Fixed Income Momentum

This link The Momentum Factor Relevant For Bonds? is a variation on and re-link of some stuff in other posts and in particular some stuff that Cullen Roche queued up recently (see also some work at NewFound for some solid quant based thinking on this topic).  This kind of thing has been my bread and butter strategy-domain since around 2010, if not earlier.  I hate to see the attention to a backwater strategy but then again I also like to see the validation.  This has been (will it continue to be? Who knows?) something that works well as an incremental strategy to add some efficiency (statistics not provided here). Good to see some of this coming out now although, being an interest rate sensitive kind of thing, we'll see how it goes from here...

Thanks to other bloggers...

Ok, I totally get it that: a) I do not promote myself whatsoever, and b) RH appeals to, shall we say, some esoteric tastes, but I am still surprised sometimes by the fact that some of this stuff doesn't get picked up more often after two years of doing this kind of thing.  Don't get me wrong. I actually really like my obscurity for the most part for reasons that, well, that are mine and mine alone.  Sometimes, however, I do get picked up and it's fun when that happens.  This chart below will reveal the pathetic numbers I have but take a look at this series of reader stats on a sequential per-post basis.  See if you can identify when I get picked up. My guess is that that one column you see will double in 3 days. Thanks to Tadas Viskanta for the link, btw...

Fwiw, every time I write "Tadas Viskanta" I double check his site.  After 59 years of having the very simplest of names misspelled as Seldon, Seldin, Sheldon, Shelden, Sullivan, etc I take the time to make sure I have other names right.  Golden rule...

Geometric mean returns and portfolio choice

I've been reading Michaud's "A Practical Framework for Portfolio Choice" about once a quarter for a while now to see if I can identify dumb mistakes that I continue to make here.  Evidently my reading is not sinking in because every time I re-read this I realize or re-realize some of the errors I keep making.  I also look for things in the link where I do not understand something all that well (and probably should) to come up with areas to explore in the future.  So far, over a couple years, I have not come away short of things to brush up on.  Michaud is not Moses with stone tablets, of course, and there are other points of view, but I have to say that there is a lot of fun stuff in there.  I doubt I would be making any serious errors by continuing to beat some of this stuff into my head.  

How big of a deal is it to randomize the discount rate in a SPV liability calc?

This is neither very thorough nor particularly scientific. This is me going back into amateur hack territory.  In some past posts I had ginned up a stochastic present value (SPV) of a spending liability in order to get a distribution of NPVs I could perhaps choose amongst for the purposes of working on a household balance sheet. But I had only randomized the cash flow (numerator) and not the discount rate (denominator, which appears to be a "more correct" way to do it) so I was kinda cheating a little bit before.  My goal here is pretty simple. I wanted to see how big of a deal it would be and what kind of impact there would be on the shape of the NPV distribution by shaking up the denominator.  In other words, would the 95th percentile be something like $3M or would it be a hundred trazillion? I'll just use one set of parameters and see if it blows up or stays normal (normal normal not statistics normal).

Here is the basic deterministic setup. Forgive me if I botch the notation:

• Cash flow : 100k, inflated at 3% stepped down 20% at 68 and 20% at 85. No randomness.
• Mortality assumptions from the SOA IAM table with G2 extension.
• Discount rate is 4% which is entirely arbitrary and for illustration.  
• Deterministic present value is calculated like an annuity: 

Here is the basic setup for the stochastic present value calc:

Telescope fever

I was telling one of my two readers recently about the 2nd of my two "Richard Dreyfus - Close Encounters" moments, you know, one of those moments where you obsessively build a Devils Tower model in your basement and then your wife leaves you...

The "2nd" moment was this blog which was an efflorescence of weird quantitative ret-fin desire that came out of nowhere (well, ok, not nowhere but getting a fake diet of "it'll be ok" from my high cost advisors and then finding out the risk was 8 times what they said it was kinda helped).

