Book mention: Retirement Income Redesigned: Master Plans for Distribution -- An Adviser's Guide

Retirement Income Redesigned: Master Plans for Distribution -- An Adviser's Guide for Funding Boomers' Best Years Hardcover – April 1, 2006

by Harold Evensky  (Editor), Deena B. Katz (Editor), Walter Updegrave (Foreword)

I had a chance to plow through this recently.  2006 already seems like long ago but the issues are still fresh.  This is not a review just a mention.  I am mentioning it because I have read about a million finance books and quite a few on retirement.  Mass market books on retirement all kinda look the same and their table of contents are more or less interchangeable.  This one is different and I liked it.  It is targeted to advisers and it shows. The book is less of a book and more of an anthology of essays by leading practitioners and researchers on a wide array of important topics related to solving the almost unsolvable retirement problem.  And they don't shy away from hard topics and quantitative analysis, which I appreciate.  Contributors include: William Bengen, Laurence Booth, Joel Bruckenstein, Rick Carey, April Caudil, Harold Evensky, Matthew Greenwald, Michael Henkel, Roger Ibbotsen, Deena Katz , Moshe Milevsky, Jim Otar, and others.  Worth a look for non-advisors that like a deep dive.  You'll come away with a pretty good appreciation of how hard the problem really is. 

Another Simple Formula for Spending or Ruin Risk Estimation

Here is another "simple formula" [1] that I picked up from a 2005 CFA article by Milevsky and Robinson.  They present there a way of estimating the risk of ruin without simulation.  The advantage here might be that the assumptions become more transparent and simple, it provides "intuition on the financial trade-off between retirement risk and return" (Milevsky), and one is freed a bit from advisors bearing black boxes. The con might be that it might cut some corners and does not match reality very well but then no simulator does that either.  I view these simple formulas and rules of thumb as useful tools when one is trying to triangulate in on retirement issues by using many tools and points of view to come to a broader conclusion about impending risk.  None of them individually has an "answer." All of them together can help a little.

Putting Optimized Dynamic Allocations Back Into a Simulator

Maybe this has been done before, maybe not.  In the end the analysis in this post does not look like it moves the needle a whole lot as far as I can tell so it probably doesn’t matter much anyway especially since I think that spending control is a much stronger lever than asset allocation when responding to changing levels of risk.  The question I am exploring here is: if one were to take the dynamic asset allocation recommendations that "backward induction optimization" results might imply, and that I tried to generate in a prior post [1], would it, if plugged into a forward-looking simulator, do anything interesting to fail rates or the duration of simulated fails in a very simple generic and artificial retirement plan? 

Weekend Links - Jan 27 17

QUOTE OF THE DAY

“One thing I’ve learned is that in the investment business when you hear the word ‘never,’ it’s about to happen.” – Jeffrey Gundlach

CHART OF THE DAY

Incarceration rates/100k 1978-2015

https://metricmaps.files.wordpress.com/2017/01/incar.gif

RETIREMENT FINANCE AND PLANNING

Applying a Systematic Investment Process to DistributivePortfolios - A 150 Year Study Demonstrating Enhanced Outcomes Through Trend Following, Blueprint Investment Partners.  …we seek to analyze the effects on the sustainability of various initial distribution rates when applying a systematic risk management strategy to traditionally constructed portfolios. The systematic risk management strategy examined in this paper will be referred to as trend following…Our findings indicate that portfolios with trend following tend to survive more frequently and last longer during failures than similarly allocated buy and hold portfolios. 

On Hacking Out a (Really) Rough Asset Allocation Optimizer by Using Backward Induction and Dynamic Stochastic Programming.

This post marks the near-completion of a three leg journey into retirement finance and other financial math that I started several years ago after I finally woke up to the risks of early retirement -- of which I had not been sufficiently aware and that I was also a bit slow to acknowledge.  There were many steps along the way, of course, and lots of other retirement math between the "here and there" but the three math legs -- or "projects" -- I'm thinking about in particular here were: 1) use mean variance optimization (or, in my case, less optimization than ex-post "mapping") to contextualize my systematic alt-risk strategy, 2) create a highly customized forward financial simulation tool, and 3) the topic of this post: attempt to calculate optimal asset allocations using dynamic stochastic programming and backward induction.  I'll synthesize what I think about all three legs some other day.  This post is just an attempt at a quick summary about what I did on number three and some thoughts on whether I learned anything along the way.

