Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for February 2023 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for February 2023 (Final)
1st ETF 2nd ETF 3rd ETF

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.

Job Openings and Stock Market Returns

Do U.S. non-farm job openings, a measurement from the Job Openings and Labor Turnover Survey run monthly by the U.S. Bureau of Labor Statistics, have implications for future U.S. stock market return? High (low) job openings rate may indicate a strong (weak) economy and/or may signal high (low) wage inflation. To investigate, we relate job openings to performance of SPDR S&P 500 (SPY) as a proxy for the stock market. Using monthly job openings (which has a release delay of about six weeks) during December 2000 through September 2021 and monthly dividend-adjusted returns for SPY during December 2000 through October 2021, we find that: Keep Reading

SACEVS with Quarterly Allocation Updates

Do quarterly allocation updates for the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS) work as well as monthly updates? These strategies allocate funds to the following asset class exchange-traded funds (ETF) according to valuations of term, credit and equity risk premiums, or to cash if no premiums are undervalued:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

Changing from monthly to quarterly allocation updates does not sacrifice information about lagged quarterly S&P 500 Index earnings, but it does sacrifice currency of term and credit premiums. To assess alternatives, we compare cumulative performances and the following key metrics for quarterly and monthly allocation updates: gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD), annual gross returns and volatilities and annual gross Sharpe ratios. Using monthly dividend-adjusted closes for the above ETFs during September 2002 (earliest alignment of months and quarters) through September 2021, we find that:

Keep Reading

Quit Rate and Future Asset Returns

Does the U.S. employment quit rate, a measurement from the Job Openings and Labor Turnover Survey run monthly by the U.S. Bureau of Labor Statistics, have implications for future U.S. stock market or U.S. Treasury bond return? A high (low) quit rate may indicate a strong (weak) economy and/or may signal high (low) wage inflation. To investigate, we relate quit rate to future performance of SPDR S&P 500 (SPY) as a proxy for the stock market and of iShares 20+ Year Treasury Bond (TLT) as a proxy for government bonds. Using monthly quit rate (which has a release delay of about six weeks) during December 2000 through August 2021 and monthly dividend-adjusted returns for SPY and TLT as available during December 2000 through September 2021, we find that: Keep Reading

Testing a Countercyclical Asset Allocation Strategy

“Countercyclical Asset Allocation Strategy” summarizes research on a simple countercyclical asset allocation strategy that systematically raises (lowers) the allocation to an asset class when its current aggregate allocation is relatively low (high). The underlying research is not specific on calculating portfolio allocations and returns. To corroborate findings, we use annual mutual fund and exchange-traded fund (ETF) allocations to stocks and bonds worldwide from the 2021 Investment Company Fact Book, Data Tables 3 and 11 to determine annual countercyclical allocations for stocks and bonds (ignoring allocations to money market funds). Specifically:

  • If actual aggregate mutual fund/ETF allocation to stocks in a given year is above (below) 60%, we set next-year portfolio allocation below (above) 60% by the same percentage.
  • If actual aggregate mutual fund/ETF allocation to bonds in a given year is above (below) 40%, we set next-year portfolio allocation below (above) 40% by the same percentage.

We then apply next-year allocations to stock (Fidelity Fund, FFIDX) and bond (Fidelity Investment Grade Bond Fund, FBNDX) mutual funds that have long histories. Based on Fact Book annual publication dates, we rebalance at the end of April each year. Using the specified actual fund allocations for 1984 through 2020 and FFIDX and FBNDX May through April total returns and end-of-April 1-year U.S. Treasury note (T-note) yields for 1985 through 2021, we find that: Keep Reading

Understanding the Variation in Equity Factor Returns

What is the best way to understand and anticipate variations in equity factor returns? Past research emphasizes factor return connections to business cycle variables or measures of investor sentiment (with little success). In his September 2021 paper entitled “The Quant Cycle”, David Blitz analyzes factor returns themselves to understand their variations, arguing that behavioral rather than economic forces drive them. He determines the quant cycle (bull and bear trends in factor returns) by qualitatively identifying peaks and troughs. He focuses on U.S. versions of four conventionally defined long-short factors frequently targeted by investors (value, quality, momentum and low-risk), emphasizing the most volatile (value and momentum). He also considers some alternative factors. Using monthly data for factors from the online data libraries of Kenneth French, Robeco and AQR spanning July 1963 through December 2020 (and for a reduced set of factors spanning January 1929 through June 1963), he finds that:

