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The iM Minimum Volatility (USMV) – Investor

  • Since the launch of IM-Best12(USMV)Qx (x=1,2,3,or 4) in 2014, these models converged to a combined holding of 18 stocks, thus future performance of each of the models is expected to be very similar.
  • There is not much to be gained by following four similar models and these are now replaced by the iM Min Volatility(USMV)-Investor.
  • This model holds 10 equal weighted stocks and the simulated performance since 1/3/2013 shows an annualized return of 22.0% versus 14.3% for SPY and an annual turnover ratio of 60%
  • As from Sunday 7 July we will disseminate to Gold Subscribers any buy/sell signals this model generates.

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Is the Stock Market Overvalued? – Update July 2019, and 10-Year Real Forward Return Estimate

  • The average of S&P 500 for Jun-2019 was 2,890. A 20% decline from this level would bring it to the Jan-2020 level of the long-term trend line.
  • The Shiller Cyclically Adjusted Price to Earnings Ratio (CAPE) is at a relatively high level of 28.9, and the CAPE’s 35-year moving average (MA35) is at 23.9.
  • The CAPE-MA35 ratio is 1.21, forecasting a 10-year annualized real return of 6.2%. This would indicate that for long-term investors the S&P 500 is currently not overvalued.
  • Investing in equities for the long-haul when the CAPE-MA35 ratio is below 1.30 should produce reasonable returns as this level of the ratio does not signifies overvaluation of the market.

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The iM-SuperTimer – Simulated on Portfolio 123

  • For a detailed model description of the system please read the original description, update No.1 and update No.2
  • We have transferred the excel data onto Portfolio 123 and will in future be providing signals and performance for the weekly, monthly and 3-month models running on Portfolio 123, all updated weekly.
  • The models’ holdings alternate between ETF (SPY) and ETF (IEF), being proxies for investments during up- and down stock market periods.

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A Winning Strategy to Profit from the Seasonal Effect in Equities

  • The seasonal effect that equities do better from November through April is well-known. Here we provide a rigorous statistical test of this and a trading strategy to profits from it.
  • From 1960 the S&P 500 with dividends returned on average 1.92% for the six months May to October, the “bad-periods”, while the “good-periods”, November to April, returned 8.47% on average.
  • Statistics provide a 65% probability that good-periods will produce higher returns than the average of all good- and bad-periods, and a similar probability that the bad-periods will produce lower returns.
  • This anomaly can be exploited by tactically shifting from more aggressive “good-period portfolios” to lower risk portfolios at the end of every April, and reversing the process end of October.
  • Switching accordingly between the S&P 500 and 10-Year Treasuries would have provided an annualized return of 12.1% from 1960 to 2019 versus 9.4% for buy-and-hold the S&P 500.

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Why Not To Invest In Vanguard’s U.S. Momentum Factor ETF (VFMO) – 1-Year On

  • In February 2018 Vanguard released a set of five actively managed sector ETF’s and one multi-factor ETF. Here we report on the performance of the Momentum Factor ETF (VFMO).
  • Shortly after the inception of VFMO we published this article “Why Not To Invest In Vanguard’s New U.S. Momentum Factor ETF” which demonstrated that Vanguard’s selection criteria was flawed.
  • In the referenced article we stated that it was unlikely that VFMO will show a higher return than the SPDR S&P 500 ETF (SPY) over the year following inception.

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How Good Are Target-Date Glidepath Savings Programs During the Accumulation Phase Towards Retirement?

  • This study analyzes Yale’s Qualified Default Investment Alternative, a retirement plan with a target-date strategy. The findings also apply in principle to target-date strategy models from Vanguard, Fidelity, and others.
  • Yale University’s new retirement plan provides a “Glidepath” Target-Date Plus Service and also allows participants to opt out from it to pick their own investments from a few select funds.
  • Backtests (1999-2019) show that Yale’s Glidepath strategy would not have performed particularly well; one would have done better selecting one’s own funds, or by following the traditional 60%Stock-40%Bond constant allocation.
  • Retirement savings were calculated for a hypothetical individual making contributions to a retirement fund from Jan-2000 onwards using various allocation strategies, including Yale’s Glidepath and also a reverse glide-path strategy.
  • Much higher savings with relatively low risks can be obtained by employing a dynamic investment strategy using models which have moderately different allocations for up- and down-market conditions.

