Equity Premium

What initial retirement portfolio withdrawal rate is sustainable over long horizons when, as currently, bond yields are well below and stock market valuations well above historical averages? In their June 2013 paper entitled “Asset Valuations and Safe Portfolio Withdrawal Rates”, David Blanchett, Michael Finke and Wade Pfau apply predictions of bond yields and stock market returns to estimate whether various initial withdrawal rates succeed over different retirement periods. They define initial withdrawal rate as a percentage of portfolio balance at retirement, escalated by inflation each year thereafter. They simulate future bond yield as a linear function of current bond yield with noise, assuming a long-term average of 5% and bounds of 1% and 10%. They simulate future U.S. stock mark return as a linear function of Cyclically Adjusted Price-to-Earnings ratio (CAPE, or P/E10), the ratio of current stock market level to average earnings over the last ten years, assuming P/E10 has a long-term average of 16.4 with noise (implying average annual return 10% with standard deviation 20%). They simulate inflation as a function of bond yield, change in bond yield, P/E10 and change in P/E10 with noise. They assume an annual portfolio management fee of 0.5%. They run 10,000 Monte Carlo simulations for each of many initial withdrawal rate scenarios, with probability of success defined as the percentage of runs not exhausting the portfolio before the end of a specified retirement period. Using initial conditions of a government bond yield of 2% and a P/E10 of 22 as of mid-April 2013, they find that: Keep Reading

Do returns for country stock markets vary systematically with the return volatilities of those markets? In their December 2012 paper entitled “Are Investors Compensated for Bearing Market Volatility in a Country?”, Samuel Liang and John Wei investigate the relationships between monthly returns and both total and idiosyncratic volatilities for country stock markets. They measure total market volatility as the standard deviation of country market daily returns over the past month. They measure idiosyncratic market volatility in two ways: (1) standard deviation of three-factor (global market, size, book-to-market ratio) model monthly country stock market return residuals over the past three years; and, (2) standard deviation of one-factor (global market) model country stock market return residuals over the past month. They then relate monthly country market raw return, global one-factor alpha and global three-factor alpha to prior-month country market volatility. Using monthly returns and characteristics for 21 developed country stock markets (indexes) and the individual stocks within those markets, and contemporaneous global equity market risk factors, during 1975 through 2010, they find that: Keep Reading

How might the capital gains tax rate affect stock market returns? First, a relatively low (high) rate might encourage (discourage) capital investment and stimulate (depress) economic growth, thereby persistently increasing (decreasing) corporate earnings and stock market returns. Second, an increase (decrease) in the rate might immediately drive lower (higher) portfolio allocations to stocks and thereby cause a temporary dip (spike) in stock market returns. To investigate, we relate the annual maximum capital gains tax rate in the U.S. to annual S&P 500 Index returns (capital gains only). When there there is a change in the tax rate during a year, we use the changed value. Using annual data for 1954 through 2012 (partial), we find that: Keep Reading

Are European stock market returns predictable? In their September 2012 paper entitled “Forecasting Returns: New European Evidence”, Steven Jordan, Andrew Vivian and Mark Wohar test the ability of fundamental, macroeconomic and technical variables to predict next-month returns in 14 developed and emerging European country stock markets both in-sample and out-of-sample. They consider four fundamental variables (using logarithms): dividend-price ratio; dividend yield; earnings-price ratio; and, dividend payout ratio (dividend-to-earnings). They consider two macroeconomic variables: the risk-free rate; and, variance of weekly stock market returns over the last 52 weeks. They consider two technical variables: monthly price pressure (ratio of number of rising stocks to number of falling stocks); and, monthly change in volume of all stocks. They test predictive power via simple linear regression, with a rolling historical window of 60 months for out-of-sample tests. They use the historical average as a benchmark forecast. To assess the economic value of forecasts, they examine whether portfolio allocations based on regression outputs beat those based on the historical average. Using monthly data for 14 European/Mediterranean stock market indexes during January 1995 (so out-of-sample tests begin in 2000) through December 2011, they find that: Keep Reading

