Un-retirement Project

In my most recent "weekend links" post I offered this link:

Intention to Unretire, ssrn.  "Although nearly half of retirees follow a nontraditional retirement path that involves partial retirement and/or unretirement (Maestas, 2010), unretirement has received little attention."  

This link reminded me that I have a "Target-2018" un-retirement project.  I am looking to find a way to re-engage with the wider world now that my children are starting to age out of my care.  Corporate is probably off the table but anything from volunteering to board-membership to "Robert Deniro in The Intern" style internship (not totally a joke; I can see why that might work) to entrepreneurial endeavors in anything from seed to mid stage ventures, etc. etc. probably are on the table. While I am sorting out the soup-mix of motives and goals that go into this type of thing, the interested and/or the opinionated are directed to my un-retirement tab... Contact me if it makes sense to do so.



 

Weekend Links - 4/21/2017

QUOTE OF THE DAY

"Retirement is expensive."

"In general, the life-cycle model research shreds conventional wisdom regarding one-size-fits-all rules of portfolio design and management. There appears to be neither an optimal allocation for all seasons nor an optimal withdrawal rule for all portfolios."

                                                         - Patrick Collins

CHART OF THE DAY

RETIREMENT FINANCE AND PLANNING

 4 Ways to ManageSequence of Returns Risk in Retirement, Pfau.  Attempting to sustain a fixed living standard using distributions from a portfolio of volatile assets is an inefficient retirement income strategy. This is a unique source of sequence risk. Four general techniques for managing sequence risk in retirement are highlighted in Exhibit 1.  

The Ultimate Guide to Safe Withdrawal Rates – Part 13: Dynamic Stock-Bond Allocation through Prime Harvesting, Earlyretirementnow.com  Prime Harvesting is an intuitive method to dynamically shift the stock/bond allocation in retirement. In the past, it would have sustained slightly higher withdrawal rates than the fixed percentage rule when it mattered the most: when stocks did poorly right after retirement. We propose one improvement to this methodology; use a smoother version that avoids selling massive amounts of equities all at once. Letting equities rest at the upper guardrail and skimming only the excess equity wealth above the guardrail seems to be a more sensible approach. It not only avoids the discontinuities and jumps in the final asset value chart above but also tends to afford slightly higher SWRs. 

Retirement Roulette, Dirk Cotton.  Like roulette, retirement funding is a series of "rounds"(typically years) during which the retiree makes a series of decisions (bets) and the universe responds. These first two characteristics define what game theorists refer to as a sequential stochastic game against nature. 

Ahhh, so that's why I want to move west and why northern MN was such a joy

No one lives there...

One more geo game - sensitivity to volatility

I've been playing a geometric return game in excel but that was limiting. It's quick to play but a burden to simulate something more than x,000 times which makes the results of simple expected value calcs sometimes unstable. So I switched to R for this game.  This time I wanted to see graphically how sensitive the cumulative geometric return is to volatility over short horizons.  Two strategies, unfairly cherry picked for the visual, are: 1) 9% return 20% std dev, 2) 7% return 10% std dev.  Then I ran each for 20 periods with a random draw each period calculating the cumulative geo return along the way. I was using an r function for the draw (rsnorm()) which I know is not realistic in terms of the return distribution but was an amateur convenience, something I'll work on later. I then repeated x times, in this case 10,000. I then did all that again with strategy 1 volatility changed in 2% increments to 22, 24 and 26% (dotted blue). Result, without interpretation:

Time Effects of Geometric Returns, Now With Spending

One of the reasons that retirement finance is interesting, in addition to the fact that at least a couple Noble Laureates have called it one of the hardest problems they have faced, is that it is quite a bit different than institutional or accumulation stage investing.  In both of those latter, the impulse is to 1) treat horizons as infinite or at least very long, 2) ignore or suppress the concept of spending or consumption, and 3) to simplify things think of it as a single period investment problem.  That's also why simulators are so common in retirement analysis. They take the concept of finite horizons, multiple periods, and spending, among other things, head on.  Here is another way to look at mutli-period short-horizon geometric returns with a spending element thrown in for flavor.

