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K4 Fund Selection lets you quickly and easily build weighted factor models to evaluate and monitor mutual funds and ETFs. Listed below are the scoring factors, filters, and weights for this model. This illustration is for Small Cap Blend Index Funds, but it can easily be adapted for other capitalizations, styles, or equity categories.
Basis for Model Design
You’d think finding an index fund would be
simple: It’s supposed to look and act like the index. You don’t care
about high alphas or wild standard deviations. You’re not worried
that the fund’s too concentrated or even if the manager has a long
tenure. All index funds are essentially the same except for a few
subtle differences, and that’s what you need to capture in your
scenario.
Although you could create one giant scenario
with all index funds and then use filters to get a specific
category, you’ll get a more meaningful comparison if you limit your
evaluation to a more specific category. As is always the case with K4
Fund Selection, once you’ve created your scenario, you can easily
copy it over to other categories.
You can include both funds and ETFs in the
scenario. Often you want to consider them separately because the
ETFs won’t fare too well versus actively managed funds when stocks have a definite trend, and their
relatively low costs can skew the range of the expense ratios. But
here you only want to consider index funds and low expenses are a
definite plus.
Instead of using 5- or 10-year statistics, it’s
better to stick with 3-year values given that many index funds don’t
have exceptionally long track records. As opposed to an actively
managed fund evaluation, it won’t make as much difference here
because index funds tend to closely track their benchmarks in all
market conditions.
| Categories |
Selections |
| Product Type |
Funds and ETFs |
| Asset Type |
Stock |
| Track Record |
3 Years |
| Domestic Equity |
Small Cap, Blend |
| Criteria |
K4 Factor |
Weight |
| Major Drag on Net Return |
Expense Ratio |
High |
| Return |
3-Year Return +/- Category Index |
Highest |
| Benchmark Relative Risk |
3-Year Beta (Category Index) |
Lowest |
| Downside Protection |
5-Year Down Market Ratio |
Medium |
| Filter |
Limits |
| Index or Active |
Index |
| 3-Year R2 (Category Index) |
≥ 95 |
| 3-Year Relative Standard Deviation |
≤ 1.08 |
| Best Fit Index |
Russell 2000 |
| Distinct Portfolio |
Yes |
Results
It might seem odd that Return +/- Category
Index is the most heavily weighted factor.
In the world of index funds, a few basis points can be a big
differentiator. While most stay close to their benchmarks, minor
differences between NAV and ask prices or an index fund’s asset-lending policies can add a few basis points here and there.
Expense Ratio is important for the same reason. Any reduction
there will have a noticeable impact on net return. It’s important
that the fund’s beta be close to that of the index because you want
the risk as well as the return to closely track the benchmark. The
Down Market Ratio is included as a gauge of the fund’s ability to
stick near the index when times are tough.
Failure to do so can quickly leave the fund well below the
index, a gap that will be difficult if not impossible to close.
Until the index filter is applied, the scenario
covers all funds in the category. The Best Fit Index filter
eliminates any funds with a best fit other than the Category Index.
In putting this model together, we found several funds (particularly
in the mid cap categories) where this occurred. You might be willing
to accept an actively managed fund with a best fit outside the
category, but that makes no sense if you are seeking an index fund
to represent the category. The R-Square filter also requires a high
correlation with the index while the Relative Standard Deviation
filter eliminates funds that are substantially riskier than the
index.
When it’s time to review the funds, K4 Fund Selection users can simply copy
and rename each scenario with the current date. When you open a
copy, you can proceed directly to the results page and view the
updated data. The update is automatic with no data downloads or information to set up. You can then
compare the current results to those in the original scenario. This
is also a simple means of creating an ongoing archive of your
analyses to track the funds over time.
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