Does a simple model based on the gap between the stock market earnings yield and an inflation-adjusted Treasury yield usefully predict the equity risk premium (ERP)? In their June 2012 paper entitled “Equities (Still) for the Long Run: A New Look at the Future Equity Premium”, Michael Crook and Brian Nick construct and test a model that compares an estimate of the future stock market earnings yield to real bond return expectations. They use the S&P 500 as a proxy for the stock market. They estimate the future stock market earnings yield as the inverse of Shiller’s cyclically adjust price-earnings ratio (P/E10). They use nominal Treasury yields with duration matched to forecast horizon and adjust this yield with inflation expectations from the Federal Reserve Bank of Cleveland. They apply simple inception-to-date linear regression to relate forecasted ERP to actual ERP. Using monthly S&P 500 Index total returns, Shiller’s P/E10 data, Treasury yields (10-year, 5-year and 2-year notes and bills) and the Cleveland Federal Reserve’s Index of Inflation (limiting the start of the sample period) during 1982 through April 2012, they find that: Keep Reading

Is price-to-earnings ratio cyclically adjusted via a 10-year average (CAPE, or P/E10) a good predictor of future stock market performance? In his October 2012 paper entitled “The Enhanced Risk Premium Factor Model & Expected Returns”, Javier Estrada examines three simple models that generate 10-year annualized stock market expected return (ER) based on P/E10 and the risk-free rate (R_f). Specifically, the three models hypothesize that ER is:

1. The product of a linear function of P/E10 and R_f:  ER = (a + b * P/E10) * R_f
2. The sum of independent linear functions of P/E10 and R_f:  ER = c + d * P/E10 + e * R_f
3. A simple linear function of P/E10:  ER = f + g * P/E10

…where parameters a, b, c, d, e, f and g derive from monthly regressions over a rolling historical window of 120 months. He assesses the performance of the models by comparing forecasted and actual future 10-year annualized stock market returns. He uses the S&P 500 as a proxy for the stock market. Using monthly S&P 500 earnings and 10-year Treasury note yields (as the risk-free rate) for December 1949 through December 2001 and monthly S&P 500 Index total returns from December 1959 through December 2011, he finds that: Keep Reading

How can positions in emerging equity markets benefit investment portfolios? In their October 2012 paper entitled “How Large are the Benefits of Emerging Market Equities?”, Mitchell Conover, Gerald Jensen and Robert Johnson examine the returns of emerging equity markets with focus on: (1) performance measures that account for return distribution risk and abnormalities; (2) performance by region; and, (3) effects of global economic/monetary environment on returns and diversification power. Using monthly local-currency and dollar-denominated stock index returns and annual GDP estimates for 20 emerging markets as available, along with monthly returns for MSCI developed market MSCI indexes (including MSCI World and MSCI USA) for comparison, during January 1976 through December 2010, they find that: Keep Reading

Which equity markets worldwide offer the best reward-to-risk ratios? In their October 2012 paper entitled “Risk-Adjusted Performances of World Equity Indices”, Yigit Atilgan and Ozgur Demirtas investigate whether 52 developed and emerging market equity indexes compensate investors equally based on reward-to-risk ratios. They consider three reward-to-risk ratios: (1) conventional Sharpe ratio based on monthly return and standard deviation of daily returns over the past 100 trading days; (2) Sharpe ratio substituting non-parametric value at risk (VAR) (magnitude of minimum daily return over past 100 trading days) for standard deviation of returns; and, (3) Sharpe ratio substituting parametric VAR (accounting for return distribution skewness and kurtosis) for standard deviation of returns. The latter two variations of the Sharpe ratio emphasize downside risk and return distribution non-normalities. Using daily returns and monthly total returns in local currencies for 28 developed and 24 emerging market value-weighted indexes, and associated risk-free rates, during January 1973 through December 2011 (38 years), they find that: Keep Reading

How should investors view the predictability of stock market returns and volatility? In sections 5 and 6 of the July 2012 version of his draft chapter entitled “Equity Market Level”, Andrew Ang examines the predictability of the equity risk premium and equity market volatility. He also addresses the exploitability of any predictive power found. Using both theoretical arguments and empirical tests based on long-run data through December 2011, he concludes that: Keep Reading

How does consideration of return distribution tails (not just linear correlations) affect assessment of global equity diversification benefits? In their May 2012 paper entitled “Is the Potential for International Diversification Disappearing? A Dynamic Copula Approach”, Peter Christoffersen, Vihang Errunza, Kris Jacobs and Hugues Langlois examine the evolution of equity market diversification benefits based on a methodology that accommodates non-linearity in the relationship between return streams. They focus on differences between developed and emerging markets. Using weekly returns in U.S. dollar for 16 developed markets during January 1973 through mid-June 2009, 13 emerging markets during late January 1989 through July 2008 and 17 emerging markets during July 1995 through mid-June 2009, they find that: Keep Reading