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Vanguard Capital Preservation Strategy: Effect Of Trade Day And Look-Back Period Length

Further analysis of the Vanguard Capital Preservation (VCP) tactical strategy is presented. The effects of trade day and look-back momentum period on performance and risk are shown. It is shown that the best trade days are end-of-month (EOM) and first day of the next month (EOM+1). Trading on other days reduces performance and increases risk. In a parametric study of look-back periods systematically varied from 10 trade days to 30 trade days, it is shown that the 21-day (one calendar month) look-back period is optimal. The final VCP strategy using a dual momentum approach and backtested to 1988 has a CAGR of 13.0%, a MaxDD of -5.8%, and a MAR of 2.2. This mutual fund strategy can be traded monthly (every 30 days) on the Vanguard platform without any costs. However, a strict schedule must be followed. Introduction to Vanguard Capital Preservation Strategy This article continues the analysis of the Vanguard Capital Preservation [VCP] strategy originally described here . The VCP strategy updates on a monthly schedule and uses a dual momentum approach. In this strategy, there are six Vanguard mutual funds in the basket of funds covering both equity and bond assets, and the two best (highest momentum) funds are selected at the end of each month. The relative strength momentum ranking is based on a one calendar month look-back period. Absolute momentum is used for risk control, i.e. the two funds with the highest relative strength momentum ranking must have returns greater than the money market asset in order to be actually selected. The out-of-market asset is VFIIX (although a money market asset can be used with little decrement in performance). The basket of funds is the following: Vanguard Convertible Securities Fund (MUTF: VCVSX ) Vanguard Health Care Fund (MUTF: VGHCX ) Vanguard High Yield Corporate Fund (MUTF: VWEHX ) Vanguard High Yield Tax-Exempt Fund (MUTF: VWAHX ) Vanguard GNMA Fund (MUTF: VFIIX ) Vanguard Intermediate Term Treasury Fund (MUTF: VFITX ) All of these funds have histories that date back to 1986 except VFITX that only goes back to 1991. To backtest to 1988, the Dreyfus U.S. Treasury Intermediate Fund (MUTF: DRGIX ) is substituted for VFITX. By backtesting to 1988, the strategy shows that it can successfully handle various market conditions including bull markets and bear markets. Please take note that a few of the funds presented in this article are slightly different than those described in the previous article. The other change is that the out-of-market asset is now VFIIX instead of a money market asset. These slight changes were made to improve the overall strategy. Any investor can take the parameters discussed above and insert them into Portfolio Visualizer [PV], a commercially-free backtest software program. PV will backtest the strategy to 1988, plus it will select what funds to select at the end of each month. Results of VCP Strategy The backtested results of the VCP strategy are shown below. The backtest results are produced by Portfolio Visualizer [PV]; the timespan is 1988 – present. Total Return: 1988 – 2015 (click to enlarge) Annual Returns: 1988 – 2015 (click to enlarge) Drawdowns: 1988 – 2015 (with S&P 500 included) (click to enlarge) Drawdowns: 1988 – 2015 (without S&P 500 included) (click to enlarge) Overall Summary: 1988 – 2015 (click to enlarge) It can be seen that the Compounded Annualized Growth Rate [CAGR] is 13.0%, the Standard Deviation [SD] is 6.7%, and the Maximum Drawdown (MaxDD) is -5.8%. This gives a MAR (CAGR/MaxDD) of 2.24. How these numbers compare to a buy & hold strategy (rebalanced annually) and the S&P 500 are presented in the table above. For the buy & hold strategy, the CAGR is 9.0%, the SD is 5.3%, and the MaxDD is -14.3%. This gives a MAR of 0.63. Thus, the tactical strategy is a significant upgrade to the buy & hold strategy. Likewise, the tactical strategy is significantly better that the S&P 500 that has a CAGR of 10.4%, a SD of 14.5%, a MaxDD of -51.0%, and a MAR of 0.20. There are no negative