The "1st" moment was a telescope I built back in 2002.  I had built one as a kid in 1970 and then I had built a couple others in-between but sometime in 2002 I had one of those Dreyfus moments.  I decided I had to build a 10" reflecting Dobsonian reflector but -- and here is the kicker -- it had to be engineered as a truss design and all parts had to be fabricated from scratch. Where the hell did that come from?  It took me me about 18 months of currying favor with my now-ex and my babysitters to make 50 runs a day to the hardware store and then a wee bit of "devil's tower" obsessiveness. At the end I had the picture below.  Everything you see with a few exceptions is hand fabricated.  Note that there is absolutely no logical reason whatsoever for the truss design for something that size. If it had been a 20-inch with a 10-12 foot trussed out structure, that makes sense. This had zero functional purpose. I just wanted to do it...I just wanted the look.   I even tapped the threads in the aluminum pieces I hand cut.  What did I do with it? Well of course I looked once or twice (it was pretty good, the image was better than another telescope I had, a 130mm refractor known to be among the best apochromatics on the planet) but after a couple looks it sat in my basement in MN and then made it to FL after a divorce I did not know was coming.  Then sitting here in FL, I decided I should give it to a local school.  They took it, of course, and promised to send me a thank you note...but didn't...ever. I should call and browbeat the science teacher that made the promise (one of the biggest things in my life as a caregiver for young children was stressing and stressing again and stressing one more time that "keep a promise" thing; I thought then and still think now that that is really really important.  I am always disappointed when the concept is breached.  That could explain some of my disappointments with living in the state of Florida). At least I kept a picture. Here it is:

3D surface of Conditional Survival Probability 50-75

This is a pretty, if maybe a "pretty depressing," picture.  I'm not totally sure I have the math exactly right but it's close.  This is the conditional survival probability (CSP) at each age (axis = survive-to age) projected out over time (axis = survive-to plus ___) given that one has survived to that particular age.  This is, I believe, the CSP I'd use at each age to calculate an annuity price or a spend liability or ruin probabilities, among other things...  This is from the SOA IAM table with the G2 extension applied to 2018.  Some day I'll ask an actuary if I got this even close to right.

Ed Thorp and AQR on Retirement

I'm appreciative of the work AQR does and I am appreciative of the work that Ed Thorp has done so when AQR interviews Ed I'm even more appreciative.  Rodney Sullivan, Antti Ilmanen and Aaron Brown interview Ed on topics such as his early career, investing today, market efficiency, retirement challenges, lessons from the global financial crisis, investment education, and heroes and mentors.

Here are some quotes from the retirement section:

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

"Individuals have a much more difficult time dealing with risk analysis and planning than institutions. Many individuals are going to come up short and it's going to be very painful for them in their later years. So, even though the institutions who have pushed for this see it in their own best interest, I don't think it's in the best interest of the retirees, and I don't think it's in the best interest of the country as a whole."

"It's the opposite of the invisible hand. It's the invisible foot in the mouth. Everybody's working for his own particular self-interest and it's damaging for the country as a whole. A great example is the lack of willingness to address climate change. It’s similar for individuals and their retirement."

"These issues are real. When people retire, they must have money to spend and it has to come from somewhere. There is a rational way to figure out how much they need."

on investment committees: "I found it difficult to persuade them that all this cerebration was a waste of our time...The network access, to me, is where the value comes in. Not the asset allocation discussion."

"Stocks have historically outperformed over moderate to longer periods by a significant amount. I believe that the equity risk premium is real, and will lead stocks to continue to outperform in the future."

"I find that retirement savings is individual specific. There's not one cookie-cutter answer for everybody."

on endowments: "We did simulations to create assurances that things would work out in a worst-case scenario. Our finding was to draw no more than 2% a year from the endowment. They have a portfolio that behaves like roughly 20% bonds and 80% equities, but they feel fairly secure."

"what they've done is to make the rules such that it's very difficult to change the investment policy. That way they may hang onto this policy and prevent defections during the tough times when there's a big downturn."

Rerun - cumulative geometric returns over 20 periods

This is a repeat of something I did last year or the year before. I ran across it this week looking for something else and thought I'd throw it out there again.  This was a simulation of the expected value of the cumulative geometric return in each period over 20 periods. The arithmetic mean return expectation here is 7% with a standard deviation of 20%. That means we can estimate the expected geometric return at infinity with an imprecise estimator like EV(g) = A - V/2 or let's call it .05.  In this sim at the 20 period mark for this run the EV of geo return was ~.052.  The point of the chart below, however, is that all sorts of interesting things can happen between t=0 and t=∞ when it comes to: a) the rate of change of the expected value (mean), and b) the shape of any particular individual path, a path that a retiree, for example, might be presented with in his or her one run through life.  No wonder this ret-fin stuff is so hard.*

* As an amateur I realize that there are a few reasons one might qualify this kind of thing...lognormal this or that, etc. So, my advice? Don't read RH, read this instead. 

