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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.

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 asset class exchange-traded funds (ETF), plus cash:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR Dow Jones REIT (RWR) through September 2004 dovetailed with Vanguard REIT ETF (VNQ) thereafter
SPDR S&P 500 (SPY)

This set of ETFs relates to four risk premiums, as specified below: (1) term; (2) credit (default); (3) real estate; and, (4) equity. We focus on the effects of adding the real estate risk premium on Compound annual growth rates (CAGR) and Maximum drawdowns (MaxDD) 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 and FTSE NAREIT Equity REITs Index during March 1989 through August 2018 (limited by availability of earnings data), and monthly dividend-adjusted closing prices for the above asset class ETFs during July 2002 through August 2018 (194 months, limited by availability of TLT and LQD), we find that: Keep Reading

Stock Market Timing Using P/E SMA Signals

A subscriber proposed four alternative ways of timing the U.S. stock market based on simple moving averages (SMA) of the market price-earnings ratio (P/E), as follows:

  1. 5-Year Binary – hold stocks (cash) when P/E is below (above) its 5-year SMA.
  2. 10-Year Binary – hold stocks (cash) when P/E is below (above) its 10-year SMA.
  3. 15-Year Binary – hold stocks (cash) when P/E is below (above) its 15-year SMA.
  4. 5-Year Scaled – hold 100% stocks (cash) when P/E is five or more units below (above) its 5-year SMA. Between these levels, scale allocations linearly.

To obtain a sample long enough for testing these rules, we use the monthly U.S. data of Robert Shiller. While offering a very long history, this source has the disadvantage of blurring monthly data as averages of daily values. How well do these alternative timing strategies work for this dataset? Using monthly data for the S&P Composite Index, annual dividends, annual P/E and 10-year government bond yield since January 1871 and monthly 3-month U.S. Treasury bill (T-bill) yield as return on cash since January 1934, all through August 2018, we find that: Keep Reading

Best Profitability Metric for Predicting Stock Returns?

Is there a best way for investors to measure firm profitability for global stock selection? In their August 2018 paper entitled “Constructing a Powerful Profitability Factor: International Evidence”, Matthias Hanauer and Daniel Huber investigate which measure of firm profitability best predicts associated stock returns. They consider six measures: return on equity; gross profitability; operating profitability calculated in two ways; cash-based operating profitability (excluding accruals); and, cash-based gross profitability (also excluding accruals). They construct a long-short profitability factor for each measure and test its power to predict stock returns both standalone and in combination with other kinds of factors (market, size, book-to-market, momentum, investment and accruals) and the other profitability factors. Using monthly returns and annual accounting data for non-financial common stocks in 49 countries (excluding the U.S.) during July 1989 through June 2016, they find that: Keep Reading

Actual Global Stock Trading Frictions

How, and how well, do institutional equity traders manage global stock trading frictions? In the April 2018 draft of their paper entitled “Trading Costs”, Andrea Frazzini, Ronen Israel and Tobias Moskowitz examine the real-world trading frictions of a large trader. They define trading frictions as the difference in results between a theoretical portfolio with zero frictions and a practical tracking portfolio with frictions. They account for all components of trading frictions: broker commissions, bid-ask spreads and price impacts of trading. They record market price at trade initiation, volume traded and execution price for each share traded, as well as type of trade (buy long, buy-to-cover, sell long or sell short). They describe how frictions vary by trade type, stock characteristics, trade size, time and exchange. Based on preliminary findings, they devise and test out-of-sample a price impact model based on market conditions, stock characteristics and trade size calibrated to actual U.S. and international trades. Using $1.7 trillion of orders and trade execution data from a large institutional money manager spanning 21 developed equity markets during August 1998 through June 2016, they find that: Keep Reading

Federal Reserve Treasuries Holdings and Asset Returns

Is the level, or changes in the level, of Federal Reserve (Fed) holdings of U.S. Treasuries (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 August 2018, we find that: Keep Reading

Timing the Dividend Risk Premium

Do stock dividends exhibit exploitable risk premiums? In their July 2018 paper entitled “A Model-Free Term Structure of U.S. Dividend Premiums”, Maxim Ulrich, Stephan Florig and Christian Wuchte construct a term structure of the dividend risk premium and test strategies to time this premium at specific horizons. They specify dividend risk premium as the spread between:

  • Expected dividend growth rate based on analyst 1-year and 2-year S&P 500 dividend forecasts, extended by analyst 5-year earnings growth estimates assuming constant future payout ratio.
  • Expected dividend growth rate derived from equity index put and call option prices across different maturities.

