.
Intel

Managing for Drawdown .

First Target Distribution ETF Seeks to Help Investors in Retirement

Managing for Drawdown

By David I. Cohen & Matthew J. Patterson / Founders, Bryant Avenue Ventures, Creators of HANDLS™ Indexes

What the world needs is real solutions for people who are in the drawdown portion of their investment life.”2

Investors Objectives are Changing from Accumulation to Drawdown
There are 79 million baby boomers and over the next 30 years a staggering 10,000 people a day will retire.  The macroeconomic and demographic demands of this post-WW2 generation will reshape the investment management industry, as the focus of baby boomers shifts from wealth accumulation to finding solutions to manage the drawdown phase of their investment lives.
  
This paradigm shift means every day more and more people need a steady, dependable and consistent distribution from their investment portfolios. Unfortunately, in an era of low interest rates, few investors have sufficient retirement savings to fund future withdrawal requirements with a portfolio of government-guaranteed bank deposits or U.S. Treasuries3, let alone maintaining any expectation of keeping pace with inflation.  Investors can seek to address this shortfall by reaching down the scale of fixed-income credit quality (to aptly named “junk” bonds) or through exposure to other income-producing asset categories that carry high levels of idiosyncratic risk. While these options may produce modestly higher levels of current income, both come at a cost of significantly increased volatility and neither addresses the reality that retirees require high-distribution tools that may minimize the risk of idiosyncratic market dislocations. 
 
The average age of retirement in the United States is 63 and the average length of retirement is 18 years, thereby making retirement a long-term liability. We propose that this fundamental shift in investor objective requires a parallel shift in the approach to asset management.  Because the 18-year average length of a retirement liability will likely experience multiple market cycles, fixing withdrawal rates and allowing the portfolio’s net asset value to fluctuate can help meet an investor’s cash-flow needs while allowing time to smooth over market volatility.   We believe this is best executed by employing a well-diversified portfolio with the objective of seeking to maximize risk-adjusted returns over time.  
 
Retirees need cash flows to address funding a lifestyle. Holding all else equal the spender may be indifferent to the source of cash flow, which could come from current income, capital gains or even return of capital.  However, all else isn’t equal as each is taxed differently, or in the case of return of capital, isn’t taxed at all. This being the case, we believe a proper drawdown strategy may incorporate return of capital as part of a distribution plan, particularly if underlying investments, such as ETFs, allow for potential capital growth while seeking to minimize capital gains distributions.
 
Modern Portfolio Theory Meets Retirement
If the question facing 10,000 new retirees every day is how to best fund an 18-year average liability, the logical next step is to study what would have been experienced over a long-term history that encompassed numerous market environments.  
 
There are three major asset classes that serve as the cornerstone for any investment portfolio: cash, fixed income (or bonds) and equities (or stocks). Many investors also invest in high yield corporate bonds, which make up a small portion of the fixed income asset class, to add additional income to their portfolios. While the future performance of each asset class is unknown, historical monthly returns offer perspective regarding how assets classes performed over past investment cycles. For the 30-year period from January 1987 through December 2016, starting with a hypothetical $1 million and assuming reinvestment of all income, the three major asset classes and high yield corporate bonds achieved the following returns4:
 

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While these historical returns are helpful, they do not consider important factors that can impact investors in the drawdown phase of their investing lives. One such factor, inflation, eats into nominal investment returns by reducing the purchasing power of dollars over time. Another factor, the distribution strategy employed by an investor, can dramatically influence investment returns and, if too aggressive relative to investment performance, lead to a complete drawdown of savings. 
 
Using Monte Carlo simulations, we can study what might happen to investment portfolios in various hypothetical scenarios using historical monthly returns, taking into account historical inflation and a high monthly distribution strategy.  A Monte Carlo simulation randomly samples historical monthly returns to produce a range of projected investment outcomes for a given asset class or investment portfolio. The range of projected investment outcomes produced by a Monte Carlo simulation can assist investors in determining the likelihood of an asset class or investment portfolio meeting a given investment objective. 
 
