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The Portfolio Timing using Absolute Momentum Tool allows users to enter a list of symbols and corresponding weightings to construct a standard "Buy and Hold" portfolio and apply an absolute momentum filter to each fund in the portfolio. When a specific fund is performing more poorly than the 'Cash' fund chosen (typically this is a treasury bill rate, but RotationInvest.com allows users to define this as any fund they would like), then the 'Cash' fund is invested in instead of the poorly performing fund.
Fund: The fund chosen here will be compared to each fund in the portfolio, if this fund is doing better (return %) over the period of time (see length below) then this fund will be invested in instead of the fund in the main portfolio. Length: The length defined here is the length of time to compare each fund in the portfolio to the 'Absolute Momentum Fund' chosen here. For Example: If 100 days is chosen as the length, IEF is the absolute momentum fund, and the portfolio is 60% SPY and 40% MDY: If IEF has a higher % return over the past 100 days than SPY but not MDY - then IEF will take over 60% of the portfolio from SPY, so the allocation will be IEF 60% and MDY 40%. Note: These features are available in the Advanced Rotation Tool as well in the 'Absolute Momentum' section. For a research paper anaylizing Absolute Momentum see: http://www.naaim.org/wp-content/uploads/2013/10/00D_Absolute-Momentum_gary_antonacci.pdf
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You may notice a few updates to Rotation Invest's tools recently.
Review List of Available Symbols in our Default Data Feed & More Information about our default data feed: Our latest addition to RotationInvest.com's suite of tools is the Risk On - Risk Off Tool. This tool uses a moving average applied to a fund or the Risk On portfolio to determine which Portfolio (the Risk On or Risk Off) should be invested in. The goal of this tool is to provide a simple way for users to backtest switching between 2 different portfolios using a moving average or 2 moving averages as market timing method. This allows the user to select a portfolio for when the stock market is doing well (Risk On), and provide a way to de-invest from this portfolio when the market is doing poorly (Risk Off). The below example is switching between a simple stock portfolio (50% SPY [S&P 500 ETF], and 50% MDY [Mid-Cap ETF]) in the Risk On portfolio, and a Low risk portfolio for the Risk Off portion comprised of 50% SHY [US Treasury 1-3 Year ETF] and 50% IEF [US Treasury 7-10 Year ETF]. The Risk On portfolio is selected when SPY is above the 175 day moving average, otherwise the Risk Off portfolio is selected. Also we made sure that even when the Risk On portfolio was not selected we kept it's allocation at 30% (so you are not entirely out of the Risk On Portfolio at any time).  Below is the Equity Curve for the strategy:  Below shows the transition map, when each portfolio was chosen. As you can see, since we selected to always have at least 30% of our funds invested in the Risk ON portfolio, the Risk OFF portfolio is only ever invested 70%:  Over the Span of the Backtest this strategy returned 12.12% with a 0.96 Sharpe Ratio, with only 15.3% maximum monthly drawdown:  Adaptive Asset Allocation is often cited as an attractive alternative to fixed or standard asset allocation so popularly used. Standard asset allocation or fixed asset allocation is the idea of allocating 10% of your portfolio to this and 25% to that and another 7% to this asset class and keeping that percentage fixed regardless of market circumstances. Adaptive Asset Allocation is quite different in how it decides how much is allocated to each asset in a portfolio, instead of using fixed numbers set by an individual or corporation at one time during one market cycle and sticking with it through thick and thin, adaptive asset allocation adjusts or adapts the portfolio weightings on a regular basis based on maximizing or minimizing a certain performance metric such as volatility or variance or even the Sharpe ratio. Market cycles as well as bear markets can be ruinous at worst and challenging at best for most fixed asset allocation models. If you recall 2008, the S&P 500 lost around 55% of it's value, but Gold didn't miss a beat until it has lost 1/3 of it's value from 2011-2013. Bonds surged while the stock market crashed in 2008, but many bonds faltered for much of 2013 while the stock market soared. Certainly you can see why diversifying a portfolio is of great value, but it begs the question "Why did we have to hold on to the stock funds we were invested in?". The first reason we could cite is a very valid one, and it is because we needed to hold onto it so we could benefit from it in the good years like 2013. The next question you may ask is "Why couldn't we reduce the amount of money we have in the stock market when it is falling or the bond market when it was falling and why