Equity Premium

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 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

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

Has 24-hour trading of equity index futures created a reliable pattern in hour-by-hour returns? In their February 2020 preliminary paper entitled “The Overnight Drift”, Nina Boyarchenko, Lars Larsen and Paul Whelan study round-the-clock U.S. stock market performance decomposing S&P 500 Index futures returns by hour, with focus on dealer inventory management. Using 24-hour high-frequency trades and quotes for S&P 500 futures contracts during January 1998 through December 2018, they find that: Keep Reading

Does success in the U.S. equity market depend on an ever- shrinking percentage of outperforming stocks? In his February 2020 paper entitled “Wealth Creation in the U.S. Public Stock Markets 1926 to 2019”, Hendrik Bessembinder updates his analysis of wealth creation in excess of 1-month U.S. Treasury bills (T-bills) across U.S. stocks since 1926 by adding 2017-2019. Wealth creation differs from market capitalization by accounting for all cash flows to and from shareholders via new share issuances, share repurchases and dividends not reinvested in stocks. Using price, share issuance/repurchase and dividend data for 26,168 U.S. stocks and the T-bill yield during 1926 through 2019, he finds that:

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Why does the coincident relationship between U.S. aggregate corporate earnings growth and stock market return change from negative in older research to positive in recent research? In their January 2020 paper entitled “Assessing the Structural Change in the Aggregate Earnings-Returns Relation”, Asher Curtis, Chang‐Jin Kim and Hyung Il Oh examine when the change in the aggregate earnings growth-market returns relationship occurs. They then examine factors explaining the change based on asset pricing theory (expected cash flow and expected discount rate). They calculate aggregate earnings growth as the value-weighted average of year-over-year change in firm quarterly earnings scaled by beginning-of-quarter stock price. They consider only U.S. firms with accounting years ending in March, June, September or December, and they exclude firms with stock prices less than $1 and firms in the top and bottom 0.5% of quarterly earnings growth. They calculate corresponding quarterly stock market returns from one month prior to two months after fiscal quarter ends to capture earnings announcement effects. Using quarterly earnings and returns data as specified for a broad sample of U.S. public firms from the first quarter of 1970 through the fourth quarter of 2016, they find that:

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How does execution delay affect the performance of the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS)? These strategies each month 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)

To investigate, we compare 22 variations of each strategy with execution days ranging from end-of-month (EOM) per the baseline strategy to 21 trading days after EOM (EOM+21). For example, an EOM+5 variation computes allocations based on EOM but delays execution until the close five trading days after EOM. We include a benchmark that each month allocates 60% to SPY and 40% to TLT (60-40) to see whether variations are unique to SACEVS. We focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance statistics. Using daily dividend-adjusted closes for the above ETFs from the end of July 2002 through January 2020, we find that:

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A subscriber requested verification of findings in “Smart Money Indicator for Stocks vs. Bonds”, where 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). To verify, we simplify somewhat the approach for calculating and testing SMI, as follows:

• Use a “modern” sample of weekly Traders in Financial Futures; Futures-and-Options Combined Reports from CFTC, starting in mid-June 2006 and extending into early February 2020.
• For each asset, take Asset Manager/Institutional positions as the smart money and Non-reporting positions as the dumb money.
• For each asset, calculate weekly net positions of smart money and dumb money as longs minus shorts. 
• For each asset, use a 52-week lookback interval to calculate weekly z-scores of smart and dumb money net positions (how unusual current net positions are). This interval should dampen any seasonality.
• For each asset, calculate weekly relative sentiment as the difference between smart money and dumb money z-scores.
• For each asset, use a 13-week lookback interval to calculate recent maximum/minimum relative sentiments between smart money and dumb money for all three inputs. The original study reports that short intervals work better than long ones, and 13 weeks is a quarterly earnings interval.
• Use a 13-week lookback interval to calculate final SMI as described in “Smart Money Indicator for Stocks vs. Bonds”.

We perform three kinds of tests to verify original study findings, using 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. We calculate asset returns based on Friday closes (or Monday closes when Friday is a holiday) because source report releases are normally the Friday after the Tuesday report date, just before the stock market close. 

1. Calculate full sample correlations between weekly final SMI and both SPY and TLT total returns for lags of 0 to 13 weeks.
2. Calculate over the full sample average weekly SPY and TLT total returns by ranked tenth (decile) of SMI for each of the next three weeks after SMI ranking.
3. Test a market timing strategy that is in SPY (cash or TLT) when SMI is positive (zero or negative), with 0.1% (0.2%) switching frictions when the alternative asset is cash (TLT). We try execution at the same Friday close as report release date and for lags of one week (as in the original study) and two weeks. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics. Buying and holding SPY is the benchmark.

Using inputs as specified above for 6/16/06 through 2/7/20, we find that: Keep Reading