…“more” is not always the best way to “Great”. Fritz
2050 here would be age 65 retirement
Gordon Irlam AAcalc.com
Rather than a rule of thumb it is also possible to compute an optimal variable withdrawal strategy if it is assumed annual returns are from a known distribution that is independent over time by using the Stochastic Dynamic Programming (SDP) algorithm ... To compute the variable withdrawal strategy using SDP it is only necessary to treat the withdrawal amount as a dimension to be optimized over analogous to an asset allocation dimension. That is working backwards by age, for each portfolio size, we consider every asset allocation and withdrawal amount and pick the best one.
RETIREMENT FINANCE AND PLANNING
Planning for a Non-Retirement, CFAinstitute
Much of financial planning focuses on retirement. But what
if your client isn’t planning to retire?
Multivariate Density Modeling for Retirement Finance, Rook,
Stevens Institute
The purpose of this research is to develop a multivariate
PDF for asset returns that is suitable for quantitative retirement plans. The
model fits any set of returns, however the curse of dimensionality will limit
the number of securities. We propose a multivariate mixture having fixed
mixture marginals using normal components. The model is motivated by the claim
that a lognormal PDF is virtually indistinguishable from a mixture of normals.
Whereas the lognormal PDF is intractable with regard to weighted sums, the normal
mixture is not. The lognormal PDF is only justifiable when short-term returns
are iid and the PDF is CLT-compatible for the given sample size. A typical
retiree could endure several market crashes and we should not expect the
historical sample to represent all possible extremes. We can stress test a
retirement plan by subjecting it to a return PDF that has been fit on the
historical sample seeded with black swan events. The normal or lognormal PDF
are unhelpful in this regard as neither can accommodate such outliers. [commentary in note [1] ]
Historical simulations show that an equity glidepath is
useful when the CAPE is high at the commencement of retirement.
As it is today! If the CAPE is below 20, glidepaths are
of no use and an aggressive static equity allocation (close to 100%!!!) has
performed best in historical simulations! … Monte Carlo
simulations miss important elements of real-world data, i.e., mean reversion of
equity valuations and changing asset return correlations. Hence, glidepaths
that were calibrated to do well in Monte Carlo
simulations (Kitces and Pfau) tend to do poorly in historical simulations.
Unless we believe that the past observed dynamics of equity returns no longer
apply in the future, we should disregard the Kitces/Pfau glidepaths because
they’d likely perform worse than even most static asset allocations.
James Altucher Is Right, Retirees Shouldn’t Own A Home,
randomroger.com
These Maps Show Where a Dollar Goes Furthest in the U.S.
VisualCapitalist.com
[commentary: duh!]
MARKETS AND INVESTING
Research Review | 15 September 2017 | Portfolio Management, CapitalSpectator
Swedroe's war on Dividends
[he is either correct, in which case all of this warring is
unnecessary or he is incorrect in which case he is misleading. I think he is a little of both. From a traditional finance theory / Miller
Modigliani perspective (and some empirical evidence to boot) he is correct and
dividend-focused strategies don't matter or maybe have some distinct
disadvantages. On the other hand I
believe there are trading situations, multiperiod decumulation scenarios, and
some utility arguments that at least would give me pause before throwing div
strategies out the window. I have posted
on this before… I like dividends for reasons that are not in Miller (or
Swedroe)]
Quantifying Kahneman-Tversky’s Value Function, Journal of
Personal Finance
…every individual is unique and neither expected utility
theory nor prospect theory appropriately capture the diversity in risk
tolerance. This paper seeks to make Kahneman-Tversky’s research on prospect
theory/behavioral economics, and their value function practical and
user-friendly, thus improving investment decision making.
ALTERNATIVE RISK
What Happens When you Data Mine 2 million Fundamental Quant Strategies, Alphaarchitect.com
After examining all the signals, the authors find only a
handlful of trading strategies that are “anomalous” and most of these
strategies make no economic sense! Now the authors do assume (through their
tests), that the Fama and French 5-factor model plus momentum explain the
cross-section of stock returns (so all the classic characteristics we all argue
about are controlled for in the study), but the author’s main contribution is
that there is little to no evidence for additional anomalies. "If we
properly account for the statistical properties of the data-generating process
and use the FDP approach, we are left with a handful of exceptional investment
opportunities. If we adopt an all-together conservative approach and control
FDP at γ= 1% (i.e., we accept one per cent of lucky discovery among all
discoveries on average or in our sample), we reject all the two million
strategies."
the correlations between the active returns of individual
systematic managers are very low, comparable to those between discretionary
managers. We present empirical evidence of systematic and discretionary
managers’ performance and risk, concluding that neither group has been
inherently better than the other one and that they have historically been good
complements.
Research and systematic investing can overcome motivated cognition, Mark Rzepczynski
The use of a systematic and disciplined investment and
research process is an effective way of reducing the confirmation biases by
making the decision process explicitly based on a set criteria that can be
tested. Decisions rules can be tested against past data and reviewed against
future performance. There may still be biases based on the weighting of the
evidence, but a systematic process can allow for testable analysis. Systematic
investing can eliminate one of the key psychological problems facing investors.