How might I project an SPV spend liability estimate out 3 or 10 years from today?

On the surface this does not look like a particularly useful question. But if RH is not here for useless retirement finance questions, well then what am I here for...

This subject of projecting future spend-liability-NPVs came to mind for two reasons.  First I remembered that I created an NPV model (not stochastic) back in 2003 for the education liability I keep on my household balance sheet.  It was complex and with three kids in independent schools -- and one would presume decent colleges later, for a total scope of around 25 years under the yoke -- the total cost in either absolute or present value terms was eye opening.  I thought I had a handle on it (and I did, it has been remarkably accurate and useful) but I was irritated that the NPV went up each year even as an old year fell off the front end.  It finally dawned on me that I had huge lumps in my cash flow and that when they were distant they were discounted exponentially and had a small impact but as years fell off the front end, that mass of lumpy liability moved closer to me out of the discounting fog and started to dominate the math.  That meant that the NPV rose each year to a critical  point a couple years ago. It now thankfully declines each year and will continue to do so all the way to zero around 2026-2027 barring 5 year programs or graduate programs I agree to help with (knock on wood for no). The same effect would presumably obtain for lumpy and/or long retirements when estimating (projecting) a spend liability.   

Second, I think I've mentioned that it sometimes bothers me that the vast majority of retirement finance lit seems like it ignores or blows by or even condescends to early retirement.  Sometimes this is implicit or accidental. Sometimes it is explicit.  Here is an example I recently ran into.  In a piece that I otherwise greatly enjoyed and found fairly useful (Determining Discount Rates Required to Fund Defined Benefit Plans, Turner et. al 2015...I know...I need another hobby) they address early retirement thusly:

Weekend Links - 1/25/18

QUOTE OF THE DAY

..the rates are uncertain and everything about the uncertainty is uncertain.  

          – Turner et. al. on the influence of mortality rates on pension liabilities.

RETIREMENT FINANCE AND PLANNING

Note: My goal was not to turn this into "Mindlin week" at RH even though he seems to he well represented today.  The point of weekend-links is not promotion of people or ideas it is, in rough terms, a list of what I've been reading over the last week that might be relevant to what I am doing.

Sequence Risk: Managing Retiree Exposure to Sequence RiskThrough Probability of Failure Based Decision Rules; Frank and Blanchett [2011]

The hypothesis for this exploration is that POF based decision rules can be developed to warn a retiree that his retirement withdrawals during adverse returns sequences may put his distributions at risk of depletion within his lifetime. With such a warning, these same decision rules may suggest a method to adjust either the portfolio allocation and/or withdrawal amount to avoid running out of money before death. The point of reference for Guyton-type decision rules is based on an initial withdrawal rate. This is, by definition, a time reference to some point in the past. But what time point is relevant for this decision? This paper will shift the point of reference for decision rules into the future by using a future oriented reference point for the time function. This paper will also use current withdrawal rates as opposed to initial withdrawal rates… Changing asset allocation as a stand-alone strategy to address sequence risk is not effective. In other words, changing portfolio allocation in response to sequence risk in ineffective… Arguably, no predictive metric may be available. However, Probability of Failure appears to be an effective management tool as a response to exposure to sequence risk.   

The Dynamic Implications of Sequence Risk on a Distribution Portfolio. Executive Summary; Frank and Blanchett [June 2010 - Journal of Financial Planning]

"sequence risk is always [emphasis added] present in the short term, and can be measured indirectly through the current [i.e., as measured or sampled in the unfolding future "present" moment] probability of failure of the current withdrawal rate over the remaining distribution period…the current year is the first year in the time sequence, regardless of how many years remain in the sequence or how many years may have passed."  [I have blogged on this fairly often before.  I am surprised by how infrequently I see this concept of an explicitly continuous retirement discussed like this] 

The Pitfalls of 'Sequential Risk', Dimitry Mindlin

The objectives and risks should reflect the best interests of the stakeholders of

investment programs in the most straightforward manner. For example, if the

primary objective is to fund a pre-determined level of post-retirement spending,

then the primary risk should be defined as “a failure to fund a pre-determined

level of post-retirement spending.” All scenarios that may lead to this failure

should be considered. Doing otherwise may lead to sub-optimal investment

solutions and be a disservice to the program’s stakeholders.

