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Simple Asset Class Momentum Strategy Applied to Mutual Funds

A subscriber inquired whether a longer test of the “Simple Asset Class ETF Momentum Strategy” is feasible using mutual funds rather than exchange-traded funds (ETF) as asset class proxies. To investigate, we consider the following set of mutual funds (partly adapted from the paper summarized in “Asset Allocation Combining Momentum, Volatility, Correlation and Crash Protection”):

Oppenheimer Commodity Strategy Total Return A (QRAAX)
Vanguard Emerging Markets Stock Index Investor Shares (VEIEX)
Fidelity Diversified International (FDIVX)
First Eagle Gold A (SGGDX)
Vanguard Total Stock Market Index Investor Shares (VTSMX)
Vanguard Small Capitalization Index Investor Shares  (NAESX)
Vanguard REIT Index Investor Shares (VGSIX)
Vanguard Long-Term Treasury Investor Shares (VUSTX)
3-month Treasury bills (Cash)

The investigation includes basic tests performed in “Simple Asset Class ETF Momentum Strategy”, robustness tests performed in “Simple Asset Class ETF Momentum Strategy Robustness/Sensitivity Tests” and some of the extensions explored in “Alternative Asset Class ETF Momentum Allocations”. The selected mutual funds all have monthly prices available as of the end of March 1997. Monthly strategy returns, as limited by the kinds of tests performed, commence in April 1998. Using monthly dividend-adjusted closing prices for the above mutual funds and the yield for Cash during March 1997 through September 2014 (212 months), we find that: Keep Reading

Survey of Recent Research on Constructing and Monitoring Portfolios

What’s the latest research on portfolio construction and risk management? In the the introduction to the July 2014 version of his (book-length) paper entitled “Many Risks, One (Optimal) Portfolio”, Cristian Homescu states: “The main focus of this paper is to analyze how to obtain a portfolio which provides above average returns while remaining robust to most risk exposures. We place emphasis on risk management for both stages of asset allocation: a) portfolio construction and b) monitoring, given our belief that obtaining above average portfolio performance strongly depends on having an effective risk management process.” Based on a comprehensive review of recent research on portfolio construction and risk management, he reports on:

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Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for September 2014. The actual total (core) inflation rate for September is lower than (about the same as) forecasted.

The new actual and forecasted inflation rates will flow into Real Earnings Yield Model projections at the end of the month.

End-of-Quarter Effect

Does the U.S. stock market offer a predictable pattern of returns around the ends of calendar quarters? Do funds deploy cash to bid stocks up at quarter ends to boost portfolio values at the end of reporting periods (with subsequent reversals)? Or, do they sell stocks to raise cash for fund redemptions? Is the end-of-quarter effect the same as the Turn-of-the-Month (TOTM) effect? To investigate, we examine average daily stock market returns from 10 trading days before to 10 trading days after the ends of calendar quarters. We compare these returns to those for turns of calendar months. Using daily closes for the S&P 500 Index for January 1950 through September 2014 (259 quarters), we find that: Keep Reading

Unemployment Rate and Stock Market Returns

The business media and expert commentators sometimes cite the U.S. unemployment rate as an indicator of economic and stock market health, generally interpreting a jump (drop) in the unemployment rate as bad (good) for stocks. Conversely, investors may interpret a falling unemployment rate as a trigger for increases in the Federal Reserve target interest rate (and adverse stock market reactions). Is this indicator in fact predictive of U.S. stock market behavior in subsequent months, quarters and years? Using the monthly unemployment rate from the U.S. Bureau of Labor Statistics (BLS) and contemporaneous S&P 500 Index data for the period January 1950 through September 2014 (777 months), we find that: Keep Reading

Employment and Stocks Over the Intermediate Term

U.S. job gains or losses are a prominent element of the monthly investment-related news cycle, with the the business media and expert commentators generally interpreting changes in employment as an indicator of future economic and stock market health. One line of reasoning is that jobs generate personal income, which spurs personal consumption, which boosts corporate earnings and lifts the stock market. Are employment trends in fact predictive of U.S. stock market behavior in subsequent months, quarters and years? Using monthly seasonally adjusted nonfarm employment data from the U.S. Bureau of Labor Statistics (BLS) and contemporaneous S&P 500 Index data for the period January 1950 through September 2014 (777 months), we find that: Keep Reading

