Finance’s Dr. Jeckyll & Mr. Hyde: How Sophisticated is your Fund & Wealth Manager?

A review of: “Practitioner Portfolio Construction and Performance Measurement: Evidence from Europe” by Noël Amenc, Felix Goltz, and Abraham Lioui (Financial Analysts Journal, June 2011).Thumbnail


What defines the difference between being a professional and an amatorial investor? Certainly, there must be a justification for the large fees fund managers charge. Regardless of how useful it is to have your money managed by a third person/entity, one might expect a certain level of sophistication in their approach to effectively managing money.

Many innovations and studies have been conducted in academia related to finance. More accurate models have been created, as risk and portfolio management theories have evolved and improved throughout the years. Yet, Amenc et Al.’s study shows us just how little of this innovation has been implemented in the investment management world.

The authors find that heuristics and arbitrary decision-making are still key drivers of the industry. Following, we will describe their study and try to decipher the reasons why the results may be such.

The Survey

The authors of this study were interested in two major concepts: what techniques do practitioners use when they try to optimize their portfolios, and what techniques do they use when they want to calculate their performance. This was done through a survey containing 8 multiple choice questions addressed to 229 different institutions from Europe, including asset managers, private banks, pension funds, insurance companies, family offices, and investment banks.

While the fact that answering to the survey was voluntary could cause sample bias (there may be differences between people willing to answer the survey voluntarily and those not willing to), the authors explain this shouldn’t be an issue as “The practitioner who is more familiar with the concepts mentioned in the questionnaire would be more likely to respond to the questionnaire than someone to whom academic concepts are new” and as the results demonstrated an under-sophistication of the techniques used by these practitioners. It is also true though, that perhaps those practitioners who do use more sophisticated techniques might not be willing to disclose it, as it may be their “ace up the sleeve” in investment management.

Up next is every question they asked, one by one. The first 5 have portfolio management as a theme, and the last 3 analyze performance measurement techniques.

Question 1: Do you set absolute risk objectives in portfolio optimization?

Absolute risk is the probability that a certain negative event will occur.

The initial results seem positive. A good 80% of the industry does take into consideration the absolute risks of their portfolios. The statistics used were mainly the VaR (Value at Risk) and CVaR (Conditional Value at Risk), which quantify how likely an extreme downside in returns is in a given time frame, and what the average loss could be if it takes place. The fact that many (particularly small) firms used only average risk measures is not reassuring though. These measures do not take into consideration “fat tails” in return distributions.

Question 2: Do you set relative risk objectives in portfolio optimization?

Relative risk is the risk of an investment when compared to another benchmark investment.

34% of respondents stated that they do not use measures of relative risk, which the authors attribute to the difficulty of choosing a benchmark, and explain that once this is made practitioners may be affected by behavioral biases. Yet, many practitioners tend to use “Tracking error” as relative risk measurements. Tracking error is not particularly sophisticated. It represents the volatility of the difference in returns of the investment versus the benchmark. More preoccupying is the fact that “Tail risk” is not measured often. This entails that practitioners often overlook the relative risk of their investments’ potential downsides, compared to those of other benchmark investments. Again, bigger fund managers tend to use more sophisticated techniques.

Question 3: When implementing portfolio optimization, how do you estimate the covariance matrix?

As we saw in Article 1, there are various models used to estimate the correlation between different assets in a portfolio. Some of these models were clearly superior to the others: covariance modeling is the one that has improved the most thanks to academic finance research. Practitioners though, mainly use the most basic type of estimation for such modeling, the sample estimate. We have shown before what kinds of biases and problems are caused by this reasoning: large unexpected movements in the market can cause practitioners to make calculations that are not of great validity.

Question 4: When implementing portfolio optimization, how do you calculate extreme risk measures?

We mentioned the extreme risk above. Here the authors dig more into the details of how the practitioners measure.

Most practitioners tend to use normal distribution VaR, or not account for extreme risk at all. While accounting for Value at Risk certainly is important, doing so through normal distribution measurements is paradoxical, as “fat tail” risks are not considered. In real life, when events such as the financial crisis, liquidity crunches, or the Covid-19 pandemic occur, returns are often much worse and are so for more prolonged periods, than what one would expect through “normal distribution” estimates.

Question 5: How do you deal with estimation risk?

Many fund managers understand the limitations of the unsophisticated measures they use and try to make up by imposing different weights on their investment choices. While this is positive, the choice of weights is often arbitrary, and as can be seen above, the more efficient, but more complex ways of making these choices are rarely used. Even “Resampling”, which is as simple as redoing the same analysis with different data subsets, is relatively unused.

Question 6: What do you use to measure absolute performance?

Absolute performance is a measure of how well the investments went and is often expressed as the ratio of return over risk, the Sharpe Ratio.

Effectively, the researchers found that most practitioners use the Sharpe Ratio, meaning that risk is accounted for in their performance measurements. This is not a fully common practice though, as many firms base their performance solely on average excess returns. Excess returns are the difference between the portfolio returns and those of the risk-free rate, a measure that when used alone can be classified as “Neanderthalian”.

Question 7: What do you use to measure relative performance?

Relative performance in this realm is important as it defines how well a fund manager has done compared to a benchmark.

The results here are not terrible, as both the information ratio and Jensen’s alpha are broadly used. Jensen’s alpha is the average return of the portfolio subtracted by the return that would be expected by the CAPM-calculated return for the same portfolio. The information ratio is slightly more arbitrary, as it requires the definition of a benchmark, but takes under consideration systematic risk, which is positive. Unfortunately, though, as for absolute performance, even here AER is used extensively. This can be very misleading if the benchmark used is not investing in securities with similar risk.

Question 8: How do you analyze managers’ Alpha?

Finally, the manager’s Alpha is a measure of how well the manager performed compared to the market.

Peer-group analysis is a widespread practice, which can be confusing as it is difficult to find peers investing in very similar securities yet gaining different returns. Additionally, multifactor models, which are potentially the most sophisticated statistics under this theme, are rarely used, potentially due to the difficulties in defining what factors should be used to measure a manager’s performance.

Conclusion & Additional Thoughts

We have seen that many practitioners do not use the most sophisticated methods and models when constructing their portfolios and measuring performance. Many fail to consider extreme risks and ignore the more advanced estimation techniques. A large proportion of these practitioners use statistic measures that even a first-year undergraduate is expected to be knowledgeable of.

Why is this so? Perhaps the answer lies in the investors: if more complex methods were used, investors may lack the sophistication necessary to understand what is “done” with their money. Or perhaps fund managers give little importance to these kinds of statistics as they consider fundamental analysis more important in making investment decisions. The fact that it is mainly smaller firms that use less sophisticated techniques could mean that lack of sophistication is due to resource scarcity, or even inclination to take on additional risk, in order to survive in a very competitive market environment.

Whatever the reason, many fund managers have proven that their excessive fees may not be worth it, but strongly rely on investors’ ignorance.

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