Transforming my cluelessness into a different kind of cluelessness

After a number of years of digging through the various gullies and sewers and back alleys of retirement finance analytics I now find myself, for my own planning purposes, sticking closer and closer to the management of the continuous present moment via a household balance sheet than by way of any type of predictions about or projections into the future. The projection project, to the extent that it is trying to predict anything beyond about 5 minutes from now, is seriously misguided. Even if the results from that effort are viewed properly as a judgment about risk that is forming in the present moment -- an insight which is sometimes useful and on which we can or should sometimes act -- it can even miss the boat there, too.  Here is Dirk Cotton on this idea: "the prediction horizon for retirement portfolio balances is a less than a year, beyond which the outcomes diverge dramatically… Retirement income studies tend to use probabilities to focus on long-term sustainability of savings as a function of market volatility alone. This approach won't catch many quickly developing expense-related crises, especially since the studies tend to ignore expense uncertainty altogether. When we say a retiree has a 5% risk of outliving her savings, we mean a 5% risk of outliving savings due solely to market volatility. But, there are other risks to those savings that should also be considered… These studies explain long, slow declines in standard of living, not catastrophic failures, in a world where market returns are normally distributed and mean-reverting and no one ever needs to spend more than their 'sustainable withdrawal.' Their recommendations – diversification and spending adjustments – provide little help in a spending crisis."  

The solution (other than working longer or lottery wins) to problems like the one Dirk is describing can often, but not always, be found by leaning a little more heavily on something like a household balance sheet as well as developing more robust systems and processes and patterns of behavior for monitoring and surveilling the developing personal and external landscape.  It's here that we might be at our best in terms of tracking judgments about the impact of spending choices as well as any evolving or impending changes in our personal lives.  On the other hand, I don't think we have completely eliminated prediction-style cluelessness by going headlong into the balance sheet, we have just transformed it a bit. 

Here is (maybe) a way to make a spend liability calc a little like a ruin-risk calc

Note: I posted this entry last week and then I subsequently withdrew it and now I have reposted it again.  I originally posted it because I think this is an interesting way to look at spend liabilities on a household balance sheet and the stochastic present value idea in particular has some affinities with both balance sheets and probabilities of success.  I withdrew it because I found I had made some material errors in the spreadsheets I was using that undermine my point...in addition to the fact that I wasn't all that confident in the idea at the time and had been cavalier about my rigor in looking into this kind of thing.  I reposted because I still think the idea is worthwhile for contemplation, I found some validation in a 2009 paper by Dimitry Mindlin (more later), I have since re-written the process in R rather than Excel, and I want the option to refer back to this when exploring this idea some more. I could fix the spreadsheet but at this point, why?   

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

This post is not legit data science because many of my assumptions and inputs don't line up or are maybe even cherry picked (omg) a little bit.  But the outcomes do line up so I am suppressing my gag reflex on my corrupt methods in order to throw it out there just for fun.  My guess is that a little bit of honest effort into being serious about lining up and rationalizing my assumptions would make my results diverge more than I want but where is the fun in that.

In this case I used my own future spending assumptions (values not shown for obvious personal reasons) to see how it all looked for my own plan -- I mean as long as I have been perseverating on this spend liability thing lately I thought I might as well.  I used the spreadsheet-spending-sim that I did in a couple recent posts but now I did it: with my real spending plan design, inflated at 3%, stepped down in custom steps at 69 and 86, varied randomly year to year, discounted at a rate closer to annuity discounting (2.8%), and, finally, with an age assumption varied between sim iterations that is plucked randomly but also mostly in line with a conservative SOA annuitant mortality curve.

