A [retirement] scheme that does not respond to the performance of the
portfolio is likely to underperform one that is responsive to portfolio
performance. - Gordan Irlam
RETIREMENT FINANCE AND PLANNING
What are Longevity Goals? Bob French at Retirement
Researcher. Life expectancy is one of
the trickiest parts of financial planning. We can look at actuarial tables and
come up with dates when you should pass away with given levels of certainty,
but those are nothing but statistical estimates. Statistics work best for large pension funds
and life insurance providers. The group of people those entities cover are so
big that they tend to end up looking like the historical averages. They may be
off a little bit, but overall, the actuarial tables are probably going to do a
pretty good job of predicting what will happen.
The Opening Game, Dirk Cotton. the opening of retirement may be the most
expensive of the three games…Key risks of the Opening Game include forced
retirement, the “Tax Torpedo”, and sequence of returns risk… The Opening Game
has its own risks and rewards and decisions made in early retirement can have a
dramatic impact on later games. A good retirement strategy will not only
consider the impacts of decisions on the Opening Game but also impacts on later
games. Plan with the understanding that the three games are different, that
each may require its own strategy, that decisions may affect more than the
current game, and that you won't know what pieces are still in play until you
almost reach the next game.
[comment: while I think a post like this might actually need a little background
and prior knowledge in retirement finance concepts I think that every point
made in this post should be required reading for both early and traditional retirees]
Dynamic Programming Methods For Retirement Income, Wade
Pfau. Due to their mathematical
complexity, dynamic programming methods are mostly discussed in the realm of
academia and have not yet become a common part of the toolkit for individual
retirees…Among the other strategies, they [Tomlinson and Irlam] find that the
closest match to their optimal dynamic programming solution is the RMD-styled
strategy with a fixed 90% stock allocation. Next is a 6.8% fixed percentage
strategy with a 90% stock allocation. In third is a constant inflation-adjusted
spending strategy with a 60% stock allocation. All of the other asset
allocation strategies result in worse outcomes because they lack sufficient
aggressiveness.
Why Monte Carlo Analysis StillMatters And The Risk Of A Retiree Black Swan Is [Probably] Overrated,
Kitces.com. So the reality really is
that planning for black swans isn’t about getting better planning software to
model them: A) because black swans basically don’t exist in annual data; and B)
because you wouldn’t really do anything different anyway, because even a
properly accounted black swan is not material enough to dramatically change a
Monte Carlo analysis or a probability of success. What it’s really about is
formulating the plans about how you’re going to respond to a black swan.
[comment: This article is correct. A while back I added a feature to my simulator that models
unlikely but big spending events. While interesting and important the impact on
fail rates is relatively muted. The more
interesting thing, which Kitces points out, is formulating an action plan for
what to do (e.g., cut spending by precisely $x) if the sceneario plays out.
I've done that a couple times and it is a useful exercise.
I forced myself to figure out exactly what I would need to do to keep spend
rates below 4 or 5 % for a while if the stock portion of my portfolio fell 30
or 40% or more for a few years. Then, to take it even further, I went to my income
statement expense data and figured out what exactly would have to go and how. Hard and unpleasant stuff but necessary]
Measuring the Risk of Running Out of Money in Retirement by
Grant Gardner, Ph.D.; and Sam Pittman, Ph.D. This paper proposes a simple way
to measure sustainability risk—mortality-adjusting—that accounts for the joint
occurrence of being alive and running out of money. Different assumptions
regarding the age of death of a client leads to very different assessments of
retirement sustainability risk. …Planning software and tool providers can help
advisers and their clients make more efficient spending and investment
decisions by incorporating lifespan uncertainty into their planning tools. The
potential errors in sustainability risk assessment caused by using an
arbitrarily chosen deterministic planning horizon, and the inefficiency that
these errors introduce into retirement decision making, are too large to
ignore. [um, well, I agree]
Breaking the 4% rule, J.P. Morgan. [marketing sheet by JP
Morgan covering a utility optimizing retirement approach. ] Maximizing expected lifetime utility (i.e.,
potential derived satisfaction) serves as a more effective benchmark of
retirement withdrawal success than typical measures, such as probability of
failure. Focusing on utility offers a way to quantify how much satisfaction
retirees receive from their portfolio withdrawals. This can help potentially
increase investors’ level of income when they are most apt to enjoy their
retirement dollars, while still avoiding the risk of premature portfolio
depletion. A dynamic approach to
managing withdrawals and asset allocations provides a more effective use of
retirement assets than traditional approaches. Adapting to changes in market
conditions and investors’ specific situations over time can help maximize the
expected lifetime utility generated by retirement assets. This type of dynamic
strategy may help provide greater payout consistency and reduce the likelihood
of either running out of money or accumulating excess wealth that is unlikely
to be used by the investor.
Portfolio Size Matters, Gordon Irlam. Journal Of Personal Finance
Volume 13, Issue 2 In
contrast to target date funds that vary asset allocation by age alone, it is
important to take into account both the client’s age and the client’s portfolio
size relative to spending goals when determining an optimal asset allocation.
