investing Without People”, Oaktree’s Howard Marks takes on quantitative investing. We look forward to reading Mr. Marks’ memos, as they are packed with valuable insights from a venerable career. But in this case, the questions he poses are all too familiar & in my opinion, exhibits faulty logic.


    The hottest headlines in technology, whether it be autonomous driving, government surveillance, targeted ads on Facebook or quantitative investment strategies have been surrounding the ubiquitous idea of “machine learning” and “big data”. Many of the largest quantitative firms are spending significant amounts of their budgets and many years researching artificial intelligence and how it can be applied to financial markets. That said, there seems to be diversity of opinion regarding effectiveness and none seem ready to completely handover their processes to the artificial intelligence robots – the quintessential black box. When doing so a firm that was likely hypothesis-driven would move to being more data-driven, which can certainly have its challenges..”