Equity Premium

A subscriber asked how the “Simple Asset Class ETF Value Strategy” (SACEVS) performs when interest rates rise. This strategy seeks to exploit relative valuation of the term risk premium, the credit (default) risk premium and the equity risk premium via exchange-traded funds (ETF). To investigate, because the sample period available for mutual funds is much longer than that available for ETFs, we use instead data from “SACEVS Applied to Mutual Funds”. Specifically, each month we reform a Best Value portfolio (picking the asset associated with the most undervalued premium, or cash if no premiums are undervalued) and a Weighted portfolio (weighting assets associated with all undervalued premiums according to degree of undervaluation, or cash if no premiums are undervalued) using the following four assets:

• Monthly average 3-Month Treasury Bill (T-bill) Secondary Market Rate as an approximation of the return on Cash.
• Vanguard GNMA Investor Shares (VFIIX).
• Vanguard Long-Term Investment Grade Investor Shares (VWESX).
• Vanguard US Growth Investor Shares (VWUSX).

The benchmark is a monthly rebalanced portfolio of 60% stocks and 40% U.S. Treasuries (60-40 VWUSX-VFIIX). We use the T-bill yield as the short-term interest rate (SR) and the 10-year Constant Maturity U.S. Treasury note (T-note) yield as the long-term interest rate (LR). We say that each rate rises or falls when the associated average monthly yield increases or decreases during the SACEVS holding month. Using monthly risk premium estimates, SR and LR, and Best Value and Weighted returns during June 1980 through June 2017 (444 months), we find that:

Keep Reading

Is the Capital Asset Pricing Model (CAPM), which relates the return of an asset to its non-diversifiable risk, called beta, worth learning? In his June 2017 paper (provocatively) entitled “Is It Ethical to Teach That Beta and CAPM Explain Something?”, Pablo Fernandez tackles this question. Based on the body of relevant research, he concludes that:

Keep Reading

Can investors predict the return of a stock from its relationship with the dispersion of returns across all stocks? In their May 2017 paper entitled “Building Efficient Portfolios Sensitive to Market Volatility”, Wei Liu, James Kolari and Jianhua Huang examine a 2-factor model which predicts the return on a stock based on its sensitivity to (1) the value-weighted stock market return (beta risk) and (2) the standard deviation of value-weighted returns for all stocks (zeta risk). They first each month estimate zeta for each stock via regressions of daily data over the past year. They then rank stocks by zeta into quantile portfolios and calculate next-month equal-weighted returns across these portfolios and various long-short combinations of these portfolios (hedge portfolios) to measure dependence of future returns on zeta. Finally, they generate performance data for aggregate zeta risk portfolios by adding value-weighted market index returns to returns for each of the long-short zeta-sorted portfolios. Using daily and monthly returns for a broad sample of U.S. stocks in the top 90% of market capitalizations for that year, monthly equity market returns and monthly U.S. Treasury bill yields as the risk-free rate during January 1965 through December 2015, they find that: Keep Reading

Are there opportunities to trade S&P 500 Index additions in the current market environment? In her May 2017 paper entitled “The Diminished Effect of Index Rebalances”, Konstantina Kappou examines returns for S&P 500 Index additions before and after the 2008 financial crisis. She focuses on additions because deletions generally involve confounding information such as restructuring, bankruptcy or merger. Current index management practices are to announce changes after market hours about five days in advance (announcement date – AD) and to implement changes at the specified close (event date – ED). She investigates returns during an event window from 15 trading days before AD through 252 trading days after ED. She calculates abnormal returns as differences between returns for added stocks and contemporaneous market returns. She considers 276 index additions during January 2002 through November 2013, with October 2008 separately pre-crisis from post-crisis. She excludes 48 of the additions due to lack of data or confounding information. Using daily returns for the remaining 228 S&P 500 Index additions during the specified sample period, she finds that: Keep Reading

Are equity sector and factor investing complementary? In their May 2017 paper entitled “Factors vs. Sectors in Asset Allocation: Stronger Together?”, Marie Briere and Ariane Szafarz compare efficient sector investing (diversifying economic risks) and efficient factor investing (diversifying across risk factors) for U.S. stocks, and then assess advantages of combining the two approaches. They first construct two efficient frontiers (sets of portfolios with the highest expected returns across the range of volatilities), one from 10 sectors and the other from 10 factors. Their sector set consists of long-only portfolios covering (1) non-durable consumer goods, (2) durable consumer goods, (3) manufacturing, (4) energy, (5) technology, (6) telecommunications, (7) shops, (8) health care, (9) utilities and (10) other. Their factor set consists of the long and short portfolios separately for (1) size, (2) book-to-market, (3) momentum, (4) profitability and (5) investment. They consider six scenarios consisting of three samples (full period, crisis subperiods and non-crisis subperiods) for long-only and long-short efficient portfolios. They define crises by combining NBER recession dates and Forbes Magazine bear market dates. Using monthly returns for sectors and factors as specified from Kenneth French’s data library and the broad market, along with yields for 1-month U.S. Treasury bills as the risk-free rate, during July 1963 through December 2016, they find that: Keep Reading