My Own Private Idaho Of Utility - A Case Study In Spending Control

Past a certain level of income, what you need is just what sits below your ego. Morgan Housel 

I have fiddled around a lot with spreadsheets and simulators and formulas over the last several years exploring things like the impact of asset allocation or return volatility or path dependence (sequence risk) on retirement outcomes. These are all important subjects of course but the topic of spending in retirement towers like a giant over all of that in the way that it can ruin outcomes and also in the way that it is one of the few levers that we really truly can control.  Spending when we read about it in academic literature ranges from the constant dollar spending of the 4% rule -- or, as Dirk Cotton once wrote: "In fact, constant-dollar spending is the only widely acknowledged spending strategy that results in portfolio ruin under reasonable spending assumptions." -- to various adaptive systems (like Waring and Seigel's ARVA) to no small number of conscious decision rule overlays (like Guyton and Klinger or Kitces Ratchet rule; I have not explored rules based systems much…yet). But academic spending-speak never feels quite right to me. It's a cool and distant equation on a page rather than lived experience (except from retiree bloggers like Cotton or Darrow Kirkpatrick).  It also, especially in it's adaptive or rule based forms, presupposes that change is easy and that, in particular, downward change is both easy and achievable and that spending doesn't jump around like crazy sometimes i.e., it presupposes it is under control, a bold assumption.  Personal experience tells me that control is elusive, change is achievable but not easy, and that spending is quite a bit more variable than it looks like on paper.  So, rather than a survey of spending systems or SWR methods, which others have done quite well, this post is merely a small attempt at a mini case study about the control of both the direction and variability of spending and how one might evaluate the evolution of spending over time.    

Weekend Links - 4/15/2017

QUOTE OF THE DAY

It’s pointless to try to figure out how much you’ll need in [retirement] savings or income if you don’t have a good understanding of how much it costs for you to live. Ben Carlson 

CHART OF THE DAY

RETIREMENT FINANCE AND PLANNING

Time for Retirement ‘Selfies’? Robert C. Merton MIT.  …neither governments nor companies are willing to bear the liabilities associated with pension obligations. This shift requires new thinking about how portfolios are managed and which instruments are available to investors. Our proposed SeLFIES (Standard of Living indexed, Forward-starting, Income-only Securities) make individuals self-reliant and are also advantageous for governments.

Using the “holistic” Actuarial Approach is a better way todevelop a spending budget than summing your sources of income.  Ken Steiner  

How Much Money Do You Need to Retire? Ben Carlson.  It’s pointless to try to figure out how much you’ll need in savings or income if you don’t have a good understanding of how much it costs for you to live…You can run through all the calculations and spreadsheets you want but life will inevitably throw you a curve ball or some of your assumptions will prove to be untrue. This is an unfortunate side effect of trying to plan in the face of never-ending uncertainty. In a way, there’s a lot of guessing involved in the process. 

Safe Withdrawal Rates—The Good News Bad News Story, Ken Steiner.

Link to: Safe Withdrawal Rates: A Guide for Early Retirees

Here is a link to "Safe Withdrawal Rates: A Guide for Early Retirees" by Earlyretirementnow.com.

I thought this was deserving of its own post.

Why?

1. I have a soft spot for non-academic, non-practitioner, skin-in-the-game, analytically-minded early retirees for obvious reasons.[1]

2. He, like I have in the past, makes the case that there are two different retirements out there: regular and early. Early has a sufficient number of characteristics and risks that are not shared by regular retirement that it can be considered an entirely different beast, a beast that is either ignored or glossed over in the ret-fin literature.

3. He makes a convincing case, as I have to myself and in this blog, that while academics and practitioners and their various studies can sometimes make it seem that a SWR of 4% (or even higher) might still be ok for some retirees some of the time (though I am seeing less and less of this), for an early retiree, in this kind of environment in 2017, it is kind of a non-starter. Better to keep it under 3.5% or even lower.

4. While he articulates at points a case for 100% equities, and in the context of his paper he is probably right, his charts whisper a slightly different story. No small number of them confirm my bias that there is, in a simplified 2-asset world anyway, a range of 40-70% equity exposure that seems to work an awful lot of the time...depending on time left and wealth, though.

5. He packs in an awful lot of the analysis that I have either done, was thinking about doing, or would do if I had thought of it or wished I had thought of. That means I can kick back and relax now.

I skimmed and need to go back and re-read but in general: good stuff.


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[1] it was pointed out to me that by saying "I like non-academics" about a PhD in econ might sound incoherent which I guess it does. But I think there is a difference between ivory tower papers on retirement and a guy with a PhD that has a job in the private economy and who is actively attempting to do an early retirement. That takes him out of the category and into the real world which was my original point.

