We have recently finished reading The Quants by Scott Patterson. We enjoyed the book and recommend it as a non-technical introduction to quantitative investing. The main focus of the book being on a handful of ultimately very successful quants and their experiences in the past decade.
The most compelling part of the book is Chapter 10 – The August Factor. In it Patterson chronicles the reactions of a handful of high profile quantitative fund managers to a massive reversal in fortune. In 2007 everything seemed to go wrong for these quants, who up until this point in time, had been coining profits.
This inevitably led to some introspection on the part of these investors as they saw their funds take massive performance hits. Nearly all were forced to reduce their positions and risks in light of this massive drawdown. In short, these investors were looking at their models seeing where they went wrong. Patterson writes:
Throttled quants everywhere were suddenly engaged in a prolonged bout of soul-searching, questioning whether all their brilliant strategies were an illusion, pure luck that happened to work during a period of dramatic growth, economic prosperity, and excessive leverage that lifted everyone’s boat.*
We all now know that things would get much worse for nearly everyone in 2008 as the global financial system teetered on the edge of collapse. The point being that some of the most successful investors of the past decade had to re-think the fundamental assumptions underlying their models.
At some point every investment strategy or method is going to run into a drawdown of a magnitude not previously seen (or modeled). It might be the result of market conditions or the model itself. In either case the portfolio manager is going to have a decision to make. Stand pat, tinker or trash the model altogether. In the case of leverage the decision to pull the plug may very well be made by someone else.
However for the non-leveraged player the decision is not so clear cut. An unexpectedly large drawdown may mark the failure of the model or may simply be the result of bad luck. The fact is that the decision will only be validated in hindsight. In either case it represents a chink in the armor of the human-free investment process. Ultimately every portfolio is run by a (fallible) human, whether they choose to admit it or not.
In this respect quantitative investing is not unlike discretionary investing. At some point every investor will face the choice of continuing to use their method despite losses or choosing to modify or replace the current methodology. So while quantitative investing may automate much of the investment process it still requires human input. In the end every quant model has a human with their hand on the power plug ready to pull it if things go badly wrong.
Jeff Miller at A Dash of Insight echoed this point when he recently wrote about the development process underlying his ETF selection model. Jeff documents the careful process they use to avoid overfitting their models and data snooping. He also correctly notes that the model should serve the needs and temperment of the user. In conclusion Miller writes:
The biggest problem for the system trader is to maintain confidence when things seem to be going wrong. Unless you have complete confidence in the development process, you will bail out on your system the first time you see losses.
August 2007 was the first time that many of these quant investors had seen significant losses. In some cases the models were flawed, in other cases the problem was how the models were implemented. In either case it should be clear that quantitative models can do many things well, but in the end they are designed and implemented by fallible people. So in the end their flaws are our flaws.
*p. 244, The Quants, Scott Patterson, Crown Business, 2010.