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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.

Lipper U.S. Fund Flows: Investors Pad The Coffers Of Money Market Funds Ahead Of Jobs Report

During the fund-flows week ended December 2, 2015, investors remained on the fence ahead of the U.S. nonfarm payrolls report, the European Central Bank’s details of its stimulus plans, and after learning that Chinese regulators were investigating two Chinese brokerage firms for securities violations. And, of course, investors were anxiously awaiting results of Black Friday and Cyber Monday sales to get a gauge of consumer demand for the upcoming holiday season. With the U.S. market closed for Thursday’s Thanksgiving Day holiday, returns were muted on Friday; investors preferred the comfort of defensive issues after energy shares once again took it on the chin following another decline in oil prices that were pressured by a strong dollar and concerns of a glut in global supply. While energy shares saw a slight boost on Monday after an uptick in oil prices, retail stocks struggled as first reads on the beginning of the holiday shopping season appeared soft. A weaker-than-expected Chicago PMI report indicated the region fell back into contraction territory, but that was partially offset by a 0.2% increase in pending home sales for October. Investors even appeared to shrug off a subpar reading of the November ISM manufacturing index, which fell to 48.9 (the lowest reading since 2009 and signaling contraction), ahead of comments from Federal Reserve Chair Janet Yellen and the nonfarm payrolls report due on Friday. Better-than-expected reports on construction spending and auto sales helped keep investors engaged. On Wednesday, however, stocks turned down as Yellen and Atlanta Fed President Dennis Lockhart both indicated a case for an imminent rate increase and as oil futures sank under $40 a barrel. Nonetheless, investors were net purchases of fund assets (including those of conventional funds and exchange-traded funds [ETFs]), injecting a net $15.2 billion for the fund-flows week ended December 2. Cautious investors turned their back on equity and fixed income funds, redeeming $0.9 billion and $2.1 billion net, respectively, for the week, but they padded the coffers of money market funds (+$17.8 billion) and municipal bond funds (+$0.4 billion) on the uncertain news. For the eighth week in a row equity ETFs witnessed net inflows, taking in $3.8 billion for the week. Despite initial concerns over the holiday season, authorized participants (APs) were net purchasers of domestic equity ETFs (+$3.4 billion), injecting money into the group for a third consecutive week. They also padded—for the second week running—the coffers of nondomestic equity ETFs (but only to the tune of +$0.4 billion). As a result of the relative risk aversion during the week, APs turned their attention to higher-quality, well-known equity offerings, with the SPDR S&P 500 ETF (NYSEARCA: SPY ) (+$2.7 billion), the iShares MSCI Eurozone ETF (NYSEARCA: EZU ) (+$0.3 billion), and the SPDR Dow Jones Industrial Average ETF (NYSEARCA: DIA ) (+$0.2 billion) attracting the largest amounts of net new money of all individual equity ETFs. At the other end of the spectrum the SPDR Gold ETF (NYSEARCA: GLD ) (-$566 million) experienced the largest net redemptions, while the iShares Nasdaq Biotech ETF (NASDAQ: IBB ) (-$267 million) suffered the second largest redemptions for the week. Once again, in contrast to equity ETF investors, for the fourth week in a row conventional fund (ex-ETF) investors were net redeemers of equity funds, redeeming $4.7 billion from the group. Domestic equity funds, handing back $3.4 billion, witnessed their fourth consecutive week of net outflows. Meanwhile, their nondomestic equity fund counterparts witnessed $1.3 billion of net outflows—suffering net redemptions for the third consecutive week. On the domestic side investors lightened up on large-cap funds and equity income funds, redeeming a net $1.7 billion and $0.7 billion, respectively, for the week. On the nondomestic side international equity funds witnessed $1.3 billion of net outflows, while emerging-market equity funds handed back some $0.7 billion. For the fourth consecutive week taxable bond funds (ex-ETFs) witnessed net outflows, handing back a little more than $1.8 billion for the week. Corporate investment-grade debt funds suffered the largest redemptions for the week, witnessing net outflows of $737 million (for their second consecutive week of net redemptions), while flexible portfolio funds witnessed the second largest net redemptions (-$654 million). Despite the increasing chance of a December interest rate increase, bank rate funds—handing back some $367 million for the week—experienced their nineteenth consecutive week of net outflows. For the ninth week in a row municipal bond funds (ex-ETFs) witnessed net inflows, taking in $331 million this past week.