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The iM-SuperTimer – Update No.2:
Timing the Market with the iM-Stock Market Confidence Level

  • For a detailed model description of the system please read the original description and previous update.
  • To make this model more user-friendly we will be providing signals for three different version of this model, all updated weekly.
  • The models’ holdings alternate between ETF (SPY) and ETF (IEF), being proxies for investments during up- and down stock market periods, respectively.
  • The strategy was modeled in excel with weekly data, and performance includes trading costs of 0.1% of the total switch trade amounts.

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The iM-SuperTimer – Update March 2019: Timing the Market with the iM-Stock Market Confidence Level

  • The system uses a composite model consisting of several market timers. It should deliver more reliable signals for profitable investment and saving plans than single market timing models.
  • Component timers are allocated a 100% stock holding percentage when the timer signals investment in the stock market, or 0% when the timer it is out of the stock market.
  • A weekly Stock Market Confidence Level (SMC level), which can range from 0% to 100%, is obtained by considering the percentage allocated to each component timer and the timer’s weight in the system.
  • A backtest of a combination model of 15 iMarketSignals timers signaled avoidance of the stock market for SMC levels <=50%, while SMC levels >50% suggest better stock market investment climates.

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Beyond Buy-and-Hold: Improving Returns on Long-Term Investments Using the Shiller CAPE-MA35 Ratio

  • Forward 10-year annualized real returns of the S&P 500 Index can be determined by regression analysis using the ratio of the Shiller CAPE-ratio and its 35-year moving average (CMA-ratio).
  • Currently this ratio stands at 1.21 and forecasts a 10-year annualized real return of 6.2%, which would indicate that the market as represented by the S&P 500 is not overvalued.
  • Since 1979, when the CMA-ratio was within +/-5% of the current value the 10-year annualized real returns for the S&P500 that followed ranged from 4.7% to 14.6%, averaging 9.8%.
  • Investing in equities for the long-haul when the CMA-ratio is at 1.50 or higher produces poor returns, as this level of the ratio signifies overvaluation of the market.

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Estimating Forward 10-Year Stock Market Returns using the Shiller CAPE Ratio and its 35-Year Moving Average. (Update Dec-2018)

  • The Dec-2018 Shiller Cyclically Adjusted Price to Earnings Ratio (CAPE-ratio) stands at 27.9, which is 11.0 above its long-term mean of 16.9, signifying overvaluation of stocks and low forward returns.
  • The MA35-CAPE-Ratio methodology references stock market valuation to a 35-year moving-average of the Shiller CAPE-ratio (MA35) instead of the 1881-2018 long-term mean which the standard forecasting method is based on.
  • The MA35-CAPE-Ratio method should be superior to the standard CAPE-ratio method as only the percentage difference between the CAPE-ratio and its MA35 is considered, and not the absolute difference.
  • The MA35-CAPE-Ratio method and the falling trend of the CAPE-ratio currently signal a forward 10-year annualized real return for stocks of about 5.8%, while the historic long-term trend forecasts 5.0%.
  • Only the ratio between the prevailing CAPE-ratio and its 35-year moving average (CAPE-ratio / MA35) is needed to easily obtain the expected 10-year forward returns from the charts in this article.

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The iM-SuperTimer: Timing the Market with the iM-Stock Market Confidence Level

  • The system uses a composite model consisting of several market timers. It should deliver more reliable signals for profitable stock market investment than single market timing models.
  • Component timers are allocated a 100% stock holding percentage when the timer signals investment in the stock market, or 0% when the timer it is out of the stock market.
  • A weekly Stock Market Confidence Level (SMC level), which can range from 0% to 100%, is obtained by considering the percentage allocated to each component timer and the timer’s weight in the system.
  • The optimal SMC level for stock market investment is found by optimizing a stock-bond model for various SMC levels considering returns and drawdowns relative to buy-and-hold the S&P 500 index.
  • A backtest of a combination model of thirteen iMarketSignals timers signaled avoidance of the stock market for SMC levels <=50%, while SMC levels >50% suggest better stock market investment climates.

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The iM-FlipSaver Models (Revised)

In order to simplify retirement investment we are replacing our iM-Vanguard/TIAA-CREF Systems (updated monthly) with three iM-FlipSaver Models (updated weekly).

Prudent investors have assets allocated to both bonds and stocks. This conservative strategy is found in the Vanguard LifeStrategy Funds that invest statically in bonds and stocks and also in Life-Cycle/Target-Date Retirement Funds.

