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Low-Risk Tactical Strategies Using Volatility Targeting

Summary In this volatility targeting approach, the allocation between equity and bond assets is varied on a monthly basis based on a specified target volatility level. Low volatility is the goal. Two strategies are presented: 1) a moderate growth version and 2) a capital preservation version. 30 years of backtesting results are presented using mutual funds as proxies for ETFs. For the moderate growth version, backtests show a CAGR of 12.6%, a MaxDD of -7.4% (based on monthly returns), and a return-to-risk (CAGR/MaxDD) of 1.7. For the capital preservation version, CAGR = 10.2%, MaxDD = -4.9%, and return-to-risk (CAGR/MaxDD) = 2.1. In live trading, ETFs can be substituted for the mutual funds. Short-term backtesting results using ETFs are presented. I must admit I am somewhat of a novice at using volatility targeting in a tactical strategy. But recently, the commercially free Portfolio Visualizer [PV] added a new backtest tool to their arsenal, so I started studying volatility targeting and how it works. Volatility targeting as used by PV is a method to adjust monthly allocations of assets within a portfolio based on the volatility of the assets over the previous month(s). In this case, we are only looking at high volatility equities and very low volatility bonds. To maintain a constant level of volatility for the portfolio, when the volatility of the equity asset(s) increases, allocation to the bond asset(s) increases because the bond asset has low volatility. And when the volatility of the equity asset(s) decrease, allocation to the bond asset(s) decreases. In PV, you can specify a target volatility level for the portfolio. Since I wanted an overall low volatility strategy with moderate growth (greater than 12% compounded annualized growth rate), I mainly focused on very low volatility target levels. I ended up using a monthly lookback period on volatility to determine the asset allocations because monthly lookbacks produced the best overall results. I quickly came to realize that high-growth equity assets are desired for the equity holdings, and a low-risk (low volatility) bond asset is preferred for the bond fund. In order to assess the strategy, I used mutual funds that have backtest histories to 1985. This enabled backtesting to Jan 1986. In live trading, ETFs that mimic the funds can be used. I will show results using the mutual funds as well as the ETFs. The equity assets I selected were Vanguard Health Care Fund (MUTF: VGHCX ) and Berkshire Hathaway (NYSE: BRK.A ) stock. Either Vanguard Health Care ETF (NYSEARCA: VHT ) or Guggenheim – Rydex S&P Equal Weight Health Care ETF (NYSEARCA: RYH ) can be substituted for VGHCX in live trading. BRK.A is, of course, a long-standing diversified stock. These two equity assets were selected because of their high performance over the years. Of course, these equities had substantial drawdowns in bear markets, something we want to avoid in our strategy. But in volatility targeting, as I have found out, it is advantageous to use the best-performing equities, not just index-based equities. Of course, it is assumed that these equities will continue to perform well in the future as they have in the past 30 years, and that may or may not be the case. For the low-risk bond asset class, I used the GNMA bond class. The selection of the GNMA bond class was made after studying performance and risk using other bond classes such as money market, short-term Treasuries, long-term Treasuries, etc. The GNMA class turned out to be the best. I selected Vanguard GNMA Fund (MUTF: VFIIX ) for backtesting, so that the backtests could extend to Jan 1986. There are a number of options for ETFs that can be used in live trading, e.g. iShares Barclays MBS Fixed-Rate Bond ETF (NYSEARCA: MBB ). Moderate Growth Version (CAGR = 12.6%) A moderate growth version is considered first. VGHCX and BRK.A are the equities always held in a 66%/34% split; VFIIX is the bond asset; and the target volatility is 6%. The backtested results from 1986-2015 are shown below compared to a buy and hold strategy of the equities (rebalanced annually). (click to enlarge) (click to enlarge) (click to enlarge) (click to enlarge) It can be seen that the compounded annualized growth rate [CAGR] is 12.6%, the maximum drawdown [MaxDD] is -7.4% (based on monthly returns), and the return-to-risk [MAR = CAGR/MaxDD] is 1.7. There