Equity Premium

Governments are largely insulated from market forces. Companies are not. Investments in stocks therefore carry substantial risk in comparison with holdings of government bonds, notes or bills. The marketplace presumably rewards risk with extra return. How much of a return premium should investors in equities expect? These blog entries examine the equity risk premium as a return benchmark for equity investors.

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Bond and Stock ETFs Lead-lag

Are there exploitable lead-lag relationships between bonds and stocks, perhaps because bond investors are generally better informed than stock investors or because there is some predictable stocks-bonds rebalancing cycle? To investigate, we examine lead-lag relationships between bond exchange-traded fund (ETF) returns and stock ETF returns. We consider iShares iBoxx $ Investment Grade Corporate Bond (LQD) and  iShares iBoxx $ High-Yield Corporate Bond (HYG) as liquid bond ETFs and SPDR S&P 500 (SPY) as a liquid stock ETF. Using dividend-adjusted daily, weekly and monthly returns for LQDHYG and SPY during mid-April 2007 (HYG inception) through mid-May 2016, we find that: Keep Reading

Preliminary Value Strategy Update

The home page and “Value Strategy” now show preliminary asset class ETF value strategy positions for June 2016. There may be small shifts in allocations based on final data.

SACEMS-SACEVS Mutual Diversification

Are the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) mutually diversifying. To check, we relate monthly returns for the SACEVS and the SACEMS exchange-traded fund (ETF) selections and look at the performance of an equally weighted portfolio of the two strategies, rebalanced monthly (50-50). Specifically, we consider: SACEVS Best Value paired with SACEMS Top 1; and, SACEVS Weighted paired with SACEMS Equally Weighted (EW) Top 3. Using monthly gross returns for SACEVS Best Value and SACEMS Top 1 since January 2003 and for SACEVS Weighted and SACEMS EW Top 3 since July 2006, all through April 2016, we find that: Keep Reading

Add REITs to SACEVS?

What happens if we extend the “Simple Asset Class ETF Value Strategy” (SACEVS) with a real estate risk premium, derived from the yield on equity Real Estate Investment Trusts (REIT), represented by the FTSE NAREIT Equity REITs Index? To investigate, we apply the SACEVS methodology to the following four asset class exchange-traded funds (ETF), plus cash:

3-month Treasury bills (Cash)
iShares 7-10 Year Treasury Bond (IEF)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR Dow Jones REIT (RWR)
SPDR S&P 500 (SPY)

This set of ETFs relates to four factor risk premiums: (1) the difference in yields between Treasury bills and Treasury notes/bonds indicates the term risk premium; (2) the difference in yields between corporate bonds and Treasury notes/bonds indicates the credit (default) risk premium; (3) the difference in yields between equity REITs and Treasury notes/bonds indicates the real estate risk premium; and, (4) the difference in yields between equities and Treasury notes/bonds indicates the equity risk premium. We consider two alternative strategies for exploiting premium undervaluation: Best Value, which picks the most undervalued premium; and, Weighted, which weights all undervalued premiums according to degree of undervaluation. Based on the assets considered, the principal benchmark is a monthly rebalanced portfolio of 60% stocks and 40% U.S. Treasury notes (60-40 SPY-IEF). Using lagged quarterly S&P 500 earnings, end-of-month S&P 500 Index levels and end-of-month yields for the 3-month Constant Maturity U.S. Treasury bill (T-bill), the 10-year Constant Maturity U.S. Treasury note (T-note), Moody’s Seasoned Baa Corporate Bonds and FTSE NAREIT Equity REITs Index during March 1989 through April 2016 (limited by availability of earnings data), and daily dividend-adjusted closing prices for the above four asset class ETFs during July 2002 through April 2016 (166 months, limited by availability of IEF and LQD), we find that: Keep Reading

Expert Estimates of 2016 Country Equity Risk Premiums

What are current estimates of annual premiums over risk-free rates demanded in each country by equity investors (equity risk premium, or ERP)? In their May 2016 paper entitled “Market Risk Premium Used in 71 Countries in 2016: A Survey with 6,932 Answers”, Pablo Fernandez, Alberto Ortiz and Isabel Acin summarize results of an April 2016 email survey of international finance/economic professors, analysts and company managers “about the Market Risk Premium (MRP) or Equity Premium used to calculate the required return to equity in different countries.” Based on 6,734 specific and credible responses spanning 71 countries (with at least eight such responses), they find that: Keep Reading

Factor Investing Wisdom?