[couldn't have said it better…]
Ways to manage risk, Blue Sky Asset Management
Dynamic hedging strategies that attempt to replicate option
payoffs are by far the cheapest form of
SOCIETY AND CAPITAL
Financial econometrics and machine learning, sr-sv.com
[NYC+50M is not what the rest of the world is like,
certainly not my world. This feels more
like NY gossip than a rational, systematic study. It may be a worthy
conversation to start no doubt but this feels a little politically fashionable
and tendentious before we even get to the facts.]
All cultures are not equal. Or at least they are not equal
in preparing people to be productive in an advanced economy. [article would
require a micro-aggression warning at most colleges today; this story made it
to the WSJ this week and is getting some serious pushback. see also this nymag post]
Why men are not earning more, Cowen
Most younger men ended up with less because they started out
earning less than their counterparts in previous years, and saw little growth
in their early years. They entered the work force with lower wages and never
caught up.
Gender and Bubbles, ssrn
We report data from double-auction experiments in China
and the U.S.
using groups of exclusively females, exclusively males and mixed gender
participants. We find that female groups in China
generate price bubbles statistically identical to those produced by exclusively
male groups in both China
and the U.S. ,
all of which are significantly larger than the bubbles produced by exclusively
female groups in the U.S.
Our results suggest that gender differences in financial markets may be
sensitive to culture.
Does Gender Matter on Wall Street? AlphaArchitect
The general literature documents that, outside Wall Street,
there is persistent gender gap with few women at the top. To climb the corporate
ladder you need both outstanding performance and positive subjective
evaluations by others. This paper adds to this literature because it shows
asymmetries between how men and women benefit from social ties.
A Price on Your Head, humbledollar.com
three assets with potentially significant value are our
regular paycheck, our Social Security retirement benefit and any traditional
employer pension we’re entitled to.
Social Proof in the Markets, Ben Carlson
Social proof is the idea that we look to others to figure
out what the correct behavior should be. We follow narratives instead of
evidence. It feels more comfortable to go along with the crowd when making
tough decisions because we look at what others are doing in times of
uncertainty.
The real innovation at the heart of what’s been named the
“sharing economy” is the realization that such peer-to-peer matching can
transform markets for services such as transportation, lodging, and general
errands. Those who carelessly speculate that these platforms will change the
role of private property will inevitably be disappointed—the so-called sharing
economy is another way that free market forces have evolved to put that
property to its best use.
---------
[1] the paper above (Multivariate Density Modeling for
Retirement Finance) was a wee bit above my pay-grade and might be geared more
towards tenure committees and co-academics rather than even the most advanced
practitioners. The paper is an exercise
in quantitative virtuosity. The quantitative virtuosity, however, will neither
uncover a way to predict the future nor will it extract new money out of thin
air so while it might move the academic needle a bit it will not change the basic retirement game in any fundamental way (yet), a game whose rules are to allocate finite
resources over an unknowable future.
(see my recent hurricane post; even the best quantitative forecasts go
stale really, really fast so the game is not to build a better forecaster it is
to do faster cycles of analysis and adaptation). I mean, if he finds more risk,
we will spend less early to account for that since there are only so many ways
to cut a pie. If he finds less, we'll maybe spend a little more or add to
legacy. But either way the need to re-evaluate periodically won't change in the
slightest.
The wisest things in this paper are not necessarily even the math. The good stuff is 1) a fairly subdued
consideration of stress testing retirement plans, which makes sense, and 2) an
idea for seeding a return distribution with some extra black swan events. That
last I like and has been on my drawing board for a while. I did it once before with "spend
shocks" with predictable results and I was thinking of doing it again with
a return distribution in my simulator in a similar fashion. Right now I either sample with replacement from
an S&P distribution for equities (in addition to bonds; I get a decent
historically-derived non-normal distribution this way) or I sample from a
skewable (haven't found a good kurtos-able one yet) distribution that
represents an alt risk premium or a third asset. I know I can either customize
the distribution -- or an equivalent probability distribution vector for
sampling -- in order to wreak havoc on the downside or alternatively introduce
crashes as a random event as a layer over the regular process. Either way, one gets a robust test of the
downside; who worries about the upside risk anyway? (remember that simulation
all by itself, if run enough times, will find some pretty bad paths that are
way worse than the historical record. That is the point of simulation. I guess
I'm not really sure how making it even worse than that adds a ton of value
other than confirming the obvious: spending a ton early in retirement -- if you
happen to have the luck of the draw later and superannuate -- is risky).
On another note, this paper is a good example of what I was
talking about in a past post. New good ideas, and there are no doubt some new
good ideas in here, need to find a more efficient way to flow downstream to
normal advisors and at least some advanced retail retirees. That won't happen
here. The paper is too dense so I know I can't do it and the probability that
retirement researchers or advanced practitioners will look at this and then
digest it and publish something usable in a meaningful timeframe for me
personally is doubtful. This thing will
sink into the analytic sands for a long while.
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