have I been holding so much Gold the last few years?". This is where a fixed allocation system simply says we set a fixed amount and we stick to it regardless of circumstances, but lets take a look at how adaptive asset allocation answers this question. Enter Adaptive Asset Allocation Adaptive Asset Allocation sets the weight of each asset in your portfolio not by a fixed percentage but as a result of optimizing different performance metrics. For example, we could optimize a portfolio's weightings to minimize volatility or minimize variance or maximize the risk adjusted return (Sharpe Ratio). Each of these optimization criterion can be used to decide how much of each asset in your portfolio instead of using a fixed percentage. As you can see, adaptive asset allocation answers the questions posed above, namely how can we reduce the allocation in a certain asset when it is doing poorly. We are now going to reduce our assets in an asset that is volatile or has a high variance or a low risk adjust return (Sharpe Ratio), and increase our assets in funds that have low volatility or low variance or a high risk adjusted return. Our Test Now that we have established the rationale for why we might want to use adaptive asset allocation let's test a sample portfolio to see if Adaptive Asset Allocation can improve returns and reduce drawdown. For this portfolio I am going to use Exchange Trade Funds (ETFs) to select US Stocks, International Stocks, Gold, and US Treasury bonds as our portfolio assets. The tickers used are SPY for US Stocks, EFA for International Stocks, GLD for Gold, and TLT for US Treasury bonds. Parameters We are going to try 3 different performance metrics for deciding our weighting, the first is minimizing volatility, the second is minimizing variance, and the third is maximizing the risk adjusted return (Sharpe Ratio). For all calculations we are just going to use the 3 month trailing volatility, variance, and Sharpe ratio as our measurement. We are going to adjust the portfolio on a monthly basis, this may be too often or not often enough, but for an introduction to these type of ideas it is what we will use. Results - Volatility  The performance for the minimum volatility weighted portfolio is: 9.42% CAGR 0.98 Sharpe Ratio 9.72% Volatility 22% maximum draw down This compares to the equally weighted version of this portfolio (25% SPY, 25% GLD, 25% EFA, 25% TLT): 8.5% CAGR 0.76 Sharpe Ratio 11.61% Volatility 28.81% maximum draw down And to the S&P 500 alone: 7.64% CAGR 0.47 Sharpe Ratio 19.96% Volatility 55.22% maximum draw down The minimum volatility adaptive asset allocation portfolio successful outperformed the S&P 500, and equally weighted portfolio in all the performance metrics shown above! As you will see in the transition map image above, the volatility adaptive asset allocation did a lot of what we mentioned to reduce risk; during the 2008 stock market crash the % in SPY and EFA dropped considerably, while the bond fund took over a large percentage of the portfolio, and recently the allocation in gold has been dropping to reduce the exposure to the falling gold market. Results - Variance  The performance for the minimum variance weighted portfolio is: 10.1% CAGR 1.12 Sharpe Ratio 8.95% Volatility 14.84% maximum draw down The minimum variance adaptive asset allocation portfolio again successful outperformed the S&P 500, and equally weighted portfolio in all the performance metrics shown above! The minimum variance adaptive asset allocation did even more than the minimum volatility portfolio to reduce risk and increase returns. During the 2008 stock market crash the % in SPY and EFA dropped to near 0%, while the bond fund took over the majority of the portfolio, the allocation in bonds dropped while stocks recovered, and recently the allocation in gold has been dropping to near 0% to reduce the exposure to the falling gold market. Results - Risk Adjusted Return (Sharpe Ratio)  The performance for the maximum Risk Adjusted Return (Sharpe Ratio) weighted portfolio is: 15.43% CAGR 1.07 Sharpe Ratio 14.4% Volatility 27.27% maximum draw down The maximum Sharpe ratio adaptive asset allocation portfolio again successful outperformed the S&P 500, and equally weighted portfolio in all the performance metrics shown above - especially returns! The maximum Sharpe Ratio adaptive asset allocation did a lot to increase returns and even managed to outperform in the areas of drawdown and volatility over the S&P 500 and equal weight portfolios. Optimizing the Sharpe ratio is definitely aggressive, you will notice how it often completely eliminates assets from the portfolio and even is only in a single asset during certain times. During the 2008 stock market crash the % in SPY and EFA dropped to 0%, while the bond fund took the entire 100% of the portfolio, the allocation in bonds dropped while stocks recovered and there were many times when stocks where 100% of the portfolio, and recently gold has been almost completely absent