are three years with essentially zero or very slightly negative returns: 1999, 2002 and 2008. The worst year (2008) had a -1.6% return. The monthly win rate is 74%. The percentage of VFIIX varies between 1% and 93% for any given month. The Vanguard Wellesley 60/40 Equity/Bond Fund (MUTF: VWINX ) is a good benchmark for this strategy. The overall performance and risk of VWINX are shown below. It can be seen that the CAGR is 9.1%, while the MaxDD is -18.9%. These performance and risk numbers are quite good for a buy and hold mutual fund, but the volatility targeting strategy produces higher CAGR and much lower MaxDD. VWINX Benchmark Results: 1986-2015 (click to enlarge) Capital Preservation Version (MAR = 2.1) For this version, the target volatility was set to a very low level of 3.5%. This volatility level produced the lowest MAR. The results using PV are shown below. (click to enlarge) (click to enlarge) (click to enlarge) (click to enlarge) It can be seen that the CAGR is 10.2%, the MaxDD is -4.9%, and the MAR is 2.1. Every year has a positive return; the worst year has a return of +0.4%. The monthly win rate is 75%. Limited Backtesting Using ETFs To show how this strategy would play out in live trading, I have substituted RYH for VGHCX and MBB for VFIIX. The second equity asset is BRK.A as before. Backtesting is limited to 2008 with these ETFs and the BRK.A stock. The backtest results are shown below. (click to enlarge) (click to enlarge) The ETF results can be compared with the mutual fund results from 2008 to 2015. The mutual fund results are shown below. (click to enlarge) (click to enlarge) It can be seen that the overall performance over these years is lower than seen over the past 30 years. The CAGR is 9.7% from 2008 to 2015 for the mutual funds and 9.3% for the ETFs. Although this performance in recent years is less than earlier performance, it is still deemed acceptable for most retired investors interested in preserving their nest egg while accumulating modest growth. The good quantitative agreement between mutual funds and ETFs between 2008 and 2015 provides some confidence that using ETFs is a viable option for this strategy. Overall Conclusions The tactical volatility targeting strategy I have presented has good potential to mitigate risk and still provides moderate growth in a retirement portfolio. The moderate growth version has a CAGR of 12.6% and a MaxDD of -7.4% in 30 years of backtesting. The capital preservation version has a CAGR of 10.2% and a MaxDD of -4.9% over this same timespan. The return-to-risk MAR using target volatility is much better than passive buy and hold approaches, especially in bear markets when large drawdowns may occur even in diversified portfolios.

Quarterly Tactical Strategy Using Fidelity Fixed-Income Mutual Funds

Summary This strategy consists of ranking four fixed-income mutual funds based on 3-month returns, and then selecting the top-ranked fund at the end of each quarter. The top-ranked fund must pass a 3-month simple moving average filter in order to be purchased. Otherwise, the money goes into a money market asset. Backtested to 1986, the CAGR is 11.1%, the MaxDD is 5.5%, the worst year is +0.73%, and the return-to-risk ratio [CAGR/MaxDD] is 2.03. The monthly win rate is 79%. The strategy appears to be very robust in terms of relative momentum look-back period length and moving average duration. The strategy can be traded between the end of quarter [EOQ] and the next four trade days without any significant detrimental effect on performance or risk. I have recently been developing monthly tactical strategies that employ mutual funds instead of ETFs (see here and here ). There are a number of benefits in trading mutual funds instead of ETFs. First, mutual funds of a certain class tend to have much less volatility than ETFs in the same class. This permits the use of shorter duration look-back periods and moving averages in a tactical strategy without as much whipsaw. Second, there is the benefit of trading at one closing price, thus avoiding slippage losses (bid/ask losses) associated with trading ETFs. Third, mutual funds tend to have higher liquidity than ETFs. This avoids sudden price changes caused by lack of asset liquidity. Fourth, there are no fees/loads at all if Vanguard funds are traded in a Vanguard account, or Fidelity funds in a Fidelity account. And fifth, using mutual funds with long histories enables backtesting of almost 30 years, back to