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• This strategy exploits the anomaly that equities perform best from November to April, and less so from May to October during most years.
• ETFs (XLV, XLI, XLY, XLB) have historically performed best from November to April, and ETFs (XLK, XLP, XLU, QQQ) have done better than the first group from May to October.
• The super-sector models termed “aggressive” and “defensive” combine, respectively, the Top5(Sector)Select models for ETFs (XLV, XLI, XLY, XLB) and ETFs (XLK, XLP, XLU, QQQ), all previously published on Seeking Alpha.
• This strategy invests alternatingly in the aggressive- and defensive super-sector models during their respective “good” 6-month periods.
• From Jan-2000 to Nov-2020 a backtest shows that this strategy would have outperformed the SPDR S&P 500 ETF Trust (SPY), producing an annualized return of 23.3% versus 6.1% for SPY.
• NOTE: Elsewhere we refer to the “aggressive” season as the winter season, and conversely the “defensive” as the summer season. This model switches all holdings bi-annually end of April and end of October.

• This 5-stock trading strategy with the stocks from Invesco QQQ Trust produces much higher returns than the ETF (QQQ).
• The universe from which stocks are selected is the Nasdaq 100 Index which represents all the point-in-time holdings of QQQ.
• The model ranks the stocks of the Nasdaq 100 Index and selects periodically the highest ranked stocks which also satisfy certain yield requirements.
• From 1/2/2009 to 10/30/2020 this strategy would have produced an annualized return (CAGR) of 28.5%, more than the 21.2% CAGR of QQQ over this period.

In a previous article, I discussed the seasonal effect in equities and showed that they perform best from November to April, the “good” period, and that replacing equities with fixed income during the “bad” period of May thru October is a winning strategy over the longer term.

Since future returns from fixed income are uncertain, the strategy proposed here is to always invest in sector equity ETFs. I defined two super-sector groups, classified as aggressive and defensive, each with four sector ETFs which have historically performed best from November to April and relatively well from May to October, respectively.

This simple strategy invests alternately in the aggressive and defensive super sectors during their respective six-month periods, switching from aggressive to defensive at the end of April, and vice versa at the end of October.

• This 5-stock trading strategy with the Consumer Discretionary Sector stocks of the S&P 500 produces much higher returns than the Consumer Discretionary Select Sector SPDR Fund (XLY).
• The universe from which stocks are selected emulates as close as possible the point-in-time holdings of XLY.
• The model ranks the stocks of this custom universe and selects periodically the highest ranked stocks which also satisfy stipulated yield requirements.
• From 1/2/2009 to 10/23/2020 this strategy would have produced an annualized return (CAGR) of 22.7%, more than the 19.3% CAGR of XLY over this period.

• This 5-stock trading strategy with the Materials Sector stocks of the S&P 500 produces much higher returns than the Materials Select Sector SPDR Fund (XLB).
• The universe from which stocks are selected holds point-in-time, the S&P 500 non-energy materials stocks of FactSet’s Reverse Business Industry Classification System.
• The model ranks the stocks of this custom universe and selects periodically the highest ranked stocks which also satisfy stipulated yield requirements.
• From 1/2/2009 to 10/21/2020 this strategy would have produced an annualized return (CAGR) of 19.2%, significantly more than the 11.5% CAGR of XLB over this period.

• This 5-stock trading strategy with the Utility Sector stocks of the S&P 500 produces much higher returns than the Utility Select Sector SPDR Fund (XLU).
• The universe from which stocks are selected holds point-in-time, the S&P 500 utility stocks of FactSet’s Reverse Business Industry Classification System.
• The model ranks the stocks of this custom universe and selects periodically the highest ranked stocks which also satisfy stipulated yield requirements.
• From 1/2/2009 to 10/1/2020 this strategy would have produced an annualized return (CAGR) of 12.7%, versus the 10.2% CAGR of XLU over this period.

• This 5-stock trading strategy with the Industrial Sector stocks of the S&P 500 produces much higher returns than the Industrial Select Sector SPDR Fund (XLI).
• The universe from which stocks are selected holds point-in-time the S&P 500 industrial stocks of FactSet’s Reverse Business Industry Classification System.
• The model ranks the stocks of this custom universe with the Portfolio123 “Greenblatt” ranking system and selects periodically the highest ranked stocks which also satisfy stipulated yield requirements.
• From 1/2/2009 to 10/2/2020 this strategy would have produced an annualized return (CAGR) of 20.6%, significantly more than the 12.7% CAGR of XLI over this period.

