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Value Investing Strategy (Read Overview)

Allocations for January 2020 (Final)
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Momentum Investing Strategy (Read Overview)

Allocations for January 2020 (Final)
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Equity Premium

Governments are largely insulated from market forces. Companies are not. Investments in stocks therefore carry substantial risk in comparison with holdings of government bonds, notes or bills. The marketplace presumably rewards risk with extra return. How much of a return premium should investors in equities expect? These blog entries examine the equity risk premium as a return benchmark for equity investors.

SACEMS-SACEVS Diversification with Mutual Funds

“SACEMS-SACEVS for Value-Momentum Diversification” finds that the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) are mutually diversifying. Do longer samples available from “SACEVS Applied to Mutual Funds” and “SACEMS Applied to Mutual Funds” confirm this finding? To check, we look at the following three equal-weighted (50-50) combinations of the two strategies, rebalanced monthly:

  1. SACEVS Best Value paired with SACEMS Top 1 (aggressive value and aggressive momentum).
  2. SACEVS Best Value paired with SACEMS Equally Weighted (EW) Top 3 (aggressive value and diversified momentum).
  3. SACEVS Weighted paired with SACEMS EW Top 3 (diversified value and diversified momentum).

Using monthly gross returns for SACEVS and SACEMS mutual fund portfolios during September 1997 through July 2019, we find that:

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SMA10 vs. OFR FSI for Stock Market Timing

In response to “OFR FSI as Stock Market Return Predictor”, a subscriber suggested overlaying a 10-month simple moving average (SMA10) technical indicator on the Office of Financial Research Financial Stress Index (OFR FSI) fundamental indicator for timing SPDR S&P 500 (SPY). The intent of the suggested overlay is to expand risk-on opportunities safely. To test the overlay, we add four strategies (4 through 7) to the prior three, each evaluated since January 2000 and since January 2009:

  1. SPY – buy and hold SPY.
  2. OFR FSI-Cash – hold SPY (cash as proxied by 3-month U.S. Treasury bills) when OFR FSI at the end of the prior month is negative or zero (positive).
  3. OFR-FSI-VFITX – hold SPY (Vanguard Intermediate-Term Treasury Fund Investor Shares, VFITX, as a more aggressive risk-off asset than cash) when OFR FSI at the end of the prior month is negative or zero (positive).
  4. SMA10-Cash – hold SPY (cash) when the S&P 500 Index is above (at or below) its SMA10 at the end of the prior month.
  5. SMA10-VFITX – hold SPY (VFITX) when the S&P 500 Index is above (at or below) its SMA10 at the end of the prior month.
  6. OFR-FSI-SMA10-Cash – hold SPY (cash) when either signal 2 or signal 4 specifies SPY. Otherwise, hold cash.
  7. OFR-FSI-SMA10-VFITX – hold SPY (cash) when either signal 3 or signal 5 specifies SPY. Otherwise, hold VFITX.

Using end-of-month values of OFR FSI, SPY total return and level of the S&P 500 Index during January 2000 (OFR FSI inception) through June 2019, we find that:

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SACEVS Applied to Mutual Funds

“Simple Asset Class ETF Value Strategy” (SACEVS) finds that investors may be able to exploit relative valuation of the term risk premium, the credit (default) risk premium and the equity risk premium via exchange-traded funds (ETF). However, the backtesting period is limited by available histories for ETFs and for series used to estimate risk premiums. To construct a longer test, we make the following substitutions for potential holdings (selected for length of available samples):

To enable estimation of risk premiums over a longer history, we also substitute:

As with ETFs, we consider two alternatives for exploiting premium undervaluation: Best Value, which picks the most undervalued premium; and, Weighted, which weights all undervalued premiums according to degree of undervaluation. Based on the assets considered, the principal benchmark is a monthly rebalanced portfolio of 60% VFINX and 40% VFIIX. Using monthly risk premium calculation data during March 1934 through July 2019 (limited by availability of T-bill data), and monthly dividend-adjusted closing prices for the three asset class mutual funds during June 1980 through July 2019 (39 years, limited by VFIIX), we find that:

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S&P 500 Volatility Indexes as an Asset Class

Should investors consider allocations to products that track equity volatility indexes? In her July 2019 paper entitled “Challenges of Indexation in S&P 500 Index Volatility Investment Strategies”, Margaret Sundberg examines whether behaviors of S&P 500 Index option-based volatility indexes justify treatment of volatility as an asset class. To assess potential strategies, she employs the following indexes:

Using daily time series for these indexes during April 2008 through March 2019, she finds that: Keep Reading

Equity Factor Time Series Momentum

In their July 2019 paper entitled “Momentum-Managed Equity Factors”, Volker Flögel, Christian Schlag and Claudia Zunft test exploitation of positive first-order autocorrelation (time series, absolute or intrinsic momentum) in monthly excess returns of seven equity factor portfolios:

  1. Market (MKT).
  2. Size – small minus big market capitalizations (SMB).
  3. Value – high minus low book-to-market ratios (HML).
  4. Momentum – winners minus losers (WML)
  5. Investment – conservative minus aggressive (CMA).
  6. Operating profitability – robust minus weak (RMW).
  7. Volatility – stable minus volatile (SMV).

