Equity Premium

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 March 2020, we find that: Keep Reading

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

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

“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:

• Extend the sample period in “Verification Tests of the Smart Money Indicator” through the end of March 2020 to see how SMI performs during the 2019 coronavirus (COVID-19) stock market crash.
• Using the same setup as in “Verification Tests of the Smart Money Indicator”, test alternate definitions of commercial traders as smart money and non-commercial traders as dumb money (from legacy reports), rather than asset manager/institutional traders and non-reportables, respectively (from more granular disaggregated reports).

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:

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What are the different ways of estimating the equity risk premium, and which one is best? In his March 2020 paper entitled “Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2020 Edition”, Aswath Damodaran updates a comprehensive overview of equity risk premium estimation and application. He examines why different approaches to estimating the premium disagree and how to choose among them. Using data from multiple countries (but focusing on the U.S.) over long periods through the end of 2019, he concludes that: Keep Reading

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

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 February 2020, we find that: Keep Reading

U.S. job gains or losses receive prominent coverage in the monthly financial news cycle, with media and expert commentators generally interpreting employment changes 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 changes in fact predictive of U.S. stock market behavior in subsequent months, quarters and years? Using monthly seasonally adjusted non-farm employment data from the U.S. Bureau of Labor Statistics (BLS) and monthly S&P 500 Index levels during January 1939 (limited by employment data) through February 2020, we find that: Keep Reading

Economic data arrive too slowly to help investors navigate crises such as the 2019 coronavirus (COVID-19) outbreak. Are there data that support quick reactions? In their March 2020 paper entitled “Coronavirus: Impact on Stock Prices and Growth Expectations”, Niels Gormsen and Ralph Koijen employ equity index dividend futures by maturity to understand the evolution of investor reactions to COVID-19 outbreak and subsequent policy actions. They argue that a stock market decline means that expected future dividends fall and/or the discount rate for future dividends rises, differently by maturity. These changes in expectations affect stock market valuation. Using daily dividend futures closing mid-quotes in the U.S. and settlement prices in the EU during January 2006 through March 25, 2020, they find that:

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Which equity factor model is best among non-U.S. global stock markets? In other words, what market/accounting variables are most important to investors screening non-U.S. stocks? In his February 2020 paper entitled “A Comparison of Global Factor Models”, Matthias Hanauer compares eight widely used equity factor models on a common dataset spanning stocks from 47 non-U.S. developed and emerging markets based on gross Sharpe ratio. The models are:

1. The Capital Asset Pricing Model (CAPM) – market.
2. FF3 (3-factor) – market, size, book-to-market.
3. FF5 (5-factor) – adds profitability based on operating profits-to-book equity and investment to FF3.
4. FF6 (6-factor) – adds momentum to FF5.
5. FF6_CP (6-factor) – substitutes cash-based operating profits-to-assets for the profitability factor used in FF6.
6. HXZ4, or q-factor (4-factor) – market, size, profitability based on return-on-equity (ROE), investment.
7. SY4, or Mispricing (4-factor) – market, size, management, performance.
8. FF6_CP,m (6-factor) – substitutes a monthly value factor for the annual value factor in FF6_CP.

He employs annual accounting data because quarterly data are unavailable in many countries at the beginning of my sample period. Using factor input and return data for 56,171 stocks across developed and emerging markets during 1990 through 2018, he finds that: Keep Reading