Tactical Asset Allocation Strategy Using Schwab-Free ETFs

by: Cliff Smith

I have written previous articles on conservative, moderate, and aggressive Tactical Asset Allocation (TAA) strategies, and some people have asked for a conservative strategy using Schwab commission-free ETFs. The cost of buying and selling ETFs on a semi-monthly basis can add up and cut into net performance in "real money" accounts, especially in small accounts ($20,000 or less). And the cost of rebalancing ETFs likewise reduces overall net performance. Thus, trading Schwab-free ETFs makes sense, especially since there are no limitations on buying and/or selling Schwab-free ETFs.

So I set out to find TAA settings for a Schwab-free strategy using the ETFreplay software. I wanted to only use Schwab commission-free ETFs from the Schwab ETF OneSource Platform. This strategy will not work on any other platform.

A number of difficulties arose. Not all of the Schwab-free ETFs are included in the ETFreplay software. And some of the Schwab-free ETFs in preferable assets classes did not go back far enough in time to significantly backtest them. I wanted to at least backtest to 2008, realizing even this timeframe (2008-2014) is not nearly sufficient to accurately represent all market conditions. But even going back to 2008 had the aforementioned challenges. The way I avoided some of these issues was to use ETF proxies for Schwab-free ETFs in some classes. I checked the correlation between the proxy and the Schwab-free ETF to ensure the proxy gave a good representation. And then after assessing a strategy, I substituted the real Schwab-free ETF for the proxy and reran the ETFreplay software for a limited timeframe to ensure the proxy accurately represented the Schwab-free ETF.

I realize these approximations make the backtest results more questionable. I also realize that backtesting only to 2008 is not long enough. Plus, I realize I am curve fitting the strategy's parameters to match the rather limited data. But the curve fitting is not just a mathematical exercise; rather the strategy employs research-proven tactical methods such as moving day average and relative strength to determine what assets are best given the existing conditions. Market trends are captured by these methods, and so the TAA strategies can correctly respond to current market conditions. The challenge is that not all market conditions are represented in the backtest data. In the big picture, there is only one bear market and one bull market from 2008-2014. And yet there are several market corrections in these years that provide added information on different market conditions. This is certainly not perfect, but it is the best we can do at the present time.

I broke the Schwab-free TAA strategy into two sub-categories: 1) an all assets except bonds (AAEB) sub-category, and 2) a bonds-only (BO) sub-category. I will discuss the ETFreplay results of each sub-category separately. And then at the end of this article, I will combine the two sub-categories into one conservative strategy and show the results of a 60/40% AAEB-to-BO mix.

I limited the Schwab-free ETFs to ones that averaged over 25,000 trades daily. The following 14 ETFs were used in the AAEB strategy:

  1. Guggenheim Zacks Multi-Asset Income Index ETF (NYSE:CVY)
  3. Guggenheim BNY Mellon Frontier Markets ETF (NYSE:FRN)
  4. SPDR S&P Emerging Asia Pacific ETF (NYSEARCA:GMF)
  5. Guggenheim Zacks International Multi-Asset Income Index ETF (NYSE:HGI)
  6. Rydex S&P Equal Stock Weight ETF (NYSEARCA:RSP)
  7. Schwab Dow Jones U.S. Small-Cap ETF (NYSEARCA:SCHA)
  8. Schwab FTSE Developed Small-Cap ex-U.S. ETF (NYSEARCA:SCHC)
  9. Schwab FTSE Emerging Market ETF (NYSEARCA:SCHE)
  10. Schwab FTSE Developed Market International Equity ETF (NYSEARCA:SCHF)
  11. Schwab Dow Jones U.S. REIT Index ETF (NYSEARCA:SCHH)
  12. Schwab Dow Jones U.S. Mid-Cap ETF (NYSEARCA:SCHM)
  13. Schwab Dow Jones U.S. Large-Cap ETF (NYSEARCA:SCHX)
  14. ETFS Physical Swiss Gold ETF (NYSEARCA:SGOL)

Cash was Schwab 1-3 Year Treasury Bond ETF (NYSEARCA:SCHO).

The proxies I chose were:

  1. Vanguard U.S. Small-Cap ETF (NYSEARCA:VB) for SCHA
  2. iShares MSCI Emerging Markets ETF (NYSEARCA:EEM) for SCHE
  3. iShares Dow Jones Real Estate REIT ETF (NYSEARCA:IYR) for SCHH
  4. S&P Mid-Cap 400 SPDR ETF (NYSEARCA:MDY) for SCHM
  5. Vanguard MSCI U.S. Large Cap ETF (NYSEARCA:VV) for SCHX
  6. SPDR Gold Shares ETF (NYSEARCA:GLD) for SGOL
  7. Barclays Low Duration 2-Year Treasury ETF (NYSEARCA:SHY) for SCHO

With these proxies, I was able to backtest to 2005 with a sufficient number of ETFs.