Stochastic Dynamic Programming (SDP) is a mathematical optimization technique
that can be used to determine optimal dynamic adjustments to asset allocation
in response to evolving portfolio wealth and time horizons. Using SDP,
portfolio size appears at least as important to asset allocation decisions as
age.
Competing Risks: Death and Ruin, Dirk Cotton. Medical research uses methods of analyzing survival studies that are novel in
retirement research. We use Kaplan-Meier estimates and competing risks analysis
to explore the conditional probability of a retiree outliving her savings as
age progresses, the relationship of the competing risks of death and ruin as
age progresses, and the timing of portfolio failures due to poor market
returns. We find that risk of ruin develops in three stages of a long
retirement: a lowrisk period early in retirement with high sensitivity to
market returns but few portfolio failures, a middle period in which portfolio
failure peaks, and a late period in which death is much more likely than
portfolio ruin.
[ comment: I think this is correct in terms of "three stages." In my own simulation exercises I see this as well even
though I am not explicitly doing Kaplan-Meier (I think I'm doing something vaguely similar but maybe backwards).
Here for example is one output from a longevity-varying simulator.
I have to be careful when looking at this
because the x axis does not represent the age at fail, it represents a "longevity expectation" where the y for a given x is the aggregate
fail rate for all wealth-age pairs that have terminated at that terminal age
and younger. The fail could have happened earlier. The point was to be able trace with one's finger along
the x, find one's longevity planning expectation and see the fail rate and then maybe go a little further out and check it again and then go all the way to an
unrealistic and hyper-conservative max terminal age. As in Dirk's article, it is easy to see the
three stages possibly implied in the chart for this particular example's assumptions: 1) short longevity expectation (58-75) lower fail risk, 2) medium longevity expectation
(75-95) rapid rise in ruin estimates, and 3) late expectation (95+) where death risk
overtakes ruin risk. I didn't keep the
data but the slope max is probably somewhere around the mode of 87]
MARKETS AND INVESTING
How Index Funds Democratize Investing, Academic criticismsof indexing lack economic logic and factual support from the real world.
WSJ. The remedies that the papers
suggest are also troubling. Some academics propose: first, prohibiting managers
of index funds from voting on behalf of shareholders; and second, limiting
investment by index funds to one company per sector, thereby eliminating the
benefit of diversification that investors have relied on since Mr. Markowitz
first published his research more than 50 years ago.
[really!? How can
otherwise smart people be so dumb?]
One of My Investing Pet Peeves, Ben Carlson. If you have 40% of your money in a relatively
stable asset during stock market losses you don’t really have 90% of your
portfolio exposed to risk.
Asset Allocation is Not for the Faint of Heart, Adam Butler, ReSolve. You see, asset allocation is pretty
complicated, and not for the faint of heart. For my money, investors have the
greatest chance of hitting long-term financial goals by investing in the most
diversified portfolio possible (choose your definition), and saving as much as
they reasonably can. Any precision beyond this level is a triumph of hope over
uncertainty.
AQR Alternative Thinking 1Q17: Capital Market Assumptions,
AQR. Our current estimate for the
long-run real return of U.S.
equities is 4.2%, somewhat lower than most other developed markets (average 4.6%)
and emerging markets (5.4%). Our current estimate for U.S.
10-year government bonds’ long-run real return is 0.7%. For U.S.
investmentgrade and high-yield credit we estimate real returns of 1.4% and
2.1%, respectively. For a riskweighted portfolio of commodities we estimate a
long-run real return of around 3%. From
a historical perspective almost all long-only investments have low expected returns
today.
Why 1/N is still a great non-optimal portfolio choice. None of the fourteen portfolio models
consistently dominates 1/N across seven separate datasets (SR and turnover). Victor
DeMiguel
ALTERNATIVE RISK
Diversification: When 1 + 1 < 2? NewFound. in an environment of high equity valuations and
low bond yields, the math for a traditionally allocated balanced portfolio led
to a long-term expected return of nearly 0% in real terms…
Why Some Technical Analysis May No Longer Be Effective: AnInterview With Michael Harris, Forbes. My
opinion based on my own research without making any generalizations is that
after prudent risk and money management is added and a trend-following program
is diversified, it becomes a smart beta strategy. But that is open to
discussion. There are always people who figure out ways of doing things
differently and more efficiently and we should not discount this possibility…the
key to profitable trading is identifying methods that in turn identify market
anomalies before others do.
SOCIETY AND CAPITAL
The Mad Scientists of Monetary Policy: The War on Cash. Ron
Rimkus, CFA Inst. In practice, if the world converted to a cashless society,
Mom could still choose what she buys, how she invests, what she does with her money.
But she would lose the freedom to withhold her money from the banking system…the
ability of the public to choose whether or not to place their money in a bank
acts as a vital restraint on the interests of banks, governments, and other
actors. The checks and balances of the system are at risk.
The power and origin of uncertainty shocks. Sr-sv.com. Uncertainty in economic theory usually means
that agents believe that the value of a relevant parameter is a random drawing
from a specific probability distribution. An uncertainty shock is a change to
that belief in form of a re-assessment of the probability distribution. This
can affect any of its moments, such as mean, standard deviation, skewness (bias
towards upside or downside) and kurtosis (probability of extreme events).
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