This is the second installment in a multi-part research series on fund ratings. In this series, we will use proprietary functionality leveraged by our clients to develop custom fund rating systems.1
In the first installment of this series, we discussed the volatility of 10-year fund ratings as the last of the Global Financial Crisis (GFC) faded from the historical window. Now, we’d like to address in more detail one of the reasons this happened.
The reason, in a nutshell, is that fund ratings―based on any statistic―identify outliers. Naturally, you might say, that’s the idea. And it is―to an extent. But the why matters. Are the top funds consistent superior managers? Did they make a shrewd tactical allocation move at the right time? Or are they rewarded for something that has nothing to do with skill?
Let’s return to our illustration using the 10-year MRAR statistic calculated in MPI Stylus.23 We compute 10-year MRAR ranks and 10-year Beta vs. the S&P 500 Index within the Large Blend Morningstar category of about 700 funds that are currently active with inception January 2008 or earlier. We first compute over the 10-year window (January 2008 through December 2017), where most of the GFC is included, as seen in the below chart.
In the below chart, we calculate the same values between March 2009 and February 2019, where the GFC is fully excluded. On both charts we highlight in green the same group of funds: top 10% of funds ranked on 10-year MRAR as of December 2017.
These two charts produce a handful of observations:
- While the GFC is included in the window, the majority of the funds with the highest 10-year rating are funds with low betas. In fact, a third of the top-ranked funds have beta of 0.8 or lower. Far fewer funds with high beta are highly rated. The picture is similar for other 10-year windows that include a substantial portion of the crisis―most of the top-rated funds are found in the top left corner with low beta.
- Once the GFC is no longer in the 10-year window, the 10-year ratings of funds with lower beta fall off a dramatically.
- We can also observe that the new 10-year ratings prefer funds with high beta―over 90% of top-rated funds as of February 2019 (second chart) have beta greater than 1.0.
- Most importantly, top-rated funds with the beta around 1.0 have the least amount of ranking changes.
The explanation is that, on average, funds with lower beta most likely had the smallest losses during the GFC. As we explained in our previous post, MRAR and other similar measures of risk-adjusted returns, like the Sharpe Ratio, are highly sensitive to large negative values and keep them in their “memory.”
As soon as the GFC is out of the estimation or ratings window of MRAR, Sharpe, etc., the ratings lever shifts to performance, and the funds with higher performance (many of which are, of course, high beta in a bull market) are pushed to the top.
A second way to look at this issue is illustrated in the two charts below. Again, we plot MRAR ratings over the same two 10-year periods vs. performance ranks in the same period. It is easy to see that while the GFC is “in the window” a lot of funds with lower performance (left edge of the charts) are rated highly from a risk-adjusted basis (top of the charts) because of the significant value given to their low risk.
Once the GFC is “out of the window,” as seen below, the relationship between 10-year MRAR ratings and total performance ratings is almost linear with very few exceptions (below the diagonal), and all top-10% ranked funds on a risk-adjusted basis are in the top-20% by performance. In other words, by many measures, right now, some of the riskiest funds are being rewarded without penalty in typical rating systems.
As stated at the start of this post, top-rated funds are outliers by definition. Some may very well be the outliers we’re all looking to identify. As we’ve just shown, however, some outliers are likely being rewarded or penalized simply for their nature as a more aggressive or conservative strategy. Others could be rewarded or penalized due to a simple misclassification. Consequently, there are good reasons to avoid a top-rated fund unless you know why it’s an outlier.
In our next post, we will delve into how to minimize the outlier effect highlighted above. To do this, we will apply our Common Style analytic, which assesses fund and portfolio similarities and differences to identify funds that could potentially be miscategorized.
Footnotes
- 1DISCLAIMER: Markov Processes International, Inc., conducts performance-based analysis and, beyond any public information, does not claim to know or insinuate actual strategy, positions or holdings of funds, portfolios or organizations discussed herein, nor are we commenting on the quality or merits of the strategies. This analysis is purely returns-based and does not reflect insights into actual holdings. Deviations between our analysis and the actual holdings, performance and/or management decisions made by funds and/or organizations are expected and inherent in any quantitative analysis. MPI and/or its affiliates, employees, third-party data providers and agents makes no warranties or guarantees as to the accuracy of the statistical analysis contained herein. This analysis should not be interpreted as legal, tax or investment advice nor does MPI take any responsibility for investment decisions made by any parties based on this analysis. The use of this document is entirely at your own risk. Under no circumstances will MPI and/or its affiliates, employees, third-party data providers and agents be liable for any losses, including but not limited to trading and investment losses, direct, indirect, incidental, consequential, special, exemplary, punitive, or any other monetary or other damages, fees, fines, penalties, or liabilities arising out of or relating in any way to this document, and/or content or information provided herein.
- 2The funds selected are those in the Large Blend category as of February 2017 for both charts. Some funds will have been moved in or out of the category. There will be some discrepancies between those selected and the funds that were actually in the Large Blend category as of December 2017.
- 3MPI uses the MRAR statistic upon which Morningstar ratings are based. We do not, however, make various other adjustments that Morningstar does. Estimated ranks will not match perfectly.