Retirement Investing With A Global ETF Portfolio

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Includes: EEM, EFA, MDY, QQQ, SHY, TLT
by: Toma Hentea

Summary

Adding two global equity ETFs to the simple U.S. based portfolio leads to significantly higher historical performance over the period from 7/2003 to 7/2015.

Over that period, the enhanced portfolio with a mean-variance optimization algorithm for a low volatility target could support 11% annual withdrawal rate.

The portfolio with a mean-variance optimization algorithm for a HIGH volatility target could support 15% annual withdrawal rate.

In response to requests from some readers, we augmented the simple U.S. based ETF portfolio from our previous article by adding a couple of international equity ETFs. We still use some of the most liquid ETFs to build a very simple portfolio for a retirement investing account. As a benchmark we analyze the performance of the portfolio with equal weight targets, rebalanced when the allocation of any asset deviates by more than 20% from the target weight. That portfolio was subjected to 13 rebalancings within the 12 year interval of the study from July 2003 to July 2015.

As an alternative, we apply monthly reallocations based on a mean-variance optimization algorithm. We investigate two versions of the strategy: a return maximization with a low volatility target, and another with a high volatility target. The version with low volatility was subjected to 138 reallocation of the assets, virtually almost every month. Except for transaction costs, the frequent trading of this strategy is not a problem in an IRA account.

Here is the list of securities used to build the portfolio:

  • iShares MSCI Emerging Markets ETF (NYSEARCA:EEM)
  • iShares MSCI EAFE ETF (NYSEARCA:EFA)
  • SPDR S&P MidCap 400 ETF (NYSEARCA:MDY)
  • PowerShares QQQ Trust ETF (NASDAQ:QQQ)
  • iShares 1-3 Year Treasury Bond ETF (NYSEARCA:SHY)
  • iShares 20+ Year Treasury Bond ETF (NYSEARCA:TLT)

Basic information about the funds was extracted from Yahoo Finance and is shown in table 1.

Table 1.

Symbol

Inception Date

Net Assets

Yield%

Category

EEM

4/7/2003

24.62B

2.25%

Emerging Markets Large Blend

EFA

8/14/2001

61.43B

2.61%

Foreign Large Blend

MDY

5/4/1995

16.34B

1.16%

Mid-Cap Blend

QQQ

3/31/1999

45B

1.01%

Technology Large-Cap

SHY

7/22/2002

11.02B

0.46%

Short Term Treasury Bond

TLT

7/22/2002

4.98B

2.68%

Long Term Treasury Bond

The data for the study were downloaded from Yahoo Finance on the Historical Prices menu for EEM, EFA, MDY, QQQ, SHY, and TLT. We use the daily price data adjusted for dividend payments.

For the adaptive allocation strategy, the portfolio is managed as dictated by the mean-variance optimization algorithm developed on the Modern Portfolio Theory (Markowitz). The allocation is rebalanced monthly at market closing of the first trading day of the month. The optimization algorithm seeks to maximize the return under a constraint on the portfolio risk determined as the standard deviation of daily returns.

In table 2 we list the total return, the compound average growth rate (CAGR%), the maximum drawdown (maxDD%), the annual volatility (VOL%), the Sharpe ratio and the Sortino ratio of the portfolios without any fund withdrawals.

Table 2. Performance of the portfolios from July 2003 to July 2015 without withdrawals.

TotRet%

CAGR%

maxDD%

VOL%

Sharpe

Sortino

Equal Weight

227.32

10.35

-46.41

17.51

0.59

0.75

AA LOW volatility

405.51

14.08

-10.66

10.96

1.29

1.79

AA HIGH volatility

940.40

21.00

-18.45

16.94

1.24

1.71

SPY

163.86

8.40

-55.19

19.13

0.44

0.53

In figures 1a and 1b we show the historical allocation of assets for the adaptive allocation strategy.

Figure 1a. Historical asset allocation for the low volatility target portfolio.

Source: All the charts in this article are based on calculations using the adjusted daily closing share prices of securities.

Figure 1b. Historical asset allocation for the high volatility target portfolio.

In figure 2 we show the equity curves for the portfolios without withdrawals.

Figure 2. Equity curves for the adaptive allocation (AA) portfolios and the equally weighted portfolio with rebalancing with no withdrawals.

In table 3 we list the excess total return after withdrawals, the compound average growth rate (CAGR%) of the excess return, the maximum drawdown (maxDD%) including the withdrawals and the annual volatility (VOL%) of returns including withdrawals.

Table 3. Performance of the portfolios from July 2003 to July 2015 with 6% annual withdrawals.

TotRet%

CAGR%

maxDD%

VOL%

Equal Weight

88.40

5.40

-51.07

18.65

AA LOW volatility

201.11

9.61

-12.28

12.54

AA HIGH volatility

639.46

18.10

-21.10

17.32

In figure 3 we show the equity curves for the portfolios with 6% annual withdrawals.

Figure 3. Equity curves for the adaptive allocation portfolios and the equally weighted portfolio with rebalancing after 6% annual withdrawals.

To see how reliable was the 6% annual withdrawal rate we estimated the excess compound annual return (CAGR %) and the maximum drawdown (Max DD %) for the three portfolios starting in July of each year between 2003 and 2014. The results are shown in the figures 4a and 4b.

Figure 4a. Compound annual return for portfolios with 6% annual withdrawal rates by starting retirement date.

Figure 4b. Maximum drawdown for portfolios with 6% annual withdrawal rates by starting retirement date.

Source: All the charts in this article are based on calculations using the adjusted daily closing share prices of securities.

Additionally , we determined the maximum annual withdrawal rate that preserved the initial investment for the portfolios with July 2003 as starting retirement date. The results are shown in table 4.

Table 4.

Portfolio

Max withdrawal rate

Excess total return

Equal weight

8%

42.09%

AA LOW volatility

11%

31.61%

AA HIGH volatility

15%

188.06%

Current allocations for August 2015:

  • LOW volatility portfolio: 43% MDY + 40% QQQ + 17% TLT
  • HIGH volatility portfolio: 100% QQQ

Conclusion

Adding two global equity ETFs to the simple U.S. based portfolio leads to significantly higher historical performance over the time period from July 2003 to August 2015. Over that period, the enhanced portfolio with a mean-variance optimization algorithm for a low volatility target could support 11% annual withdrawal rate versus 9% for the simple U.S. based portfolio. The enhanced portfolio with a mean-variance optimization algorithm for a HIGH volatility target could support an exceptionally large 15% annual withdrawal rate.

Disclosure: I am/we are long QQQ, SHY.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.