While a Monte Carlo simulation can offer insight into how asset classes or investment portfolios may perform in a variety of scenarios, the process employs critical assumptions that should be taken into consideration. Most significantly, to the extent it employs actual past returns to simulate future returns, a Monte Carlo simulation implicitly assumes that future return distributions will be like past return distributions, which may not be the case. Sampling from a long period of historical data can help ensure a variety of market cycles are incorporated into historical return data, but there can be no assurance that markets will exhibit similar patterns of performance in the future as in the past. 
 
Using a Monte Carlo simulation tool available at www.portfoliovisualizer.com, we sought to simulate projected investment outcomes for the three major asset classes described above and a few combinations thereof using historical monthly returns for the 30-year period from January 1987 through December 2016, an era that saw both bull and bear market cycles and multiple periods of extreme market dislocation. We made the following assumptions for each Monte Carlo simulation: 
 
  • $1MM initial portfolio
  • $5,833 monthly distribution
  • 20-year simulation
  • Monthly Historical returns
  • Single-year Model
  • Historical Inflation
 
It is important to understand that the projections or other information generated by these Monte Carlo simulations regarding the likelihood of various investment outcomes are hypothetical in nature, do not reflect actual investment results and are not guarantees of future results.  The results of a Monte Carlo simulation may vary with each use and over time.
 
Cash and Equities: The Two Extremes
While cash may lose purchasing power, it never loses its nominal value5.   On the other extreme, equities have the highest historical return, but may expose investors to significant risk of catastrophic loss. The two charts below illustrate these potential outcomes in a side-by-side comparison of Monte Carlo simulations for either a 100% Cash Portfolio or a 100% Equity Portfolio where the investor has $1MM in assets and requires a drawdown of $70,000 per year ($5,833 per month).
 

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From the perspective of drawdown management, where a 100% maximum drawdown means that an investor has completely exhausted his savings through a combination of market performance and distributions, one can conclude that cash is by far riskier than stocks – even though cash has no risk of loss in nominal terms. For the 100% Cash Portfolio, more than 70% of all simulations failed to survive all withdrawals over a 20-year period while on the equity side only 12% failed. To make matters worse, only 10% of the cash portfolios had enough residual value to cover drawdowns even into just a 21st year.   In terms of keeping pace with inflation on top of withdrawals, no cash simulations were able to come close, while the median portfolio of equities exceeded the inflation rate to have an ending inflation-adjusted balance of $1.5MM on top of 20 years of $70,000 annual cash flow.
 
Bonds and High Yield: Risk-Return Tradeoff
Using the same assumptions, a 100% Bond Portfolio was able to achieve the long-term funding goal in 97% of simulations, meaning it retained its ability to pay a 7% annual distribution over 20 years. However, only 10% of bond portfolios in the simulation maintained nominal value and nearly all failed to keep pace with inflation by between 35 and 90%. One approach investors take is to slide down the credit quality scale to high yield corporates, or junk bonds, to enhance future returns.  While a 100% High Yield Bond Portfolio demonstrates the potential in simulations to significantly outperform a 100% Bond Portfolio, 10% of simulations for the 100% High Yield Bond Portfolio lost all value and failed to meet distribution requirements and only 25% were able to maintain the drawdown and achieve an inflation-adjusted end balance equal to the initial $1MM.
 

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Balanced Portfolios = Higher Sharpe Ratio
One of the key insights of modern portfolio theory is that investors with a greater preference for risk have historically earned higher risk-adjusted returns by increasing exposure to a well-diversified portfolio than by investing in a portfolio of concentrated investments in riskier assets. This may be accomplished by increasing the exposure to a diversified portfolio to a multiple of 1.3x using leverage. Investors can add leverage to their portfolios by use of margin accounts or by investing in funds that employ leverage (by borrowing to invest in securities). While investors should be aware that leverage increases the expected return of a given portfolio, it also increases expected volatility, or risk. 
 
