Laurence B. Siegel
Gary P. Brinson Director of Research
CFA Institute Research Foundation
Luis Garcia-Feijóo, CFA, CIPM
Associate Research Director
Florida Atlantic University
William Sharpe said that retirement finance was the toughest engineering problem he had ever worked on. It is difficult to figure out how much to save because (1) you do not know how long you are going to live, (2) you do not know how much money you will need in each year you survive after retirement, (3) you do not know what return you will make on your investments, and (4) if you did know how much you needed to save, you would probably have a heart attack and die (solving the first problem expeditiously).
Jacques Lussier’s Secure Retirement: Connecting Financial Theory and Human Behavior is an engineer’s approach to solving these problems. He uses a series of Monte Carlo simulations to show the impact of each important decision on retirement outcomes. He also shows the impact of combined decisions (say, the decision to save more and invest more aggressively), which is a neat trick made possible by Monte Carlo technology.
Lussier’s analytics are not the only way to address retirement. His monograph is only one in an extensive series of RF works on this general topic. We have, over the years, also published three conference proceedings monographs on retirement organized by Zvi Bodie (2007, 2009, 2012); a literature review on Longevity Risk and Retirement Income Planning (2015); Moshe Milevsky’s 2013 monograph on life annuities; the 2007 monograph Lifetime Financial Advice: Human Capital, Asset Allocation, and Insurance, by Roger Ibbotson and three coauthors; and, this year, a brief on tontines (described subsequently).
In Statman’s words, normal people “want three kinds of benefits—utilitarian, expressive, and emotional—from all activities, products, and services, including financial activities, products, and services.” It is not irrational to want these benefits, so normal people’s wants are consistent with the classical economist’s assumption that people are utility maximizers. Statman’s BF 2.0 offers a different and more complete perspective on what people value—that is, on what they are maximizing when they seek to increase their utility.
A better potential solution to this problem is right in front of our faces. In Tontines: A Practitioner’s Guide to Mortality-Pooled Investments, Richard K. Fullmer, CFA, reaches back in time to an invention by Lorenzo de Tonti, a 17th-century Italian banker (hence the name “tontine”). A group of people pool their assets so that as each one dies, the surviving group members share the remainder.
To address this fiasco, New York University and the Annual Review of Financial Economics convened a group of central bankers and economic researchers to reflect on the causes, events, and long-run out-comes of the Global Financial Crisis. This event took place around the 10th anniversary of the crisis, hence the title of the Research Foundation’s brief based on the conference, Ten Years After: Reflections on the Global Financial Crisis.
The highlight was the central banker roundtable, which featured Stanley Fischer as moderator(!) and Ben Bernanke, Lord Mervyn King, and Jean-Claude Trichet as the principal speakers. All four were heads of central banks at the time of the crisis.2 Their comments, as well as those of the many other economists who participated in the conference, are available on video at the RF website as well as in the brief.
One of the oddest missing links in the financial system is the absence of a secure, transparent, and low-cost mechanism for investors planning for retirement to insure against longevity risk. The mirror image of mortality risk (the risk of dying), longevity risk is the risk of outliving one’s money. Life annuities are a step in the right direction, but most are overpriced and opaque as to both costs and benefits.
Fullmer’s RF brief develops the tontine idea into a full-fledged suite of hypothetical financial products. He shows the math behind a design that would allow participants of different ages and/or life expectancies to enter a tontine pool at actuarially fair prices. This approach would make the formation of large, anonymous pools practical.
Endowment funds play a central role in supporting the long-term survival and smooth operations of colleges and universities, but donations and market performance fluctuate, making sound management imperative. In this context, Richard Franz and Stephan Kranner’s brief, University Endowments: A Primer, offers an excellent overview of both how endowments work and how they should work. Assets under management at college and university endowments in the United States totaled approximately $570 billion as of June 2017.
Endowments have some unique features. Their time horizon is not merely long term; it is perpetual, giving them an advantage in earning an illiquidity premium. But illiquidity can also pose a risk, because schools typically have the greatest need of liquidity for spending when it is least available. Franz and Kranner cover these and other important issues in endowment management.
Setting spending rates for individuals with a limited but unknown length of life is difficult because of longevity risk: We do not know how long the money needs to last. But why does setting spending rates for perpetual endowment funds and for other long-lived trusts seem equally hard? Universities, foundations, and families have wrestled with this problem seemingly forever and have come up with a variety of conflicting answers:
The second-to-last answer, a fixed percentage of market value (or some variant, such as a fixed percentage of a rolling three-year average of market values), is currently in favor. The Ford Foundation advocated this method in an influential 1969 report, and the approach is still with us a half-century later. But the question remains open.
Although “income” can be a legal or accounting concept, the word also has an economic meaning: namely, what you can consume in a given period without being worse off at the end of the period (in real terms) than at the beginning, assuming no change in market valuations. In the RF brief A Cash-Flow Focus for Endowments and Trusts, James Garland, CFA, builds on this concept to define the “fecundity” of an asset in exactly that way. (Fecundity, in biology, is the fertility of an organism per unit of time—high for rabbits, low for elephants.) For bonds, fecundity is yield, minus an allowance for defaults if the bond is risky; for stocks, it is a concept akin to free cash flow, representing the economic profit of the company.
A spending rule tied to fecundity (and ignoring market values) will give very different results from the currently popular rule specifying a percentage of market value. It will increase spending, relative to the market-value rule, when markets are cheap and will decrease it when markets are expensive. It will also produce a smoother spending path. Each institution or individual will have to decide which spending rule makes the most sense in that institution’s or individual’s particular situation.
Demographic trends and economic growth expectations indicate that future investment opportunities with attractive risk–return profiles will likely come from outside developed markets. In particular, African markets are regarded with promise. They are, however, not a unitary construct. For example, MSCI classifies South Africa and Egypt as emerging; Kenya, Mauritius, Morocco, Nigeria, Tunisia, and the countries in the West African Economic and Monetary Union as frontier; and Botswana and Zimbabwe as neither emerging nor frontier, although they have their own standalone market indices. Some countries export oil and gas; others, precious metals and minerals or coffee; and yet others, non-commodities such as textiles. Furthermore, sovereign and corporate bond markets, as well as exchange-traded funds, need to be understood in these countries.
The brief African Capital Markets: Challenges and Opportunities, produced in collaboration with the African Securities Exchanges Association (ASEA), addresses these issues for South Africa, Namibia, Botswana, Zimbabwe, Mauritius, Kenya, Tanzania, Uganda, Rwanda, Nigeria, Ghana, Egypt, and Morocco and presents aggregate data on the size and liquidity of the equity and fixed-income markets in these countries.
1Nicola Gennaioli, Andrei Shleifer, and Robert Vishny, “Money Doctors,” Journal of Finance 70 (February 2015): 91–114.
2Respectively, in Israel, the United States, the United Kingdom and at the European Central Bank.