Investment Menu Construction within ERISA Plans in Light of the 2022 Supreme Court Ruling

By: Jeffery A. Acheson

Jeffery A. Acheson, CPWA®, CFP®, CPFATM, AIF®, CEPA®, NQPATM, CAPPTM has 40 + years in the financial services and retirement plan industries, creating a value proposition that is an exceptional and diversified integration of credentialed education, experience-based knowledge, and industry leadership. His fiduciary based business model focuses on enhancing his ability to be a trusted advisor to high-net-worth individuals, families, businesses, and their mission-critical employees, in addition to retirement plan sponsors and their participants through his private practice, Advanced Strategies Group. Mr. Acheson serves as the Chief Business Development Officer for Independent Financial Partners headquartered in Tampa, FL. He also volunteers within the National Association of Plan Advisors having served as the Chair of the Government Affairs Committee, a member of its Leadership Council and as President of the organization. He currently is an active member of the American Retirement Association’s Board of Directors and is Chairman of NAPA’s nonqualified plan certificate program and annual conference.

In my last column within this publication [The Journal of Pension Benefits, Vol. 29, No. 4, Summer 2022], I provided perspective on the Supreme Court ruling and commentary specific to the Hughes vs. Northwestern University 403(b) case and the potential impact on menu construction within ERISA governed plans going forward. I closed that column by stating that in this subsequent column, I would extend the perspective by offering thoughtful and defensible best practices in menu construction considering the liability exposures that may ultimately manifest driven by this Supreme Court ruling. The following thoughts will be equally applicable to menu construction for newly established plans and menu revisions to already established plans.

The First Line of Defense

As is well established, the Employee Retirement Income Security Act of 1974 (ERISA) holds fiduciaries to a standard of prudence when making investment-related decisions for qualified ERISA plans. The Department of Labor (DOL) and the courts have determined that said standard of prudence can best be determined by a fiduciary’s process, or procedures, used in making such decisions. Although ERISA does not explicitly require a written Investment Policy Statement (IPS), it is considered a best practice to create and maintain one to assist in guiding fiduciaries in plan-related investment decisions. To that end, it is common for DOL investigators to routinely request a copy of the governing IPS during an investigation. Further, the courts have regularly looked to the provisions of a plan’s IPS to determine if fiduciaries undertook a prudent process in making decisions on behalf of the plan and executed their duties in a timely manner as outlined within the document.

This scrutiny of the language within an IPS can be a delicate dance. Best practices warrant the document and its processes neither be too vague nor overly strict to allow plan fiduciaries some degree of latitude in their decision-making process and the timing thereof. However, an overly vague IPS creates no discernable evidence of process that can be used as evidence of a defensible decision-making process if prudence is ever questioned. If a court of law cannot determine the actual steps being taken in determining fiduciary action, the IPS is virtually toothless as a line of defense. Ultimately, documented adherence to memorialized process and procedure outlined in a thoughtfully designed IPS is a fiduciary’s first and best line of defense when prudence is called into question by a regulator or litigator.

Follow the Yellow Brick Road!

You may recall Glinda, the Good Witch of the North in the Wizard of Oz, and her guidance to Dorothy and her traveling companions to “follow the yellow brick road!” in their journey to the Emerald City. The Oxford Learner’s Dictionary subsequently translated that guidance to mean “a course of action that a person takes believing it will lead to good things.” Ultimately, that is the purpose of a well-designed and carefully followed IPS.

One of the key components within an IPS is language outlining the investment menu construction and subsequent monitoring and management process. The first and best place to start is meeting the “low-bar” requirement set forth within ERISA Section 404(c) requiring a “broad range of investment options.” Given that “broad range” is defined as three diversified investment alternatives with risk/return characteristics different from each other, most plans’ investment menus easily clear that hurdle already. In practice, the two questions plan fiduciaries should be asking themselves are:

  1. “How many is too many” in terms of available
    options. This is now especially true given the
    recent Supreme Court rulings that clearly value
    quality over quantity.
  2. How do we best define and measure quality in the
    balancing act between scrutinized cost and performance,
    both historical and expected?