As can be seen in the following charts, diversification provides investors a better overall mix of potential outcomes.  Nearly 99% of both the 1.0x and 1.3x versions of the 70/30 Fixed Income/Equity Portfolio survived all distribution requirements and both experienced lower maximum drawdowns than any of the prior individual asset simulations. 
 

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Moreover, while the use of leverage in the 1.3x 70/30 Total US Bond/US Equity Portfolio did not result in a significantly greater likelihood of the portfolio failing to survive all withdrawals, it did increase its performance compared to the unleveraged version of the portfolio. Not only was the median 1.3x 70/30 Total US Bond/US Equity Portfolio able to fund the $70,000 annual drawdown while keeping pace with inflation, its inflation-adjusted ending balance of just slightly over the $1MM initial investment bested all but the $1.5MM inflation-adjusted ending balance of the median 100% Equity Portfolio.  
 
The approach does carry uncertainty regarding future returns.  While the Monte Carlo Simulation showed the median 1.3x 70/30 Total US Bond/US Equity portfolio was able to support the $70,000 distribution and keep pace with inflation, 50% of those outcomes were better and 50% were worse. Nevertheless, Monte Carlo simulations based on historical performance data suggest, at least over long holding periods, that the use of some leverage in a well-diversified portfolio has the potential to improve investor outcomes without significantly increasing the risk of a portfolio experiencing a complete drawdown even after accounting for a 7% annual distribution rate.
 
Charting a Course in Unpredictable Markets
 The following chart illustrates the actual historical performance (as opposed to a Monte Carlo simulation) of a 1.3x 70/30 Total US Bond/US Equity Portfolio for the period from January 1987 to December 2017. The analysis assumes a starting value of $500,000 and a 7% annual distribution paid monthly. The vertical axis shows the portfolio’s net asset value (“NAV”) and cumulative distributions. 
 

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Clearly an investor's experience is path dependent.  An investor undertaking this strategy in January 1987 started with $500,000.  Over the next 10 years, the portfolio grew to $844,969 (NAV) and the investor received $477,849 of distributions (nearly all the initial investment received in just 10 years).   However, an investor entering with $844,969 in 1997 over the next 20 years received just a little over $1.1 MM in distributions but had an ending balance or NAV of only 788,671.   The most ill-timed investor bought in 1998 and held until 2008.  In that case the investor bought at $930,000, had an ending NAV of $659,000 and received $587,000 in distributions.  While a difficult market led to $271,000 in principal degradation, the balanced approach fared far better than the 100% equity portfolio which lost $432,000 over the same period.  In this scenario, buying at the absolute high took 17 years to recapture the initial investment of $930,000 purely from distributions.
  
The 1.3x 70/30 Total US Bond/US Equity Portfolio does have risk, meaning the future value is uncertain; but investors need some risk to have any possibility of generating sufficient future returns to support a portfolio that pays out high distributions. There is also volatility in the distribution because by being tied to a percentage of the value of the portfolio.  In 1997 the annual distribution for this hypothetical portfolio was $51,191.  In 2007, it was $51,355 and in 2017 it was $54,515.  But in 1998 at the peak of valuations for the cycle, the distribution was over $60,000.  After the peak, valuations and distributions declined; however, distributions remained higher than the $35,000 that was earned on an initial value of $500,000 in 1987. The alternative to not having risk, though, is the 90-day Treasury bill, which currently earns 1.68% and would potentially provide an investor experience similar to the 100% Cash Portfolio previously modeled. 
 
A Potentially Superior Approach
To support the claim that taking a 1.3x position in a well-diversified portfolio is superior to concentrating assets in high-yielding assets with more idiosyncratic risk, we provide a side by side comparison of the historical performance of a 1.3x 70/30 Total US Bond/US Equity Portfolio compared to the to the 100% High Yield Bond Portfolio from January 1987 through December 2017. In exchange for roughly the same experienced volatility, the 1.3x 70/30 Total US Bond/US Equity Portfolio earned an average annual internal rate of return 226 bps greater than the 100% High Yield Bond Portfolio. 
 

