It’s the start of the sprint to the end of the calendar year, when activity picks up as firms and individuals try to get to the upper quartile of performance or the top of the league table. Don’t forget to take some time to think big thoughts. Some prompts for you:
First principles
Ted Seides published a great piece about “The Real Yale Model.” He went back to David Swensen’s Pioneering Portfolio Management to consider the differences between Swensen’s stated beliefs and the common interpretations of the Yale model.
Among the misconceptions is that “embracing illiquidity” is at the heart of the model. Seides says that is not a first principle:
To the extent David tilted towards illiquid assets, it was primarily because others did not.
Similar disconnects exist regarding asset allocation, active management, rebalancing, and private equity and venture capital. Regarding those two asset classes:
It’s ironic that Pioneering Portfolio Management became ignition fuel for capital flows to private markets. . . . David essentially begged all but the most well-resourced and sophisticated investors to play a different game.
The Yale model (or endowment model, if you want to use that term) is now used by organizations and individuals who do not have the resources to implement it in the way that Swensen said was required, while trumpeting elements of their approach in Swensen’s name. Seides effectively says, “Wait a minute.”
His summary of the real first principles: an equity bias, diversification, the alignment of interests, and a search for inefficiencies. The last two in particular are often overlooked by those who would otherwise seek to copy Swensen’s success as an allocator.
The missing piece
In “Harry Markowitz and the Philosopher’s Stone,” Stephen Sexauer and Laurence Siegel quote (and add the italics to) the groundbreaking 1952 article on portfolio selection by Markowitz:
The process of selecting a portfolio may be divided into two stages. The first stage starts with observation and experience and ends with beliefs about the future performances of available securities. The second stage starts with the relevant beliefs about future performances and ends with the choice of portfolio. This paper is concerned with the second stage.
As for that first stage, Markowitz wrote that statistical techniques and judgments should be used to come up with the estimates needed as inputs for his mathematical model. Sexauer and Siegel:
In this essay, we make the case that, while Markowitz adeptly solved for an optimal portfolio of stocks given accurate inputs, its extensive use today is incomplete, because almost every user — from pension and endowment boards to investment consultants and advisors all the way down to individual users — ignores or assumes away the question of whether the inputs are any good.
The article covers a lot of ground, reviewing the work of Markowitz and other pioneers of financial theory, as well as the challenges for practitioners in applying their models to the real world. For one thing, mean-variance optimization is an “error maximizer,” which turns “bad risk and return inputs into even worse outputs (portfolio allocations).”
When pressed by practitioners to give advice on how to arrive at better inputs, Markowitz would reply, “That’s your job, not mine.” At the end of their article, the authors offer a method for doing so.
(See also the correlation chart and discussion that appears further down in this posting.)
Winning lessons
In “An industry rearranged,” Casey Quirk outlines what it sees as the lessons learned by observing asset managers since the financial crisis. The twenty major firms that had net new revenues over that time (those due to positive flows) increased their share of industry revenue from 24% to 32%.
The report uses the construct “Winning firms . . .” to detail common aspects of success among those firms, grouping the specific actions into investments in growth, modernizations in operating models, and the institution of financial discipline. It is a recounting of what has worked for the biggest companies, with a focus on business outcomes rather than investment ones. (Asset management firms, large and small, need to consider a full range of innovation initiatives to succeed in the future.)
Other reads
“The Endowment Model: Key Considerations,” The Brandes Center. A discussion between Robert Maynard and Steve McCourt that summarizes their opposing views regarding the endowment model.
“Shining Light into the Machine Learning Black Box,” Wanjun Jin, Man Institute.
While ML models may have improved returns, investors are currently somewhat blind to where those returns are coming from.
“Future State of the Investment Industry,” Rhodri Preece, et. al, CFA Institute. This report focuses on four areas that are already affecting the investment arena: “diverging worlds” (deglobalization and other disruptions to the status quo macro assumptions), sustainable finance, digital transformation, and the end of cheap money.
“Comparability is crucial for informed investment decisions,” Steve Cooper and Dennis Jullens, The Footnotes Analyst.
Do not assume that financial statement data is always comparable, even if companies report under the same accounting standards.
“5 Signs of a Flyover Stock,” Todd Wenning, Flyover Stocks. Some attributes that cause a company to be overlooked; when coupled with good financial performance, they contribute to sustained market returns.
“How Blackstone Sprinted Ahead of Its Peers in AI,” Jonathan Kandell, Institutional Investor.
With $1 trillion of assets under management spread over more than 230 portfolio companies and massive holdings in real estate, private equity, and credit funds, Blackstone owns a plethora of the proprietary data that is the lifeblood of AI.
“Negative Lollapalooza Effects,” The Rational Walk. On the potential psychological traps that can result from making your investment ideas public.
“15 Ideas, Frameworks, and Lessons from 15 Years,” Corey Hoffstein, Flirting with Models.
If you have a portfolio and can introduce a novel source of diversifying beta, it’s not only likely to be cheaper than any alpha you can access, but you can probably ascribe a much higher degree of confidence to its risk premium.
“Making Conferences Great Again,” Adam Schwab. Suggestions on how to improve the experience for attendees; this is specific to institutional investor gatherings, but applies to other kinds of conferences as well.
“Analysts’ Institutional Client Catering and Reputation Tradeoff: Strategic Timing of Recommendations,” Anna Agapova and Uliana Filatova, SSRN.
This catering behavior to institutional investors with an objective to aid “window dressing” of managed portfolios around reporting dates is present for actively managed mutual funds, but not other types of institutional investors.
“The Santa problem,” Seth Godin, Seth’s Blog. How to know when you’re in an echo chamber within your group; “sooner or later reality arrives.”
“As subscription fees become popular, SEC warns investors to ‘do the math’,” Payton Guion, Citywire RIA.
The SEC urged investors to evaluate the services they’re paying for and determine if the services match the fees paid.
“The Morningstar ESG Commitment Level Landscape,” Morningstar. 108 asset managers are given qualitative ratings regarding their ESG efforts — fodder for advocates and detractors of the approach.
Human capital
“It doesn’t make sense to hire smart people and tell them what to do; we hire smart people so they can tell us what to do.” — Steve Jobs.
All over the place
In the paper on Markowitz referenced above, the authors state that “standard deviation and correlations are typically estimated from historical data.” The chart above, from “Empirical evidence on the stock-bond correlation,” by Roderick Molenaar and others, illustrates a potential problem:
The obstacle one faces when estimating the correlation between stocks and bonds is that it fluctuates extensively across periods. Volatility of assets classes can vary widely inside of business cycle but remain relatively stable over longer horizons. Correlations between stocks and bonds, on the other hand can switch signs for very long periods.
One of many good images in the paper, this shows the three-year values of average inflation and correlation mapped across time. The earlier periods had wider ranges of inflation (and deflation, something that hasn’t been seen in a long time), but even the most recent period shows the dramatic range in the correlation between stocks and bonds. A big unknown for portfolio construction that doesn’t receive the attention it should.
Postings
“Identifying the Complexity Risks in an Organization” looks at the gravitational pull of complexity in regard to investment choices and the complications regarding due diligence, communication, operations, technology, and regulation.
Under any circumstances, market stress can amplify organizational stress and business model stress. Increased complexity of whatever sort means that periods of pressure can be more unpredictable than they otherwise would be. In all respects it pays to gauge the payoff for complexity and the potential downsides involved, so a multidimensional analysis of it should be part of your strategic planning process.
All of the content published by The Investment Ecosystem is available in the archives.
Thanks for reading. Many happy total returns.
Published: September 11, 2023
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