from the portfolio even though it played a strong role in the portfolio when it was surging upwards earlier in the backtest. One More Thing... One thing that may be problematic is the fact that some of these allocations involve entirely or nearly getting rid of an investment, especially the Sharpe Ratio portfolio. One thing we can do to combat this is what I call "dampen" the weighting algorithms. This involves "dampening" the effects of each weighting algorithm by only allowing the weighting algorithm to go so far. For example you could decide you want to hold no less than 5% of a certain asset, you could then call 0-4% 5% and adjust the other weightings accordingly to effectively "dampen" the effect of the weighting algorithm to make the portfolio more like a fixed allocation strategy. So lets take a quick look at "dampening" the Sharpe Ratio Adaptive Asset Allocation portfolio to see what a little more conservative switching can do: Results - Risk Adjusted Return (Sharpe Ratio) with ~7% Minimum per Asset  The performance for the maximum Risk Adjusted Return (Sharpe Ratio) weighted portfolio with dampening is: 13.68% CAGR 1.13 Sharpe Ratio 12.04% Volatility 24.68% maximum draw down So we reduced the full effect of the Sharpe Ratio weighting and we got a portfolio that did not entirely eliminate any symbol, but also wasn't afraid to aggressively reduce its allocation in assets when they had a bad risk adjusted return value. The results show a smaller CAGR return %, but improvements in the area of Sharpe Ratio, volatility, and maximum drawdown. Conclusion Adaptive Asset Allocation can be used to re-weight our portfolio to reduce drawdown and increase returns in the ever changing markets as opposed to a more traditional fixed asset allocation. We tested 3 techniques that can be used to weighted a portfolio and noticed how each responded to the changes in the stock markets, bond markets, and gold market. Each strategy was able to outperform a standard buy and hold approach and the stock market, while also delivering better volatility and drawdown numbers in the backtests presented above. With ever increasing uncertainty in the direction of the markets and how best to diversify a portfolio adaptive asset allocation may be one answer of how to eliminate guesswork and provide a foundation for adjusting allocations to compensate for the winds and waves of the markets. 2018 UPDATE: 2015 & 2016 proved difficult years for the Sharpe and Information Ratio asset allocation strategies while variance and volatility based asset allocation seemed to do well in these years as well as in 2017 and 2018:   Rotation strategies, also know as momentum strategies or sometimes sector rotation strategies, operate on a simple premise. The premise is we will buy the fund (ETF in this case) that has done the best (is the strongest) over the last few months or weeks or years, then we will hold it for a specific length of time then we will do our calculations again to see which fund is the strongest then will we switch our investment from the old fund to the new better performing fund. The purpose of this strategy is to follow a trending market up, and rotate into a stronger fund as that market is getting weaker. Strength is often measure as momentum only (or relative strength), and sometimes volatility is factored in, instead will build a rotation strategy using ETFs (exchange trade funds) that uses the Sharpe Ratio as the deciding factor as to which assets will be invested in. The Sharpe Ratio is basically a composite of the momentum and volatility, it is refered to as the risk adjusted performance, that is how much performance do you get for a unit or risk. A Sharpe Ratio of 1 means you get the same amount of return as the benchmark (typically a low duration bond at around 2.5%) for a fixed amount of risk. So a portfolio with a Sharpe Ratio of 1 may have very different amounts of total return but the amount of risk for the higher return portfolios is more. When selecting funds for a strategy keep in mind the funds goal, and its volatility and general performance. For example mixing a short term bond fund like SHY and a high volatility fund such as ZIV or EDV would not work well together if all you looked at was volatility in the backtest. Since SHY is very low volatility and the other fund is much higher results using volatility alone would just result in SHY being chosen almost 100% of the time. Instead you could calculate the strength for the rotation based on momentum or use the Sharpe ratio with a low volatility factor (sometimes called F Factor). This volatility factor modifies the traditional calculation of the Sharpe Ratio and is only used to adjust the position scoring inside the backtesting tool, all Sharpe ratios posted are calculated using traditional methods. We are going to choose to rotate between SPY, EFA, and GLD. This isn't the highest performing option you can choose, it just intends to show off how using the Sharpe Ratio as the scoring technique, and using market filters or inversely correlated asset classes can improve results substantially. If we select 3 month lookback for the Sharpe Ratio we get the following chart, it gives 15.8% annual return, 0.86 Sharpe Ratio, 19.24% volatility, and a 46.26% draw down. The purple line below shows the effect of choosing the top 1 for rotation, and the light blue line shows the result of an equal weighted portfolio of SPY, EFA and GLD.  