Musings on a Famous Portfolio Asset Allocation Study

Some Critics Claim Brinson, Hood, and Beebower Conclusions Wrong

[By Dr. David Edward Marcinko MBA CMP™]

http://www.CertifiedMedicalPlanner.org

[Publisher-in-Chief]

Frequently, we hear the axiom that asset allocation is the most important investment decision, explaining 93.6% of portfolio returns. The presumption has been that once the risk tolerance and time horizon have been established, investing is simply a matter of implementing a fixed mix of stocks, bonds, and cash using mutual funds selected for this purpose. This axiom is based on a famous study by Brinson, Hood, and Beebower (BHB) published in the Financial Analysts Journal in July/August 1986. It is the stuff of most modern business school and graduate students in economics and finance.

Enter the Critics

One critic claims that BHB’s conclusions and the interpretation of their conclusions are wrong, stating that because of several methodological problems, BHB needed to make certain assumptions for their analysis to go forward. They assumed that the average asset-class weights for the 10-year period studied are the same as the actual normal policy weights; that investments in foreign stocks, real estate, private placements, and venture capital can be proxied by a mix of stocks, bonds, and cash; and that the benchmarks for stocks, bonds, and cash against which fund performance was measured are appropriate. The author believes that each of these assumptions can lead to a faulty measurement of success or failure at market timing and stock selection.

The Jahnke Study

William Jahnke claims that BHB erred in their focus on explaining the variation of quarterly portfolio returns rather than portfolio returns over the 10-year period studied. According to the study, asset allocation policy explains only a small fraction of the range of 10-year portfolio returns earned by the pension funds reported in the study. The author concluded that this discrepancy is caused by the effect of compounding returns. He adds that BHB were wrong to use variance of quarterly returns rather than the standard deviation. Use of standard deviation would reduce the often cited 93.6% to about 79%. Moreover, BHB did not consider the cost of investing, such as operating expenses, management fees, brokerage commissions, and other trading costs, which are more significant for individual investors than for the pension plans studied. Jahnke claims that excessive costs can reduce wealth accumulation by 50%.

Note: (“The Asset Allocation Hoax,” William W. Jahnke, Journal of Financial Planning, February 1997, Institute of Certified Financial Planners [303] 759-4900).

Assessment

Finally, the author takes issue with establishing long-term fixed asset class weights. Asset allocation should be a dynamic process. Higher equity return expectations should in turn produce larger equity allocations, other things being equal.

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Conclusion

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Thinking Beyond Portfolio Asset Allocation

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Don’t Forget Your Spending Policy – Doctors

By Dr. David Edward Marcinko MBA CMP™

www.CertifiedMedicalPlanner.com

[Publisher-in-Chief]

If you are economically literate – or read the ME-P regularly – you may be tired of hearing the familiar saw, “the single most important determinant of investment results over time is asset allocation.”

But, as most of us realize, this glosses over critical obstacles to building personal wealth—taxes, inflation, and spending policy. A doctor’s spending policy itself is as critical as asset allocation in preserving wealth, as well as for all investors who understand the trade-offs: there are both allocation and spending strategies that stand to preserve wealth and insulate against excessive equity risk at the same time.

Income versus Security

In proving his point a decade ago, the author—Roger Hertog in “Income Versus Security”— traced the growth of a $1 million portfolio during the period of 1960–1994. He showed that while an all-stock portfolio would have experienced a compound growth rate of 10.1%, an all-bond portfolio of 7.4%, and an all T-bill portfolio of 6.1%, these growth rates dropped to 8%, 5%, and 3.7%, respectively, after taxes and conservative transaction costs. When further reduced by inflation, they dropped to 3.1%, 0.2%, and -1%, respectively. Stocks still nearly tripled in real value after taxes.

Next, Hertog factored in spending. He showed that the greater the equity exposure, the more likely investors will preserve or increase their levels of real spending and wealth. Also, he demonstrated how a spending policy of a fixed percentage of the portfolio; or of spending all the income is ill-suited to estate building. He arrived at an optimum allocation of 60% stocks and 40% bonds with a policy of spending all stock dividends but only spending interest to the extent it exceeds inflation. This latter spending policy adjusts for the fact that in – unlike today but perhaps again in the near future – an inflationary environment a portion of bond interest is a return of principal. This type of asset allocation and spending policy resulted in the greatest amount of growth over the years and gained on inflation. Hertog contends that the 60/40 allocation provides an appealing combination of growth and protection.