The ETFs were ranked three times using relative growth (4 months and 5 days) and volatility (20 days). The rankings were weighted 30%, 35%, and 35% respectively to arrive at a final overall ranking. The top two ranked ETFs were selected if they passed a 4-month moving average filter; otherwise SHY was selected. Semi-monthly updating was used.

The backtested results from 2005-2014 using ETFreplay are shown in the figure below. The compounded annual growth rate - CAGR - was 16.8% and the maximum drawdown was 12.4%. This compared to the benchmark S&P 500 Index ETF (NYSEARCA:SPY) that had a CAGR of 6.6% and a maximum drawdown of 55.2%. The annual performance of the AAEB strategy showed positive growth every year, and it beat SPY every year except 2012 and 2013. In 2012, the annual performance with the proxies was 10.7%, while in 2013, the annual performance was 18.1%. This compares to 17.1% and 27.8% respectively for SPY. When the actual Schwab-Free ETFs were used instead of the proxies in years 2012-2013, there was an improvement in the yearly performance. In 2012, 10.7% annual growth with the proxies increased to 16.2%, and in 2013, 18.1% annual growth with the proxies increased to 23.3%. So although the AAEB strategy was still less than SPY in 2012-2013 when the actual ETFs were tested, the difference was lessened and the annual growth was almost equal to SPY in those years.

The BO strategy uses the following ETFs:

  1. Guggenheim BulletShares 2015 High Yield Corporate Bond ETF (NYSEARCA:BSJF)
  2. Guggenheim BulletShares 2016 High Yield Corporate Bond ETF (NYSEARCA:BSJG)
  3. SPDR Barcap Global Ex-U.S. Bond ETF (NYSEARCA:BWX)
  4. SPDR Barclays Convertible Bond ETF (NYSEARCA:CWB)
  5. SPDR Nuveen S&P High Yield Muni Bond ETF (NYSEARCA:HYMB)
  6. SPDR Barclays International Corporate Bond ETF (NYSEARCA:IBND)
  7. PowerShares Emerging Markets Bond ETF (NYSE:PCY)
  8. PowerShares RAFI High Yield Bond ETF (NYSE:PHB)
  9. SCHO
  10. Schwab Barclays Capital U.S. TIPS ETF (NYSEARCA:SCHP)
  11. Schwab Intermediate Treasury ETF (NYSEARCA:SCHR)
  12. SPDR Barclays Long Term Treasury ETF (TLO).

Proxies were the following:

  1. AdvisorShares Peritus High Yield Bond ETF (NYSEARCA:HYLD) for BSJG
  2. PIMCO BofA ML 0-5 Year High Yield Bond ETF (NYSEARCA:HYS) for BSJF
  3. SPDR 1-3 Month T-Bill ETF (NYSEARCA:BIL) and SHY for SCHO
  4. iShares Barclays Long Term Treasury ETF (NYSEARCA:TLT) for TLO.

With these proxies I was able to backtest to 2008.

The ranking categories were 4-month and 20-day relative growth and 20-day relative volume. To calculate final rankings, weighting factors of 40%, 30%, and 30% respectively were used. There was no moving average filter for the BO strategy. The top two ranked ETFs were selected each period. Semi-monthly updating was employed.

The backtested results from 2008-2014 are shown in the figure below. The CAGR was 16.0% and the maximum drawdown was 10.6%. In this case, iShares Core Total U.S. Bond ETF (NYSEARCA:AGG) was selected as the benchmark, and the CAGR for AGG was 4.4% and the maximum drawdown was 12.8%. The BO strategy beat AGG every single year except for the early results of 2014, and there were no negative growth years.

The AAEB and BO strategies were combined (60%-40% mix) to produce a conservative Schwab-free strategy. The backtested results from 2007-2014 are shown in the figure below. The CAGR was 16.6% with a maximum drawdown of only 8.7%. For comparison, the selected benchmark Vanguard 60/40 Balanced ETF (MUTF:VBINX) had a CAGR of 6.0% and a maximum drawdown of 36.0%. $100,000 invested in 2007 would have grown to $294,700 at the present time, while the same investment in VBINX would have only grown to $150,900. The Schwab-free strategy beat VBINX every full year.

The recommendations of the Schwab-free strategy will be included in semi-monthly updates I post on my Instablog, along with recommendations of other strategies I have developed. The next update is Monday, February 3rd. The ETF selections should be posted on Friday evening, January 31st.

Disclosure: I have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.