Looking at 8 Retirement "Rules of Thumb" in Action

See the line below this paragraph? Below that line will be very little perfect science or solidly defend-able math (by me, I mean).  What you will see is what might be called a bad case of pseudo-quantitative impressionism.  Ready? Here is the line; you know what you are getting into after you pass it:

-------------------------------the line---------------------------------------------

Now that we have that established, what I want to do is take a look at the eight rules of thumb I have written about before and see how they work with respect to longevity expectations and real return assumptions.  I might have done this before but there are a couple new rules since then. I like rules of thumb because I hate black boxes and I especially hate being asked to pay for black boxes that are proprietary.  Rules of thumb are freedom.  They also give individual retirees a toolbox that allows them to adapt to changing circumstances…which they should.  Here is a super-retirement-quant (my label) on adaptation.  

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 

That quote might seem thin at first glance but there is a lot packed into it if you think about it enough, which I have.  Here is the link to the writeup on the eightrules of thumb (ROT).  Go there to see the actual formulas. Note that it's really more like six rules because two of them (#4 and 8) are the same and one of them (#1) is different because it calculates money duration rather than consumption but we can let #1 stand in as the baseline 4% rule while we calculate duration.  In brief, here are the rules: 

Weekend Links - Jan 20 17

QUOTE OF THE DAY

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 

CHART OF THE DAY

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]

Modern longevity shifts vs simulated fail rate estimates

In this link to a Kitces.com article, I highlighted their point of view on the current trend in longevity shifts.  I think their analysis is correct and important.  To quote:

One of the most important assumptions in any financial plan is life expectancy. Assuming too short of a lifespan can result in an excessively high withdrawal rate that depletes all of a client’s assets prior to death. However, despite a desire from financial planners to avoid ever seeing clients run out of money, assuming an unrealistically long lifespan is problematic as well. Excessively low withdrawal rates may lead to a lower quality of life in retirement, a larger than desired legacy inheritance (which the heirs probably won’t complain about, but the retiree might regret!), unfulfilled life goals, and—assuming there may be a relationship between life satisfaction and longevity—possibly even a reduction in lifespan itself!

The basic point of the post, if I have it right, is that while terminal age expectation might be pegged around where it's always been, around 115, the modal expectation is shifting upwards. Or rather the point is that the average longevity expectation is moving up but nothing else much is changing.  The chart from Kitces on this that sums it up is here:

Looking to borrow a copy of the RIIA 2013 curriculum book for RMA

If you have one, I'm interested in taking a look at the RIIA curriculum book for RMA.  I am mostly curious to see what's in there and how it is presented.  It is available on Amazon for $2,000.00.  Why borrow?

1. I'm not planning on going for  an RMA
2. At $2k, buying a copy would be very inconsistent with my no-unecessary-over-consumption policy
3. There appears to be no used market on Amazon or ABE

I'm thinking a month or two at most.

5 simple formulas for retirement are now 9

Update to a previous Post on simple retirement math formulas:

Here are five [now nine, maybe 10 depending on how I count] great, simple little formulas for retirement planning -- all of which are not, you'll notice, the 4% rule. Retirement planning can often be complex if not a completely unanswerable problem domain or as Wade Pfau once opined: "A truly safe withdrawal rate is unknown and unknowable". On the other hand, by using some simple, deterministic formulas to wrap your head around what might or might not work before you get into the deep weeds with a planner and his or her complex, proprietary models I think you can, if not necessarily DIY, at least save a little time and money and make the conversations-to-come more efficient. Moshe Milevsky, in a great article on de-emphasizing complex simulator-based retirement ruin calculations (It’s Time to Retire Ruin (Probabilities) ), points out the pros and big cons of stochastic modeling (e.g., simulators). He tells us, helpfully, that "the highly technical and subtle stochastic 2.0 lecture makes no sense until the deterministic 1.0 lecture is crystal clear." This just means walk with simple formulas before you run with a heavy duty model. So this post is part of the 1.0-walk "lecture." In my mind, some of the benefits of starting with the deterministic 1.0 level include:

Postscript on my spending variance post...

On Dec 30 I posted about spending variance in "More Than I (Or You) Ever Wanted To Know About Spending Variance Vs. Fail Rates."  The conclusion for generic assumptions was that spending variance has almost no impact and that for an unholy mix of assumptions slightly closer to my own there might be some effects but pretty small.

I went back and used assumptions for me as close as I could and then added a new function that creates a custom skewed distribution for spending that looks as close as I could get to what little I know about my own spending variance over the last seven years. When I re-run a sim with and without the custom spending variance (keep in mind it still varies by random inflation either way its just that I add an extra mini-variance "shock" each year in one case) it looks like this (I hate too much personal data out in public so I deleted out the actual fail rates but I'm somewhere less that 10%...down from 80-100% in 2010):

Conclusions:

Systematic Alt-Risk Performance in Review: 2014-2016

Since I have been trading for quite a few years, been through a few hard economic cycles, "retired" (sort of), moved to a foreign country (ok it's only FL but you know what I mean), watched a family split in half, ran an albatross angel-investing-consulting strategy, helped boot-strap and close a hedge fund start-up, etc. etc I thought I'd take a moment to look at some good stuff, performance-wise, while I'm at a relative high point…and while I still can. 