the mid-1980s. This is in contrast to ETFs that have very short histories, especially bond ETFs, that limit the timeframe of backtests. One of the negatives against mutual funds is the higher management expenses, but in some cases mutual funds actually have similar expenses as ETFs (e.g. Vanguard Admiral funds versus corresponding ETFs). And then there are the practical issues of trading mutual funds. These practical issues are challenging, but can be solved. Until recently, mutual funds did not permit monthly trading; severe short-term redemption penalties were charged or frequent-trading restrictions were imposed. But these penalties/restrictions have been lifted so that monthly trading is now permissible on some platforms, most notably Vanguard and Fidelity. This is the case as long as trades are made at least 30 calendar days apart. So a strict trading schedule must be followed that I have discussed previously (see here ). However, most of the trading issues are eliminated if a quarterly strategy is implemented. In past articles, I have presented monthly strategies using Vanguard mutual funds. But in this article, I am proposing a fixed-income asset allocation strategy that uses Fidelity mutual funds and trades on a quarterly basis. So the trading issues are greatly reduced. Four asset classes are used in the strategy: High yield corporate bonds: Fidelity Capital and Income Fund (MUTF: FAGIX ) High yield municipal bonds: Fidelity California Municipal Income Fund (MUTF: FCTFX ) Mortgaged-backed bonds: Fidelity Mortgage Securities Fund (MUTF: FMSFX ) Money market: CASHX (in Portfolio Visualizer). The overall objectives of this moderate growth/low risk strategy are: To attain a 10% compounded annualized growth rate [CAGR]; To achieve a maximum drawdown [MaxDD] of -5.0% (based on monthly returns); To produce a return-to-risk MAR [CAGR/MaxDD] of 2 or greater; To have positive returns every year in backtesting; and To attain a monthly win rate over 75%. The correlations between these funds are shown below, taken from Portfolio Visualizer [PV]. It can be seen that the funds have low correlation to each other, as desired. (click to enlarge) The strategy consists of ranking the 3-month total returns of each fund, and selecting the top-ranked fund at the end-of-the-quarter [EOQ]. The top-ranked fund must then pass a 3-month simple moving average [SMA] screen in order to be purchased. Otherwise, the money goes to the money market fund. It’s a pretty simple set of rules. What seems to make this strategy work is the relatively high return of FAGIX and its low correlation to FCTFX and FMSFX that have moderate return. CASHX is included as an absolute momentum filter to control risk. Backtest Results Using Portfolio Visualizer The strategy was first backtested using the PV software. All of the funds have histories that date back to at least 1985, so the backtesting went from Jan 1986 to Nov 2015. By using only Fidelity funds and trading on a quarterly basis, there are no trading costs, loads or restrictions if a Fidelity platform is used. The backtest results are shown below. Trading is done at the EOQ. (click to enlarge) (click to enlarge) (click to enlarge) (click to enlarge) The tactical strategy is compared with a buy & hold strategy in which the four funds are held continuously and rebalanced annually. The thing that jumps out at you is the large annual returns in 2003 and 2009; the rest of the time the tactical strategy has more modest returns as expected. The overall results show that the tactical strategy has a much higher CAGR (11.1% to 6.5%) and much lower MaxDD (-5.5% to -10.0%) than the buy & hold strategy. The worst year for the tactical strategy is a positive 0.7% (in 2008), while the buy and hold strategy has a worst year of negative 8.6%. It can be seen that the tactical strategy matches the buy & hold strategy over much of the timeframe, but in times of market stress, the tactical strategy performs much better than buy & hold. Backtest Results Using the Haynes’ Backtester The next step in backtesting was to assess the effects of look-back period length, SMA length, number of assets held, and trade day on the performance and risk of the tactical strategy. These calculations were performed using Herbert Haynes’ backtester. We first made sure that the Haynes’ backtester matched PV’s results for EOQ calculations. The comparative results are: PV’s Summary Results, CAGR = 11.1%, MaxDD = 5.5% (monthly basis); Haynes’ Summary Results, CAGR = 11.2%, MaxDD = 5.5% (monthly basis). Overall, we see very good agreement. All of the quarterly selections were exactly the same. The very small difference between CAGRs is probably caused by small variations in the adjusted price data between the two calculations. We proceeded to look at the effects of SMA duration. Rather than looking at calendar months, the SMA duration was switched to trade days. Twenty-one trade days corresponds to one calendar month, forty-two trade days corresponds to two calendar months, etc. The SMA duration was varied from 20 trade days to 70 trade days, and it was seen that SMA length had little impact on the results. CAGR varied from 11.2% to 11.3%, and MaxDD remained fixed at 5.5% Next, we studied the effect of the relative momentum lookback period. The lookback period was varied between 20 trade days and 84 trade days while the SMA screen was varied between 20 trade days and 50 trade days. As long as the SMA duration was 40 trade days or greater, the lookback period could be 2-months (42 trade days), 3-months (63 trade days) or 4-months (84 trade days) without any significant difference in CAGR or MaxDD. A final matrix was run in which the number of assets (1 to 3) and trade day (EOQ-20 to EOQ+20) were independently varied. For this matrix, the lookback period was fixed at 3 calendar months and the SMA screen duration was maintained at 63 days. The tabulated values and heatmaps are shown below for CAGR, MaxDD, and MAR. The tabulated values have the trade day on the top line (EOQ-20, EOQ-18, etc.) and the number of assets (1 to 3) in the first column. CAGR Results: Range = 6.1% [red] to 11.2 [blue] (click to enlarge) (click to enlarge) MaxDD Results: Range = -16.7% [red] to -4.0% [blue] (click to enlarge) (click to enlarge) MAR Results: Range = 0.5 [red] to 2.0 [blue] (click to enlarge) (click to enlarge) As expected, increasing the number of assets results in lower performance and lower risk. In terms of the return-to-risk metric [MAR], the optimal number of assets is one. One asset also produces the highest CAGR. The optimal trade days for one asset is seen to be EOQ to EOQ+4. It should be noted that this is not the equivalent of making a selection using EOQ data and waiting up to four days before making the trade. The way the program assessed the effect of trade day was to determine the fund selection and make the trade on the same day. Conclusions from Backtesting The tactical strategy is very robust in terms of the lookback duration length and SMA duration length. Significant variation of these parameters does not seem to greatly affect the backtest results. The selection of one asset each quarter (versus two or three assets) produces the best overall performance and risk adjusted returns. When only one fund is selected each quarter, the optimal trade day is EOQ to EOQ+4. Other trade days produce inferior results based on backtesting. 30-years of backtest results (1986 – 2015) show a CAGR of 11.1%, a MaxDD of 5.5%, and a MAR of 2.03. There are no losing years, and the monthly win rate is 79%. Some Practical Considerations These funds distribute their dividends on a monthly basis at the end of the month [EOM]. The dividend distribution does not make its way into the daily data until a number of days later. Thus, the selection that PV makes at EOQ may be in error until the correct data is available. The problem is that we don’t know exactly when the latest distribution information has been added to the adjusted data in PV’s selections. So a quarterly fund selection made by PV at the latest EOQ might change a few days later. Thus, each investor cannot just blindly use PV’s selection at the EOQ. Rather, each investor needs to look at the 3-month returns of the funds based on data that include the latest dividend distribution. There are two ways to determine the correct 3-month returns. One way is to take adjusted data from Yahoo and correct it for the latest dividend distribution. A second way is to use stockcharts.com (after the dividend distribution has been added to their data). Either way will work. There is an added benefit that can be achieved from this strategy that I want to discuss. It turns out that high yield mutual funds have a unique characteristic that I do not totally understand: when distributions occur on ex-div day, the price of the fund doesn’t drop by the amount of the distribution. For most funds, ETFs and stocks, whenever a dividend