Another look at a spend liability distribution

In a past post (Running some different numbers on a spend-liability estimate for a balance sheet) I was playing around with the estimation of a spend liability for a balance sheet.  For a balance sheet it seems to make sense to have a single number as an approximation for the present value of future expected spending.  This is often done with a discounted cash flow approach using a cash flow that is either run to a fixed age (and then discounted) or alternatively it can be run to infinity with probability weights at each age based on conditional survival probability (CSP) from a life table or elsewhere.  I kinda like the latter because it mimics (but will always be a different from) an annuity calc that uses the same math and it is relatively easy to come up with. On the other hand in the last post I also wanted to illustrate, to at least myself, that the estimate is maybe better visualized as a distribution when the numerator (the future forthcoming expected cash flow) is allowed to vary a bit by using some rudimentary simulation.  In that post I did the illustration by doing a simple spreadsheet sim letting inflation vary a little bit (though not very realistically). I also had a step-down function in spending at age 69 and 86 to add a custom element to the spend path that some particular person might follow.  I ran the simple sim 3000 times.  The "distribution" of results that comes out the back end is the pile of different spending liability NPVs that come from shaking up the assumptions inside each year of each sim. I then used the distribution to see how close the average was to the more deterministic DCF estimate and also to see how wide the distribution might be if I were to want to use something like the 90th or 95th percentile as a more conservative estimate for a balance sheet liability number[1].

This time I wanted to see what happens if in addition to inflation and step downs I added some random spending variance on top of all that.  My theory (and experience) is that spending can hop around for all sorts of reasons other than inflation or planned changes.  I wanted to see what happens incrementally to the distribution of the NPVs when we do this in a model.  I am using the same set of assumptions as before but notably it is $100k in spending, inflated at 3%, stepped down 20% at 69 and then 86 and discounted to PV at 4% (though subsequent to that post I'm thinking a lower rate/higher liability might make more sense). 

Weekend Links - 1/18/18

QUOTE OF THE DAY

“Any intelligent fool can make things bigger, more complex, and more violent. It takes a touch of genius — and a lot of courage — to move in the opposite direction.”

— Ernst F. Schumacher 

RETIREMENT FINANCE AND PLANNING

What Monte Carlo Projections Leave Out (...And Why You Can Still Trust Them), Cordant Wealth Partners

these inconsistencies at the extremes are important to acknowledge. As with any model, understanding it’s sensitivities and limitations is essential so that we don’t use it to answer a question it isn’t fit to reliably answer. 

If you're expecting a long life, take time to adjust your financial plan, CNBC

43 percent of retirees underestimate by at least five years, the life expectancy for someone of their age and gender, the Society of Actuaries reports. Planning for longevity might include working longer, adjusting investment strategies, and planning for incapacitating health problems.  

Spending in Retirement…or not? Blackrock

Future retirees will face a much different retirement landscape and will need to adopt new sets of skills—behavioral and financial—that will help them systematically tap into retirement savings to support future spending…. research conducted by the BlackRock Retirement Institute (BRI) in conjunction with the Employee Benefit Research Institute (EBRI) found that on average across all wealth levels, most current retirees still have 80% of their pre-retirement savings after almost two decades in retirement…These findings begin to challenge industry norms and academic theories about lifecycle consumption especially during the retirement phase… Across all wealth levels measured, more than one third of current retirees grew their assets—leaving considerable potential consumption on the table… Late in life out-of-pocket medical expenses—a major reason to retain assets—do not appear to be warranted except for a very small portion of the population… The financial landscape for future retirees will most likely be more challenging, requiring different savings and spending behaviors.  

Periodic or Generational Actuarial Tables: Which One to Choose? Swiss Finance Institute

In this paper, we demonstrate that contrary to this common belief, institutions that use generational tables (namely, tables including future mortality evolution) will most likely need to make more important adjustments (positive or negative) to their liabilities than will institutions using periodic (static) tables whenever a new table is released. By using three very different models to project mortality, we demonstrate that our findings are inherent in the required long horizons of the forecasts needed in the generational approach, with the uncertainty surrounding the forecast values increasing with the horizon. Therefore, generational tables may introduce more instability in a pension institution’s accounts than periodic tables. [keep an eye on one's counter-parties I say…]

Blackrock Study on Retirement Spending

The Blackrock Study Spending in Retirement…or not? in my opinion successfully captures a fundamental mental framing that I think more than a few retirees have.  Here again is a good example of the difference between Homo economicus and the rest of us. 