years for the VCP strategy; the worst year has a positive 1.9% return (in 2002). This compares with a worst year of negative 37.0% for the S&P 500 (in 2008) and a worst year of negative 10.3% (in 2008) for the buy & hold strategy. Further Assessment of VCP Strategy In this article, further analysis of the VCP strategy will be presented. In particular, the effect of trade day on backtest results will be assessed, as will the effect of look-back period length. Herbert Haynes has developed a backtester that can be used to study these effects. Haynes’ backtester using dual momentum was set up a little different that the dual momentum approach by PV. In particular, the absolute momentum part of the Haynes’ backtester is slightly different than PV’s absolute momentum test. Haynes followed the conventional absolute momentum technique by Gary Antonacci that uses pure cash or any other asset as the absolute momentum test, and then uses that same asset as the out-of-market asset. In PV, the absolute momentum test is always money market (i.e. 1-month T-Bill returns), and the out-of-market asset can be anything specified by the user. So for the VCP strategy using PV, the absolute momentum test was money market, and the out-of-market asset was VFIIX. For the Haynes’ backtester, the absolute momentum test was VFIIX, and the out-of-market asset was VFIIX. This slight variation between calculations did not cause any significant difference between PV results and Haynes’ backtester results for EOM calculations. First Parametric Study: Trade Day vs. Number of Assets Using the Haynes’ backtester, we first looked at the effect of trade day on performance and risk. For this parametric study, we independently varied the number of assets selected each month (1, 2, and 3) and the trade day. The trade day was varied between EOM-10 trade days and EOM+10 trade days. Heatmap results are shown below. They were skillfully created by Herbert Haynes. Heatmaps are presented for CAGR, MaxDD, and Sharpe Ratio. The colors range from red being worst to blue being best. So cold spots [blue] are desired for each variable. The numbers on the top of each heatmap (-10 to 10) correspond to the trade day. Zero (not actually specified) corresponds to the EOM. The number [-1] stands for EOM-1. The number [1] signified EOM+1. The numbers on the left (1 to 3) correspond to the number of assets selected each month in the VCP strategy. CAGR: Range = 8.5% [red] to 15.3% [blue] (click to enlarge) MaxDD: Range = -27.5% [red] to -6.8% [blue] (click to enlarge) Sharpe Ratio (CAGR/SD): Range = 0.85 [red] to 2.04 [blue] The heatmaps show that the best trading days center around EOM-1 to EOM+1. The optimal number of assets seems to be two when both CAGR and MaxDD are considered. Second Parametric Study: Trade Day vs. Look-back Length The number of assets was set to two, and another parametric was run on Haynes’ backtester. In this parametric study, trade day and look-back length were independently varied. The results are shown below in the form of heatmaps. Heatmaps are presented for CAGR, MaxDD, Volatility (Standard Deviation), and MAR (CAGR/MaxDD). The numbers on the top of each heatmap are the trade days as previously discussed, and the numbers to the left of each heatmap are the look-back trade days for the relative strength momentum. The look-back trade days range from 10 days to 30 days. CAGR: Range = 8.2% [red] to 14.1% [blue] (click to enlarge) MaxDD: Range = -27.3% [red] to -6.3% [blue] (click to enlarge) Volatility [SD]: Range = 6.3% [red] to 8.3% [blue] (click to enlarge) MAR [CAGR/MaxDD]: Range = 0.3 [red] to 2.1 [blue] (click to enlarge) For CAGR, an optimum band is seen going from the upper left corner to the lower right corner. Short look-back periods (11 to 14 days) combined with trading between EOM-8 to EOM-1 seem to be optimal and robust. But the MaxDD results show a different optimal window: look-back periods between 20 – 23 days and trade days between EOM and EOM+2. In terms of volatility, a vertical optimal band is seen that occurs between EOM and EOM+2. The MAR heatmap shows an optimal window between look-back periods of 20 days and 