Can investors improve retirement glide paths via judicious use of smart beta funds? In their March 2017 paper entitled “Life Cycle Investing and Smart Beta Strategies”, Bill Carson, Sara Shores and Nicholas Nefouse augment a conventional equities-bonds life cycle investing glide path with smart beta strategies. They use a conventional glide path, which gradually decreases the allocation to equities with age to a constant after retirement, to determine target risk levels over the life cycle. When the investor is young, they tilt equities toward the MSCI USA Diversified Multiple-Factor (DMF) Index to boost returns via value, size momentum and quality beta exposures. As the investor approaches retirement, they shift equities to the MSCI USA Minimum Volatility Index, designed to match the market return at lower risk. For bonds, they use the Barclays Constant Weights Index, which has greater diversification and higher Sharpe ratio than a conventional market capitalization-based bond index. They incorporate the specified smart beta indexes into the glide path via a procedure that maximizes Sharpe ratio while matching the risk of the conventional glide path. Specifically, they: (1) deviate no more than 3% from conventional glide path risk; (2) constrain smart beta equities beta relative to the Russell 1000 Index and the MSCI World Index ex U.S. to within 5% of the benchmark equities beta; (3) constrain smart beta bond index duration to within 0.05 years of the benchmark bonds duration; and, (4) require at least 1% allocation to bonds for all target date portfolios. Using monthly data for conventional capitalization-weighted U.S. equity and bond indexes and for the specified smart beta indexes during 2007 through 2016, they find that: Keep Reading

Are anomaly premiums (expected winners minus losers among assets within a class, based on some asset characteristic) more or less predictable than broad market returns? In their April 2017 paper entitled “Predicting Relative Returns”, Valentin Haddad, Serhiy Kozak and Shrihari Santosh apply principal component analysis to assess the predictability of premiums for published asset pricing anomalies spanning stocks, U.S. Treasuries and currencies. For tractability, they simplify asset classes by forming portfolios of assets within them, as follows:

• For stocks, they consider the long and short legs of portfolios reformed monthly into tenths (deciles) based on each of the characteristics associated with 26 published stock return anomalies (monthly data for 1973 through 2015).
• They sort zero-coupon U.S. Treasuries by maturity from one to 15 years to assess term premiums (yield data for 1985 through 2014).
• They sort individual exchange rates into five portfolios reformed daily based on interest rate differentials with the U.S. to assess the carry trade premium (daily data as available for December 1975 through December 2016).

Using the specified data, they find that: Keep Reading

Is there a straightforward way to interpret the state of the yield curve as a manifestation of how efficiently the economy is processing information? In his March 2017 paper entitled “Simple New Method to Predict Bear Markets (The Entropic Linkage between Equity and Bond Market Dynamics)”, Edgar Parker Jr. presents and tests a way to understand interaction between bond and equity markets based on arrival and consumption of economic information. He employs Shannon entropy to model the economy’s implied information processing ratio (R/C), with interpretations as follows:

1. R/C ≈ 1: healthy continuously upward-sloping yield curve when information arrival and consumption rates are approximately equal.
2. R/C >> 1: low end of the yield curve inverts when information is arriving much faster than it can be consumed.
3. R/C << 1: high end of the yield curve inverts when information is arriving much slower than it can be consumed.

Under the latter two conditions, massive information loss (entropy growth) occurs, and firms cannot confidently plan. These conditions delay/depress economic growth and produce equity bear markets. He tests this approach by matching actual yield curve data with standardized (normal) R and C distributions that both have zero mean and standard deviation one (such that standardized R and C may be negative). Using daily yields for U.S. Treasuries across durations and daily S&P 500 Index levels during 1990 through 2016, he finds that: Keep Reading

Does disentangling measures of stock illiquidity and market capitalization (size) support belief in an illiquidity premium (a reward for holding illiquid assets)? In the December 2016 version of their paper entitled “The Value of True Liquidity”, Robin Borcherding and Michael Stein investigate this question by controlling the most widely used stock illiquidity metric for size. Specifically they define and calculate true stock liquidities by:

• Calculating for each stock the conventional Amihud monthly measure of illiquidity (average absolute price impact of dollar trading volume during a month).
• Capture unexplained residuals from a regression that controls for the linear relationship (negative correlation) between this conventional illiquidity metric and size.
• Sorting stocks by size and capturing more detail regression residuals within size ranges to control for the non-linear relationship between conventional illiquidity and size.

They then form double-sorted portfolios to compare interactions of conventional and true liquidity with stock volatility and size. Using daily returns, trading data and characteristics for 4,739 U.S. common stocks during January 1990 through September 2015, they find that: Keep Reading

How do widely studied anomalies relate to representative stocks-bonds portfolio returns (rather than the risk-free rate)? In his March 2017 paper entitled “Understanding Anomalies”, Filip Bekjarovski proposes an approach to asset pricing wherein a representative portfolio of stocks and bonds is the benchmark and stock anomalies are a set of investment opportunities that may enhance the benchmark. He therefore employs benchmark-adjusted returns, rather than excess returns, to determine anomaly significance. Specifically, his benchmark portfolio captures the equity, term and default premiums. He considers 10 potentially enhancing anomalies: size, value, profitability, investment, momentum, idiosyncratic volatility, quality, betting against beta, accruals and net share issuance. He estimates each anomaly premium as returns to a portfolio that is each month long (short) the value-weighted tenth, or decile, of stocks with the highest (lowest) expected returns for that anomaly. He assesses the potential of each anomaly in three ways: (1) alphas from time series regressions that control for equity, term and default premiums; (2) performances during economic recessions; and, (3) crash proneness. He measures the attractiveness of adding anomaly premiums to the benchmark portfolio by comparing Sharpe ratios, Sortino ratios and performances during recessions of five portfolios: (1) a traditional portfolio (TP) that equally weights equity, term and default premiums; (2) an equal weighting of size, value and momentum premiums (SVM) as a basic anomaly portfolio; (3) a factor portfolio (FP) that equally weights all 10 anomaly premiums; (4) a mixed portfolio (MP) that equally weights all 13 premiums; and, (5) a balanced portfolio (BP) that equally weights TP and FP. Using monthly returns for the 13 premiums specified above from a broad sample of U.S. stocks and NBER recession dates during July 1963 through December 2014, he finds that: Keep Reading