In
Revenue Procedure 2015-36, the Internal Revenue Service (IRS) expands its
pre-approved plans program to include defined benefit plans with cash balance
plan features and defined contribution plans with employee stock ownership plan
(ESOP) features.
An
entity wishing to submit a master and prototype (M&P) or volume submitter
(VS) basic plan needs to represent to the IRS that it has at least 15 employer
clients that will adopt the plan. If submitting two or more basic plan documents,
the entity must represent to the IRS that it has at least 30 employer clients
in the aggregate, each of which is expected to adopt at least one of the
sponsor’s basic plan documents.
The
Revenue Procedure sets forth additional provisions required by M&P ESOPs
and cash balance plans, and modifies prior rules with regard to the eligibility
of employees to participate in an ESOP.
Opinion
letters will not be issued by the IRS for multiemployer plans, union plans,
stock bonus plans other than ESOPs, ESOPs that are a combination of a stock
bonus plan and a money purchase plan, and ESOPs that provide for the holding of
preferred employer stock. Opinion letters also will not be issued for hybrid
plans that contain certain features.
The
IRS previously announced its expectation to modify Revenue Procedure 2011-49 to expand the preapproved
program to include cash balance plans and ESOPs, and that tools would be
available before June 30 to assist plan sponsors in drafting these plans.
Revenue Procedure
2015-36 also extends to October 30, 2015, the deadline for submitting on-cycle
applications for opinion and advisory letters for pre-approved defined benefit
plans for the plans’ second six-year remedial amendment cycle. Text of the
Revenue Procedure is here.
Behavioral Finance Q&A with Shachar Kariv – Part 1
PLANSPONSOR talks with U.C. Berkeley's economics department chair about the role of “decision science” in improving participant decisionmaking and closing the retirement income gap.
Shachar Kariv is the Benjamin N. Ward Professor of Economics
and the Economics Department Chair at University of California, Berkeley. Like other thought leaders,
Kariv believes “decision science” is redefining the way people save and invest
money, especially for retirement.
He admits retirement readiness and decision theory aren’t
exactly the standard fare for economists in his position—but the trillions of
dollars Americans have saved in the form of tax-qualified retirement assets
comprise a critical piece of the U.S. investing landscape, he says. Beyond
this, it is vital for a healthy economic future that Americans save enough to
take financial responsibility for themselves and their families in retirement.
Finally—unlike economic challenges that so commonly break
down by income quartile or political affiliation—everyone who hopes to retire
one day, at any income level, must confront the difficult task of giving up
resources today for the benefit of one’s future self.
Q: How does
the chair of economics at U.C. Berkley get interested in personal saving issues
and the thinking around retirement readiness and behavioral finance?
I am a game
theorist and a decision theorist by training. I was drawn to economics because
of the big unanswered questions surrounding financial decision making – such
as, who accumulates wealth and why? And how do we make people better decisions
makers? Our tools are creativity, invention and the language of
mathematics combined with exponential computing power.
Experimental
data and natural data if gathered in sufficiently large and rich data sets can
begin to unlock and explain the actions of human beings and human behavior. With this, we can help people with
retirement, with savings and with the tradeoffs they face in their financial
life.
We are not
mathematicians, we’re not doing this for the sake of the theory—we are using
mathematics and data analytics to help improve financial decision making in the
real world. It takes a universal language to solve a universal problem such as
retirement and mathematics is that language. If you give me a better language,
I will gladly adopt it.
Q: Can you
expand on that? How do you think traditional models that are used in retirement
planning have missed the mark?
More and
more I became concerned about how many of the tools used to understand
individuals are actually wrong, especially in the retirement space. Overreliance
on stated preferences and inconclusive psychology based tools invented by the
industry and behavioral “experts” are geared toward entertaining us with our
quirks and minimally meeting regulatory standards. We must do better and reach for more robust
and innovative techniques to understand and measure people’s preferences. Once we understand peoples’ preferences, only
then can we advise them.
Many times
when I hear criticism about economics, I try to remind people that, economists
and psychologists are trying to explain many of the same areas of human and
group behavior, Economists, however, are willing to be strong and wrong – in
short, we are willing to see our explanations’ of human behavior falsified.
In fact we welcome that. Once proven wrong, then economists can
revise their models and you can identify their mistakes.
We are in a
new age and we need to rely on those methods that are scientifically rigorous
and allow for continuous improvement and innovation. I can argue that in
an era of smart data, psychology, is not equipped to solve the big problems of
retirement and financial decision making. People cannot remember what they purchased
yesterday, let alone accurately state to you their preferences toward risk,
time or legacy issues.
The future
retirement solutions must be based in science in order to scale across millions
of individuals each with unique and differing risk, time and social
preferences. Our research is focused on uncovering individual preferences using
mathematical models to give us more precise measures of human behavior—to
predict human behavior and in turn help retirement decision makers understand
themselves and their options.
A big leap
forward in game theory and decision theory has been the digital revolution—we
have started working with data and collecting huge amounts of data that really
illuminate what we were only able to speculate about before. We had some of
these ideas earlier, but now we can really test them experimentally and in a
scientifically rigorous way.
Working
with data is so important in economics—today we are running experiments that do
not have to rely on the way people say they will spend and save their money.
Instead, we’re actually able to run analyses on real money and the real
financial decisions that individuals have made in the past and over time. This is
a huge leap forward for the quality of the results.