26 days, and trade days between EOM and EOM+2. Overall, the optimal window seems to be around one-month in look-back length, and EOM and EOM+1 in trade days. Conclusions The analysis presented in this article indicates that two assets should be selected in the VCP strategy (from a basket of six assets). The analysis also indicates that the VCP strategy should be traded at EOM or EOM+1. Trading on other days may significantly reduce returns and increase drawdown. The optimal momentum look-back period is one calendar month. Some Practical Issues After further study, it now seems that trading mutual funds on a monthly schedule can only be accomplished using the same family of mutual funds. When different families of funds are used in a monthly strategy, sell and buy trades cannot be executed on the same day. This prevents the execution of a monthly tactical strategy using mutual funds if funds from different families are used. This issue is circumvented when the basket of funds are all in the same family. Then you can sell and buy funds on the same day. That is why only Vanguard funds are used in the actual application of this strategy. This is important because Vanguard blocks the buying of a fund for 30 calendar days after the fund has been redeemed. But this 30-day trade restriction can be accommodated in a monthly schedule if the trade day moves around slightly between EOM and EOM+1. I have presented a trading schedule in my previous article that will satisfy the 30-day trading restriction. It must be followed rigorously, or the trade day will slip downstream. And, as shown, trading on days other than EOM or EOM+1 reduces return and increases risk. The only drawback in this application is that selections must sometimes be made before EOM data are available. In these cases, EOM-1 data must be used to make the selections, with the caveat that there will be some selections that differ from the EOM selections. Going back to 2007, it was seen that EOM-1 selections differed from EOM selections about 17% of the time (averaging 4 selections out of 24 selections each year). This percentage was rather constant over the years. It was also observed that the EOM-1 selections out-performed the EOM selections over the next month about half the time. This seemed to indicate that using EOM-1 data to determine selections is not overly problematic. It is rather easy to use EOM-1 data to come up with fund selections by using StockCharts.com. Using PerfCharts, the list of funds is inserted into the symbol box, and the number of days (that varies each month between 20 days and 24 days) is inserted into the slider box. Set the start date at EOM-1 of the preceding month and the end date at EOM-1 of the current month. The percent return is seen to the right in the resulting figure. As an example, the PerfCharts plot for December selections is shown below. The slider box has 21 days for this month. It can be seen that VCVSX and VWAHX are the selections. And please note that they are both greater than absolute momentum, i.e. zero percent return. (click to enlarge) We have also found another issue in using EOM PV selections that readers need to be aware of. Many investors will look at PV’s selections at EOM and trade accordingly on EOM+1. It turns out that the latest EOM dividend distributions for mutual funds are not usually included in the EOM data feed. This means the adjusted prices are not correct at EOM, and so the selections by PV at EOM may be in error because total returns do not include the latest dividend distribution. The correct adjusted price data are not provided to PV until a number of days after EOM. Thus, the backtest results are correct, but the selections at EOM may be in error using PV. The only way around this challenge is to calculate total returns yourself by using historical data from a data source such as Yahoo. The Yahoo data will also be in error because the dividend distribution at EOM will not be included. Thus, Yahoo adjusted price data must be modified so that the effect of the latest dividend distribution is included. This is very easy to do and could be automated by skilled Excel users.