For factors 2-7, monthly returns derive from portfolios that are long (short) the value-weighted fifth of stocks with the highest (lowest) expected returns. In general, factor momentum timing means each month scaling investment in a factor from 0 to 1 according its how high its last-month excess return is relative to an inception-to-date window of past levels. They consider also two variations that smooth the simple timing signal to suppress the incremental trading that it drives. In assessing costs of this incremental trading, they assume (based on other papers) that realistic one-way trading frictions are in the range 0.1% to 0.5%. Using monthly data for a broad sample of U.S. common stocks during July 1963 through November 2014, they find that: Keep Reading

SACEVS Input Risk Premiums and EFFR

The “Simple Asset Class ETF Value Strategy” (SACEVS) seeks diversification across a small set of asset class exchanged-traded funds (ETF), plus a monthly tactical edge from potential undervaluation of three risk premiums:

  1. Term – monthly difference between the 10-year Constant Maturity U.S. Treasury note (T-note) yield and the 3-month Constant Maturity U.S. Treasury bill (T-bill) yield.
  2. Credit – monthly difference between the Moody’s Seasoned Baa Corporate Bonds yield and the T-note yield.
  3. Equity – monthly difference between S&P 500 operating earnings yield and the T-note yield.

Premium valuations are relative to historical averages. How might this strategy react to changes in the Effective Federal Funds Rate (EFFR)? Using end-of-month values of the three risk premiums, EFFRtotal 12-month U.S. inflation and core 12-month U.S. inflation during March 1989 (limited by availability of operating earnings data) through June 2019, we find that: Keep Reading

Federal Reserve Treasuries Holdings and Asset Returns

Is the level, or changes in the level, of Federal Reserve (Fed) holdings of U.S. Treasuries (bills, notes, bonds and TIPS, measured weekly as of Wednesday) an indicator of future stock market and/or Treasuries returns? To investigate, we take dividend-adjusted SPDR S&P 500 (SPY) and iShares Barclays 20+ Year Treasury Bond (TLT) as tradable proxies for the U.S. stock and Treasuries markets, respectively. Using weekly Fed holdings of Treasuries, SPY and TLT during mid-December 2002 through mid-July 2019, we find that: Keep Reading

OFR FSI as Stock Market Return Predictor

Is the Office of Financial Research Financial Stress Index (OFR FSI), described in “The OFR Financial Stress Index”, useful as a U.S. stock market return predictor? OFR FSI is a daily snapshot of global financial market stress, distilling more than 30 indicators via a dynamic weighting scheme. The index drops and adds indicators over time as some become obsolete and new ones become available. Unlike some other financial stress indicators, past OFR FSI series values do not change due to any periodic renormalization and are therefore suitable for backtesting. To investigate OFR FSI power to predict U.S. stock market returns, we relate level of and change in OFR FSI to SPDR S&P 500 (SPY) returns. Using daily and monthly values of OFR FSI and SPY total returns during January 2000 (OFR FSI inception) through June 2019, we find that:

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T-bills Beat Most Global Stocks?

Do most stocks worldwide beat the risk-free rate of return? In their July 2019 paper entitled “Do Global Stocks Outperform US Treasury Bills?”, Hendrik Bessembinder, Te-Feng Chen, Goeun Choi and John Wei  compare returns of individual global common stocks to that of 1-month U.S. Treasury bills (T-bills). They screen stock price data for obvious errors and filter/correct accordingly. For delisted stocks with no delisting return available, they set the final return to -30%. Using monthly returns with reinvested dividends in U.S. dollars for 17,505 U.S. and 44,476 non-U.S. stocks across 41 other countries (25 developed and 16 emerging) and monthly T-bill yield during 1990 through 2018, they find that:

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Sentiment Indexes and Next-Month Stock Market Return

Do sentiment indexes usefully predict U.S. stock market returns? In his May 2018 doctoral thesis entitled “Forecasting Market Direction with Sentiment Indices”, flagged by a subscriber, David Mascio tests whether the following five sentiment indexes predict next-month S&P 500 Index performance:

  1. Investor Sentiment – the Baker-Wurgler Index, which combines six sentiment proxies.
  2. Improved Investor Sentiment – a modification of the Baker-Wurgler Index that suppresses noise among input sentiment proxies.
  3. Current Business Conditions – the ADS Index of the Philadelphia Federal Reserve Bank, which combines six economic variables measured quarterly, monthly and weekly to develop an outlook for the overall economy.
  4. Credit Spread – an index based on the difference in price between between U.S. corporate bonds and U.S. Treasury instruments with matched cash flows. (See “Credit Spread as an Asset Return Predictor” for a simplified approach.)
  5. Financial Uncertainty – an index that combines forecasting errors for large sets of economic and financial variables to assess overall economic/financial uncertainty.

He also tests two combinations of these indexes, a multivariate regression including all sentiment indexes and a LASSO approach. He each month for each index/combination predicts next-month S&P 500 Index return based on a rolling historical regression of 120 months. He tests predictive power by holding (shorting) the S&P 500 Index when the prediction is for the market to go up (down). In his assessment, he considers: frequency of correctly predicting up and down movements; effectiveness in predicting market crashes; and, significance of predictions. Using monthly data for the five sentiment indexes and S&P 500 Index returns during January 1973 through April 2014, he finds that: Keep Reading

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