Martin Zweig’s Four Percent Model

A reader inquired about the validity of Martin Zweig’s Four Percent Model, which states (from pages 93-94 of the 1994 version of Martin Zweig’s Winning on Wall Street):

“The Four Percent Model for the stock market works as follows. First, It uses the Value Line Composite Index…an unweighted price index of approximately seventeen hundred stocks… All you need to construct this model is the weekly close of the Value Line Composite. You can ignore the daily numbers if you wish… This trend-following model gives a buy signal when the weekly Value Line Index rallies 4% or more from any weekly close. It then gives a sell signal when the weekly close of the Value Line Composite drops by 4% or more from any weekly peak. …That’s all there is to it. …The model is designed to force you to stay with the market trend.”

We execute this description as follows (after identifying the first signal):

  • After a buy signal, generate the next sell signal upon a 4% or greater decline from a subsequent high water mark (including the buy signal level).
  • After a sell signal, generate the next buy signal upon a 4% or greater advance from a subsequent low water mark (including the sell signal level).

We test the usefulness of the signals on the following exchange-traded funds (ETF) over their entire available histories: SPDR S&P 500 (SPY), PowerShares QQQ (QQQ), iShares Russell 2000 Index (IWM) and Guggenheim S&P 500 Equal Weight (RSP). Using weekly closes of the Value Line Geometric Index and the dividend-adjusted weekly opens of  the selected ETFs from their respective inceptions through September 2014, we find that:

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Weekly Summary of Research Findings: 10/13/14 – 10/17/14

Below is a weekly summary of our research findings for 10/13/14 through 10/17/14. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Simple Tests of Sy Harding’s Seasonal Timing Strategy

Several readers have inquired about the performance of Sy Harding’s Street Smart Report Online, which includes the Seasonal Timing Strategy. This strategy combines “the market’s best average calendar entry [October 16] and exit [April 20] days with a technical indicator, the Moving Average Convergence Divergence (MACD).” According to Street Smart Report Online, applying this strategy to a Dow Jones Industrial Average (DJIA) index fund generated a cumulative return of 213% during 1999 through 2012, compared to 93% for the DJIA itself. As a robustness test, we apply this strategy to the SPDR S&P 500 (SPY) exchange-traded fund since its inception. Using daily dividend-adjusted closing prices for SPY and daily 13-week Treasury bill (T-bill) yields during 1/29/93 (inception of SPY) through 9/30/14, we find that: Keep Reading

Kaeppel’s Sector Seasonality Strategy

A reader suggested looking at the strategy described in “Kaeppel’s Corner: Sector Seasonality” (from November 2005) and updated in “Kaeppel’s Corner: Get Me Back, Clarence” (from October 2007). The steps of this calendar-based sector strategy are:

  1. Buy Fidelity Select Technology (FSPTX) at the October close.
  2. Switch from FSPTX to Fidelity Select Energy (FSENX) at the January close.
  3. Switch from FSENX to cash at the May close.
  4. Switch from cash to Fidelity Select Gold (FSAGX) at the August close.
  5. Switch from FSAGX to cash at the September close.
  6. Repeat by switching from cash to FSPTX at the October close.

Does this strategy materially and persistently outperform? To investigate, we compare results for three alternative strategies: (1) Kaeppel’s Sector Seasonality strategy (Sector Seasonality); (2) buy and hold Vanguard 500 Index Investor (VFINX) as an investable broad index benchmark (VFINX); and, (3) a simplified seasonal strategy using only VFINX from the October close through the May close and cash otherwise (VFINX /Cash). Using monthly dividend-adjusted closing levels for FSPTX, FSENX, FSAGX, the 13-week Treasury bill (T-bill) yield as the return on cash and VFINX over the period December 1985 through September 2014 (almost 29 years), we find that: Keep Reading

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