Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for May 2024 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for May 2024 (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.

Simple Sector ETF Momentum Strategy Update/Extension

“Simple Sector ETF Momentum Strategy” investigates performances of simple momentum trading strategies for the following nine sector exchange-traded funds (ETF) executed with Standard & Poor’s Depository Receipts (SPDR):

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

Here, we update the principal strategy and extend it by adding equally weighted combinations of the top two and top three sector ETFs, along with corresponding robustness tests and benchmarks. We present findings in formats similar to those used for the Simple Asset Class ETF Momentum Strategy and the Simple Asset Class ETF Value Strategy. Using monthly dividend-adjusted closing prices for the sector ETFs and SPDR S&P 500 (SPY), 3-month U.S. Treasury bill (T-bill) yield and S&P 500 Index level during December 1998 through December 2021, we find that: Keep Reading

TLT-SPY Return Delta as Stock Market Crash Indicator

A subscriber hypothesized that a very large delta between daily iShares 20+ Year Treasury Bond (TLT) and SPDR S&P 500 (SPY) returns presages a stock market collapse, and asked for verification. To investigate, we consider two tests:

  1. Calculate correlations between daily TLT-SPY return delta and daily SPY returns over the next month (21 trading days). A stock market collapse during this interval should exhibit very negative correlations.
  2. Compute average next-day SPY returns by ranked tenth (decile) of daily TLT-SPY return deltas. Average SPY returns should be relatively very low for high deciles.

Using daily dividend-adjusted prices for TLT and SPY during late July 2002 (limited by TLT) through mid-December 2021, we find that: Keep Reading

Defensive-in-May Sector Rotation

A subscriber asked about a strategy that holds a portfolio of cyclical sectors and small capitalization stocks during November through April and a portfolio of defensive sectors during May through October, as follows:

We use NAESX for small stocks to obtain a history as long as those for the equity sectors. We weight components of the cyclical and defensive portfolios equally. We use buy-and-hold NAESX and an equal-weighted, semiannually rebalanced portfolio of all seven funds (Sector EW) as benchmarks. We focus on semiannual return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using semiannual dividend-adjusted prices for the selected funds during April 1999 through October 2021 (defining the first and last available semiannual intervals), we find that: Keep Reading

ESG Realities

How meaningful is the term Environmental, Social, and Corporate Governance (ESG) as a descriptor of firm valuation and investment performance? In his November 2021 paper entitled “ESG: Hyperboles and Reality”, George Serafeim assesses beliefs about ESG, including those involving firm valuation and ESG firm/fund investment performance. Drawing on more than a decade of research, he concludes that: Keep Reading

Realistic Returns for Investing in the Stock Market

Is the conventional way of estimating the equity risk premium based on total shareholder return (TSR, assuming reinvestment of all dividends into stocks) reasonable? In their November 2021 paper entitled “Stock Investors’ Returns are Exaggerated”, Jesse Fried, Paul Ma and Charles Wang examine the realism of TSR as a measure of aggregate U.S. equity investor performance and therefore as the basis for U.S. equity premium estimation. They define an alternative all-shareholder return (ASR) that explicitly incorporates feasible reinvestment possibilities for cash distributions. Specifically, ASR:

  • Rejects the infeasible total dividend reinvestment assumption and alternatively assumes shareholders reallocate dividends into U.S. Treasuries of different maturities, corporate bonds or housing.
  • Takes into account all sources of cash flows from firms to investors, including repurchases and equity issuances (net distributions).

Using monthly estimated returns and cash distributions for the U.S. stock market and returns for the specified alternatives for cash distribution during January 1926 through December 2015, they find that: Keep Reading

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:

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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:

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