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Outperforming the S&P 500 by Trading the Top 10 Stocks from 40 Large Hedge Funds

  • Using the quarterly 13F filings we define a stock universe by extracting the 50 consensus stock from 40 large hedge funds, each fund with more than $3.5 billion Assets Under Management.
  • Instead of holding a portfolio of all 50 stocks, a rule based trading strategy periodically selects 10 stocks.
  • From 02/24/08 – 03/05/21 this trading strategy would have produced an annualized return (CAGR) of 22.3%, significantly more than the 10.5% CAGR of the S&P 500 ETF (SPY).

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The Covid Recession Probably Ended In October 2020

  • The Covid triggered recession probably ended in October 2020 according to two reliable recession indicator models.
  • Analyzing the series data of both the Conference Board’s Leading Economic Index and the American Chemistry Council’s Chemical Activity Barometer results this conclusion.
  • The National Bureau of Economic Research Business Cycle Dating Committee has yet to announce the end of the recession.

The start of the 2020 recession was reported on March 22, 2020 when we announced that the anticipated March 2020 Unemployment Rate would signal the start of a recession.
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Outperforming the S&P 500 with 50 Consensus Stock Holdings of 40 Large Hedge Funds

  • This investment strategy holds a maximum of 50 consensus stock picks from 40 hedge funds with more than $3,500 million Assets Under Management.
  • Changes in the holdings occur only every three months when the end of the quarter 13F filings becomes public information; the latest date was February 15, 2021.
  • From 02/24/08 – 02/19/21 this strategy would have produced an annualized return (CAGR) of 18.7%, significantly more than the 10.7% CAGR of the S&P 500 ETF (SPY).
  • Here we report the most recent holdings, and also list the stocks removed and added as of the week ending 2/19/2021.

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The iM-ModSum/YieldCurve Timer Model

  • The ModSum/YieldCurve Timer seeks to find optimum investment periods for equities. It uses two market timing algorithms: The ModSum Timer (weighted sum of five market timers), and the YieldCurve Timer.
  • The ModSum Timer switches between SPY and IEF, and the YieldCurve Timer adds a hedge with GLD.
  • For the period 1/02/2000 to 1/27/20121 this strategy would have produced an annualized return of 22.4% with a maximum drawdown of -14.3%.

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The S&P 500 Is Overvalued, Expect Low 10-Year Forward Returns

  • The average of S&P 500 for December 2020 is 3695. This is 1293 points higher than the long-term trend value of 2402.
  • The current percentage difference of S&P 500 level relative to the current long-term trend level is 54%, a value never exceeded in the recent past since 2002.
  • The Shiller Cyclically Adjusted Price to Earnings Ratio (CAPE) is at a level of 33.7. That is 37% higher than its 35-year moving average (MA35), currently at 24.7.
  • The CAPE-MA35 ratio is 1.37, forecasting a 10-year annualized real return of about 5.0%. Should the CAPE-MA35 ratio increase further, then 10-year forward returns will be even lower.
  • The long-term trend is forecasting a 10-year annualized real return of 2.1%. The most likely forecast 10-year forward return would be about 3.5%, the average of 5.0% and 2.1%.

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Stock – Gold Switch Signals From The Yield Curve And The Federal Funds Rate

  • The Yield Curve / Federal Funds Rate Timer signals the switches from stocks to gold and vice versa near or during recession periods.
  • Only three parameters are needed; the Effective Federal Funds Rate and the 2-year and 10-year U.S. Treasury yields to determine the periods when the yield curve is inverted.
  • The timing rules are based on the state of yield curve and on the trend of the Effective Federal Funds Rate.

This timer signals switches from stocks (S&P500 Total Return) to gold and vice versa near or during recession periods.
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iM-Trading Models: Links, Performance and Holdings as of 11/25/2020

Table of links to our trading models

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iM-Weekly Unemployment Monitor – Update 11/25/2020

  • 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 12.6% of the labor force.
  • Monitoring of the weekly insured unemployed could provide early indication of recovery from the COVID-19 crisis.
  • The current UIWC report shows that the economic recovery from the COVID-19 crisis seems to improve.

The 11/26/2020 DOL Unemployment Insurance indicates a continued improvement in the insured employment situation even though the initial claims remain high and have increased over last week, but seemingly trending towards the long-term average.
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A Stock Model That Profits From The Seasonal Performance Anomaly Of The S&P 500.

  • 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.

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Profiting From Trading The Stocks Of The Invesco QQQ Trust: (iM-Top5(QQQ)Select)

  • 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.

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Profiting from a Seasonal Super-Sector Investment Strategy

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.

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Profiting From Trading Stocks Of The S&P 500 Consumer Discretionary Sector: (iM-Top5(XLY)Select)

  • 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.

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Profiting From Trading Stocks Of The S&P 500 Materials Sector (iM-Top5(XLB)Select)

  • 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.

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Profiting From Trading Stocks Of The S&P 500 Utility Sector (iM-Top5(XLU)Select)

  • 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.

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Profiting From Trading Stocks Of The S&P 500 Industrial Sector (iM-Top5(XLI)Select)

  • 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.

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Profiting From Trading Stocks Of The S&P 500 Healthcare Sector (iM-Top5(XLV)Select)

  • 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.

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Profiting From Trading Stocks Of The S&P 500 Consumer Staples Sector (iM-Top5(XLP)Select)

  • 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.

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Profiting from Trading Stocks of the Technology Select Sector SPDR Fund (XLK) — iM-Top5(XLK)Select

  • 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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Profiting from the Consensus Stock Holdings of Ten Large Hedge Funds (iM-10LargeHedgeFundSelect)

  • 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.

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A Dividend Growth Strategy for Perennial Income

  • 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.

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