While these two questions will certainly lead to a
lively, long, and unsettled debate among investment
professionals (and their critics), for the sake of this
column, we will focus on a menu construction format
used by many. That format being scrutinized
and monitored selection in each major asset class
as illustrated by the two Morningstar-style boxes

There are two general approaches to populating
an option in each of the core asset class boxes. One
approach is to simply use an index option that mirrors
the market for that asset class in terms of performance
and volatility for the lowest cost possible. Given how
many fiduciary lawsuits have focused on cost above all
else, who could blame a fiduciary for going that route?
However, it is important to note that of late, several
lawsuits have been filed against fiduciaries who were
accused of having overly focused on low costs to the
point they neglected to determine whether investment
returns were inferior to other similar available options.
It will be interesting to see how far the pendulum now
swings in that direction.
Pure index funds provide a passive approach built
to replicate and match, not beat, the performance of an
asset class benchmark. However, this strategy provides
no opportunity for market volatility management,
improved investment performance over what the market
delivers, nor the ability to potentially mitigate negative
triggers that various behavioral finance studies have
shown cause participants to make decisions that are
detrimental to achieving longer-term positive outcomes.

The alternative to just accepting market-based
returns through low-cost index options are actively
managed funds run by investment managers who try
to outperform an assigned benchmark over time, such
as the S&P 500 or Russel 2000. It is this pursuit of
outperformance, in one measure or another, by an
actively managed fund that is the premise for justifying
higher fees than are typically charged by index
fund managers. (See Exhibit 1).

The Importance, Types and Utilization of Benchmarking

Active versus passive management results and cost
comparisons can be made in broad-based terms using
general investment objectives (that is growth vs.
value vs. blend) or within specific asset classes (for
example, large cap growth, small cap value, mid-cap
blend, etc.). However, the deepest and most actionable
analysis opportunities come when comparing a specific
actively managed fund to an index fund that mirrors
the benchmark for a specific asset class. The reason is
the asset class specific index becomes the logical choice
if a more attractive, actively managed option cannot
be identified. The process can be relatively straightforward
to initiate as individual funds and investment
portfolios will generally identify “best-fit” benchmarks
for standard comparative analysis.
As a refresher, a benchmark is a standard or measure
that can be used to analyze the allocation, risk,
return, volatility and cost of an alternative portfolio.
Benchmarks are built to represent a portfolio
of unmanaged securities representing a designated
market segment. Institutions have built index funds
to replicate these hypothetical portfolios known
as indexes as it is not possible to invest in a benchmark
itself. A benchmark can include broad measures,
such as the Russell 1000 or specific asset classes like
US small-cap growth stocks, high-yield bonds, or emerging markets and can extend to very granular and focused options such as sector or country specific funds.
As an alternative, some benchmarking analysis tools
are built around Morningstar category averages which
are equal-weighted category returns. The calculation
is simply the average of the returns for all the funds
in each category or asset class. The standard category
average calculation is based on constituents of the
category at the end of the period. As with a benchmark,
it is also not possible to invest directly in a
Morningstar category average.

The Devil is in the Details!

With any kind of analysis, the devil is in the
details and the interpretation thereof. When it comes
to deciding whether to invest in an asset class specific
fund that is a low-cost index, or a higher priced
actively managed option, the question comes down to
whether the active manager can provide something of
value to warrant the higher cost. In laymen’s language,
“Is the juice worth the squeeze?” Many of the current
analytical tools available to retirement plan advisors
use 10 to 15 data points to try and assess whether the
extra “squeeze” of added expense delivers sufficient
“extra juice” to warrant selecting a more expensive
active manager over a less expensive passive option. I
have selected a few data points to help illustrate the

Absolute Performance

Absolute Performance comparatives over historical
periods (that is, 1, 3, 5, 10 years) are used to
determine “Alpha” which is a term used in investing
to describe an investment manager’s or strategy’s
ability to “beat the market” in both up and down
markets. Alpha is often referred to as “excess return”
or “abnormal rate of return,” which refers to the idea
that markets are efficient, and as such, there is no way
to systematically earn returns that exceed the broad
market as a whole over time. Alpha can be either
negative or positive in any given measurement period.
Following is a chart built from an analytical tool used
by this columnist to make such assessments. The
names and tickers of the active and passive funds used
in this comparative have been purposely withheld as
this chart and its data are for illustrative and educational
purposes only. As you will see with this comparative,
the actively managed fund delivered positive
alpha and returns in excess of both the available asset
class-specific index fund and the applicable benchmarks,
thus overcoming its higher expense ratio and
laying the groundwork for its selection over the index
fund as being “prudent.” Of important note, the index
fund typically will have negative alpha due to the drag
of the expense ratio. (See Exhibit 2)