They model an S&P 500 dividend capture portfolio for a given horizon as: long an S&P 500 Index put option of maturity matching the horizon; short an index call option of same maturity and strike price; long the index; and, short the money market in an amount matched to the option strike price. They test two strategies for capturing this premium at a 12-month horizon: (1) each month (last trading day) reform and hold the dividend capture portfolio; or, (2) each month reform and hold the dividend capture portfolio only when the dividend risk premium is positive (analyst-estimated dividends are higher than options-implied dividends). They model the risk-free rate/money market rate across horizons using the U.S. Dollar Overnight Index Swap rate for one day to 10 years. For the S&P 500 Index, they assume annual expense ratio 0.07% and 0.01% average bid-ask spread. For options, they estimate trading frictions with actual bid-ask spreads. Using S&P 500 Index/options and analyst forecast data as specified during January 2004 through October 2017, they find that:

Keep Reading

Bringing Order to the Factor Zoo?

From a purely statistical perspective, how many factors are optimal for explaining both time series and cross-sectional variations in stock anomaly/stock returns, and how do these statistical factors relate to stock/firm characteristics? In their July 2018 paper entitled “Factors That Fit the Time Series and Cross-Section of Stock Returns”, Martin Lettau and Markus Pelger search for the optimal set of equity factors via a generalized Principal Component Analysis (PCA) that includes a penalty on return prediction errors returns. They apply this approach to three datasets:

  1. Monthly returns during July 1963 through December 2017 for two sets of 25 portfolios formed by double sorting into fifths (quintiles) first on size and then on either accruals or short-term reversal.
  2. Monthly returns during July 1963 through December 2017 for 370 portfolios formed by sorting into tenths (deciles) for each of 37 stock/firm characteristics.
  3. Monthly excess returns for 270 individual stocks that are at some time components of the S&P 500 Index during January 1972 through December 2014.

They compare performance of their generalized PCA to that of conventional PCA. Using the specified datasets, they find that: Keep Reading

Avoiding Negative Stock Market Returns

Is there an exploitable way to predict when short-term stock market return will be negative? In his June 2018 paper entitled “Predictable Downturns”, Carter Davis tests a random forest regression-based forecasting model to predict next-day U.S. stock market downturns. He uses the value-weighted return of a portfolio of the 10 U.S. stocks with the largest market capitalizations at the end of the prior year minus the U.S. Treasury bill (T-bill) yield as a proxy for excess market return. He employs a two-step test process:

  1. Use a rolling 10-year historical window of 143 input variables (economic, equity factor, market volatility, stock trading, calendar) to find when the probability of negative portfolio daily excess return is at least 55%.
  2. Calculate whether the average portfolio gross excess return of all such days is in fact significantly less than zero.

He corrects for data snooping bias associated with the modeling approach. He further investigates which input variables are most important and tests a market timing strategy that holds the 10-stock portfolio (T-bills) when predicted portfolio return is negative (non-negative) as specified above. Using data for the input variables and returns for test portfolio stocks during July 1926 through July 2017, he finds that: Keep Reading

SACEVS with Quarterly Allocation Updates

Do quarterly allocation updates for the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS) work as well as monthly updates? These strategies 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)

Changing from monthly to quarterly allocation updates does not sacrifice information about lagged quarterly S&P 500 Index earnings, but it does sacrifice currency of term and credit premiums. To assess alternatives, we compare cumulative performances and the following key metrics for quarterly and monthly allocation updates: gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD) and annual returns and volatilities. Using monthly dividend-adjusted closes for the above ETFs during September 2002 (earliest alignment of months and quarters) through June 2018, we find that:

Keep Reading

Excluding Bad Stock Factor Exposures

The many factor-based indexes and exchange-traded funds (ETFs) that track them now available enable investors to construct multi-factor portfolios piecemeal. Is such piecemeal construction suboptimal? In their July 2018 paper entitled “The Characteristics of Factor Investing”, David Blitz and Milan Vidojevic apply a multi-factor expected return linear regression model to explore behaviors of long-only factor portfolios. They consider six factors: value-weighted market, size, book-to-market ratio, momentum, operating profitability and investment(change in assets). Their model generates expected returns for each stock each month, and further aggregates individual stock expectations into factor-portfolio expectations holding all other factors constant. They use the model to assess performance differences between a group of long-only single-factor portfolios and an integrated multi-factor portfolio of stocks based on combined rankings across factors. The focus on gross monthly excess (relative to the 10-year U.S. Treasury note yield) returns as a performance metric. Using data for a broad sample of U.S. common stocks among the top 80% of NYSE market capitalizations and priced at least $1 during June 1963 through December 2017, they find that: Keep Reading

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