distribution occurs on ex-div day, the price of the asset drops by that amount. However, this does not occur for high yield mutual funds. I’m not exactly sure why the actual price does not drop on ex-div day, but it doesn’t. We can use this aspect of high yield mutual funds to our benefit. Thus, it will be better to always move from money market to FAGIX or FCTFX on EOQ-1 rather than on EOQ or later. In this way, you will receive the dividend without any accompanying loss in price. It’s like getting a free dividend payment. Likewise, if you are moving from FAGIX or FCTFX to money market, it will always be better to sell on EOQ or later (after the distribution is given). Because FMSFX has a relatively small distribution, the same rules apply to it too, i.e. selling FMSFX and buying FAGIX or FCTFX should be done on EOQ-1, and selling FAGIX or FCTFX and buying FMSFX should be done on EOQ or later. An Alternate Basket of Funds for Schwab Accounts For those investors who have Schwab accounts, an alternative basket of funds is recommended. Although there will be small costs for trading some of these funds, the costs will not be excessive because only one fund is traded each quarter. The basket of funds I recommend for use on the Schwab platform are the following: FAGIX, the Nuveen High Yield Municipal Fund (MUTF: NHMAX ), the Vanguard GNMA Fund (MUTF: VFIIX ), and a Schwab money market fund [CASHX in PV]. These funds can only be backtested from 2000 – 2015, and the results using PV are shown below, compared to the Fidelity version over the same years. Schwab Version (2000 – 2015) (click to enlarge) Fidelity Version (2000 – 2015) (click to enlarge) It can be seen that the Schwab version gives superior results in terms of CAGR (13.2% to 12.0%) while maintaining the same MaxDD (-5.5%). This is mainly caused by the superior returns of NHMAX compared to FCTFX.

Enhanced Version Of Low Volatility Momentum Strategy

Summary This article continues the work of my previous article on a tactical asset allocation strategy for Schwab or Fidelity platforms using bond mutual funds with very low volatility. The original basket of funds was modified by exchanging one fund for a less volatile fund, and adding a floating-rate loan fund to enhance the strategy when rates are rising. The backtested results show a CAGR of 12.8%, a MaxDD of -2.9%, and a MAR (defining reward/risk) of 4.4. The worst year from 2000 – 2015 had a +5.6% return. Additional details are presented to help understand the practical implementation of the strategy on Schwab or Fidelity platforms. Funds are traded without costs except for a $50 short-term trading fee. The purpose of this article is to present an enhanced version of the Low Volatility Strategy [LVS] that I presented previously (see here ). Based on comments and further study, I have slightly modified the original LVS-1. The -1 designation means one fund is selected each month from a basket of funds. The original LVS-1 had a basket of four mutual funds coming from four different bond classes. Each fund had very low volatility (i.e. daily standard deviations [DSDs] of 0.35% or less) and the funds were mostly non-correlated to each other. A relative strength approach was used in which the funds were ranked based on their total returns over the previous ten trading days. The top-ranked fund was selected at the end of each month unless it failed a 10-day simple moving average [SMA] test, in which case the money went to a safe harbor. The safe harbor was a money market fund. Further details are explained in the previous article. The original basket of funds for application to the Schwab or Fidelity platforms were: Nuveen High Yield Municipal Bond Fund (MUTF: NHMAX ) Principal High Yield Fund (MUTF: CPHYX ) PIMCO Mortgage-Backed Fund (MUTF: PTMDX ) Dreyfus U.S. Treasury Intermediate Term Fund (MUTF: DRGIX ) Changes to Original Basket and Backtest Results After further study, I have replaced DRGIX with the Loomis Sayles Limited Term Government and Agency Fund (MUTF: NEFLX ) because of its reduced risk (reduced DSD that resulted in lower MaxDD). More importantly, I added a floating rate loan fund to the basket in order to improve performance in a rising rate environment. Since I decided to concentrate on the basket of funds for the Schwab and Fidelity platforms, NHMAX limited how far back I could go in a backtest (2000). Thus, I needed a floating rate loan fund with an inception date in 1999 or before. There were