This is a major improvement as far as return %, Sharpe ratio, drawdown and volatility as compared to the S&P 500 index or even an equally weighted portfolio. For example over the same time period the S&P 500 (SPY) including dividends had a performance of 8.45% annually, a 0.51 Sharpe ratio, 19.57% volatility and a 55.44% draw down. The big thing you will notice is the Rotation Strategy still is subject to a drawdown during bear markets. Lets examine two ways of solving this issue. Idea #1 is to switch to a cash fund or bond fund when the market is dropping, I am calling this a cash filter. Idea #2 is to add a few bonds or cash like ETFs to the portfolio so the rotation can rotate into those ETFs if the others are weak. We will choose US Treasury bonds as the cash ETF since they usually have a negative correlation to stocks. First lets just test Idea #1 (Cash Filter) with SHY as a cash filter using the 150 day Simple Moving Average:  A big improvement, no more large drawdown and an increase in overall performance. 18.24% annual return, 1.07 Sharpe Ratio, 17.09% volatility, and a draw down of 24.87%. Now lets look at the performance of Idea #1 (adding bond as cash filter) and Idea #2 (adding bond into rotation) for a few different bond funds, specifically SHY, IEF and TLT: Idea #1 - Cash Filter - Results Testing with Different ETFs  Idea #2 - Adding Bond Funds to the Rotation - Results Testing with Different ETFs  It looks like adding that cash filter really improved results, and adding an ETF to the rotation helped some and hurt some. Since TLT is the more volatile fund I think it was a better match to the other funds thus it performed better when added to the rotation. In this case the cash filter did better than just adding another fund to the rotation, but if you can match the volatility of the funds (such as adding TLT) the results of adding the fund to the rotation can be good. Below are the results for the winning cash filter of TLT at 150 period Simple Moving Average.  Final Sharpe Rotation Strategy:  Rotating Funds: SPY, EFA, GLD Cash Filter: TLT Rules: Invest in the top 1 fund based on the 3 month Sharpe Ratio (volatility factor of 0.5) Cash Filter Rules: If the top fund is below it's 150 day moving average then invest in the bond ETF (TLT) Annual Performance of our Final Strategy  Robustness Are the values I choose above just flukes or is the strategy going to work well regardless of how exactly we choose the values presented above? Sharpe Ratio Length (months)  Sharpe Ratio Volatility Factor (F Factor) [Using 3 Month Sharpe]  Cash Filter Length (Days) [Using 3 Month Sharpe]  The results show a wide range of inputs working well for the strategy, all results yielded better returns, Sharpe Ratios, volatility and drawdown numbers than the S&P 500 and an equally weighted portfolio. We could choose better settings, like lowering the Volatility factor (thus making it closer to a momentum only strategy), and reduced the cash filter moving average length. Conclusion This is an example of how using a simple rotation strategy and adding in a moving average cash filter or inversely correlated assets to the rotation can help reduce draw downs, increase returns and eliminate the guesswork of investing. Rotation strategies can reduce your risk while providing much higher returns than the market and high Sharpe Ratios (reward to risk). The strategy presented here certainly isn't the end all be all of rotational strategies, but it shows how rotating funds into the strongest fund and avoiding market corrections with either inversely correlated asset classes (bond fund in this case) mixed into the rotation or by using a simple moving average filter to detect market corrections can result in a robust and realistic strategy. Sharpe Ratio is another way to measure the strength of a fund in addition to purely using momentum and/or volatility, Sharpe ratio rotation can add value in comparing funds risk adjusted return against each other instead of just comparing their momentum or volatility alone. 2018 UPDATE: Many rotation based strategies suffered in 2015/2016 years, this strategy was no exception. The 2015 year ended up at -2.4% with a 12.5% draw down, and 2016 ended up at -5.7% with a 10.8% draw down. However in 2017 and 2018 there was a sharp turn around, 23% (with only 2.2% drawdown) returns in 2017 and 11.8% (with 10.1% drawdown) so far in 2018.  