Upside/Downside Capture Ratio

However, end-of-period raw performance does not
measure the volatility endured during a selected measurement
period by an active manager, even one with
positive alpha outcome. The term “Upside/Downside
Capture Ratio” might sound geeky, but the concept
is straightforward. In short, the statistics show
whether a given fund has outperformed—gained
more or lost less than—a broad market benchmark
during periods of market strength and weakness, and
if so, by how much. An upside capture ratio over one
hundred indicates a fund generally has outperformed
the benchmark during periods of positive returns.
Meanwhile, a downside capture ratio of less than one
hundred indicates that a fund has lost less than its
benchmark in periods when the benchmark has been
in the red. An optimal historical outcome would be outperformance in an up-market period while losing less than the benchmark when the market turned negative. (See Exhibit 3)

Value at Risk

Value at Risk (VaR) is another statistic that
attempts to quantify the possible severity of financial
losses within a firm, portfolio, or position over a specific
period. There are three main methods of computing
VaR which are (1) historical, (2) Monte Carlo, and
(3) variance-covariance.
The historical method looks at prior return history
and orders them from worst losses to greatest gains—
following the premise that past returns experience will
inform future outcomes.
A second approach to VaR is to conduct a Monte
Carlo simulation. This technique uses computational
models to simulate projected returns over hundreds
or thousands of iterations. Then, it takes the chance
that a loss will occur, say 35 percent of the time, and
reveals the impact.

This column focuses on the final approach which
is the variance-covariance method, otherwise known
as standard deviation. Rather than assuming the past
will inform the future, this method assumes that gains
and losses are normally distributed over time. This
way, potential losses can be framed in terms of standard
deviation events from the mean (that is, expected
return for the measurement period) allowing for
probabilities of certain outcome ranges. The smaller
an investment’s standard deviation, the less volatility
incurred. The larger the standard deviation, the more
dispersed those returns are and thus the riskier the
investment is. As a metaphor, think of a bowling alley
with bumpers in the gutters. How often and how far
from center either way—positive or negative—will the
ball drift before incurring a correction by a bumper in
pursuit of the proverbial “strike right down the middle”
and deliver the expected return? In other words,
what percentage of the returns stay toward the middle
versus the extremes, whether positive or negative?
A visual of this methodology would be the Bell
Curve (see Exhibit 4)

illustrating that 68 percent of the historical returns were within one standard deviation of a certain percentage range above or below an
expected mean return with 95 percent of the historical
returns within two standard deviations, etc.
Ultimately, this is an exercise of using probabilities to measure risk exposure to find a level of comfort with loss exposures.
As an example, using our actively managed sample fund data, if the historical mean return was8 percent, and our historical standard deviation was measured at a +/- 17.39 percent, our expected
range of returns for any given period would between
+25.39 percent (8 percent + 17.39 percent) and
-9.39 percent (8 percent – 17.39 percent). Returns
during two standard deviation environments (volatile
bull or bear market) would be in a range of
+42.78 (8 percent + 17.39 percent + 17.39 percent)
or -26.78 percent (8 percent – 17.39 percent -17.39
percent). The standard deviation to the negative
helps quantify the Value at Risk at any given
time. Needless to say, three or even four Standard
Deviation time frames are possible (that is, 2000-
2002, 2008-2009, and the COVID start to 2020)
which exacerbated the Value at Risk realization. It
is also important to note in the following example, the
Standard Deviation is less for the actively managed
fund than for the passive index indication historically
the active option had been less volatile or “risky.”
Again, important to remember past performance does
not guarantee future performance.
As an example, using our actively managed sample
fund data, if the historical mean return was determined
to be 8 percent, and the historical standard
deviation was measured at +/- 17.39 percent, it would
indicate our expected range of returns 68 percent of
the time would be between a positive 25.39 percent
(8 percent + 17.39 percent) and negative 9.39 percent
(8 percent – 17.39 percent). Further, 95 percent of the
time the returns would be between positive 42.78 (8
percent + 17.39 percent + 17.39 percent) and negative
26.78 percent (8 percent – 17.39 percent -17.39
percent). The negative standard deviation from the
mean helps quantify the “Value at Risk” at any given
time. Of course, performance extending out three
or even four Standard Deviations are possible as was
the case in 2000-2002, 2008-2009 and the COVID
start to 2020 which exacerbated the Value at Risk
realization. It is also important to note in following
comparative, the Standard Deviation was less for
the actively managed fund than for the passive index
indicating the active option had historically been less
volatile or “risky.” Again, important to remember past
performance does not guarantee future performance.
(See Exhibit 5)

