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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.

Open Source Stock Predictor Data and Code

Are published studies that predict higher returns for some U.S. stocks and lower for others based on firm accounting, stock trading and other data reproducible? In their May 2020 paper entitled “Open Source Cross-Sectional Asset Pricing”, Andrew Chen and Tom Zimmermann make available data and code that reproduce many published cross-sectional stock return predictors, allowing other researchers to modify and extend past studies. They commit to annual updates of their study. Defining statistical significance as achieving at least 95% confidence in predictive power, they include:

  • 180 clear predictors that exhibit statistical significance in original studies and are easily reproducible.
  • 30 likely predictors that exhibit statistical significance in original studies but are not precisely reproducible.
  • 315 additional predictors covered in past studies that were not clearly tested or failed, or are variations of these predictors. They further extend this group by separately testing 1-month, 3-month, 6-month and 12-month portfolio reformation frequencies (1,260 total tests).

They compute all predictors on a monthly basis and create for each a long-short portfolio based on the specifications and the sample period of its original study. They check predictive power of each using data available at the end of each month to evaluate long-short portfolio returns the next month. They assume a 6-month lag for availability of annual accounting data and a 1-quarter lag for quarterly accounting data. They make no attempt to account for portfolio reformation frictions or to winnow predictors based on similarity. Using data and sample periods for U.S. firms/stocks as specified in original published studies as described above, they find that: Keep Reading

Investor Access to Factor Premiums via Funds

Are widely accepted equity factor exposures available in fact to investors via “smart beta” mutual funds and exchange-traded funds (ETF)? In their May 2020 paper entitled “Smart Beta Made Smart”, Andreas Johansson, Riccardo Sabbatucci and Andrea Tamoni test effectiveness of individual U.S. equity mutual funds and ETFs and combinations of these funds for exploiting several major equity risk factors (value, size, profitability and momentum). After assembling a sample of funds with names that indicate smart beta strategies, they iteratively (annually for size, value and profitability and daily for momentum):

  1. Apply a double-regression to each fund to identify those that are actually “closet” market index funds.
  2. Refine factor exposures of each true smart beta fund based on actual fund holdings.
  3. Construct separately for institutional and retail investors tradable long-side (mutual funds and ETFs) and short-side (ETFs only) risk factors via value-weighted combinations of the 10 funds with the strongest exposures to each factor.

Using daily, monthly, and quarterly data for U.S. equity mutual funds and ETFs with (1) names indicating smart beta strategies, (2) at least one year of returns and (3)assets over $1 billion, data for their individual component U.S. stocks and specified factor returns during January 2003 through May 2019, they find that: Keep Reading

Exploit U.S. Stock Market Dips with Margin?

A subscriber requested evaluation of a strategy that seeks to exploit U.S stock market reversion after dips by temporarily applying margin. Specifically, the strategy:

  • At all times holds the U.S. stock market.
  • When the stock market closes down more than 7% from its high over the past year, augments stock market holdings by applying 50% margin.
  • Closes each margin position after two months.

To investigate, we assume:

  • The S&P 500 Index represents the U.S. stock market for calculating drawdown over the past year (252 trading days).
  • SPDR S&P 500 (SPY) represents the market from a portfolio perspective.
  • We start a margin augmentation at the same daily close as the drawdown signal by slightly anticipating the drawdown at the close.
  • 50% margin is set at the opening of each augmentation and there is no rebalancing to maintain 50% margin during the two months (42 trading days) it is open.
  • If S&P 500 Index drawdown over the past year is still greater than 7% after ending a margin augmentation, we start a new margin augmentation at the next close.
  • Baseline margin interest is U.S. Treasury bill (T-bill) yield plus 1%, debited daily.
  • Baseline one-way trading frictions for starting and ending margin augmentations are 0.1% of margin account value.
  • There are no tax implications of trading.

We use buying and holding SPY without margin augmentation as a benchmark. Using daily levels of the S&P 500 Index, daily dividend-adjusted SPY prices and daily T-bill yields from the end of January 1993 (limited by SPY) through May 2020, we find that: Keep Reading

Returns for Leveraged Securities

Are investors willing to pay for easy access to leverage? In the April 2020 version of their draft paper entitled “Embedded Leverage”, Andrea Frazzini and Lasse Pedersen investigate the relationship between the leverage of a financial asset (absolute percentage price change per one percent change in the underlying) and its return. They consider equity index options and individual stock options for different maturities and levels of moneyness, and leveraged exchange-traded funds (ETF). They consider three ways to test whether securities with more embedded leverage offer lower (monthly) returns: (1) portfolios sorted on leverage; (2) long-short factors that bet against leverage; and, (3) regression analysis. They consider alphas based on four (market, size, book-to-market, momentum) and five (plus market volatility) risk factors. Using groomed daily data for options on around 3,300 individual stocks and 12 stock indexes during 1996 through 2018, and daily data for seven 2X leveraged ETFs for seven major U.S. stock indexes during 2006 through 2018, they find that: Keep Reading

U.S. Equity Premium?