This article will discuss a simple method to use moving averages and moving average channels to increase a portfolio's return, while reducing risk, and avoiding over-trading or highly active management of a portfolio. Adjusting a portfolio on a monthly basis offers several disadvantages to a buy and hold portfolio. Namely, these disadvantages include difficulty in executing trades in a timely manner each and every month, and short term trades that eat away at returns in a taxable account and incur often unnecessary commissions on all accounts. Overtrading gives an investor a reason to doubt their strategy and the trades their strategy is executing each and every month, or possibly more often than this. Between doubting trades, and not wanting to take the time to execute every single month, investors often don't trade a complex strategy, and end up missing the best of what the strategy has to offer, and ultimately the results are often less than optimal. Overtrading also has very tangible negative consequences in taxable accounts, that is incurring short term gains instead of long term gains, thus increasing the amount of taxes you need to pay on your gains. 10% gains taxed at up to 39.6% for short term gains is worth a lot less than 10% gains taxed at the long term gains rates of 0-20% at the end of the year (US tax rates)! The simple, and default strategy for avoiding overtrading and short term gains in an account is a buy and hold strategy. If rebalanced yearly there is only 1 trade per year, and no short term gains. However, as we will see, a buy and hold strategy is susceptible to large downturns in the market. In order to gain exposure to different sectors upswing, we also have to be involved in their respective down turns using classical buy and hold methods. So the question comes to mind, "Can we avoid the down turns in a market, but be involved in the upswing of each market?". In our article we want to backtest a simple, and practical method for building a US sector portfolio and then seeing if applying a moving average and moving average channels can benefit this simple strategy. Of course these timing strategies will result in more trading than a buy and hold portfolio, so we will look at how often they trade and how many short term gains we expect to see. Portfolio Timing Logic The portfolio we will test from January 2000 to September 2016, the portfolio will consisting of equal parts XLP (Consumer Staples), XLY (Consumer Discretionary), XLE (Energy), XLU (Utilities), XLI (Industrial), XLB (Materials), XLF (Financial), XLK (Technology), and XLV (Health Care). When any of these funds falls below the 175 day simple moving average, we will de-invest in the respective fund, and instead invest in a short term bond, cash like fund. Portfolio Channels Logic We will use the same portfolio, and the same 175 day simple moving average. However we will only invest in a fund if it goes 2.5% above this moving average, and we will only de-invest and move our money to cash if a fund goes 2.5% below this moving average. The purpose of the Channels is to reduce the possibility of getting in and out of the market over and over again when it is not necessary.  Simple Buy and Hold Portfolio With Yearly Rebalancing Below are the results for rebalancing a buy and hold portfolio with these funds in equal amounts since 2000:    The results show 6.46% return per year, with yearly rebalancing there are no short term gains to report for a taxable account, but there are large drawdowns during poor market conditions (Maximum drawdown is 52.68%). Portfolio Timing with Cash (SHY) Below are the results for the portfolio timing strategy using a cash like fund (SHY) as the de-invested fund since 2000:     Timing improved the results from 6.46% return per year to 9.16% return per year while reducing drawdown from 52.68% to 19.11%. The average trade length shows most trades over 3% lasting over a year. A vast improvement just by adding 1 simple moving average filter, a simple rule with drastic benefits to return. Portfolio Timing with 7-10 Year Treasury Bond (IEF) Below are the results for the portfolio timing strategy using a 7-10 Year Treasury Bond (IEF) as the de-invested fund since 2000:     Using IEF instead of SHY resulted in a solid increase in returns to 10.71% return per year. Portfolio Channels with Cash (SHY) Below are the results for the portfolio channels strategy using a cash like fund (SHY) as the de-invested fund since 2000:     The Channels strategy is a vast improvement over buy and hold, and also better than the simple Portfolio Timing strategy. The average trade length shows almost all trades over 3% lasting over a year. This means less trading and less short term income to report on taxable accounts when compared to the simpler Portfolio Timing options. Portfolio Channels with 7-10 Year Treasury Bond (IEF) Below are the results for the portfolio timing strategy using a 7-10 Year Treasury Bond (IEF) as the de-invested fund since 2000:     The channels strategy with IEF obtains 10.9% return over the duration of the test, with the same longer trades as the SHY model.