Low Volatility Bond Strategy Using Momentum With Short Timing Periods

Summary Most momentum strategies utilize long timing periods, but because of inherently shorter latencies, shorter timing periods would in principle be preferable if whipsaws could be kept within acceptable limits. Shorter timing periods are more likely to work well with low volatility funds. A basket of four funds with daily standard deviations A relative strength tactical strategy named LVS is presented with a 10-day look-back period and 10-day simple moving average cash filter. One fund or two funds are selected each month. Backtested to 1988, the one fund version (LVS-1) has CAGR = 11.6% and MaxDD = -6.6%. The two fund version (LVS-2) has CAGR = 9.4% and MaxDD = -3.4%. LVS implementation is addressed by selecting NTF funds from both Fidelity and Schwab brokerages, and backtesting from 2000 – 2015 with these funds. Monthly win rates over 81% are observed. Tactical momentum strategies rely on look-back periods to establish the ranking of a basket of funds, and then select the best fund(s) to be held each month. The best funds are then filtered by absolute momentum or moving averages. If the funds do not pass their filter, then the money is diverted to a cash fund (usually a money market fund). There are a number of possible momentum strategies that can be used, such as 1) relative strength momentum with cash filters based on absolute momentum. This is commonly called dual momentum; 2) relative strength momentum with cash filters based on moving averages; and 3) a basket of funds with cash filters on each fund based on moving averages. The length of time periods, both for relative strength and for moving averages, is a parameter selected by the developer of a tactical strategy. Developers choose different lengths or combination of lengths based on extensive (or not so extensive) backtesting and/or the research literature. For relative strength, look-back periods ranging from 3-months to 6-months are commonly used. For moving averages, even longer time periods are typically used, e.g. 10-months or 12-months. For moving averages, it is well-known that the optimal moving average can change between asset groups, and can be substantially different under various market conditions. So picking a timing period is not a simple task. In my recent development of momentum strategies (e.g. see here , here , and here ), I have found that shorter timing periods are generally preferred in order to respond quickly to market trends. But short timing periods usually result in whipsaw and poor performance. So the big question is how can we effectively use short timing periods and avoid whipsaw. My answer is that we need to select low volatility funds in our basket of assets, funds with daily standard deviations [DSDs] of 0.35% or less. I arrived at this DSD number after studying what DSD level is needed for effective use of short duration timing periods in tactical strategies. In other words, I determined what DSD level is needed in order for short duration SMAs to produce returns higher than buy & hold, and for maximum drawdowns to be 33% or less of the maximum drawdown of buy & hold. In reality, a DSD of 0.35% is a rather arbitrary number, but it is in the right ball park. To get a feel for DSD numbers for various assets, I have listed DSD numbers for various ETFs and mutual funds from 2009 – 2015: SPDR S&P 500 ETF (NYSEARCA: SPY ): 1.02% Vanguard S&P 500 Index Fund (MUTF: VFINX ): 1.03% PowerShares Nasdaq-100 Index ETF (NASDAQ: QQQ ): 1.11% Vanguard Small Cap Index Fund (MUTF: NAESX ): 1.33% iShares Barclays Long-Term Treasury ETF – 20+ Years (NYSEARCA: TLT ): 0.98% Vanguard Long-Term Treasury Fund – 15+ Years (MUTF: VUSTX ): 0.84% SPDR Barclays Convertible Bond ETF (NYSEARCA: CWB ): 0.69% Vanguard Convertible Securities Fund (MUTF: VCVSX ): 0.59% Vanguard High Yield Corporate Bond Fund (MUTF: VWEHX ): 0.26% Dreyfus U.S. Treasury Intermediate Term Fund (MUTF: DRGIX ): 0.19% Barclays Low Duration Treasury ETF (NYSEARCA: SHY ): 0.06%. All equity assets have DSDs that are substantially greater than 0.35%, and so they can be eliminated from consideration. Some bond assets also have DSD numbers that are too high for use, including long-term treasuries and convertible securities. But there are some bond asset classes that meet the DSD requirement, and also have reasonably high returns. We do not want a fund to have low DSD at