• This 5-stock trading strategy with the Healthcare Sector stocks of the S&P 500 produces much higher returns than the Healthcare Select Sector SPDR Fund (XLV).
• The universe from which stocks are selected holds point-in-time, the S&P 500 healthcare stocks of FactSet’s Reverse Business Industry Classification System.
• The model ranks the stocks of this custom universe with the Portfolio123 “Greenblatt” ranking system and selects periodically the highest ranked stocks which also satisfy stipulated industry and yield requirements.
• From 1/2/2009 to 9/28/2020 this strategy would have produced an annualized return (CAGR) of 23.6%, significantly more than the 13.8% CAGR of XLV over this period.

• This 5-stock trading strategy with the Consumer Staples Sector stocks of the S&P 500 produces much higher returns than Consumer Staples Select Sector SPDR Fund (XLP).
• The universe from which stocks are selected holds point-in-time, the S&P 500 consumer non-cyclical stocks of FactSet’s Reverse Business Industry Classification System.
• The model ranks the stocks of this custom universe with a modified “Greenblatt” ranking system and selects periodically the highest ranked stocks which also satisfy stipulated yield requirements.
• From 1/2/2009 to 9/23/2020 this strategy would have produced an annualized return (CAGR) of 25.3%, significantly more than the 11.4% CAGR of XLP over this period.

• This trading strategy with five stocks from those of ETF (XLK), mainly with a dividend yield greater than that of the S&P 500 index, produces much higher returns than XLK.
• The universe from which stocks are selected holds point-in-time, the S&P 500 technology stocks of FactSet’s Reverse Business Industry Classification System and some electronic payments industry stocks, similar to XLK.
• The model ranks the stocks of this custom universe with the Portfolio 123 “Greenblatt” ranking system and selects periodically the highest ranked stocks which also satisfy the stipulated yield requirement.
• From 1/2/2009 to 9/8/2020 this strategy would have produced an annualized return (CAGR) of 31.8%, significantly more than the 20.1% CAGR of XLK over this period.

The analysis was performed on the on-line portfolio simulation platform Portfolio 123.
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• This is a copycat trading strategy based on the quarterly 13F filings of 10 large hedge funds with assets under management (AUM) greater than $3.5-Billion.
• The algorithm looks at the top 20 largest holdings from each of the 10 filers and then picks the 15 most frequently held stocks among all of the filers.
• The model selects 12 of the 15 consensus picks from this hedge fund group with a ranking system based on quality.
• Changes in the holdings occur only every three months, about 45 days after the end of a quarter when 13F filings become public information, February, May, August, and November.
• From Feb-2008 to Aug-2020 this strategy would have produced an annualized return (CAGR) of 27.6%, significantly more than the 10.1% CAGR of the S&P 500 ETF (SPY) over this period.

• A simulation of this strategy with annual withdrawal rates of up to 10% still showed long-term growth which exceeded that of buy-and-hold the S&P 500 ETF (SPY).
• The backtests use the FactSet stock database and FactSet’s Revere Business Industry Classifications System (RBICS).
• The model holds equal-weight 10 stocks of the Russell 1000 index which are ranked with a simple ranking system to identify shares of the highest “quality” companies.
• The strategy provides a high dividend yield because a minimum yield excess (depending on RBICS sector type) over the yield of SPY is a critirium for stock selection.
• From Jan-2000 to Jun-2020 this strategy without withdrawals would have produced an annualized return (CAGR) of 21.5%, much more than the 5.6% CAGR obtained from SPY over the same period.

• A truer picture of the employment situation is extracted from the Unemployment Insurance Weekly Claims (UIWC) report.
• Persons receiving some form of unemployment benefit account for 18.6% of the labor force and not 13.3% – the official unemployment rate. 
• Monitoring of the weekly insured unemployed can provide early indication of recovery from the Covid-19 crisis.
• No meaningful economic recovery is identified from the current UIWC report.