Conclusion A simple buy and hold portfolio has a simple once a year update schedule with no short term trades, at the expense of lower performance, more drawdown, and more volatility. The Portfolio Timing with longer term bonds shows the much better performance, but has a number of short term trades that could negatively impact a taxable account as well as make portfolio management an ongoing task each month. The Portfolio Channels results shows performance numbers even better than the simple moving average crossover of the Portfolio Timing Tool, with less short term trades, and less trading overall, making this option a possible compromise between a moving average strategy and a pure buy and hold portfolio. Overtrading was reduced by using a channel method instead of a simple moving average crossover strategy. Overtrading can cause psychological difficulties for investors as the market moves up and down, doubt and busyness get in the way of executing strategies correctly. Overtrading also increases your short term tax burden in taxable accounts, reducing trades to a minimum eliminates short term trades and taxable events in an account. The Calmar Ratio is a comparison of the returns over a specific period of time, compared to the maximum drawdown suffered during that time period. There are many ratios, such as the Sharpe Ratio, that compare returns to risk, the Calmar Ratio is another ratio that attempts to do this. Unlike the Sharpe Ratio the Calmar ratio compares returns (Sharpe and Calmar do calculate returns differently, but ultimately it is something akin to returns over a period of time) to the maximum drawdown instead of volatility, giving investors another way to measure risk in a market. Below we have put together a simple strategy using our Advanced Rotation tool that rotates between US Large Cap, US Mid Cap, Total World, and Long Term US Government Bonds, using 50% 3-month momentum and 50% 65 day Calmar Ratio as the criterion for determining which fund is the strongest, and thus gets chosen. Settings: Rotating between US Large Cap, US Mid Cap, Total World Fund, Long Term US Government Bonds 50% 3 month momentum, 50% 65 day Calmar Ratio Equity/Drawdown     Introduction This article will show a simple way to monitor fixed income investors entry and exit from the high risk market to the low risk market. High Yield Bonds (Junk Bonds) are considered high risk, since the companies a high yield fund is investing in have lower credit ratings than investment grade or treasury bonds. On the opposite end of the spectrum are US treasury bonds, no real credit risks exist with these funds. When economic situations are turning down fixed income investors flee high risk bonds, and move to bonds with low or no risk. In this article we will take a look at how a simple fund switching strategy has performed recently using ETFs, and then move on and look at the performance since 1969 using index data. Logic Using a simple moving average we will monitor when investors are fleeing junk bonds, and moving to treasury bonds by examining the ratio of how junk bonds are doing compared with treasury bonds. We will test a strategy that switches between JNK [SPDR Barclays Capital High Yield Bnd ETF] & IEF [iShares 7-10 Year Treasury Bond ETF], based on if the price of the JNK fund is above or below a 130 day moving average. JNK [SPDR Barclays Capital High Yield Bnd ETF] & IEF [iShares 7-10 Year Treasury Bond ETF] Switching Results   Simply switching a high yield bond fund to a treasury fund when the price of JNK is falling has resulted in significant performance, 11.84% annual return, with a high 1.88 Sharpe Ratio, and only 7% monthly drawdown. Long Term Testing Due to limited history this strategy may be suspect, we only had one large downturn in the market during this time period, and . Fortunately we can backtest using index data from Bank of America and Barclays back to 1969. We will use the same ratio of the US High Yield to Intermediate Term Treasury, and switch funds based on the same 130 day moving average. US High Yield Data Index: BofA Merrill Lynch US High Yield Master II Intermediate Term Treasury Data Index: Barclays US Government Intermediate Term    The worst year for the moving average timing strategy was -4.8% in 1969, the best year for the moving average timing system was 36.2% in 1992. This pattern of gaining exposure to a majority of uptrends, and avoiding downtrends has been a shown to exist for almost 50 years of backtesting. Fundamentally investors have been fleeing high risk securities and moving to minimum risk bonds for at least this long. Fixed income investors always are looking to find yield, if they can't get the highest yields found in junk bonds due to riskiness they move somewhere, and that somewhere is at least sometimes treasury bonds. Money always goes somewhere, when it leaves fund with high credit risk it moves to low risk funds, by tracking this phenomenon we can effectively switch between these funds to achieve high yields with low risk. Subscribe to our blog to see this strategy being updated and tracked on a Monthly Basis! Members Can Get the Latest Signals Here
