Life lived in the rearview mirror… you’ll crash.
Life staring out the windshield… you’ll risk missing hazards behind.
Drive forward but understand your blind spots.
This thought came to me while teaching my 16-year-old daughter to drive. As you recall when learning to drive, it is difficult to know when to take your eyes off the road to check the mirrors. Changing lanes at high speeds makes it even harder. I tried to explain when to look in the side mirror and when to use the rearview mirror. It’s a strange concept to check your blind-spots without taking your mind off the traffic ahead.
Hundreds of decisions to make and very little time to react. Over time, and with practice, it becomes rote. But for a rookie driver, not having a clear instruction to follow can be stressful.
When my daughter hesitated while switching lanes, I said, “You don’t have perfect information. Just be ok with a 99% probability.” I continued, “If you take it smoothly, and a car is there, they will warn you of danger with a honk.” I’m not sure if the advice is correct, but it was the best guide I could offer.
In the investment world, we also don’t have perfect information. Many recommendations and models rely on what happened in the rearview mirror as we know how portfolio managers performed in previous markets. Manager research analysts do their due diligence to best understand an investment manager’s blind spots. They run stress tests on how portfolio managers did in difference downturns (hazards). Analysts interview portfolio managers to discover how they have reacted in past markets and try to get inside their brains to predict how they may respond in the future. The biggest challenge for an analyst is what lies ahead (through the windshield). Chief investment officers make predictions and asset allocators do their best with the information given to them to make decisions. Unfortunately, this is more art than science.
Wouldn’t it be nice to have a little more visibility about which products will be best positioned for the drive ahead? Aapryl uses machine learning to help analysts find managers with the most skill and who will most likely outperform their peers over the next 3 years. This is not a black box or magic bullet. It gives analysts data to combine with their deep understanding of the behaviors of managers to make better decisions, adding more science to the process.
I am trying to help my daughter balance the science and the art of driving to make the best decisions possible… Drive forward but understand your blind spots.