three candidates: Oppenheimer Senior Float-Rate Fund (MUTF: OOSAX ): Annualized Return = 4.66%, DSD = 0.18% Invesco Floating-Rate Fund (MUTF: AFRAX ): Annualized Return = 3.62%, DSD = 0.20% Blackrocks Floating Rate Income Portfolio Fund (MUTF: BFRAX ): Annualized Return = 3.76%, DSD = 0.21% OOSAX was selected because it has the highest annualized return and lowest DSD. Thus, the final basket for use on Schwab or Fidelity platforms is: NHMAX, CPHYX, PTMDX, NEFLX and OOSAX. A correlation matrix is shown below, together with annualized returns and various forms of volatility numbers. It can be seen that all funds are noncorrelated except for PTMDX and NEFLX that have a correlation of 0.81. (click to enlarge) Using these funds, LVS-1 was run on Portfolio Visualizer, a commercially-free software package. The backtest was limited to 2000 – 2015 due to the histories of the selected mutual funds. In this article, I am only going to focus on the LVS-1 using mutual funds we will trade on Schwab and Fidelity. However, it should be noted that, in the previous article, this basic strategy was backtested to 1988 using proxies, and good performance and low risk were demonstrated. The results of LVS-1 are shown below, along with results for a buy & hold, equal weight portfolio. Total Return: 2000 – 2015 (click to enlarge) Annual Return (click to enlarge) Tabulated Annual Return (click to enlarge) Drawdown (click to enlarge) Summary Table (click to enlarge) It can be seen that the Compounded Annualized Growth Rate [CAGR] is 12.8%, the standard deviation [SD] is 5.5%, the worst year is +5.4%, and the maximum drawdown [MaxDD] is -2.9%. There are no losing years, and the monthly win rate is 84%. In terms of reward/risk, the MAR (CAGR/MaxDD) is 4.4. This strategy is appropriate for an investor who wants moderate growth and very low risk. Further Thoughts on Implementing LVS-1 on Schwab and Fidelity Platforms The funds that were selected are no load /no fee funds on Schwab and Fidelity. This means the loads are waived, and there are no commission fees. The only fee you will pay is a short-term trading fee of $49.95 if you sell a fund within 90 calendar days on Schwab or within 60 calendar days on Fidelity. So in some instances, you will hold a fund for multiple months, and avoid the short-term trading fee. But most of the time, there will be a charge when you sell a fund. LVS-1 averages about 8 trades per year. That means it will cost about $400 in short-term trading fees per year. For a $100K account, this will come out to 0.4% per year. But there are no other fees. I also looked at the prospectus of each fund pertaining to trading frequency restrictions. All of the funds warn about excessive trading, but they combat excessive trading in different ways. Round-trips are sometimes used to define excessive trading. A round-trip is the buying and selling of one fund in one account. Excessive trading for the mutual funds of interest are: NHMAX: Limited to two round-trips in a 60-day period. CPHYX: Must hold the fund for 30 days before selling. PTMDX: Nothing specific stated. NEFLX: Limited to two round-trips within a rolling 90-day period. OOSAX: 30-day exchange limit. Fund is blocked for 30 days. Thus, there are no limitations that will stop the trading of the LVS-1 strategy as long as we make our trades 30 days apart. This means if we trade on March 1st, our next trade cannot occur until March 31st at the earliest. Conclusion In conclusion, the LVS-1 shows the potential to achieve 12% net growth on average with maximum drawdown (based on monthly returns) of less than 3%. More realistically, this strategy probably has the potential to earn 10% per year with a maximum drawdown of 5%. The monthly win rate should be higher than 80% according to backtesting. As far as I can tell, this strategy should be viable in Schwab or Fidelity accounts as long as the trades are made 30 calendar days apart. To maintain a spacing of 30 days between trades, a schedule is presented in this article . Recently Herbert Haynes has duplicated this strategy and has looked at the effect of trade day on the results. He has shown that trade day is of paramount importance; the only trade days that produce good results are end-of-the-month [EOM] and first day-of-the-month. It is not clear what causes this seasonality of the strategy. Perhaps it is the effect of using funds with large dividends that occur at EOM, or perhaps it is the effect of a short timing period.