• This is a copycat trading strategy based on the quarterly 13F filings of five hedge funds.
• The model holds the top 20 consensus picks from a group of five hedge funds.
• Changes in the holdings occur only every three months when the end-of-the-month 13F filings becomes public information.
• From Jan-2007 to May-2020 this strategy would have produced an annualized return (CAGR) of 27.1%, much more than the 7.8% CAGR of the S&P 500 ETF (SPY).

Rational for a Copycat Strategy

Research from Barclay and Novus published in October 2019 found that a stock selection copycat strategy that combines conviction and consensus of fund managers that have longer-term views outperformed the S&P 500 by 3.80% on average annually from Q1 2004 to Q2 2019.

• The ratio of federal debt to the GDP is expected to rise dramatically due to the covid-19 pandemic fiscal stimulus. This should result in a significant gold price rally.
• The analysis shows that a trading strategy for gold miners is preferable to a buy-and-hold investment strategy of individual mining stocks.
• This momentum strategy selects periodically one gold mining stock from a set of three: AngloGold Ashanti Ltd (AU), Newmont Corp. (NEM) and Sibanye-Stillwater Ltd. (SBSW).
• The selection is based on the momentum of the percentage price change and the up/down volume ratio of the stocks.
• From Jan-2016 to Apr-2019 this strategy would have produced an annualized return (CAGR) of 80.6%, much more than that of the best performing single stock of the three considered.

The ratio of federal debt to the economic output of the U.S. is expected to rise dramatically by the end of 2020 as a result of the covid-19 pandemic fiscal stimulus. This, and low interest rates should result in a significant rally in gold, similar to the post 2008 gold price increase, as shown in the figure below.

(click to enlarge)
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• The average of S&P500 for March-2020 was 2652. A 12% decline, or 325 point drop, would bring it to 2327, the end of March level of the long-term trend line.
• A recession appears to be imminent. Stocks could lose up to 50% if the S&P reaches the lower prediction band line of the long-term trend.
• If the percentage decline matches the loss during 2009-09 recession then the S&P500 could reach a low of 1560, a decline of about 35% from the current value.
• The Shiller Cyclically Adjusted Price to Earnings Ratio (CAPE) is at a level of 24.5, almost identical to the 35-year moving average (MA35) of the CAPE of 24.3.
• The CAPE-MA35 ratio is 1.01, forecasting a 10-year annualized real return of 7.9%. Should the CAPE-MA35 ratio decline further then 10-year forward returns will be higher.

• A reliable source for recession forecasting is the unemployment rate (UER), which can provide signals for the beginnings and ends of recessions.
• The February 2020 UER is 3.5%, signifying that no recession was imminent. However, if the March 2020 UER is 3.9% then a recession will be signaled, according to the model.
• According to the Washington Post more than a million workers are expected to lose their jobs by the end of March, a dramatic turnaround from February.
• Goldman Sachs estimates that 2.25 million Americans filed for their first week of unemployment benefits in the week ending March 20.
• If the number of unemployed rises only by one million than the March UER will be 4.1%, if it rises by 2.25 million it will be 4.9%.

• 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 “Why Not To Invest In Vanguard’s New U.S. Momentum Factor ETF” which demonstrated that Vanguard’s selection criteria for this fund was flawed.
• In the referenced article we stated that it was unlikely that VFMO would show a higher return than the SPDR S&P 500 ETF (SPY) over the year following inception.
• In April 2019 in a follow up article we showed that the actual performance of VFMO since inception was 6.8% lower than that of SPY, confirming the conclusion in the bullet-point above.
• Again, VFMO has underperformed SPY, and we come to the same conclusion for the following year, namely that the one-year return to Feb-2021 will be less than that of SPY.

• 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, repectively.
• The iM-1wk-SuperTimer (SPY-IEF) would have produced an annualized return of 19.9% with a max drawdown of about -10%.
• Appendix 2 shows that a (50%SPY+50%VCIT)-(IEF) strategy reduces drawdowns to -6.2% but would still have achieved an annualized return of 14%.
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• The new Federal Reserve Bank of Chicago Brave-Butters-Kelley Indexes ( BBK ) provide useful input for recession forecasting.
• In the past, low estimates of BBK GDP growth related to the respective recessions, this allow the extraction of a recession warning signal from this growth series.
• We combine two BBK indexes with the Conference Board LEI and iMarketSignals’ Business Cycle Index BCIg to derive our Long Leading Index (iM-LLI) for the US economy.
• Currently neither index signals a recession warning.