Another obvious reason to put the squeeze on vol

I forgot in my last post on geometric returns (Some Thoughts On the Trade-offs Of Strategy Switching or Aggressive Tactical Allocation and the Virtues of Sometimes Taking a Tax Hit) about the most obvious thing about being aware of the longer term effects of geometric returns: choosing between two strategies that have more-or-less similar return profiles but radically different vol profiles.  One strategy might always be expected to win and it might not be the higher return strategy.  For example, using the same very-rudimentary back of the envelope excel sim (1000 random returns per period, 20 periods of  geometric compounding, all 1000 paths averaged to an expected value, and then the whole run x times) and two vaguely similar strategies: a) 7% return, 20% std dev, and b) 6% return and 10% std dev it could look like this:

Lower return wins. And it wins within a time-scale (on this run anyway) that can make sense with respect to a retiree's planning horizon.

Now, the individual paths could be insanely different (see notes in the referenced post) and each of the measly 100 times I did this all looked different, too... but 7% won or tied only twice at the 20-period expected value level out of a hundred times I ran it .  That may have more to do with my sketchy excel modeling but it is, probably, indicative of general expectations.  This would play out well on a mean variance map too but this is another analytical way of thinking about it.

First baby step in hacking out a resampling technique

I'm only half way through R. Michaud's 2008 book Efficient Asset Management: A Practical Guide to Stock Portfolio Optimization and Asset Allocation and I have printed but not yet read Bernd Sherer's paper "Portfolio Resampling: Review and Critique" 2002.  Before I go any further, to make sure I grasp what they are talking about, I thought I'd try my hand at what I gather to be the first step: showing that the efficient frontier is more subjective than it looks or at least prone to estimation error.

Celebrating Almost One Year of Blogging

I started this blog last year around this time. I'd celebrate on the exact anniversary of the first post but I actually started earlier on LinkedIn.  So here's to "about a year."  I now have three subscribers and one active reader/interlocutor. I have no advertising which is probably a good thing right now.  All of this is clearly not much to crow about but my goal was never subscribers it was to force myself to learn new things by writing them down and thereby hoping to help at least one of the seven billion other people on earth if they had something similar going on.  Cheers.  

Some Thoughts On the Trade-offs Of Strategy Switching or Aggressive Tactical Allocation and the Virtues of Sometimes Taking a Tax Hit

The small number of people that read this blog probably know that I have been mining, if not perseverating on, a paper by R. Michaud (A Practical Framework For Portfolio Choice, Robert Michaud, Journal of Investment Management, 2003).  In that article he has a great example on the pros and cons of selling a high volatility one-stock portfolio in exchange for a more diversified fund especially when it comes to thinking about the upfront tax hit that stops many people from doing the smart long-term thing.  His point is that many people misapprehend the issue because they mis-understand the concept of geometric returns and the effects of time and volatility.  As a way to help myself understand his point I wanted to see if I could recreate some of that analysis especially as it applies to the concept of flipping between high and low risk strategies based on systematic rules or some kind of adaptive optimization framework.  I was originally thinking about this late last  year when I ran through an exercise in backward induction (BI).  My amateur attempt and the results of the BI optimization told me at that time, right or wrong, that I might want to start with a conservative allocation (in a two asset world, let's say a 30/70 or 40/60 stock/bond allocation) and then switch between that and a riskier portfolio (let's say 70/30 to 100% risk allocation) based on age and scale of wealth in any given year.  The project was fun but the thought of selling assets at any scale to switch strategies back and forth gave me the tax willies.  Michaud's paper attacks this problem head-on. 

Weekend Links - 4/7/17

QUOTE OF THE DAY

One of the hardest questions to answer as an investor is the following: Am I anchoring to an investment strategy that doesn’t work anymore or staying disciplined to a good process?  Ben Carlson.  

RETIREMENT FINANCE AND PLANNING

My Appreciation of Annuities Has Grown, Dirk Cotton.   

Life Cycle Investing and Smart Beta Strategies, Blackrock. we develop a smart beta glide path which seeks to take advantage of broad, persistent patterns within asset classes to identify securities with higher risk-adjusted returns than the market. Within equities, investors can shift from return-enhancing strategies — like value, momentum, size, and quality — to risk-reducing strategies like minimum volatility as they move through their life cycles. Adopting smart beta glide paths may improve Sharpe ratios by up to 20% over a standard equity-bond glide path. 

