Equity Premium

Do analysts systematically ignore the connection between future firm earnings and current economic conditions? In their July 2024 paper entitled “Predicting Analysts’ S&P 500 Earnings Forecast Errors and Stock Market Returns Using Macroeconomic Data and Nowcasts”, Steven Sharpe and Antonio Gil de Rubio Cruz examine the quality of bottom-up forecasts of near-term S&P 500 earnings aggregated from analyst forecasts across individual firms. Specifically, they:

• Model expected aggregate S&P 500 quarterly earnings growth as a function of GDP growth, output and wage inflation and change in dollar exchange rate. They also consider a simplified model based only on real GDP growth and change in the dollar exchange rate.
• Calculate the gap between modeled S&P 500 earnings growth and analyst-forecasted growth.
• Estimate how well this forecast gap predicts analyst forecast errors.
• Test the extent to which the forecast gap predicts S&P 500 Index total returns.

Using quarterly actual and forecasted S&P 500 earnings, S&P 500 Index total return and values for the specified economic variables during 1993 through 2023, they find that: Keep Reading

Has ease of access to, and processing of, firm accounting data suppressed stock anomalies by leveling the information playing field? In their July 2024 paper entitled “The Effect of New Information Technologies on Asset Pricing Anomalies”, David Hirshleifer and Liang Ma test the effects of mandating Electronic Data Gathering, Analysis and Retrieval (EDGAR) during April 1993 to May 1996 and eXtensible Business Reporting Language (XBRL) during 2009 to 2011 on well-known stock return anomalies attributed to mispricing. EDGAR makes firm accounting data available electronically, and XBRL reduces the cost of processing such data by making it machine readable. They focus on eight anomalies, five of which rely on accounting data (accruals, net operating assets, investment-to-assets ratio, asset growth and gross profitability) and three of which rely on market data (momentum, net stock issuance and composite equity issuance). They examine effects of EDGAR/XBRL implementations on each anomaly individually, on the five accounting anomalies in aggregate and on the three non-accounting anomalies in aggregate. They carefully consider EDGAR/XBRL implementation dates and fiscal years by firm to compare anomalies for implemented and non-implemented sets of stocks. Using firm characteristics and monthly returns for a broad sample of U.S. common stocks during July 1992 through June 1997 (July 2009 through June 2012) for the EDGAR (XBRL) sample, they find that: Keep Reading

What is the distribution of U.S. common stock outcomes over the past century? In the July 2024 draft of his paper entitled “Which U.S. Stocks Generated the Highest Long-Term Returns?”, Hendrik Bessembinder presents cumulative returns and compound annual growth rates (CAGR) for all 29,078 publicly listed U.S. common stocks in the Center for Research in Security Prices (CRSP) databases through 2023, from initial appearance in CRSP until delisting or the end of the sample period. He assumes immediate reinvestment of all dividends. Using daily price/dividend data for all U.S. common stocks during December 1925 through December 2023, he finds that:

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“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 equal-weighted combinations of the top two and top three sector ETFs, along with corresponding robustness tests and benchmarks. Using monthly dividend-adjusted closing prices for the sector ETFs and SPDR S&P 500 ETF Trust (SPY), 3-month U.S. Treasury bill (T-bill) yield and S&P 500 Index level during December 1998 through June 2024, we find that: Keep Reading

A subscriber suggested review of the February 2017 paper “Bubbles for Fama”, in which Robin Greenwood, Andrei Shleifer and Yang You assess Eugene Fama’s claim that stock prices do not exhibit bubbles. They define a bubble candidate as a value-weighted U.S. industry or international sector that rises over 100% in both raw and net of market returns over the prior two years, as well as 50% or more raw return over the prior five years. They define a crash as a 40% drawdown within a two-year interval. They also look at characteristics of industry/sector portfolios identified bubble candidates, including level and change in volatility, level and change in turnover, firm age, return on new versus old companies, stock issuance, book-to-market ratio, sales growth, price-earnings ratio and price acceleration (abruptness of price run-up). They evaluate timing strategies that switch from an industry portfolio to either the market portfolio or cash (with risk-free yield) based on a price run-up signal, or a signal that combines price run-up and other characteristics. Their benchmark is buying and holding the industry portfolio. Using value-weighted returns for 48 U.S. industries (based on SIC code) during January 1926 through March 2014 and for 11 international sectors (based on GICS codes) during October 1985 through December 2014, they find that:

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Do exchange-traded funds selecting stocks based on environmental, social, and governance characteristics (ESG ETF) typically offer attractive performance? To investigate, we compare performance statistics of eight ESG ETFs, all currently available, to those of simple and liquid benchmark ETFs, as follows:

