The number one shortcoming that all money managers suffer from and what should investors do to mitigate its impact


Asset owners deal with money managers of all types. There are fundamental managers who rely on internally generated research, quantitative managers who build sophisticated algorithms, and then there are those managers who rely mostly on technical analysis by looking at chart patterns (there are more managers in this group than care/dare to admit). There are obviously some other types of investment managers but these 3 categories cover a great majority of money managers out there.  Yet, all of these managers share a common trait and it is overconfidence.

By definition, fundamental managers are overconfident in their abilities to uncover and interpret information relative to other investors. Quantitative managers claim that the lack of emotions that is the hallmark of their automatic model-driven trading makes them superior to fundamental managers. However, their shortcoming is that they are overconfident in the robustness and adaptability (current and future) of their algorithms in a constantly evolving market. What may have worked in the past, whether due to chance or skill, may not work in the future nor is it guaranteed that a quant manager’s new tweaked models will. Technical analysts are not only overconfident that future chart patterns will imitate, or at least rhyme, with the past but that it will when they think it will.

So, if overconfidence is so universal among money managers then what should asset owners do? The answer is simple. It is to focus on what will happen when a manager’s strategy goes wrong rather than what will happen when it goes right. It is to focus on a manager’s risk management strategy and the tools used to mitigate losses. As a student of financial risk management for the past 15 years, I can tell you that most practitioners and a big deal of the literature on the subject focus far more on risk measurement than risk management. Whether it is using sensitivity analysis, scenario analysis, back-testing or coming up with measures such as VAR, or value at risk, all of these techniques primarily predict what could happen (primarily given the past) but don’t tell you what to do when you actually experience a predicted, or as often is the case, unpredicted loss. Granted, you have to measure something in order to manage it but confusing measurement with management gives a false sense of assurance.

I will explain what I consider to be a proper risk management framework in a future post but what I can tell you from my 13 years as a money manager that any robust risk management framework will have 3 key anchors: 1) identifying and quantifying the proper downside risk you are willing and able to take, 2) having positions that will be profitable when your thesis is wrong in a magnitude that reduces the volatility of your returns, and 3) having optionality in your portfolio.

So to conclude, asset owners should expect that money managers they hire will have a certain degree of overconfidence. They should make sure that those they hire have a robust, reasonable, and actionable plan that mitigates losses and that their risk management strategy is not only one of a theoretical framework that hopes to predict the magnitude of potential losses. Money managers should realize this shortcoming and have a framework that will mitigate losses and assure clients.

BCM Staff January 7, 2016 No Comments

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