Keep Reading

Extended Sample Tests of Established Equity Premium Predictors

Do equity premium predictors published in the past still work after extending their respective discovery samples through 2020? In their September 2021 paper entitled “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction II”, Amit Goyal, Ivo Welch and Athanasse Zafirov reexamine the power of 29 variables found to predict the equity premium (stock market return minus U.S. Treasury bill yield) in 26 prominent published papers, with data samples ending between 2000 and 2017, by extending these samples through 2020. They test not only their predictive powers, but also their performances when applied to four simple market timing strategies with Treasury bills as the alternative asset:

  1. Untilted, Unscaled – go long the equity premium when the predictor is above its historical median and short otherwise.
  2. Tilted, Unscaled – go long the equity premium unless the predictor is below its historical 25th percentile.
  3. Untilted, Z-scaled – first calculate a Z-score by subtracting the historical median from the current value and dividing by the historical standard deviation, and then scale the equity premium allocation by the Z-score.
  4. Tilted, Z-scaled – first calculate a Z-score by subtracting the historical 25th percentile value from the current value and dividing by the historical standard deviation, and then scale the equity premium allocation by the Z-score.

The benchmark for these strategies is buying and holding the equity premium. Extending the original discovery sample for each of the 29 predictors through December 2020 (typically about 10 years additional data), they find that: Keep Reading

DJIA-Gold Ratio as a Stock Market Indicator

A reader requested a test of the following hypothesis from the article “Gold’s Bluff – Is a 30 Percent Drop Next?” [no longer available]: “Ironically, gold is more than just a hedge against market turmoil. Gold is actually one of the most accurate indicators of the stock market’s long-term direction. The Dow Jones measured in gold is a forward looking indicator.” To test this assertion, we examine relationships between the spot price of gold and the level of the Dow Jones Industrial Average (DJIA). Using monthly data for the spot price of gold in dollars per ounce and DJIA over the period January 1971 through August 2021, we find that: Keep Reading

Update of Credit as a Tactical Asset Allocation Signal

Do credit portfolio managers adjust their portfolios more expeditiously than equity managers, thereby offering a means to time the equity market? In his August 2021 paper entitled “Credit-Informed Tactical Asset Allocation – 10 Years On”, David Klein updates and enhances the strategy presented in his paper of June 2011 (see “Credit as a Tactical Asset Allocation Signal”). The strategy holds stocks (short-term Treasuries) when stocks appear undervalued (overvalued) relative to corporate bonds based on data from a rolling 6-month historical interval. His proxy for corporate bonds is the ICE BofA Single-B US High Yield Index Option-Adjusted Spread (converted to a default probability) and for stocks is the Russell 2000 Index (with dividends). He hypothesizes that stock prices tend to fall when credit spread widens, and small capitalization stocks are more sensitive to credit conditions than large capitalization stocks. Two enhancements to the original strategy are: (1) shorten the lookback from six to three months; and (2) increase the equity allocation by adding a premium to equity values. A third enhancement is taking a 120% long position in stocks when they are undervalued and a 20% short position in stocks when they are overvalued (with 2% estimated annual costs for implementation). Updating and enhancing this strategy with 10 years of new daily data through June 2021, he finds that:

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Pure ESG?

Is it possible to isolate environmental, social and governance characteristics (ESG) effects on stock returns from those of other stock characteristics? In their July 2021 paper entitled “Chasing The ESG Factor”, Abraham Lioui and Andrea Tarelli specify a cross-sectional long-short ESG factor that neutralizes exposures to other firm characteristics, such as size and book-to-market ratio. By creating a pure ESG factor, they are able to isolate ESG alpha and estimate its separate E, S and G contributions. Their approach also suppresses effects of arbitrary ESG rating scales. They further construct an ESG sentiment index based on media attention to ESG-related topics and employ it to understand variations in pure ESG alpha. Using monthly firm ESG ratings from three sources as available during 1991-2019 and associated stock characteristics and returns during December 1992 through December 2020, with tests spanning December 2002 through December 2020, they find that: Keep Reading

Unemployment Rate and Stock Market Returns

Financial media and expert commentators often 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 variable in fact predictive of U.S. stock market behavior in subsequent months, quarters and years? Using monthly seasonally adjusted unemployment rate from the U.S. Bureau of Labor Statistics (BLS) and monthly S&P 500 Index levels during January 1948 (limited by unemployment rate data) through June 2021, we find that: Keep Reading

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