Most retirees in our study appear to have coped and managed pretty well in retirement. Many could have afforded to withdraw a little and, in some cases, a lot more from their retirement accounts but chose not to, potentially leaving in some cases large amounts of hard-earned savings unspent. While many might find this puzzling, research suggests16 that people would rather not touch their savings and instead adjust their lifestyle, making cutbacks where necessary and shifting to “needs” over “wants.” Others may feel the need to hold on to wealth as a form of self-insurance instead of acquiring an annuity to deal with a number of life’s uncertainties, such as longevity risk. Retirees may also hold back due to deeper behavioral biases or tendencies. After being told to “save, save, save,” for decades, the idea of shifting to “spend, spend, spend” underweights the power of inertia and the comfort associated with the status quo.17 The common framing of decumulation as a time to withdraw or remove assets rather than say gaining new experiences faces a strong “loss aversion” bias as well.18 Even the uncertainty or ambiguity regarding longevity itself can lead people to select more certain, but possibly sub-optimal decisions.19 These biases can be exacerbated given the person’s risk tolerances, experience (or lack of) with the investment industry and investing and the overall saving/spending relationship (often family influenced). Retirement planning and financial advice that acknowledges and incorporates solutions to these types of biases could help mitigate behaviors getting in the way of retirees spending a bit more on themselves and using those assets saved over decades.

...and on the other hand...

Future retirees may not be so lucky. Many will likely retire into an environment with multiple headwinds and face growing pressure to save more and maximize the value of their entire retirement savings—principal and all—unless they are willing/able to make dramatic cuts in their retirement lifestyle...Future retirees will face obstacles not seen by prior generations and many of the apparent behavioral biases possibly holding back current retirees from spending will be at play among future retirees as well. Whether they can gain the confidence to spend retirement assets if and when needed—or not and potentially see major adjustments to their lifestyle instead—remains to be seen.

US force of mortality over time

From "Modeling Longevity Risk Using Consistent Dynamics Affinee Mortality Models" by Islem and Bedoui Univ. of Sousse - Laboratory Research for Economy, Management and Quantitative Finance

"Figure.1 shows the plot of the average force of mortality given by m[bar](t,T) for population aged between 50 and 100 for the years 1933-2010. we use this mortality curve data to represent the model fitting. We can see through this figure the exponential shape of the mortality curve and the improvement in mortality over the periode of the study , this improvements have appeared at various rates for different ages."

A custom spend liability calc vs. a fixed annuity

An annuity is one way to step off the longevity risk treadmill by shifting that late age risk to a third party that can pool it over a large population (something we cannot replicate ourselves, btw).  That makes the price of an annuity that approximates the net present value of a forthcoming retirement spending plan a good proxy for a "free boundary" i.e., a boundary below which is -- when a retirement balance sheet is initially funded and valued with non-annuity assets -- an infeasible retirement and above which is a retirement that can be feasible with or perhaps without annuitization depending on, well, depending on a bunch of stuff.  Ignore for the moment that: a) that means annuities might be relevant to only a very tiny cohort, those at or near or quickly descending towards the "boundary" from above, and b) in practice we'd look at an entire household balance sheet rather than just the spending plan in isolation.  The focus here rather is the idea that the spending plan, whether by design or by the nature of normal random variance in spending will never, ever match the cash flow from an annuity.  And that's before we factor in something like spending "shocks." This could be a problem don't you think?

A simple fixed (non inflation adjusted) annuity price is relatively simple to estimate.  A spending plan is also relatively simple to "price" as well and in theory it can be done with the same or similar math.  The problem is that the spending plan can vary greatly from the annuity path before we even get to the idea of random variance in spending.  That means that if annuitization ever does occur then it seems like there must be periods of cash flow under-funding or over-funding which also means, in simple terms, that one has to deal with this somehow.  I have no idea how the pros like CFAs or retirement specialists handle this kind of thing but I presume that one would have to have some kind of a side account that would reserve overfunding and either put it in market assets or into incremental annuities (or TIPS) to satisfy some of the future liability and especially those future liabilities that end up underfunded.  One could also imagine a borrowing facility to deal with some amount of underfunding when needed.