IOW: It gives investors a milder ride.

Assessment

Over the 35-year period studied, a 60/40 mix returned almost as much as the all-stock portfolio both before taxes and after taxes and achieved some 75% of its real after-tax growth. Also, the portfolio’s worst year was only half as bad as the all-stock portfolio. Hertog believed that balancing with bonds softened the downside. But – what about the “flash-crash” of 2008-09?

Note: “Income Versus Security: Do You Have To Choose?” Roger Hertog, Trust & Estates, March 1997, pp. 44–62, Intertec Publishing Corporation.

Conclusion

And so, your thoughts and comments on this ME-P are appreciated. Is the bull market in bonds over? Do you believe Hertog? Feel free to review our top-left column, and top-right sidebar materials, links, URLs and related websites, too. Then, subscribe to the ME-P. It is fast, free and secure.

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PODCAST: Hospital SUPPLY CHAIN Status

By Staff Reporters

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Hospital Supply Chain Optimization Status: Survey Results

A recent survey from Syft of 100 hospital and supply chain leaders found:

 •  65% said better supply chain management could improve margins by 1-3%, with 23% of respondents believing margins can improve by more than 3%.
 •  94% agreed that supply chain analytics can reduce supply chain costs. 76% said it can improve quality.
 •  24% said their organizations identify supply standardization opportunities very well.
 •  32% said it would cost their organizations more than $500,000 annually to meet new supply chain regulations like California Assembly Bill 2357.

Source: Syft via. PRNewswire, December 8, 2021

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UPDATE: The Markets, Gasoline, Recession and the Bear

By Staff Reporters

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For the domestic markets, the S&P 500 closed down 151 points, or 3.88%. It’s down nearly 22% since January. The Dow was down 876 points (2.79%) and the NASDAQ dropped 530 points (4.68%). And, investors were disappointed to learn that inflation is moving in the wrong direction. U.S. consumer prices surged 8.6% year-over-year in May, to a fresh 40-year high, led by higher prices for energy, food and housing.

For the first time in history, a gallon of regular gasoline now costs $5 on average nationwide, according to AAA, and experts predict gas prices could average $6 a gallon by August.

Moreover, nearly 70% of leading economists expect the US to tumble into a recession as the country grapples with inflation. In a Financial Times poll, the bulk of economists said they expect a recession to be declared in the first half of 2023. The poll comes after US inflation soared to 8.6% in May, outstripping economists’ expectations and piling the pressure on the Fed.

Finally, S&P Global says a 20% decline in the S&P 500 on a closing basis from its previous peak is all it takes to define a bear market. Which means that this bear market is already more than five months old, since the S&P 500 all-time high came on January 3rd, 2022.

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Financial Monte Carlo Simulation’s FLAW and FIXES

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Physicians Must Understand Deus ex Machina

[By Wayne J. Firebaugh Jr; CPA, CFP®, CMP™]

SPONSOR: http://www.CertifiedMedicalPlanner.org

wayne-firebaughNamed after Monte Carlo, Monaco, which is famous for its games of chance, MCS is a software technique that randomly changes a variable over numerous iterations in order to simulate an outcome and develop a probability forecast of successfully achieving an outcome.

Endowment Fund Perspective

In private portfolio and fund endowment management, MCS is used to demonstrate the probability of “success” as defined by achieving the endowment’s asset growth and payout goals. In other words, MCS can provide the endowment manager with a comfort level that a given payout policy and asset allocation success will not deplete the real value of the endowment.

Divorce from Judgment

The problem with many quantitative software and other tools is the divorce of judgment from their use. Although useful, both mean variance optimization MVO and MCS have limitations that make it so they should not supplant the physician investor or endowment manager’s experience. MVO generates an efficient frontier by relying upon several inputs: expected return, expected volatility, and correlation coefficients. These variables are commonly input using historical measures as proxies for estimated future performance. This poses a variety of problems.

Problems with MCS 

First, the MVO will generally assume that returns are normally distributed and that this distribution is stationary. As such, asset classes with high historical returns are assumed to have high future returns.