This article will discuss a simple method to use moving averages and moving average channels to increase a portfolio's return, while reducing risk, and avoiding over-trading or highly active management of a portfolio. Adjusting a portfolio on a monthly basis offers several disadvantages to a buy and hold portfolio. Namely, these disadvantages include difficulty in executing trades in a timely manner each and every month, and short term trades that eat away at returns in a taxable account and incur often unnecessary commissions on all accounts. Overtrading gives an investor a reason to doubt their strategy and the trades their strategy is executing each and every month, or possibly more often than this. Between doubting trades, and not wanting to take the time to execute every single month, investors often don't trade a complex strategy, and end up missing the best of what the strategy has to offer, and ultimately the results are often less than optimal. Overtrading also has very tangible negative consequences in taxable accounts, that is incurring short term gains instead of long term gains, thus increasing the amount of taxes you need to pay on your gains. 10% gains taxed at up to 39.6% for short term gains is worth a lot less than 10% gains taxed at the long term gains rates of 0-20% at the end of the year (US tax rates)! The simple, and default strategy for avoiding overtrading and short term gains in an account is a buy and hold strategy. If rebalanced yearly there is only 1 trade per year, and no short term gains. However, as we will see, a buy and hold strategy is susceptible to large downturns in the market. In order to gain exposure to different sectors upswing, we also have to be involved in their respective down turns using classical buy and hold methods. So the question comes to mind, "Can we avoid the down turns in a market, but be involved in the upswing of each market?". In our article we want to backtest a simple, and practical method for building a US sector portfolio and then seeing if applying a moving average and moving average channels can benefit this simple strategy. Of course these timing strategies will result in more trading than a buy and hold portfolio, so we will look at how often they trade and how many short term gains we expect to see. Portfolio Timing Logic The portfolio we will test from January 2000 to September 2016, the portfolio will consisting of equal parts XLP (Consumer Staples), XLY (Consumer Discretionary), XLE (Energy), XLU (Utilities), XLI (Industrial), XLB (Materials), XLF (Financial), XLK (Technology), and XLV (Health Care). When any of these funds falls below the 175 day simple moving average, we will de-invest in the respective fund, and instead invest in a short term bond, cash like fund. Portfolio Channels Logic We will use the same portfolio, and the same 175 day simple moving average. However we will only invest in a fund if it goes 2.5% above this moving average, and we will only de-invest and move our money to cash if a fund goes 2.5% below this moving average. The purpose of the Channels is to reduce the possibility of getting in and out of the market over and over again when it is not necessary.  Simple Buy and Hold Portfolio With Yearly Rebalancing Below are the results for rebalancing a buy and hold portfolio with these funds in equal amounts since 2000:    The results show 6.46% return per year, with yearly rebalancing there are no short term gains to report for a taxable account, but there are large drawdowns during poor market conditions (Maximum drawdown is 52.68%). Portfolio Timing with Cash (SHY) Below are the results for the portfolio timing strategy using a cash like fund (SHY) as the de-invested fund since 2000:     Timing improved the results from 6.46% return per year to 9.16% return per year while reducing drawdown from 52.68% to 19.11%. The average trade length shows most trades over 3% lasting over a year. A vast improvement just by adding 1 simple moving average filter, a simple rule with drastic benefits to return. Portfolio Timing with 7-10 Year Treasury Bond (IEF) Below are the results for the portfolio timing strategy using a 7-10 Year Treasury Bond (IEF) as the de-invested fund since 2000:     Using IEF instead of SHY resulted in a solid increase in returns to 10.71% return per year. Portfolio Channels with Cash (SHY) Below are the results for the portfolio channels strategy using a cash like fund (SHY) as the de-invested fund since 2000:     The Channels strategy is a vast improvement over buy and hold, and also better than the simple Portfolio Timing strategy. The average trade length shows almost all trades over 3% lasting over a year. This means less trading and less short term income to report on taxable accounts when compared to the simpler Portfolio Timing options. Portfolio Channels with 7-10 Year Treasury Bond (IEF) Below are the results for the portfolio timing strategy using a 7-10 Year Treasury Bond (IEF) as the de-invested fund since 2000:     The channels strategy with IEF obtains 10.9% return over the duration of the test, with the same longer trades as the SHY model.