To price the annuity we can use standard formulas from any intro finance book.  Here I'll use the annuity formula from Milevsky's 7 Equations book because I happen to have it on my desk.  From the first page of chapter 3 it looks like this:

Weekend Links - Jan 11 2017

QUOTE OF THE DAY

We might retire from the workforce, but we should never retire from the pursuit of a fulfilling life. - Johnathan Clements  

RETIREMENT FINANCE AND PLANNING

How to Invest and Spend Wealth in Retirement? A Utility-Based Analysis. Bilsen, Bovenberg, Laeven. 

This paper explores the optimal consumption and investment behavior of a retiree who derives utility from the ratio between consumption and an endogenous habit. By developing a non-trivial linearization to the budget constraint, we are able to derive closed-form policies. This enables us to explicitly characterize how the preference parameters determine the optimal return and consumption smoothing mechanism and investment strategy. We also consider an extension which decouples relative risk aversion from the elasticity of intertemporal substitution. We show that under this extension habit formation no longer leads to unrealistically high median growth rates of consumption at the end of life. 

Hello, I’m Not Mr. Money Mustache, Chris Mamula @ canIretireyet.com

While people “outside the cult” waste time arguing over why someone else is not really retired, they miss the much bigger point.  

Occam’s – Are Bitcoins a Good Tool For Retirement Investors?, Bob French

That’s why you have your money: so that you can do what you want with it. But the odds of you going home with more money than you started with are vanishingly small. It’s possible…  

Minimizing Regret, Dirk Cotton.

We can't make a blanket statement about the outcomes, true enough, but we can make a blanket statement about the quality of the decision…The dollar amount of regret can be defined as the difference between the outcome you expect and the outcome that would have resulted from clairvoyance  [Markowitz once admitted to defaulting to a 50/50 asset allocation based on a theory of regret.  I am not immune to the allure of regret-based analysis]  

A different, non-finance retirement analysis - for me

Florida is not what I consider home and for a variety of reasons Minnesota is currently off the table. Since my children will age out of my care relatively soon, home will be wherever I decide it is.  That, if and when I make it happen, for better or worse, will be west.  I have no idea where my daughters will end up after college so I'm thinking I want to be not only "west" but somewhere within two hours of a large or at least medium sized hub airport.  I have some specific ideas about where that will be but just for fun I thought I'd open it up a little bit just to see what the scope of the problem is.  So here were some first level screening criteria before we even get to things like state selection, taxes, specific cities/towns, health care availability, services, walk-ability, cultural amenities, etc etc:

- Within something like 2 hours (65 mph) of a decent sized hub airport

- Hub defined as > 400k enplanements (2014-2015 data)

- West of the 100th meridian

- South of Canada

- North of Mexico but effectively north of Phoenix

- East of the Pacific but ex-California

Running some different numbers on a spend-liability estimate for a balance sheet

I've been toying around lately with the valuation of the spend liability on my balance sheet (a household meta-balance sheet which should include, in addition to financial assets and debt, the NPV of flow or future assets like social security, human capital, annuities, pensions, spending, long term care, etc.). This toying around came after some recent discussions I've been having on this subject with some people with wiser heads than mine.  I haven't changed the way I look at this in years (see # 1 below) but there are probably an infinite number of ways to estimate the liability so why not reconsider. I'm thinking in this post about three ways in particular:  

1. Discounted cash flow over a conservative fixed period, say to age 95,
2. Discounted cash flow over an infinite or really long time frame but weighted by conditional survival probability, and
3. Rather than just a point estimate, we could also create a distribution of many spend liabilities by breathing some variability into some of the pieces that make up the spend liability calc (in this case I'm focusing on inflation and longevity but that randomizing impulse could snare other pieces like spending variance before we even get to inflation, spend shocks, maybe even discount rates, whatever…) in which case we would look at either an average or a median or maybe some percentile on the cumulative distribution as a "bad" or "worst" case for planning.

The Idea

I've done #1 and #2 before but not #3.  I'm not sure if it (3) is really necessary or even a legit exercise (someday I'll ask an econ or finance prof) but I was curious what it would look like.  It's achieved through simulation of course but it's also an NPV or, rather, a distribution of NPVs. I'm not really sure what it is.  I could get the same reality-check by testing the sensitivity of a spend liability NPV to different inflation or age range assumptions but I also like the idea of a seeing the shape of a distribution. Even if this is not a legit thing to do, let's take a look anyway just for the  hell of it.