Trend Following in Retirement Portfolios - another quick look

I started to take a look at trend following in retirement portfolios here and here.  This kind of thing has been covered elsewhere before so this idea is not new (see references). I am just trying to get my head around it on my own in a way that I can understand.  So far, it, trend following, seems accretive.  Today, however, I am going to look at something else a little bit different.

IF we can say (not sure we can) the following:

1. Trend following, either as an allocation or as an overlay on an asset structure, has a convex pay-off structure, and
2. The convex pay off structure (return smoothing) enhances retirement consumption capacity, and
3. Trend following is not strictly a return premium for taking on risk but rather an exploitation of a behavioral anomaly, and 
4. Non-real-premia strategies can sometimes suffer from crowding and decay  

THEN it might be fair to ask the following

1. Is trend following suffering any decay these days, and
2. If there is decay, does it matter, or 
3. Are there different ways to look at the accretiveness of trend following? 

Letter to a 20-something on training, fasting, and health

Yes, I know I perseverate on retirement finance. But I'm now thinking about something else: what is ret-fin if we are weak and prematurely-old and unhealthy. The following letter below is an email I sent to a 20-something with a minor health thing that should be a wake up call to that person to pay attention to his or her health now rather than later.  I am writing this post as a change-up to my typical ret-fin stuff. I also do this kind of thing on twitter and facebook as a way to commit myself to the path I'm on and to reinforce and accelerate what I am trying to do.  It's like those millennial apps where people publicly commit to things so that they follow through.

On Outliers or a response that got away from me

I received a decent comment from a reader to which I started to reply. But (1) the reply got away from me, and (2) I can't really answer it very well. This is my response and, if anyone is interested, a challenge to the world to help me articulate a better answer. This was in response to what I considered a casual exploratory post
Another half-a** look at trend following and retirement portfolios.  The "half-a**" was the tell on my casualness. 

Here is the comment:

"I guess we can confirm that there is a fat tail, ..." 

Is it really a fat tail, or is it a normal distribution with outliers? Would a Shapiro–Wilk, Kolmogorov–Smirnov, Lilliefors, or Anderson–Darling test run on your raw data help you infer an answer?  

Also, you're binning data. That could the assailability of your "fat tail" conclusion, couldn't it? 

Here is my tentative response, tentative because it should properly be the start of a dialogue rather than an answer. I mean, really, how much do I really know about this kind of thing...yet.

Another half-a** look at trend following and retirement portfolios

This has probably been done a thousand times before, and I'm not totally sure I can do what I am trying to do here, but I wanted to hold the data in my own hands to see what it looks like.  Here in this post I wanted to:

1) Get some real market data and see how non-normal it looks using monthly data and the imperfectness of yahoo finance data, and then

2) Look at the relationship between market (S&P, which isn't really "the market") and some trend following data to see if I could see the "convexity thing" and then

3) See if I had anything to say about this particular look-see...

Trial run: effects on "Perfect Withdrawal Rates" from allocations to trend following

I think I've done this before and I know that others have done something like this in some papers I found on SSRN[1] but I'm trying to think through some things for some future posts. That makes this post a trial run or an early shake out of some R-script just for the hellovit.  Doesn't hurt to think this through again.

The goal

See what happens to the stats and visuals in a PWR (perfect withdrawal rate) distribution by knocking the fat tail off of an input return distribution. This is based on zero real data and is merely a modeling hypothetical that knows nothing outside the model.

Word on my last post...

"One day I might die reading these spend rates and withdrawal rate articles." 

-- Ng Lip Hong Kyith via Facebook about my last post Perfect Withdrawal Rates and Random Lifetime

to which I'd say...

"Me too."

Perfect Withdrawal Rates and Random Lifetime

This is another one of my "want to see what it looked like" posts. In this case I wanted to see what a PWR distribution looked like at different ages using a point on a random lifetime distribution as the "horizon."  PWR we've seen before[1]. Instead of holding spending to a number and letting terminal wealth in a net wealth process disperse over a planning horizon, here we hold terminal wealth to zero and let spending be dispersed. The math, as before looks like this:

On "Post-Modern" Planning Horizons

It may be that someone somewhere in 2019, given our various and not-so-benign cultural upheavals, has claimed that death is a social construct and that 30 year planning horizons are a form of violence. Maybe, but we can at least say that 30-year horizons are a type of modeling violence for people that either (a) retire early, and/or (b) do continuous forms of monitoring at all plan ages.  This can be important because much of Modern Portfolio Theory is based on single period math and very long or infinite institutional horizons and also because so many retirement finance papers use some form of MPT logic along with a common -- or at least I imagine it this way -- life-cycle parameterization of "male, 65, and 30 years."

The shape of conditional mortality

There is no purpose for this. Like a lot of my posts, this is more about "seeing what it looks like" than anything else. I was looking at an idea for a post and ended up running this chart just for the hell of it. This is SOA Individual Annuitant Mortality 2012 table data with G2 extension to 2018 rendered like this:

- Age x is age attained with mortality probability conditional on that age attained
- x axis is time t from age x in years
- y axis is my discrete probability of mortality based on the table and age x
- the lines: each age x from 60 to 100

I was intrigued by the shape of the chart. The end.

Inflection Point

With the completion of my draft of my "Five Retirement Processes" thing (manuscript? really long blog posts? maybe ~150 pages in total) this week, I've hit an inflection point. If I got this stuff wrong, I have some more work to do so I don't misdirect anyone. If I am half right, that's about as much esoteric retirement quant stuff that I can handle for any six month period or at least it is for a while. Plus, the real question is "now what?" The blog has been fun but not so remunerative in terms of dollars or feedback or sense of service....except for my 20 readers (up from 2, so my growth rate is stratospheric). So what, exactly do I do now? Choices so far include:

- Stop this insanity and go do "life," i.e., go retire for real...
- Keep going and try to monetize something here via blog, book or services
- Limp along and keep doing posts but maybe more review and commentary
- Go back to work
- Partner with someone in the Ret-fin domain to do whatever
- Contribute knowledge cap via some type of volunteer service
- Other TBD

Anyone?