Style Drift

Style Drift is the divergence of a fund from its
investment style or objective. Style drift can result
naturally from capital appreciation in one asset relative
to others in a portfolio. It can also occur from a
change in the fund’s management or a manager who
begins to diverge without warning from the portfolio’s
mandate. Generally, a portfolio manager staying true
to their advertised stated investment management
style or focus is a positive investment quality. For
obvious reasons, consistency in this area is preferable
to style drift for several reasons. First, Managers chasing
performance by using different strategies are often
counterproductive and duplicative of other options on
the investment menu. In addition, it will potentially
not only change the risk-return profile of the fund but
also the plan’s overall style box equilibrium, setting
the stage for potential unintended consequences for
the plan’s fiduciaries.
There are many other quantitative and qualitative
measurements than can and should be used to subjectively
assess the quality of potential 401(k) options.
I have focused on these select few to emphasize the
importance of risk and volatility assessment alongside
cost and raw performance when assessing the impact
on participant behavior and potential fiduciary liability

Keep Their Eyes on the Prize

Common sense dictates participants are better long-term
investors when returns are consistent and less volatile.
In addition, basic math dictates an easier path to
better long-term returns is to try and avoid large losses
along the way. The following exhibits illustrate these
points using two hypothetical funds. (See Exhibit 6)
The average return for both funds over the last 10
years was 10 percent. However, each took a different
path in arriving at the common outcome. Let’s look at
how each fund came—in any given year—to the average
10 percent.

In Exhibit 7, XYZ Fund only realized an actual 10
percent return during Year 9. In the other years, the
return was much higher (Year 7) or much lower
(Year 2). The behavioral finance psychology of 401(k)
investors and the attorneys who make their living finding fault with Plan Sponsors and service providers’ decisions, may take issue with a selection if they discount a long-term perspective and only focus on
performance at a specific point in time in a market
cycle that is negative. In other words, short-term perspective
can trump long-term objectives.

Now, let’s take a look at the annual returns of ABC
Fund, which also had a 10 percent average return for
the last 10 years. (See Exhibit 8)

Ultimately, behavioral finance studies show that
participants are better long-term investors and less inclined to be victims of their short-term emotions if annual returns are more in line with their expectations. As such, funds with “smoother rides” while in
pursuit of comparable returns to more volatile options,
tend to elicit better investor behavior when it comes
to sticking to long-term objectives. This is primarily
due to alleviating the fear of an insurmountable loss
overriding their optimism that a long-term goal can
still be reached if they simply ride out the storm of

The Math of Loss Recovery

There is no argument that investment performance
and costs are critically important when comparing and
analyzing potential options. However, just as important,
is analyzing and comparing past performance
consistency, volatility, and the estimated value at risk
in down markets. The math of loss recovery can be
overwhelming if losses are incurred by the faint of
heart or by those close to the changeover from accumulation
to deaccumulation in their retirement planning.
Exhibit 9 clearly illustrates the math that strikes
fear into the hearts of many investors and usually right
before an ill-timed decision to sell low and later buy
high if, they get back into the market at all.

Final Thoughts

Ultimately, prudence in menu construction comes
down to applying a thoughtful process and methodology
on an asset class by asset class and fund by fund basis. The concept of quantity over quality as a defensible position has been deemed invalid by the Supreme Court case referenced. As such, a plan fiduciary would
be well-advised to have a renewed focus on a defensible
integration of analyzed cost, performance, volatility,
and risk assessments.
One strategy would be to start with the premise of
an appropriate index fund for each asset class within
the overall master style box configuration for the
plan. Then, work with an advisor who will perform
exhaustive due diligence to determine if actively managed
alternatives are available and provide sufficient historical performance to validate that fund’s past ability to overcome the cost differential when compared to the index fund. That validation may be in the form
of improved raw performance over time driven by
Alpha, or by delivering attractive annual performance
consistency or reduced volatility when compared to
its applicable index alternative. While past performance
is never a guarantee of future performance, it
is at least a place to start when building a case for
documented adherence to a memorialized process and

Copyright © 2022 CCH Incorporated. All Rights Reserved.

Reprinted from Journal of Pension Benefits, Summer 2022, Volume 29, Number 4, pages 39–42, with permission from Wolters Kluwer, New York, NY, 1-800-638-8437,