Second, an MVO optimizer is not generally time sensitive. In other words, the optimizer may ignore current environmental conditions that would cause a secular shift in a given asset class returns.

Finally, an MVO optimizer may be subject to selection bias for certain asset classes. For example, private equity firms that fail will no longer report results and will be eliminated from the index used to provide the optimizer’s historical data [1].

Example:

As an example, David Loeper, CEO of Wealthcare Capital Management, made the following observation regarding optimization:

Take a small cap “bet” for our theoretical [endowment] with an S&P 500 investment policy. It is hard to imagine that someone in 1979, looking at a 9% small cap stock return premium and corresponding 14% higher standard deviation for the last twenty years, would forecast the relationship over the next twenty years to shift to small caps under-performing large caps by nearly 2% and their standard deviation being less than 2% higher than the 20-year standard deviation of large caps in 1979 [2].

Table: Compares the returns, standard deviations for large and small cap stocks for the 20-year periods ended in 1979 and 1999.  Twenty Year Risk & Return Small Cap vs. Large Cap (Ibbotson Data).

1979 1999
Risk Return Correlation Risk Return Correlation
Small Cap Stocks 30.8% 17.4% 78.0% 18.1% 16.9% 59.0%
Large Cap Stocks 16.5% 8.1% 13.1% 18.6%

Reproduced from “Asset Allocation Math, Methods and Mistakes.” Wealthcare Capital Management White Paper, David B. Loeper, CIMA, CIMC (June 2, 2001).

More Problems with MCS

David Nawrocki identified a number of problems with typical MCS as being that most optimizers assume “normal distributions and correlation coefficients of zero, neither of which are typical in the world of financial markets.”

Dr. Nawrocki subsequently describes a number of other issues with MCS including nonstationary distributions and nonlinear correlations.

Finally, Dr. Nawrocki quotes Harold Evensky who eloquently notes that “[t]he problem is the confusion of risk with uncertainty.

Risk assumes knowledge of the distribution of future outcomes (i.e., the input to the Monte Carlo simulation).

Uncertainty or ambiguity describes a world (our world) in which the shape and location of the distribution is open to question.

Contrary to academic orthodoxy, the distribution of U.S. stock market returns is far from “normal” [3]. Other critics have noted that many MCS simulators do not run enough iterations to provide a meaningful probability analysis.

Assessment

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Some of these criticisms have been addressed by using MCS simulators with more robust correlation assumptions and with a greater number of iterative trials. In addition, some simulators now combine MVO and MCS to determine probabilities along the efficient frontier.

Conclusion

Your thoughts and comments on this ME-P are appreciated. Feel free to review our top-left column, and top-right sidebar materials, links, URLs and related websites, too. Then, subscribe to the ME-P. It is fast, free and secure.

Link: http://feeds.feedburner.com/HealthcareFinancialsthePostForcxos

Speaker: If you need a moderator or speaker for an upcoming event, Dr. David E. Marcinko; MBA – Publisher-in-Chief of the Medical Executive-Post – is available for seminar or speaking engagements. Contact: MarcinkoAdvisors@msn.com

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References:

1. Clark, S.E. and Yates, T.T., Jr. “How Efficient is your Frontier?” Commonfund Institute White Paper (November 2003).

2. Loeper, D.B., CIMA, CIMC. “Asset Allocation Math, Methods, and Mistakes.” Wealthcare Capital Management White Paper (June 2001).

3. Nawrocki, D., Ph.D. “The Problems with Monte Carlo Simulation.” FPA Journal (November 2001).

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What is an [Famous] ECONOMIST?

The Top 15 Most Famous?

By Staff Reporters
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15 Famous Economists and Their Contributions That’ll Truly Amaze You

According to Wikipedia, an economist is a professional and practitioner in the social science discipline of economics. The individual may also study, develop, and apply theories and concepts from economics and write about economic policy. Within this field there are many sub-fields, ranging from the broad philosophical theories to the focused study of minutiae within specific markets, macroeconomic analysis, microeconomic analysis or financial statement analysis, involving analytical methods and tools such as econometrics, statistics, economics computational models, financial economics, mathematical finance and mathematical economics.

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The Famous 15

READ: https://historyplex.com/famous-economists

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