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And, in a side-by-side comparison of Monte Carlo simulations comparing the two portfolios, the dispersion of probable returns appears more attractive (both higher lows and higher highs) for the 1.3x 70/30 Total US Bond/US Equity portfolio than for high yield corporate bonds. 
 

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The 50th percentile is also called the median and can be compared across the various investment options.   Clearly the 1.3x 70/30 Total US Bond/US Equity option offers the potential to maintain its-inflation adjusted value while paying out a 7% annual distribution on a monthly basis.  
 
50th Percentile Monte Carlo Simulation Summary Statistics
 

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Introducing Target Distribution Indexes
 
Based upon the preceding insights, Bryant Avenue Ventures and Nasdaq Global Indexes created Nasdaq HANDLS™ (High-Distribution AND Liquid Solutions) Indexes to help address the income needs of retirees in the drawdown phase of their investing lives.
 
The first of these indexes, the Nasdaq 7HANDL Index, powered by Nasdaq Dorsey Wright, consists of three critical components:
  1. A well-diversified, multi-asset portfolio of Exchange-Traded Funds (ETFs) that seeks to provide a high level of income;
  2. 1.3x exposure to enhance the income generation and total return potential of the underlying portfolio (1.3x exposure is the equivalent of 23% leverage); and
  3. The goal, but not the guarantee, of achieving a total return sufficient, over time and after expenses, to support a seven percent (7.0%) annual distribution rate.
 
About the Nasdaq 7HANDL Index 
The Nasdaq 7HANDL Index is a 100% rules-based index defining a diversified portfolio of exchange-traded funds that was specifically developed with the goal to support, from total return, a 7% annual distribution rate. Accordingly, returns for the index are calculated assuming 1.3x exposure on the underlying portfolio (net of the estimated borrowing cost). The Nasdaq 7HANDL™ Index has risk characteristics similar to the broad US capital markets and can be expected to generally rise and fall with prevailing market conditions. The index design is intended to offer a variation of the traditional 1.3x 70/30 Fixed Income / Equity Portfolio discussed above that incorporates a tactical component to a portion of the portfolio. 
 
The Nasdaq 7HANDL™ Index is split into two components, with a 50% allocation to a Core Portfolio and a 50% allocation to a Dorsey Wright Explore Portfolio. 
 

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The Core Portfolio embeds a long-term foundation by providing static exposure to the U.S. fixed-income and equity markets.  Allocations for the Core Portfolio are fixed at 70% U.S. aggregate fixed-income and 30% US large cap equity.  
 
The Nasdaq Dorsey Wright Explore Portfolio component of the Nasdaq 7HANDL™ Index employs a 100% rules-based proprietary tactical asset allocation methodology to provide a tactical exposure to ETFs across a range of asset categories that have historically provided high levels of current income and exhibit largely uncorrelated return streams. The 12 asset categories represented in the Explore Portfolio include the following:
 
Table 1 Nasdaq 7HANDL Dorsey Wright Explore Portfolio Asset Categories
 

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Each asset category in the Explore Portfolio is represented by a single ETF6.  The 12 ETF representatives of the Explore Portfolio asset categories are weighted on a monthly basis using a tactical asset allocation methodology developed in consultation with Nasdaq Dorsey Wright Investment Research & Analysis that seeks to incorporate momentum, yield and risk7
 
When combined, the Core Portfolio and Nasdaq Dorsey Wright Explore Portfolio provide a balance between long-term beta and short-term smart beta investment strategies across a group of constituents whose underlying securities total an estimated 34,000 positions representing an estimated 20,000 individual CUSIPS8
 
 

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The teachings of modern portfolio theory can be effectively deployed to help manage withdrawal requirements for retirees.  Research suggests that enhancing risk-adjusted returns may be achieved by modestly increasing the exposure to a well-diversified, balanced portfolio while minimizing the idiosyncratic risk posed by concentrated investments. Combining these principles with a high managed distribution provides an additional potential benefit to investors -- minimizing the need to maintain an excessive amount of cash reserves; an asset class virtually guaranteed to not keep pace with inflation. 
 
