The Management Expense Ratio (MER) represents the combined total of all the fees applied to a Mutual Fund. This includes the management fee, operating expenses and taxes charged to a fund during a given year. It is expressed as a percentage of a fund’s average net assets for that year.
As a Financial Application Designer who has to build mathematical models, accounting for MERs when comparing different assets classes and investments or representing the results of a financial plan is the bane of our existence. How do you do this? This question came up when designing one of the functions of our newest financial application, MortyGlobal.
MortyGlobal, as a mortgage application, provides all of the functionality required to deal with anything involving mortgages. One of the functions provides the consumer the ability to see whether or not he will need a Reverse Mortgage to supplement his retirement income in the event his retirement plan has an asset shortfall.
It has been established through many studies that consumers can lose up to 40% of all of their retirement invested assets though MERs. This means this variable will play a critical role on whether or not a consumer will have a retirement assets shortfall. But how do we account for MERs in evaluating this shortfall?
Solution 1: Using Total Returns: The easy answer and wrong answer!
It would be easy to decide to use the total return associated with the benchmark for the fund we are analyzing. Let’s assume the benchmark has a return of 8% over 10 years and Fund A has a MER of 3% and Fund B has a MER of 1%. We could state in comparing Fund A and Fund B, that the net return of Fund B will be 7% and better than Fund A by 2%.
However this does not work because the total return of both fund are not the total return of the benchmark particularly if we are comparing an Index fund to a Managed fund. As a result, dealing with the MER at the level of total returns would be in fact inaccurate.
In fact, most managed funds are able to achieve a total return greater than the benchmark. For example, the fund managers could be able to beat by the benchmark by 1% and achieve 9% in the case of Fund A. It is when the MER is applied that the return of the fund falls below the benchmark. This is why most mutual funds do worst than their benchmark. Since we cannot compare apple to oranges, we cannot use Total Returns in our mathematical model.
Solution 2: Using the Net Returns/Forgetting the MER: Still an easy answer and a wrong answer…
Since we cannot use Total Returns, we are left with Net Returns (after MER have been deducted). Since the MER have been deducted, how can we account for that variable. In fact, should we account for it at all?
Let’s assume a client buys two funds which provides the same return of 5%. One fund was not managed and followed the Index with a MER of 1%. The other fund was managed and also returned 5% with a MER of 3%.
Based on this observation, from the perspective of the consumer, we could conclude that the MER did not impact his financial results and the final return he earned. He would have received 5% whether he purchased Fund A or B.
Let’s look at this closely. The fund manager of fund B was able through his management and decisions to beat the total return of the Index by about 2%. So this is the value he created. However this value went directly in the pocket of the fund manager because of the MER of 3%. As a result, the consumer saw or did not see any of this value. Basically the consumer loaned his money to the fund manager who invested it and who kept all of the rewards while the consumer retained all of the risks. In fact, many consumers end up paying a fee to the fund manager to do less than the benchmark because of the MER.
As a result, we can see that MERs do have an impact and we have to account for it in any mathematical model we create. However we can’t account for it under the form of returns as this will create a catch22 problem which has no solutions. We have to account for it in terms of risk and probability.
Solution 3: Net Return and probability. The only possible solution..
Let’s use Fund A with a MER of 1% and Fund B with a MER of 3%, both having achieved a return of 5% to understand how we can account for MERs in any financial or mathematical model. If I was to show the impact of MERs through the use of images, this is what I would use.
The first image illustrates Fund A. Basically the consumer is able to achieve 5% by taking an easy and safe road. The second image illustrates Fund B with a MER of 3%. Here the road is less than safe and in fact there is a high probability that something could go wrong and that disaster could strike. Both roads lead to the same destination, but the journey will be entirely different. If you were to assign a probability of getting to destination for both roads you have to agree that road A would offer the best probability.
As a result, this is how MERs has to be accounted in any mathematical model. Showing net returns is not sufficient. You cannot show a financial plan done at 5% where 100% of funds are investment into a managed Canadian equity fund without attaching a probability to this 5%.
The plan would have to show 5% with a probability of 10-20% of achieving that return and a probability of 80-90% of doing worst. This is what we show in our picture of our app for reverse mortgage.
What are the variables that change the probability of meeting a retirement goal?
Without revealing our algorithm used to calculate this probability, we have determined that 4 variables influence this probability directly and significantly. The variables are:
- MER
- Income/Asset ratio (Utilization rate of the retirement assets percent income over assets)
- Volatility
- Historical return of the benchmark