1. iShares MSCI USA ESG Select ETF (SUSA), with SPDR S&P 500 ETF Trust (SPY) as a benchmark.
2. iShares MSCI KLD 400 Social ETF (DSI), with SPY as a benchmark.
3. iShares ESG MSCI EM ETF (ESGE), with iShares MSCI Emerging Markets ETF (EEM) as a benchmark.
4. iShares ESG Aware MSCI EAFE ETF (ESGD), with iShares MSCI EAFE ETF (EFA) as a benchmark
5. iShares ESG MSCI USA ETF (ESGU), with SPY as a benchmark.
6. Nuveen ESG Small-Cap ETF (NUSC), with iShares Russell 2000 ETF (IWM) as a benchmark.
7. Vanguard ESG U.S. Stock ETF (ESGV), with SPY as a benchmark.
8. Vanguard ESG International Stock ETF (VSGX), with Vanguard FTSE All-World ex-US Index Fund ETF (VEU) as a benchmark.

We focus on average return, standard deviation of returns, reward/risk (average return divided by standard deviation of returns), compound annual growth rate (CAGR) and maximum drawdown (MaxDD), all based on monthly data. Using monthly dividend-adjusted returns for all specified ETFs since inceptions and for all benchmarks over matched sample periods through June 2024, we find that: Keep Reading

What happens if we extend the “Simple Asset Class ETF Value Strategy” (SACEVS) with a utilities risk premium, derived from the yield on Utilities Select Sector SPDR Fund (XLU)? To investigate, we apply the SACEVS methodology to the following asset class exchange-traded funds (ETF), plus cash:

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

This set of ETFs relates to four risk premiums, as specified below: (1) term; (2) credit (default); (3) utilities; and, (4) equity. We focus on effects of adding the utilities risk premium on gross compound annual growth rates (CAGR), maximum drawdowns (MaxDD) and annual Sharpe ratios of the Best Value (picking the most undervalued premium) and Weighted (weighting all undervalued premiums according to degree of undervaluation) versions of SACEVS. Using lagged quarterly S&P 500 earnings, monthly S&P 500 Index levels and monthly yields for 3-month U.S. Treasury bill (T-bill), the 10-year Constant Maturity U.S. Treasury note (T-note), Moody’s Seasoned Baa Corporate Bonds since March 1989 (limited by availability of earnings data), XLU prices and dividends since December 1998 (inception) and monthly dividend-adjusted closing prices for the above asset class ETFs since July 2002, all through May 2024, we find that: Keep Reading

Does the S&P 500 Index (SPX) or the CBOE Volatility Index (VIX) yield better short-term trading signals for stocks and VIX futures? In the May 2024 revision of his paper entitled “Chicken and Egg: Should you use the VIX to time the SPX? Or use the SPX to time the VIX?”, Robert Hanna explores mutual predictive relationships between SPX and VIX, with an eye toward exploitation via market timing strategies. He considers several long-term trend indicators to investigate whether SPX or VIX data offers better SPX return predictions. He considers two types of short-term overbought/oversold predictive rules: (1) short-term relative strength index (RSI) readings of 2, 3 and 4 days; and, (2) short-term high and low readings of 5 to 25 days in length. He applies both sets of short-term rules separately to SPX and VIX to predict movements of SPX and VIX futures. Using daily SPX and VIX levels since 1990 and short-term VIX futures prices since 2007, all through 2023, he finds that: Keep Reading

“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 2024 Investment Company Fact Book data tables 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 2023 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 2024, we find that: Keep Reading

Are the industry membership of a firm, as designated by Standard Industrial Classification (SIC) code, and the position of the firm within its industry good predictors of the performance of its stock? In their May 2024 paper entitled “Decoding Cross-Stock Predictability: Peer Strength versus Firm-Peer Disparities”, Doron Avramov, Shuyi Ge, Shaoran Li and Oliver Linton devise the following two industry related stock metrics and test their abilities to predict stock returns:

1. Peer Index (PI) – calculated for each firm via a multi-input, inception-to-date regression to predict next-month stock return, replacing firm characteristics by the contemporaneous average values for all firms in its industry as inputs.
2. Peer-Deviation Index (PDI) – calculated for each firm via a multi-input, inception-to-date regression to predict next-month stock return using firm characteristics minus the contemporaneous average values of these characteristics for all firms in its industry as inputs (indicating the standing of the firm within its industry).

Inputs consist of 94 firm-specific characteristics and 8 industry-related characteristics, organized into six groups: momentum, value versus growth, investment, profitability, trading frictions and intangibles. Using monthly values for the selected 102 firm/industry characteristics and monthly returns for common stocks in the top 80% of AMEX/NYSE/NASDAQ  market capitalizations during January 1980 through March 2022, they find that: Keep Reading