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When you set out to analyze mutual funds or managers, you probably have some specific characteristics in mind. Klein Decisions’ K4 Fund Selection and K4 Manager Selection allow you to assign levels of importance to these factors and then use them to rank order the products based on their actual results. In addition, they also allow you to eliminate any fund or manager that lacks essential or “must have” attributes. These are two very different processes and rely upon the use of “Preferences” and “Filters.”
The widely used standard screening process employs filters. These are the basis for pass/fail tests that are applied to each fund or manager. Failure to pass just one results in elimination. Filters work the same way in the K4 tools, but they are generally not applied until after the funds or managers have been ranked. The ranking is based upon preferences rather than filters.
Preferences are the characteristics you believe are important for superior fund or manager performance. All else being equal, you want to select products that score well on them, although a disappointing result on one (or possibly several) won’t automatically result in elimination - it’s just a low score for that particular attribute.
You’ve probably noticed that many of the items you can use as preferences are also filter options. Obviously the difference isn’t in the factors themselves but in their effect on the analysis. The key to using K4 effectively is to properly determine which factors should be preferences and which should be filters. The simplest test is to consider if a successful fund or manager absolutely must have a specific level of a particular factor. If yes, the factor is a filter. If no, it’s a preference.
Here are four examples to illustrate the point. Each uses the same factors, but in some instances they’re used as filters, in others as preferences, and some are even used as both in the same scenario.
Factors for each example:
- 5-Year Return vs. Category Index
- Manager Tenure
- 5-Year Sharpe Ratio
- 5-Year Batting Average
Example 1
Goals:
- Long-term risk-adjusted return that compares favorably with the Category Index
- Manager must have at least three years' tenure with the product
- Consistent performance with good risk-adjusted return
One of the surest marks of a filter is a specific quantifiable cutoff. In this example, the fact that managers must have at least three years' tenure means this factor should be used as a filter. It can then serve as pass/fail test to eliminate funds or managers.
Certainly you would prefer higher to lower values for the other three factors (i.e. a higher 5-year return vs. the Category Index vs. a lower one, a higher Sharpe Ratio, and a higher 5-year batting average). However, the example doesn’t state a specific cutoff for any of these factors so they are best used as preferences. Products with higher values will rank above those with lower. In addition, since they aren’t filters, poor scores on one or all won’t eliminate the candidates from inclusion in the rankings.
Example 2
Goals:
- Long-term return that compares favorably with Category Index
- Manager must have at least three years' tenure with the product
- Must have a positive Sharpe Ratio
- Prefer consistent performers
Once again there's a specific quantifiable cutoff for manager tenure, so it should be used as a filter. There's actually a cutoff for Sharpe Ratio, too, although it's not as obvious. Requiring it to be positive is equivalent to saying it must be greater than zero, so the 5-Year Sharpe Ration should also be used as a filter. There are no similar cutoffs for 5-year return vs. Category Index or 5-year batting average, so they should be preferences.
Example 3
Goals:
- Long-term return greater than the Category Index
- Manager must have at least three years' tenure
- Must have a positive Sharpe Ratio
- 5-year batting average must be over 60%
Everything here has a quantifiable cutoff. There are no preferences. This is essentially a pass/fail screen so after designating the category for the scenario, you can skip right to the Results. Select the four factors as filters and enter the appropriate values for each. The surviving products will appear in alphabetical order.
Although you could do this, it doesn’t make a lot of sense. While you might think all four factors are important, do you really think they’re equally important? Probably not, but that can’t be reflected in this basic screen. You’ve got an alphabetical list of funds, but which should you prefer? The basic screen doesn’t help with this decision either.
A better way to approach this example would be to rethink the importance of the four factors and consider eliminating the cutoffs from those where you are willing to accept greater flexibility. These would be your preferences. Alternatively, you might want to add other factors that are important to success but don’t have quantifiable cutoffs like the original four. Either approach will allow you to turn your screen into a weighted factor model for more meaningful results.
Example 4
Goals:
- Long-term risk-adjusted return that compares favorably with the Category Index
- Prefer longer manager tenure but don’t even want to consider products where manager tenure is less than 3 years
- Consistent performance with good risk-adjusted return
This is identical to Example 1 but with one important difference: Here, you not only want to require minimum manager tenure of three years, you also want to favor those with longer tenure over those with less. In this case, manager tenure should be used as both a preference and a filter. (You might want to consider this approach for some of the factors in Example 3, too.) When you use it as a preference, you designate its importance relative to the other factors. The weight you assign will then help determine where products fall in the rankings. Then when manager tenure is used as a filter, it will work just as it did in Example 1, eliminating those where the current manager has been at the helm for less than three years. The other preferences and filters should be the same as in Example 1.
As these examples illustrate, in most instances the factors themselves don’t determine how they’re used, you do. The critical issue is whether you want to utilize them as a means of ranking products or whether you need them to eliminate candidates that fall short of specific standards. As you can see from the examples, your answers will have a major impact on the scenario as well as its results. One important thing to remember—in K4, filters can be applied after preference ranking, so no matter which way you go, you have the ability to see all the potential funds in rank order and then view only the ones that actually meet the filter criteria.