Feb 20, 2017

A checkpoint on my current "geo-location" in retirement finance

The other day I responded to an email from an academic/professor that I had received on a question I had sent to him where I had asked about portfolio optimization.  In writing my response to his response, which included a paragraph on why I was doing what I was doing, I realized it was a concise summary of my ret-fin journey to date.  Since I have been down this road for about three or four years now I think it's reasonable to pause, step to the side, take a breath, and see where I am. This is consistent with the purpose of the blog which is to publicly record my journey by which means I intend to consolidate my own understanding of what I am doing and maybe (or maybe not) help at least one person out there that might or might not be on the same parallel path.   

If you temporarily ignore what I really think about the parlous state of the current retirement world where an uncomfortably large percentage of about-to-be and just- retired people are woefully, uncomfortably, and dangerously underfunded for a retirement, the paragraph below is a fair statement of what I currently think about retirement finance so far.  I'm just a beginner in all of this so my opinions will likely change. I endeavor every day to be open and available to any new evidence-based approaches that can change my mind. 

GMM here refers to Geometric Mean Maximization [Estrada 2010] or sometimes: the Kelly Criterion, growth optimal portfolio, maximum expected log, etc. It has a long and sometimes controversial history in economic circles.  MV refers to Mean-variance optimization (MPT/Markowitz). MC stands for Monte Carlo.

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"I got interested in life-cycle finance a few years ago when going through the door of a too-early retirement made the concept of portfolio outcomes go from abstract to a little too real.  I started by building a 5-asset-class MV optimizer -- but that's single period stuff with no spending constraint and anyway expected return estimates seem to go stale pretty fast.  Then I built both a custom MC simulator and an amateur backward induction engine using dynamic programming (with help) because they had multiple-period perspective and consumption constraints.  My conclusion after all that, plus a lot of reading and modeling, was that: a) risk allocation does matter a bit within certain ranges[1] (but spending control is better), b) portfolios seem like they can tolerate high risk allocations under certain conditions related to plan year and portfolio size, and c) that satisfying the consumption constraint with some kind of partial-to-full floor (whether using pooled risk or something else) is probably a better idea than not.  This is where GMM gets interesting.  While the volatility profile and time horizons of GMM may be ill suited to retirees in general, once a "floor" is in place, the excess non-committed portion of a portfolio, to the extent it is not a reserve for contingent expenses, becomes a very long term proposition and pretty tolerant of risk and even leverage which means it probably is a good fit for what I know of GMM-style thinking. The excess portfolio turns, in fact, into a long-term option on upside potential and it likely accrues more to legacy goals than consumption. This approach, if it works, also moots a bit, I think, the Samuelson critique of GMM.  I'd like to simulate it out some day if I can make it understandable in my own layperson terms." 

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[1] I didn't say but my current un-proved opinion is that the smaller the portfolio and the earlier in the plan, the more likely that the range is something like 40-70% committed to risk assets depending on the level of ever-changing risk-aversion. The later in the plan and the larger the portfolio the more likely that one is perhaps at 70% or even beyond, up to and including the possibility of 100% or even leveraged risk portfolios.  


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