Q: And what
is the data showing us?
Some of the
modeling that is the most compelling and explains the most about the way people
make decisions is what I like to call a ‘trade-off model.’
Let me
explain this: When we look at the way human beings are making decisions,
financial or otherwise, we can derive three fundamental trade-offs that they
are considering and which really offer a strong framework which we can use to
predict the way people will react to certain circumstances. I believe all the
decisions we make in our lives, financial or otherwise, are determined by a
mixture of these three trade-offs.
So the
three big trade-offs are 1) risk potential versus return potential; 2)
gratification today versus gratification tomorrow; and 3) the self versus the
other—meaning both the self versus other people and the current self versus the
future self.
Let’s think
about how this applies to a big financial decision—a question like, how much
money should I save for retirement and where should I keep this money? Of
course we can see how the risk versus return question plays out—do I want to
keep my money in a risk product that could grow quickly and fall quickly? Or do
I want to store this under my mattress and try to protect it until tomorrow?
But we also
have to consider whether we want to trade off income today for the potential of
collecting the income in the future—there is a strong psychological bias to
favor the present self, and the ability to overcome this bias is a huge
indicator of whether someone will be successful in saving for retirement. Quantifying what we call “present bias” is not
as simple as asking someone “are you present biased” – you must recover if this
is true from their decisions. This is
what we do.
The final
determinant is whether the individual cares deeply about things like leaving
money to the next generation, or saving enough so that the younger
generation—family or society in general—will not be burdened in taking care of
the older generation.
If you know
how a person will come down on these three trade-offs, you’ll have a pretty
good idea about how to make them successful in the retirement savings effort. Generic education of retirees without a good
understanding of what we call their Economic Fingerprint is too imprecise—it’s why
we lose people. We need to target educational
content or training to help them overcome psychological biases, such as favoring
current consumption, fearing losses to the detriment of future outcomes.
Q: What
have you learned about how individuals come down on these trade-offs?
Our biggest
finding is that people are very heterogeneous and do not fit into the neat
buckets that the industry has forced them into.
The existing investor profiling methods are crude and statistically
unjustifiable.
You cannot explain
preferences by standard observables such as age, gender, occupation, income or
even IQ. [In the U.S. work force,] if
you take a population of any kind you will find as much or even more diversity
within that group than across the groups. The industry is missing the chance to
better understand investors.
We’ve
learned quite a lot about how people solve the trade-offs from major,
academically verified research panels and studies that we have conducted across
the U.S., Netherlands and forthcoming in China and South Korea.
Q: How does
this apply practically for plan sponsors and advisers? Should they ask these
three questions?
Being that
I’m a game theorist, it won’t surprise you that I’m a big fan of gamification.
That’s why I have gotten involved in a company called Capital Preferences.
Together
with the firm, we have created a series of ‘risk and ambiguity games,’ through
which individuals are asked to make a series of theoretical decisions, which
are structured like retirement investments.
In
each decision you are asked to choose between two investment opportunities,
Investment A and Investment B. In the risk game, there is a 50% probability
that one of the investments will provide a positive profit while the other
returns $0. In the ambiguity game, the probabilities change from a known 50% to
an unknown probability that ranges between 20% and 80%. The potential
combinations of allocations to Investments A and B are represented on a
randomly generated downward sloping budget line where you are asked to pick a point
that represents your ‘portfolio.’ The profit from these portfolios is intended
to represent a meaningful percentage of one’s overall net worth.
Using this
type of a gaming environment helps us get around psychological biases—it helps
individuals look at their own situation more objectively. We encourage them
just to react to the individual questions and to be as honest as possible—and
this is easier because they are not talking about their own wealth, but instead
just a theoretical and abstract choice.
By learning
peoples’ risk preferences this way, we should be able to build the proper
portfolio for their risk needs.
Q: What
other insights have emerged through this psychological and behavioral approach?
I think the
key thing for sponsors and advisers to learn is that there is often a really
big and really important gap between an individuals’ stated risk preferences
and their true risk preferences—what we like to call their revealed risk
preferences, which only come to light through looking at historical data and
through the gamification approach I’ve already described.
We all know
how this happens—we like to share our good intentions when we are asked about
financial decisionmaking.
Today far
too many plan sponsors and advisers are driving their decisions based on plan
participants’ stated preferences, which they gather through a short
questionnaire in many cases. We’ve all seen these—they ask the client to report
their own preferences.
Now, there
are a couple problems here. There are situations in which the client is
attempting to manipulate the results—perhaps they have lost money in
investments before and they have decided they don’t want to take any more risk
in the markets again, so they purposefully answer the questions to make them
seem really risk averse, even knowing they could probably take on some risk and
be fine.
In other
cases, even when the client has no incentive to deceive, they’re just not in
touch with their own true risk preferences. They don’t really know how much
risk they can take, usually because they don’t know what they’ll need in
retirement, so they try to answer out of good intentions. The result is the
same—the person ends up with the wrong portfolio allocation.
Especially
in the retirement space, thinking about what you may want or need 40 years down
the road is next to impossible. One thing we have learned from the data is that
people’s fundamental risk outlook doesn’t tend to change that much over their
lifetime—while other characteristics about them can change substantially. This
is encouraging because if we can get people saving the right way early on, we
can hopefully serve them very well and keep them on track over time.