Equity Premium

Past research relating U.S. stock market returns to the party holding the Presidency mostly concludes that Democratic presidents are better for the stock market than Republican presidents. However, Presidents share power conferred by the electorate with Congress. Does historical data confirm that Democratic control of Congress is also better for stock market returns than Republican control of Congress? Is control of the smaller Senate more decisive than control of the House of Representatives? To check, we relate annual U.S. stock market (S&P 500 Index) returns to various combinations of party control of the Presidency, the Senate and the House of Representatives. Using party in power data and annual levels of the S&P 500 Index for December 1927 through December 2019 (92 years), we find that: Keep Reading

Are portfolios of U.S. stocks that are double-sorted to capture benefits of two complementary return anomalies attractive? In their July 2020 paper entitled “Interacting Anomalies”, Karsten Müller and Simon Schmickler test all possible double-sorted portfolios across 102 stock return anomalies (10,302 double-sorts). They employ 5×5 double-sorts, first ranking stocks into fifths (quintiles) for one anomaly and then re-sorting each of these quintiles into fifths for the second anomaly. They focus on the four “corner” portfolios involving the extreme high and low quintiles for both anomalies. They evaluate average returns, Sharpe ratios and factor model alphas of both equal-weighted (EW) and value-weighted (VW) versions of these portfolios, emphasizing performance gains from anomaly interactions. They correct for multiple hypothesis testing (data snooping bias) using the Bonferroni correction. Using trading and accounting data for a broad sample of U.S. common stocks with annual (quarterly) accounting data lagged by six (four) months during 1970 through 2017, they find that:

Keep Reading

The “Simple Asset Class ETF Value Strategy” (SACEVS) seeks diversification across a small set of asset class exchanged-traded funds (ETF), plus a monthly tactical edge from potential undervaluation of three risk premiums:

1. Term – monthly difference between the 10-year Constant Maturity U.S. Treasury note (T-note) yield and the 3-month Constant Maturity U.S. Treasury bill (T-bill) yield.
2. Credit – monthly difference between the Moody’s Seasoned Baa Corporate Bonds yield and the T-note yield.
3. Equity – monthly difference between S&P 500 operating earnings yield and the T-note yield.

Premium valuations are relative to historical averages. How might this strategy react to changes in the Effective Federal Funds Rate (EFFR)? Using end-of-month values of the three risk premiums, EFFR, total 12-month U.S. inflation and core 12-month U.S. inflation during March 1989 (limited by availability of operating earnings data) through July 2020, we find that: Keep Reading

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 seven 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 MSCI USA ETF (ESGU), with SPY as a benchmark.
5. Nuveen ESG Small-Cap ETF (NUSC), with iShares Russell 2000 ETF (IWM) as a benchmark.
6. Vanguard ESG U.S. Stock ETF (ESGV), with SPY as a benchmark.
7. 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, 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 2020, we find 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 2020 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 2019 and FFIDX and FBNDX May through April total returns and April 1-year U.S. Treasury note (T-note) yields for 1985 through 2020, we find that: Keep Reading

Is the level, or changes in the level, of Federal Reserve (Fed) holdings of U.S. Treasuries (bills, notes, bonds and TIPS, measured weekly as of Wednesday) an indicator of future stock market and/or Treasuries returns? To investigate, we take dividend-adjusted SPDR S&P 500 (SPY) and iShares Barclays 20+ Year Treasury Bond (TLT) as tradable proxies for the U.S. stock and Treasuries markets, respectively. Using weekly Fed holdings of Treasuries, SPY and TLT during mid-December 2002 through early July 2020, we find that: Keep Reading

How should investors think about the interactions between working years (retirement account contributions) and retirement years (retirement account withdrawals)? In his June 2020 paper entitled “Retirement Planning: From Z to A”, Javier Estrada integrates working and retirement periods to estimate how much an individual should save and how they should invest to achieve a desired retirement income and ultimate bequest to heirs. He illustrates his analytical solution empirically for U.S. stocks and bonds, first using a base case plus sensitivity analysis and then using Monte Carlo simulations. His base case assumes:

• Work will last 40 years with a 60%/40% stocks/bonds retirement portfolio.
• Retirement will last 30 years with beginning-of-year real (inflation-adjusted) withdrawals of $60,000 from a 40%/60% stocks/bonds retirement portfolio and ultimate bequest $300,000.

Using annual data for U.S. stocks (the S&P 500 Index total return), bonds (10-year U.S. Treasury notes) and U.S. inflation during 1928 through 2019, he finds that: Keep Reading

Most stock data sources present Total Return (TR), 100% reinvestment of dividends with no participation in firm rights issuances and share issuances/repurchases, as representative of investment performance. An alternative perspective is Total Return for All Shareholders (TRAS), the return for an investor who maintains a constant fraction of issued shares (see the table below). Can these two measures of returns to investors differ materially? In his May 2020 paper entitled “Total Return (TR) and Total Return for All Shareholders (TRAS). Difference for the Companies in the S&P 100”, Pablo Fernandez compares recent TR and TRAS for stocks in the S&P 100 as of April 2020 that have histories back to the end of 2004 (88 stocks). Using price, dividend, rights issuance and share issuance/repurchase data during December 2004 through April 2020, he finds that: Keep Reading

Is investor attraction to high-reward/high-risk (lottery) stocks a crucial contributor to stock return anomalies? In their May 2020 paper entitled “Lottery Preference and Anomalies”, Lei Jiang, Quan Wen, Guofu Zhou and Yifeng Zhu aggregate 16 measures of lottery preference into a single long-short factor via time-varying linear combination. Examples of the 16 measures are: maximum daily return last month; average of the five highest daily returns last month; difference between maximum and minimum daily returns last month; and, skewness of daily returns the past three months. They then test the ability of this lottery preference factor to help explain a set of 19 stock return anomalies previously unexplained by a widely used 4-factor (market, size, investment and profitability) model of stock returns. They further study interactions between the lottery preference factor and 11 well-known anomalies by each month during 1980-2018 double-sorting stocks first into fifths (quintiles) based on lottery preference and then within each lottery preference quintile into sub-quintiles based on each anomaly characteristic. Using firm/stock data for a broad sample of U.S. common stocks priced over $1 during January 1962 through December 2018, they find that:

Keep Reading

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