Instead of a static bond/stock ratio, these models change allocation in accordance with stock market conditions; e.g. during up-market periods the models hold more stocks than bonds, and during down-market periods the allocation “flips” to holding less stocks than bonds. This should improve performance and reduce drawdowns.

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Countdown To The 34th S&P 500 Death Cross; Update 12/6/2018

  • The 34th occurrence (since 1950) of the 50-day moving average of the S&P 500 crossing its 200-day moving average to the downside is imminent.
  • With the S&P 500 closing at 2,700.07 on 12/4/2018 the Death Cross is expected, with high probability, on Friday December 7.
  • Will the arrest of Huawei’s chief financial officer drive the S&P 500 below 2602 today for an earlier Death Cross?
  • The looming Death Cross could indicate the potential for a major selloff.

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Countdown To The 34th S&P 500 Death Cross; Update 11/29/2018

  • The 34th occurrence (since 1950) of the 50-day moving average of the S&P 500 crossing its 200-day moving average to the downside is imminent.
  • The Death Cross is inevitable, and will occur early December.
  • The looming Death Cross could indicate the potential for a major selloff.

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Can we avoid the 34th S&P 500 Death Cross?

  • The 34th occurrence (since 1950) of the 50-day moving average of the S&P 500 crossing its 200-day moving average to the downside is imminent.
  • Only a 7.9% upturn in the S&P 500 can avoid the “Death Cross”

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The iM-Low Frequency Timer

  • Over the last 20 years this Timer provided only two exit periods for the stock market.
  • By being out of the stock market during those periods one would have avoided most of the two bear markets and losses of 35% and 43%, respectively.

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Timing the Market with Google Trends Search Volume Data

  • Past research suggests that the relative change in the volume of Google searches for financial terms such as “debt” or “stocks” can be used to anticipate stock market trends.
  • In this analysis the search term “debt” was used to obtain monthly search volume data from Google Trends.
  • The analysis shows, that a decrease in search volume typically preceded price increases of the S&P 500 index, and vice versa.
  • Switching between ETF (SPY) and ETF (IEF) based on monthly search volume data from 2005 to 2018, would have made a profit of 634% versus 220% for buy-and-hold SPY.

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Out-Performing the US Market With the iM-Country Rotation System

  • The iM-Country Rotation System periodically selects one country ETF from six countries, USA, Canada, Japan, Australia, Germany and Sweden, based on the performance of their respective currency ETFs.
  • Backtests from 2009 to 2018 (the bull market period) show that each foreign country ETF under-performed the US stock market over the full backtest period.
  • However, when periodically selecting ETFs using a ranking system based on the performance of the countries’ respective currency ETFs, the model significantly out-performed the US stock market.
  • Over the period 3/9/2009-7/21/2018 the system showed a simulated annualized return of 29.4% versus 18.7% for the SPDR S&P 500 ETF Trust (SPY), with similar maximum draw-downs of about -19%.

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Better Returns From Seasonal Investing In The S&P 500 (1950-2018)

  • From 1950 to 2018 the S&P 500 performed best from November to April, and significantly worse from May to October during most years.
  • From 1950-2018 the real annualized return for the S&P 500 was 6.71%. Had one only invested from November to April each year the return would have been 6.60%, almost the same.
  • Investing in a money-market fund from May to October each year and the remaining time in the S&P 500 would have provided a higher real annualized return of 7.17%.
  • For the 32 year period of rising interest rates (1950-1982) the real return of the S&P 500 was only 5.40%, much less than for following 36 years of falling interest rates.

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The iM Seasonal ETF Switching Strategy

  • This strategy exploits the anomaly that Cyclical Sectors and Small Caps perform best from November to April, and Defensive Sectors do better from May to October during most years.
  • In this analysis only one ETF is periodically selected by a simple ranking system from the cyclical and defensive groups, respectively, and held for six months.
  • Out of the 37 six-month periods, 36 periods showed gains ranging from 0.1% to 28.1%, while only one six-month period produced a loss of -9.3%.
  • For the approximately 18.5 year period from end of Oct-1999 to May-2018 the backtest showed an annualized return of 19.8% with a maximum drawdown of -30%.
  • For an “inverted” switching strategy, when cyclicals ETFs are used for the May-October period and defensive ETFs during November-April period, the annualized return was 3.2% and maximum drawdown was -60%.

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