How should investors think about stock factor investing? In his April 2016 paper entitled “The Siren Song of Factor Timing”, Clifford Asness summarizes his current beliefs on exploiting stock factor premiums. He defines factors as ways to select individual stocks based on such firm/stock variables as market capitalization, value (in many flavors), momentum, carry (yield) and quality. He equates factor, smart beta and style investing. He describes factor timing as attempting to predict and exploit variations in factor premiums. Based on past research on U.S. stocks mostly for the past 50 years, he concludes that: Keep Reading

Enhancing Stock Market Prediction with Distilled Economic Variables

Can investors exploit economic data for monthly stock market timing? In their September 2015 paper entitled “Getting the Most Out of Macroeconomic Information for Predicting Excess Stock Returns”, Cem Cakmaklı and Dick van Dijk test whether a model employing 118 economic variables improves prediction of monthly U.S. stock market (S&P 500 Index) excess returns based on conventional valuation ratios (dividend yield and price-earnings ratio) and interest rate indicators (risk-free rate, change in risk-free rate and credit spread). Excess return means above the risk-free rate. They each month apply principal component analysis to distill from the 118 economic variables (or from subsets of these variables with the most individual power to predict S&P 500 Index returns) a small group of independent predictive factors. They then regress next-month S&P 500 Index excess returns linearly on these factors and conventional valuation ratios/interest rate indicators over a rolling 10-year historical window to generate excess return predictions. They measure effectiveness of the economic inputs in two ways:

  1. Directional accuracy of forecasts (proportion of forecasts that accurately predict the sign of next-month excess returns).
  2. Explicit economic value of forecasts via mean-variance optimal stocks-cash investment strategies that each month range from 200% long to 100% short the stock index depending on monthly excess return predictions as specified and monthly volatility predictions based on daily index returns over the past month, with transaction costs of 0.0%, 0.1% or 0.3%.

Using monthly values of the 118 economic variables (lagged one month to assure availability), conventional ratios/indicators and monthly and daily S&P 500 Index levels during January 1967 through December 2014, they find that: Keep Reading

“Best” Equity Risk Premium

What are the different ways of estimating the equity risk premium, and which one is best? In his March 2016 paper entitled “Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2016 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 2015, he concludes that: Keep Reading

Breaking Down Smart Beta

What kinds of smart beta work best? In their January 2016 paper entitled “A Taxonomy of Beta Based on Investment Outcomes”, Sanne De Boer, Michael LaBella and Sarah Reifsteck compare and contrast smart beta (simple, transparent, rules-based) strategies via backtesting of 12 long-only smart beta stock portfolios. They assign these portfolios to a framework that translates diversification, fundamental weighting and factor investing into core equity exposure and style investing (see the figure below). They constrain backtests to long-only positions, relatively investable/liquid stocks and quarterly rebalancing, treating developed and emerging markets separately. Backtest outputs address gross performance, benchmark tracking accuracy and portfolio turnover. Using beta-related data for developed market stocks during 1979 through 2014 and emerging market stocks during 2001 through 2014, they find that: Keep Reading

Alternative Beta Index Implementations

Do alternative beta (factor-weighted) stock indexes present an exploitable advantage over traditional market capitalization weighting? In their February 2016 paper entitled “Alternative Beta Strategies”, Frank Benham, Roberto Obregon, Edmund Walsh and Timur Yontar analyze performance and practicality aspects of alternative beta stock indexes that target high value, high momentum, low volatility and high quality/profitability premiums. They also model multi-beta portfolios to assess the net benefits of beta diversification. Using monthly returns for market capitalization-weighted benchmark indexes and various alternative beta indexes as available through March 2015, they find that: Keep Reading

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The asset with the highest allocation is the holding of the Best Value strategy.
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