A subscriber requested measurement of a “premium” associated with U.S. stocks relative to those of other developed markets by looking at the difference in returns between the following two exchange-traded funds (ETF):

  • SPDR S&P 500 (SPY)
  • iShares MSCI EAFE Index Fund (EFA)

Using monthly dividend-adjusted closing prices for these ETFs during August 2001 (limited by EFA) through March 2020, we find that: Keep Reading

Tech Equity Premium?

A subscriber requested measurement of a “premium” associated with stocks of innovative technology firms by looking at the difference in returns between the following two exchange-traded funds (ETF):

Using monthly dividend-adjusted closing prices for these ETFs during March 1999 (limited by QQQ) through March 2020, we find that: Keep Reading

Expert Estimates of 2020 Country Equity Risk Premiums and Risk-free Rates

What are current estimates of equity risk premiums (ERP) and risk-free rates around the world? In their March 2020 paper entitled “Survey: Market Risk Premium and Risk-Free Rate used for 81 countries in 2020”, Pablo Fernandez, Eduardo de Apellániz and Javier Acín summarize results of a February-March 2020 email survey of international finance/economic professors, analysts and company managers “about the Market Risk Premium (MRP or Equity Premium) and Risk-Free Rate that companies, analysts, regulators and professors use to calculate the required return on equity in different countries.” Results are in local currencies. Based on 5,235 specific and credible premium estimates spanning 81 countries, they find that: Keep Reading

TIPS-based Equity Risk Premium Estimate

How can investors account for inflation expectations in estimating attractiveness of equities? In their March 2020 article entitled “The Equity Risk Premium: A Novel Perspective on the Past Fifty Years”, James White and Victor Haghani offer a perspective on stock market long-term (10-year) attractiveness based on Equity Risk Premium (ERP) calculated as the difference between:

  1. Cyclically adjusted earnings yield as the real expected long-term stock market return. This measure is the inverse of cyclically adjusted price-to-earnings ratio (CAPE, or P/E10); and,
  2. The yield on 10-year U.S. Treasury Inflation Protected Securities (TIPS) as the long-term risk-free return.

Using monthly values of P/E10 since 1970, modeled yield of 10-year TIPS until their initial issue in 1999 and actual yield of 10-year TIPS as issued thereafter, all through March 18, 2020, they find that: Keep Reading

Smart Money Indicator Verification Update

“Verification Tests of the Smart Money Indicator” performs tests of ideas and setup features described in “Smart Money Indicator for Stocks vs. Bonds”. The Smart Money Indicator (SMI) is a complicated variable that exploits differences in futures and options positions in the S&P 500 Index, U.S. Treasury bonds and 10-year U.S. Treasury notes between institutional investors (smart money) and retail investors (dumb money) as published in Commodity Futures Trading Commission Commitments of Traders (COT) reports. Since findings for some variations in that test are attractive, we add two further robustness tests:

Using COT report data, dividend-adjusted SPDR S&P 500 (SPY) as a proxy for a stock market total return index, 3-month Treasury bill (T-bill) yield as return on cash (Cash) and dividend-adjusted iShares 20+ Year Treasury Bond (TLT) as a proxy for government bonds during 6/16/06 through 4/3/20, we find that:

Keep Reading

COVID-19 and U.S. Stock Returns

What does the U.S. stock market at industry/firm levels say about investor expectations during and after the 2019 coronavirus (COVID-19) pandemic? In the April 2020 update of their paper entitled “Feverish Stock Price Reactions to COVID-19”, Stefano Ramelli and Alexander Wagner examine and interpret industry/firm-level reactions to COVID-19 across three pandemic phases:

  1. Incubation: January 2-17,
  2. Outbreak: January 20-February 21,
  3. Fever: February 24-March 20.

They estimate each stock’s abnormal return during these phases as its 1-factor (market) alpha minus its beta times the market excess return. They estimate alpha and beta via regression of daily excess stock returns on daily excess value-weighted market returns during 2019. They use the yield on 1-month U.S. Treasury bills (T-bill) as the risk-free rate for calculating excess return. Using daily dividend-adjusted stock prices for Russell 3000 stocks (excluding financial stocks for leverage-related analyses), market returns and T-bill yields during December 31, 2018 through March 20, 2020, they find that: Keep Reading

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