The Nasdaq HANDLS™ indexing methodology uses the teachings of modern portfolio theory to help deliver income-oriented investment solutions to investors in the drawdown portion of their investment lives. By building well-diversified, balanced portfolios of low-cost ETFs and enhancing potential returns, investors seeking high monthly cash flow have the opportunity to minimize idiosyncratic risk posed by concentrated investments and earn higher risk-adjusted returns. 
 
Notes on simulations and results: Past performance is not a guarantee of future returns and data and other errors may exist. CAGR = Compound Annual Growth Rate. IRR = Internal Rate of Return taking into account the periodic contributions/withdrawals. St Dev is a number used to tell how measurements for a group are spread out from the average (mean) or expected value. A low standard deviation means that most of the numbers are very close to the average. A high standard deviation means that the numbers are spread out. Figures presented herein are the annualized standard deviation of monthly returns. Sharpe Ratio is a risk-adjusted measure of performance that considers the volatility of an investment’s returns. A higher Sharpe Ratio indicates a greater return per unit of volatility. It is calculated and annualized from monthly excess returns over the risk-free rate (1-month T-bills). Drawdowns are calculated based on monthly returns. Monthly return series of the selected benchmark is used for results comparisons The results include monthly rebalancing of portfolio assets to match the specified allocation The results use total return and assume that all dividends and distributions are reinvested. Taxes and transaction fees are not included. 
 
Source: PortfolioVIsualizer © Silicon Cloud Technologies LLC 2013-2017. 

HANDLS™ and HANDL™ are trademarks of Bryant Avenue Ventures LLC.

Nasdaq® is a registered trademark of Nasdaq, Inc. The information contained above is provided for informational and educational purposes only, and nothing contained herein should be construed as investment advice, either on behalf of a particular security or an overall investment strategy. Neither Nasdaq, Inc. nor any of its affiliates makes any recommendation to buy or sell any security or any representation about the financial condition of any company. Statements regarding Nasdaq-listed companies or Nasdaq proprietary indexes are not guarantees of future performance. Actual results may differ materially from those expressed or implied. Past performance is not indicative of future results. Investors should undertake their own due diligence and carefully evaluate companies before investing. ADVICE FROM A SECURITIES PROFESSIONAL IS STRONGLY ADVISED. 

© 2017. Nasdaq, Inc. All Rights Reserved

1The authors of this article have created or co-created ETFs that have raised approximately $14 billion in assets under management, including various first-to-market concepts (multi-asset income, BRIC, frontier, solar, timber, insider-activity, spin-off and shipping) and the popular BulletShares franchise. 

2“Rethinking ‘Fixed Income,’” Dave Nadig, ETF.com (available at http://www.etf.com/publications/etfr/rethinking-fixed-income). 

3The United States government guarantees certain deposits at FDIC-regulated banks, subject to various restrictions, and U.S. Treasuries are backed by the full faith and credit of the United States government. 

4Source for all historical performance data: www.portfoliovisualizer.com 

5“Nominal value” refers to the face value of cash without taking into account any loss of value due to inflation. 

6By default, the ETF representative of an asset category is the largest ETF (by assets under management) in the asset category. In certain circumstances, an alternative ETF with a lower expense ratio may instead serve as the ETF representative of an asset category. 

7The Explore Category allocation uses a proprietary methodology developed by Nasdaq Dorsey Wright (DWA). The 12 ETFs comprising the Dorsey Wright Explore Category are weighted based on the DWA Managed Momentum Score which is calculated as Relative Strength Weighting multiplied by Yield to Risk Weighting and rebased proportionally to 100% in order to calculate the Index Weight.

8Estimated number of non-overlapping constituents that make up the underlying ETFs that serve as Nasdaq 7HANDL Index constituents.

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