the expense of having low annualized return. Thus, a short-term treasury like SHY is not a viable candidate. I have shown previously that a strategy that employs short duration moving averages can be quite effective when used on a low volatility asset. One example is to use the crossover of 3-day and 25-day simple moving averages [SMAs] on the high yield mutual fund VWEHX. If the 3-day SMA is greater than the 25-day SMA, the strategy holds VWEHX. If not, then the strategy is in cash. Total returns and drawdown for this strategy from 2000 – 2014 can be found here . The strategy is quite effective. The DSD of VWEHX over this timespan is 0.25%. In comparison, another high yield asset that is commonly used in tactical bond strategies, SPDR Barclays Capital High Yield Bond ETF (NYSEARCA: JNK ), has a much higher DSD of 0.58% from 2009 – present, and short duration SMAs do not seem to work as well on it as they do for VWEHX. So if my hypothesis is correct, then we need to find a basket of funds that: 1) are non-correlated, 2) have DSDs less than 0.35%, and 3) have relatively high annualized return (> 5%). It turns out that mutual funds are our best option (rather than ETFs) to meet these criteria, and only certain bond classes of mutual funds are suitable. I have found four classes of bond mutual funds that meet the stated criteria. The asset categories are listed below: 1) High yield municipal bond, 2) High yield corporate bond, 3) Mortgage securities, and 4) Intermediate-term treasuries. To show the viability of this approach, I selected four funds that meet the criteria (one from each category). They all have early inception dates. The four funds that I selected are: 1. Oppenheimer Rochester AMT-Free Municipals Fund (MUTF: OPTAX ), 2. Federated High Yield Trust Fund (MUTF: FHYTX ), 3. Fidelity Mortgage Securities Fund (MUTF: FMSFX ), and 4. Dreyfus U.S. Treasury Intermediate Term Fund . The correlations of the funds, together with various forms of standard deviations and annualized returns, are shown below for the timeframe 3/27/1987 to present. These results are taken from Portfolio Visualizer, a commercially-free software package. It can be seen that the funds have relatively low correlation to each other except for FMSFX and DRGIX that have a correlation of 0.74. Notice that all of the funds essentially meet the DSD and annualized return criteria; the only exception is the annualized return of OPTAX that is slightly less than 5%. FHYTX has the highest DSD (0.33%) along with the highest annualized return (7.62%). (click to enlarge) The total returns of the funds from 1999 – present are shown below in a composite figure from StockCharts.com. Please note that the funds complement each other well, i.e. when one or more funds have a downtrend, one or more of the funds have a corresponding uptrend. And, of course, when all funds are trending down, the strategy should put the portfolio money into a money market fund. When all of the funds are trending up, the strategy will select the fund(s) with the greatest momentum. (click to enlarge) I was able to backtest these funds to 1988. I was hoping to go back to 1987, but I could not find an intermediate term treasury fund that had an inception date before 1987. I used the relative strength approach with a SMA as a cash filter. The safe harbor was a money market fund, i.e. CASHX in PV. Because of the low volatility of the funds, a 10-day look-back period for ranking could be used, and a 10-day SMA could be used as a cash filter. These are, obviously, much shorter timing periods than are commonly used in tactical strategies. They can only be used because of the low volatility of the funds. The top-ranked fund was selected at the end of each month. The results of the Low Volatility Strategy selecting one fund each month (LVS-1) are shown below. Portfolio Visualizer [PV] was used to calculate the results. In addition to LVS-1, results are also presented for a buy & hold strategy (updated annually) and for the S&P 500. The S&P 500 was used as a benchmark only because PV did not have a bond benchmark. LVS-1 Using OPTAX, FHYTX, FMSFX, and DRGIX: Total Return, 1988 – 2015 (click to enlarge) LVS-1 Using OPTAX, FHYTX, FMSFX, DRGIX: Annual Returns, 1988 – 2015 (click to enlarge) LVS-1 Using OPTAX, FHYTX, FMSFX, DRGIX: Summary, 1988 – 2015 (click to enlarge) It can be seen that LVS-1 has a Compounded Annual Growth Rate [CAGR] of 11.6%, an annualized