Conclusion A simple buy and hold portfolio has a simple once a year update schedule with no short term trades, at the expense of lower performance, more drawdown, and more volatility. The Portfolio Timing with longer term bonds shows the much better performance, but has a number of short term trades that could negatively impact a taxable account as well as make portfolio management an ongoing task each month. The Portfolio Channels results shows performance numbers even better than the simple moving average crossover of the Portfolio Timing Tool, with less short term trades, and less trading overall, making this option a possible compromise between a moving average strategy and a pure buy and hold portfolio. Overtrading was reduced by using a channel method instead of a simple moving average crossover strategy. Overtrading can cause psychological difficulties for investors as the market moves up and down, doubt and busyness get in the way of executing strategies correctly. Overtrading also increases your short term tax burden in taxable accounts, reducing trades to a minimum eliminates short term trades and taxable events in an account. Rotation strategies have often been touted as a way to increase returns, reduce drawdown, and avoid the nasty drawdowns associated with buy and hold portfolios and improving risk adjusted returns for holding single funds or stocks. Bear markets and corrections have proven difficult for buy and hold strategies, resulting in drawdowns, and less than stellar performance in great markets. Bonds surged while the stock market crashed in 2008, but many bonds faltered for much of 2013 while the stock market soared. If you recall 2008, the S&P 500 lost around 55% of it's value, but Gold didn't miss a beat until it has lost 1/3 of it's value from 2011-2013. Recently the stock market has been volatile but the bonds markets have surged upward. Why do we hold onto funds in our portfolios that are performing so poorly and are very volatile instead of just removing them from our portfolio or only investing in the best funds? One obvious answer is to just get rid of funds that are doing poorly and wait for them to do better again before getting back into that fund. That is the idea rotation investing attempts to accomplish quantitatively instead of just relying on emotions or guesses about when funds are doing poorly. Rotation Based Strategies The idea behind rotation strategies is simple, each month (or week or quarter...) you check a list of stocks, ETFs and/or mutual funds to see which is stronger and buy the top 1 or more funds and sell the old funds. This results in a portfolio constructed of only the strongest funds each month. These type of strategies have proven very effective in avoiding bear markets in the past while gaining exposure to uptrends. So instead of holding onto funds when they are dropping or very volatility like buy and hold lets test out rotating or swapping out these funds for stronger funds. Many rotation strategies have suffered in 2015, however my adding mean reversion into strategies there has been a lot of success in the past few years. Building a high return strategy using RotationInvest.com's Tools Tool: RotationInvest.com's Advanced Rotation Tool Funds: QLD, MIDU, and TMF Settings: Choose top 2 funds, 40% 3 month momentum, 60% 10 day mean reversion.      The performance for this rotation strategy is: 2040% Total Return over the period 49.99% CAGR 1.39 Sharpe Ratio 33.33% Volatility 21.94% maximum monthly drawdown No single year had a negative return Monte Carlo Warning The Monte Carlo simulation allows users to run a random sampling of possible performances and stress test a portfolio's results. This shows users possible outcomes given a return and volatility. What this simulation shows is for this particular strategy the higher volatility numbers mean a much larger range of possible outcomes, anywhere from ~$2M to more than $25M! The Monte Carlo simulation reveals an average of $8,050,000 average account balance after only 10 years with $100K starting balance and $1K monthly deposit, however the 90th Percentile and 10th Percentile (really good vs. really bad outcomes) vary dramatically due to strategy volatility! This difference in best possible outcome vs. a bad outcome shows how a high return strategy like this can result in vastly different outcomes even when they perform well in the future.  
Strategies presented anywhere on this site are ideas only, and only for education. This is not investment advice, or suggested financial information.
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