Simple ETF Portfolio: Mean-Variance Optimization Strategy Underperforms Since January 2015

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

Summary

The adaptive monthly allocation strategies applied to the simple ETF portfolio underperformed lately.

All adaptive allocation strategies produced negative returns in the first quarter of 2016.

The equal weight with rebalancing strategy performs better than adaptive allocation during extended periods of high market uncertainty.

The simple ETF portfolio was introduced in an article published in August 2015. Since then it has not performed well, and anybody who considered using it may have suffered great disappointment. For this reason, I decided to analyze its historical performance in greater detail. We already know that this portfolio did very well during the 2008-09 financial crises. The question I am trying to answer is the following: is the current underperformance unique, or there were, historically, other periods when it lagged the general market.

The portfolio is made up of the following four ETFs:

  • 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 marketwatch.com and it is shown in table 1.

Table 1.

Symbol

Inception Date

Net Assets

Yield%

Category

MDY

5/04/1995

14.23B

1.41%

Mid-Cap Blend

QQQ

3/03/1999

36.93B

0.96%

Large Growth

SHY

7/22/2002

13.11B

0.48%

Short Term Treasury Bond

TLT

7/22/2002

6.41B

2.62%

Long Term Treasury Bond

Click to enlarge

The data for the study were downloaded from Yahoo Finance on the Historical Prices menu for 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.

The portfolios are optimized for three levels of risk: LOW, MID and HIGH. As a benchmark for comparison we added the performance of an equal weight strategy with rebalancing at 20% deviation from the target. The rebalancing is done whenever the allocation of an asset reaches 20% or 30%. This strategy rebalanced the portfolio 12 times during the simulation period of this study.

In Table 2 we show the performance of the strategy applied monthly from November 2002 to March 2016.

Table 2. Performance of MVO algorithm applied monthly versus an equal weight portfolio.

TotRet% CAGR% VOL% maxDD% Sharpe Sortino 2015-16

LOW R 210.50 8.82 6.52 -7.15 1.35 1.90 1.10%

MID R 385.87 12.52 10.50 -13.16 1.19 1.67 -1.91%

HIGH R 600.54 15.63 16.09 -20.78 0.97 1.36 -9.60%

EQW 225.03 9.43 9.58 -24.51 0.98 1.34 5.49%

Click to enlarge

From Table 2 we see that the LOW risk strategy had a slightly lower return than the equal weight with rebalancing, but with much lower volatility and drawdown. Overall, the LOW risk strategy has the largest risk adjusted return, i.e. the largest Sharpe ratio.

The 2015-16 returns column shows that equal weight strategy was the best during this 15 months period. The HIGH risk portfolio exhibited heavy losses, while the MID risk produces small losses, and the LOW risk gave small gains

The equity curves for all adaptive strategies are shown in Figure 1.

Figure 1. Equity curves of the portfolios with MVO monthly optimization.

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

To analyze in more detail the performance of the adaptive allocation strategies versus the equal weight strategy, in Table 3 we display their annual returns.

Table 3. Annual returns of the equal weight (EQW), and adaptive strategies.

EQW

LOW RISK

MID RISK

HIGH RISK

2003

21.74%

14.76%

20.27%

36.51%

2004

9.04%

3.69%

3.17%

-0.15%

2005

6.29%

1.49%

0.51%

-3.33%

2006

5.64%

6.14%

10.59%

13.47%

2007

10.98%

11.53%

14.62%

12.52%

2008

-10.97%

6.48%

9.34%

20.50%

2009

16.14%

8.13%

13.04%

21.99%

2010

16.78%

10.71%

15.83%

18.21%

2011

9.76%

19.28%

30.85%

39.27%

2012

9.84%

0.90%

-0.48%

-4.71%

2013

13.87%

14.54%

28.57%

41.09%

2014

13.99%

16.96%

19.45%

17.66%

2015

1.44%

2.19%

0.40%

-3.99%

2016

4.00%

-1.06%

-2.30%

-5.85%

Click to enlarge

In table 3 we see that the equal weight strategy had a single loosing year, 2008. During 2008 all adaptive strategies had large, positive returns.

The equal weight strategy outperformed all adaptive strategies during the following years: 2004, 2005, 2012, and the first quarter of 2016. It would be of great interest to determine what market events caused this type of market behavior.

The HIGH risk strategy was the big winner in seven years and the loser in five years. Fortunately, while the winnings were huge, the losses were all minor. As shown by the total return, the HIGH risk strategy won the competition by a large margin.

The LOW risk strategy has not had any loosing year, except for the first quarter of 2016, when it exhibited a very small loss of about 1%. It also had the fewest winning years; it won in 2015 with a slightly over 2% gain.

Finally, in table 4 we show the current allocations for all adaptive strategies.

Table 4. Current allocations for April 2016.

MDY

QQQ

SHY

TLT

LOW risk

22%

0%

24%

54%

MID risk

12.5%

0%

0%

87.5%

HIGH risk

0%

0%

0%

100%

Click to enlarge

As seen in table 4 all portfolios have a heavy weighting in long term treasuries. That goes against the prevailing market opinion that long term bonds are entering a secular bear market, and therefore, should be avoided. The low risk portfolio has also some equities and short term treasuries, while the high risk is 100% in long term treasuries.

Conclusion

From the data presented above, we are coming to the conclusion that the current underperformance of the adaptive allocation strategies is by no means unique, and that most likely, it is temporary. Nevertheless, it may be advisable that investors reduce the level of risk by overweighting treasuries.

Additional disclosure: The article was written for educational purposes and should not be considered as specific investment advice.

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

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.