But before we get to the numbers, let's first look at why I went down this path in the fist place so I can connect results to the original reasons for seeking them.  Let's also ignore the underlying purpose of why I do what I do (covered here already) and focus more on the technical reasons why I rolled my own systematic alt risk rather than either "do nothing" or outsource to something like a hedge fund.  My rationale looked like this:

Weekend Links - Jan 13 2017

QUOTE OF THE DAY

“It may be a more difficult time for investors,” Mr. Masters said. But he wouldn’t issue stock market price targets. “I don’t think it makes sense to do that very often,” he said. “The world is too complex for that. We can analyze trends, we can give some probabilities — we can’t really predict the future.”  -- NYT ( Seth J. Masters, chief investment officer of Bernstein Private Wealth Management, predicted [in 2012] that the Dow Jones industrial average would reach 20,000 within the decade)  

CHART OF THE DAY

RETIREMENT FINANCE AND PLANNING

The Opening, the Middle Game and the Endgame, Dirk Cotton, TheRetirementCafe.com key characteristics of retirement finance change as retirement progresses and create what are essentially distinct phases – early retirement, mid-retirement and late retirement – that require unique strategies. A single retirement finance strategy is unlikely to be optimal in all three stages. [Comment:  I agree.  I've been rolling a three stage point of view for 5 years now.] 

Five games one can play with a longevity-varying simulator

This post has less to do with any real conclusions about retirement finance or the practical application of analytical tools and more to do with how I squander my time in an early retirement.  You are on your own when it comes to what any of this really means or whether there is any practical use.

I have a amateur-created (that's me) simulator designed for an audience of one (me again) that, among other things (including some idiosyncratic design and assumptions), lets longevity vary by either a Gompertz distribution or the SS 2013 Life table, the latter of which is my default. Most simulators make you pick an end age or a fixed planning duration.  Since longevity uncertainty is one of the big uncertainties I always thought that a fixed duration analysis was a little weird.

Once longevity varies it gets a little harder to interpret, of course, but on the other hand it also gets a little bit more interesting.  Here below are some of the ways I am starting to look at sim output (or rather let's call this the software games I play when I am not cooking or cleaning or picking up after three kids) now that I have a little more flexibility in my simulator.   For the moment let's forget some of the very deep weaknesses in leaning on simulators for any kind of hard conclusions about retirement (See for example Milevsky and Blanchett on simulation shortcomings). This is just for fun.

Here is an empirical window into the real "4% rule"

So I was in a conversation yesterday with a couple financial advisors that have managed clients from average/normal to ultra high net worth over a period of decades.  When we were talking (ok, I was talking about it because this is a cause with me) about the outsize impact that spending behavior has on retirements -- over and above asset allocation or fees or even taxes -- one of them let slip that no small number of what we might call their "privileged" clients (esp the ones that had to traverse the early 2000s and 2008 and 9) had to go back to work at an age where they didn't want to, or even couldn't, go back to work because they had hewed way too closely to the 4% rule and it's supposed safety over longish retirements. Their clients destroyed themselves through over-consumption and overconfidence. RIP and QED on 4%! Me? I am not going down that path.  Wade Pfau makes a case that it might be closer to 2.6% now given current market conditions. Evan Inglis has a great super-simple rule of thumb that says divide age by 20 (so for a 60 year old, that's 3%; even I can do that math) for a safe spend rate per age that is also quite a bit below 4 for most of us.  Buyer beware and shame on advisors that have a plan for spending 5 or 6%.  Maybe it will work out for you and maybe they have super special ultra spending rules they put in that folder they are sliding towards you on a conference room table but I can guarantee you this: they will NOT be there for you when it doesn't work out. Maybe your kids will be there but even that is a long shot.   As much as I detest people quoting Buffett, I have to go back to the well: "We never want to count on the kindness of strangers in order to meet tomorrow's obligations."

Max Human Age Probably Isn't Changing But How We Get There Is

From Kitces.com: Squaring-The-Survival-Curve And What It Means For Retirement Planning

But the fundamental point is simply to understand that the ongoing rise in life expectancies doesn’t necessarily mean that someday everyone is going to live to age 150 and beyond. It may simply mean that more of us will live to approach what appears to be a “maximum” human lifespan around age 115… and in fact, recent shifts in who is living longer (and who is not) suggests that we may have already hit that longevity wall.