Standard Deviation [SD] of 6.3%, a worst year of +0.5%, and a Maximum Drawdown [MaxDD] of -6.6%. In terms of growth/risk, the Sharpe Ratio is 1.2, the Sortino Ratio is 2.7, and MAR (CAGR/MaxDD) is 1.8. Comparison of these numbers to those obtained with a buy & hold strategy and the S&P 500 can be seen in the summary table. Notice that the LVS-1 has the highest CAGR and the lowest MaxDD of the three scenarios. And the MAR of LVS-1 is 1.8 versus 0.5 for buy & hold and 0.2 for the S&P 500. The results for LVS when the two highest-ranked funds are selected (LVS-2) are presented below. From the summary table we see that CAGR drops to 9.4%, but the risk is significantly reduced: SD = 4.5% and MaxDD = -3.4%. The MAR is increased to 2.8. LVS-2 Using OPTAX,FHYTX,FMSFX,DRGIX: Total Return, 1988 – 2015 (click to enlarge) LVS-2 Using OPTAX,FHYTX,FMSFX,DRGIX: Annual Returns, 1988 – 2015 (click to enlarge) LVS-2 Using OPTAX,FHYTX,FMSFX,DRGIX: Summary, 1988 – 2015 (click to enlarge) I will now present a way to practically implement the LVS on Fidelity or Schwab platforms. This requires finding mutual funds with No Transaction Fees [NTF] on Fidelity and Schwab that mimic the funds that have longer historical data (that we have just discussed). I have tried to select NTF funds that do not have any redemption fees, can be traded every 30 days, and have favorable round-trip restrictions per their prospectus. I also tried to find funds that Morningstar rated four stars or higher. The basket is composed of: 1. Nuveen High Yield Municipal Bond Fund (MUTF: NHMAX ), 2. Principal Fields Inc High Yield Fund (MUTF: CPHYX ), 3. PIMCO Mortgage-Back Securities Fund (MUTF: PTMDX ), and 4. Dreyfus U.S. Treasury Intermediate Term Fund . I ran the LVS-1 and LVS-2 with this basket of funds. The backtesting is limited to 2000 – 2015 for this basket because of the limited historical data of NHMAX. The results of LVS-1 are shown below. LVS-1 Using NHMAX, CPHYX, PTMDX, DRGIX: Total Returns, 2000 – 2015 (click to enlarge) LVS-1 Using NHMAX, CPHYX, PTMDX, DRGIX: Annual Returns, 2000 – 2015 (click to enlarge) LVS-1 Using NHMAX, CPHYX, PTMDX, DRGIX: Summary, 2000 – 2015 (click to enlarge) It can be seen that CAGR = 11.7%, SD = 6.0%, Worst Year = +4.0%, MaxDD = -6.5%, and MAR = 1.8. The monthly win rate is 81%; this win rate should be compared with a 60% – 65% monthly win rate for most backtested strategies I have seen. The results compare well with the results using OPTAX, FHYTX, FMSFX, and DRGIX backtested from 1988 – 2015. This strategy is good for an investor who wants high growth and low risk. The results of LVS-2 for NHMAX, CPHYX, PTMDX and DRGIX are presented below. LVS-2 Using NHMAX, CPHYX, PTMDX, DRGIX: Total Returns, 2000 – 2015 (click to enlarge) LVS-2 Using NHMAX, CPHYX, PTMDX, DRGIX: Annual Returns, 2000 – 2015 (click to enlarge) LVS-2 Using NHMAX, CPHYX, PTMDX, DRGIX: Summary, 2000 – 2015 (click to enlarge) It can be seen that CAGR = 9.9%, SD = 4.2%, Worst Year = +3.6%, MaxDD = -2.6%, and MAR = 3.8. The monthly win rate is 83%, an exceptionally high number in a tactical strategy. There are only 33 months with negative returns, out of a total of 190 months. These results also compare well with LVS-2 results using OPTAX, FHYTX, FMSFX, and DRGIX backtested from 1988 – 2015. This strategy is good for an investor who desires moderate growth and very low risk. In summary, a new approach has been conceived that allows the use of short timing periods in tactical strategies without seeing the associated whipsaw effects. To enable the use of short timing periods, each mutual fund in the basket of funds must have a daily standard deviation less than 0.35%. To maximize return, each fund must have adjusted annualized returns over 5%. A tactical strategy named Low Volatility Strategy [LVS] is presented that uses relative strength ranking based on total returns of the last 10 trade days, and a 10-day SMA to filter the top-ranked fund(s). After backtesting LVS to 1988 by using mutual funds with early inception dates, the implementation of the strategy was addressed. NTF mutual funds were selected for use on Fidelity or Schwab platforms. LVS-1 and LVS-2 backtest results from 2000 – 2015 showed monthly win rates over 81%. LVS-1 had a CAGR of 11.7%, a MaxDD of -6.5%, and a MAR of 1.8. LVS-2 had a CAGR of 9.9%, a MaxDD of -2.6%, and a MAR of 3.8. There were no losing years for LVS-1 or LVS-2, with either set of mutual funds.