Approximate Solutions to Retirement Spending Problems and the Optimality of Ruin, Habib, Huaxiong, Milevsky.  We solve the retirement income problem when investment returns are indeed stochastic using numerical PDE methods, assuming the principles of stochastic control theory and dynamic programming. But then -- and this is key -- we compare the proper optimal spending rates to the analytic approach presented in Milevsky and Huang (2011) by updating the portfolio wealth inputs to current market values. Our main practical conclusion is that this simplistic approximation when calibrated properly and frequently can indeed be used as an accurate guide for rational retirement spending policy.  

Quote from Abnormal Returns.  "For a diversified investor, returns will be what they’ll be; the best you can do is accept that and don’t let your emotions get in the way. A far better use of your time and energy is to focus on what you can control: moving forward in your career, living within your means, and saving more." (Charlie Bilello)  [emphasis added] 

Exploring some ideas on the interaction between low vol portfolios, leverage, spending, and fail rates

As a diligent and trustworthy early retiree I was thinking the other day about some random components of finance theory that we are supposed to know, such as:

• There is, of course, an efficient frontier of portfolio risk and return where for a given level of return there is a minimum variance or for a given level of variance there is a maximum return,
• The supposedly optimal portfolio, as is known, is the one that is where a line coming from the risk free rate is tangent to the efficient frontier,  
• According to Markowitz (2016) the optimal MV portfolio and max utility are likely the same under some bounded assumptions 
• If an investor seeks a higher level of return than the tangency portfolio for whatever reason, then levering the tangency portfolio is more efficient than selecting a portfolio allocation that is riskier than the optimal portfolio 
• The effect of volatility and time can seriously take the shine off of the single-period expected returns of even optimal portfolios and that can usually be seen in multi-period (or limit of) geometric return estimates if not the final realized returns
• Spending, in collusion with time and volatility, can lay waste to a portfolio especially when returns are bad at the wrong time or bad all the time or when spending is at or above unsustainable levels for too long, or........ 

And all of that got me thinking[1] about trying to simulate different spending rates over long periods of time against a levered theoretically-correct low-volatility portfolio.  This seemed not just vaguely interesting, it seemed, according to what we are supposed to know, mandatory if not essential.  I don't think this is new or that is has not been done before, but I have not seen it addressed by anyone myself and I wanted to see what it looked like.  First, let's ignore pesky things[2] like estimation error (if not actual facts) in coming up with returns and variance or things like constraints on leverage, though that is supposedly a reason for the low-volatility anomaly: people can't lever low vol assets to get higher return if they need it so they take more risk and in the process bid up  high risk assets driving down expected returns below where they might otherwise be.  In fact, even though I'll try to use some realistic data after ignoring the previous stuff, let's keep in mind that all of this is hypothetical and is ready and willing to fall apart on close examination.

Revisiting the Geometric Mean Frontier

I wanted to take a look at the geometric mean frontier again.  This time I wanted to use some real data over a reasonable time frame (looking back) and then sorta project that into a MV frontier going forward and then do two things: 1) look at the four different equations that Mindlin uses to estimate the geo mean return in On the Relationship between Arithmetic and Geometric Returns, Dimitry Mindlin, CDI Advisors 2011, and then 2) take at least one of the equations in #1 and then arbitrarily and unfairly change the risky asset's vol in incremental steps to see how long it takes to get the geo-frontier to have an inflection point.  That last effort in #2 is interesting given my recent foray into estimation problems with standard deviation.  Using a monthly series seems to underestimate annual standard deviation by quite a bit.  So maybe there are some estimation errors when trying to come up with these geo-mean frontier things, too.  If so, maybe the risk of getting to one of Michaud's "critical points," or what I am calling an inflection point, comes up faster and more insidiously than I thought.  Something to think about, which I am.

At Play in the Fields of the Sim

This post has no real analytical meaning or purpose. Certainly there is no pedegogy intended.  I am just messing around with a simulator for the hell of it.  Since I have been reading and thinking about geometric returns and utility functions as well as trying to fine-tune my understanding of return variance over the last few weeks I thought I'd fold some of what I've absorbed into the software.  So I added, for each sim-life (in this particular case age 60-90), a synopsis, at the end of the sim-life, of the realized geometric return (product of (1+Rt) for each t=sim year) and the standard deviation of the vector of returns (= stddev[ln(1+r)] for r in each sim-year)  over the life.  Then, of course, I wanted to see what it looked like. Assuming I did not fall flat on my face programming-wise, here below are some snapshots from my sim travels. It's meaning and purpose and application will come some other day...