Constant Relative Risk Aversion Utility and Retirement Investing/Consumption

I used to have a direct reporting relationship to a CEO-boss of a mid-size US public company where if he said something once it was generally regarded as imperative and immediately so.  If he said it twice yellow lights needed to start flashing in my head.  And if he said it three times: "mayday mayday we're going down." I learned to tune into patterns of repetition.  So, now three times in the last year when I have read something about retirement finance I have run into something in the article or footnotes about relative-risk utility functions and certainty equivalents as it relates to retirement investing or consumption. I won't call it a maday situation here but I did feel that I needed to make sure that I understood what they were talking about.  Here are three examples of links to something where this has come up:

• Joe Tomlinson, Combining SPIAs and Smoothing to Improve Retirement Outcomes
• David Blanchett's PhD dissertation, Three Essays In Retirement Investing
• John Walton, A New Framework for Comparing Withdrawal Strategies

OK, so I dove into the math and I think I finally made sense of at least part of it after a couple days -- including solving for certainty equivalents by inverting a U function used against different probabilistic outcomes.  I won't bore us with the details here. While there are many utility functions that can map to reasonable real-world behavior I at least have a bead on how I might fold it into my own analysis (future posts will reveal if I actually made sense of it or if I made a totally embarrassing hash of it). In the meantime I thought that this quote from Aswath Damodaran at the Stern School puts all of this utility stuff in a proper context before I get too far down the road and before I take the research efforts and conclusions of others too seriously:

While utility functions have been mined by economists to derive elegant and powerful models, there are niggling details about them that should give us pause. The first is that no single utility function seems to fit aggregate human behavior very well. The second is that the utility functions that are easiest to work with, such as the quadratic utility functions, yield profoundly counter intuitive predictions about how humans will react to risk.  The third is that there are such wide differences across individuals when it comes to risk aversion that finding a utility function to fit the representative investor or individual seems like an exercise in futility. Notwithstanding these limitations, a working knowledge of the basics of utility theory is a prerequisite for sensible risk management. 

And we haven't even discussed at this point whether or how risk aversion changes over time or in response to current changing conditions.  Mine probably changes every day because I am at least as irrational as everyone else.

Another R project, This Time for Futures Options

Just for fun I rewrote my futures-options premium-hunter tool in R in addition to my other projects.  Actually it was more than for fun.  I don't do one of my better "have-an-edge" strategies very often for two reasons: 1) it has intimidating non-linear risk, and 2) it's a hassle to fiddle around with the data I like to see to make a decision; often it takes me 45 min or more so I usually say "why bother, time for lunch or I gotta pick up the kids at school soon." Now I can kick it out in 30 seconds rather than an hour.  It still has scary non-linear risk but that's a different issue.  Here is the R version so far with example using March soybeans (ZS); focus is on short options:

Weekend Links - Jan 6

QUOTE OF THE DAY

"We never want to count on the kindness of strangers in order to meet tomorrow's obligations." - attributed to Buffet (I am not necessarily a big fan of Buffett hagiography or quotes...but I do like this one for reasons that are all my own).  

CHART OF THE DAY

Optical Connections to Light

Just for fun, I pulled this out of the closet. This is an adapted essay I wrote 13 years ago on optics and the nature of light. I don't think much has changed since 2004 although I hear that some scientists now think that the speed of light has not always been constant.  This has no purpose in a retirement finance blog but then again, why not?

A Preliminary Look at Optimizing Equity Allocations for Spending Shocks

I realize that: A) simulators need to be taken, for a lot of reasons, with a big fat grain of salt, no matter who designs or runs them, and B) mine has an assortment of idiosyncratic design assumptions that might get some derisory treatment from researchers or practitioners, but…I thought this was interesting.  The chart below uses a grab bag of not completely realistic input assumptions (not to mention the design assumptions again) -- that I won't regurgitate here -- that include a mash-up of some of my own data as well as some generic hypotheticals…so that this is probably not very practical in terms of being extensible to anyone anywhere. But for the design and assumptions I did use it looks like it might be fair to  conclude that an expectation of some reasonable probability of a spending shock[1] might require (if one has not reserved capital for the possibility, I guess) nudging equity allocations a little higher…again, for this hybrid set of assumptions only.  At some point I'll run this either on me or a more consistently generic and robust set of assumptions and seen what happens.  

-------------------

[1] in this particular case, one that I was just using to shake out some software testing stuff,  the toggle variable was adding a 5% chance of a (3 x initial spend) shock to any given sim-year.