3 Dividend ETFs With Yields Over 3% And 1 Coming Respectably Close

Summary These four dividend ETFs have similar expense ratios but substantially different holdings. DVY looks like the ETF with the highest chance to go on sale in December if the Fed Funds rate is increased. DVY and DTN have zero exposure to real estate which may be favorable for investors concerned about income taxes on REITs. One of the areas I frequently cover is ETFs. I’ve been a large proponent of investors holding the core of their portfolio in high quality ETFs with very low expense ratios. The same argument can be made for passive mutual funds with very low expense ratios, though there are fewer of those. In this argument I’m doing a quick comparison of several of the ETFs I have covered and explaining what I like and don’t like about each in the current environment. The Four ETFs Ticker Name Index QDF FlexShares Quality Dividend Index ETF Northern Trust Quality Dividend Index DHS WisdomTree Equity Income ETF WisdomTree High Dividend Index DTN WisdomTree Dividend ex-Financials ETF WisdomTree Dividend ex-Financials Index DVY iShares Select Dividend ETF Dow Jones U.S. Select Dividend Index By covering several of these ETFs in the same article I hope to provide some clarity on the relative attractiveness of the ETFs. One reason investors may struggle to reconcile positions is that investments must be compared on a relative basis and the market is constantly changing which will increase and decrease the relative attractiveness. For investors that want to see precisely which assets I’m holding, I opened my portfolio near the end of November. Dividend Yields I charted the dividend yields from Yahoo Finance for each portfolio. The FlexShares Quality Dividend Index ETF is the weakest of the batch on dividend yields, but I wouldn’t consider 2.78% even remotely bad. That is a very respectable dividend yield for an equity portfolio that is not focused on carrying REITs, BDCs, or other very high yield investments. The two WisdomTree funds both come in with very high dividend yields. (click to enlarge) Expense Ratios These funds are all extremely similar on expense ratios. (click to enlarge) Sector Even if an investor was going to focus on dividend yields, there are three funds with yields that are materially above 3%. The expense ratios are also very similar which reinforces that investors need to be looking at the sector allocations to make the determination of which ETF makes the most sense for them. I built a fairly nice table for comparing the sector allocations across dividend ETFs to make it substantially easier to get a quick feel for the risk factors: (click to enlarge) First Glance I imagine most readers looking at that glance first noticed the exceptionally tall purple bar representing the utility allocation for DVY. This is a dividend growth fund that has a fairly huge allocation to the utility sector. DVY DVY uses a very heavy allocation to utilities. For investors that already build their own utility positions in their portfolio, this wouldn’t be a great fit since it would double up on the exposure. On the other hand, for the investor that does not have utility exposure in their portfolio, the ETF could be a great fit. The utility sector often demonstrates some correlation with bonds because investors treat it as an alternative source of income. This may be a fairly volatile sector going into December because investors are expecting the Federal Reserve to raise rates and if a rate increase is confirmed it could send bond yields higher and utility stocks would be expected to fall at the same time so that the dividend yields would increase. I won’t be surprised if the Federal Reserve raises rates in December, but if they manage to raise rates 5 more times within the next year and a half I would be quite surprised. I don’t expect great results on the increase in rates, so I don’t think the following years will see further increases. I wouldn’t be surprised if see the Federal Reserve’s short term rate fall back to 0% before it makes it up to 1%. DTN and DVY In addition to being heavy on utilities, DVY joins DTN in having no allocation to real estate. I don’t mind the exclusion of real estate since I expect many investors may want to use this kind of dividend growth ETF in a taxable account while pushing their REIT exposure into a tax exempt account. For an investor putting a large part of their portfolio in either of these ETFs, it would be reasonable to look for some exposure to REITs somewhere else in the portfolio. I’m using equity ETFs for around 20% to 25% of my portfolio and I may look to increase that in December and going into next year if the REITs are on sale following an increase in the Fed Funds rate. DTN also has virtually no exposure to the financial services sector. Since their name includes “ex-Financials”, I think that makes a great deal of sense. DTN would fit best in a portfolio where the investor was manually choosing their own bank stocks and REITs for the portfolio. QDF QDF offers the lowest dividend yield and when I look at the sector allocations it appears fairly aggressive for a dividend portfolio. The allocation to utilities and consumer defensive are both fairly low and in both cases QDF has the lowest allocation in the portfolio. In my opinion, the best scenario for QDF relative to the other ETFs would be a longer bull market where more aggressive allocations would be rewarded. Compared to an actual aggressive allocation, this would be fairly tame but when compared to other high yield portfolios it is less defensive. What do You Think? Which dividend ETF makes the most sense for you? Do you use DVY to get your utility allocation, or do you pick your own utilities (or use a different ETF)? Is the dividend yield on DVY or DTN enough to bring you into the ETF? The only major weakness I see for this batch of ETFs is that the expense ratios are higher than I would like to see. However, when choosing between these four ETFs the ratios are very comparable.