Big Ideas

Some experts use the mean-variance analysis of Modern Portfolio Theory (MPT), which penalizes large upside volatility, to measure portfolio efficiency. Others use Second-order Stochastic Dominance (SSD) analysis, purer mathematically than MPT but open to unrealistic investor behavior. Is there a better way? In the February 2012 version of his paper entitled “The Passive Stock Market Portfolio is Highly Inefficient for Almost All Investors”, Thierry Post describes and tests a portfolio efficiency measure based on an Almost Second-order Stochastic Dominance (ASSD) that aims to exclude unrealistic investor behaviors. He applies the measure to a market portfolio (value-weighted average of NYSE, AMEX and NASDAQ stocks) and three alternative sets of ten equity portfolios formed using NYSE decile breakpoints for: (1) market capitalization (size); (2) book-to-market ratio; and, (3) past 11-month return with skip month (momentum). He considers investment horizons of one, 12 and 120 months over sample periods of 1926-2011 and 1963-2011. Using monthly value-weighted returns and contemporaneous stock/firm characteristics from July 1926 through December 2011 (1,026 months), along with the contemporaneous one-month Treasury bill yield as the risk-free rate, he finds that: Keep Reading

Do the poor returns and high volatility of the “buy-and-hold-is-dead” U.S. stock market since the beginning of 2000 represent a tailwind for market timers? In other words, is buy-and-hold effective as a benchmark for distinguishing between market timer luck and skill in recent years? To check, we measure the performances of various simple monthly market timing approaches (equal weighting with cash, 10-month simple moving average signals, momentum, and coin-flipping) during the 2000s. Using monthly closes for the dividend-adjusted S&P 500 Depository Receipts (SPY), the 3-month Treasury bill (T-bill) yield and the S&P 500 Index from December 1999 through October 2011 (earlier for S&P 500 Index signal calculations), we find that: Keep Reading

How can long-term investors maximize their edge of strategic patience? In their November 2011 paper entitled “Investing for the Long Run”, Andrew Ang and Knut Kjaer offer advice on successful long-term investing (such as by pension funds).  They define a long-term investor as one having no material short-term liabilities or liquidity demands. Using the California Public Employee’s Retirement System and other large institutions as examples, they conclude that: Keep Reading

How long does it take stock markets to revert to their long-run means? In their April 2010 paper entitled “Mean Reversion in International Stock Markets: An Empirical Analysis of the 20 th Century”, Laura Spierdijk, Jacob Bikker and Pieter van den Hoek analyze mean reversion in 17 developed countries (Australia, Belgium, Canada, Denmark, France, Germany, Ireland, Italy, Japan, the Netherlands, Norway, South-Africa, Spain, Sweden, Switzerland, United Kingdom and the United States) over 109 years based on annual data. Using annual levels of 17 country stock market indexes and a composite worldwide index during 1900 through 2008, they find that: Keep Reading

The conventional binary animal metaphor for markets is bull (good returns, low volatility) and bear (poor returns, high volatility). Does rigorous analysis of empirical evidence support belief in (just) two market states? In their September 2011 paper entitled “The Number of Regimes Across Asset Returns: Identification and Economic Value”, Mathieu Gatumel and Florian Ielpo apply a regime-switching model and Monte Carlo simulations to determine the likely number of regimes implicit in the returns of 19 asset classes. Their general approach is to increase the number of regimes included in the model until adding a regime no longer materially improves the fit of the model to the actual return distribution. In other words, among statistically equivalent models, they always choose the one with the smallest number of regimes. They discuss the persistence of and performance under each regime discovered.  Using weekly return data for various stock  and bond indexes, currency exchange rates and commodity indexes over the period April 1998 through mid-December 2010 (650 weeks or 12.5 years), they find that: Keep Reading

Analyses such as those described in “Demographic Headwind for U.S. Stock Market?” and “Classic Research: Demography and the Stock Market” assess the impact of demographic changes on the stock market by focusing on market valuation as measured by price-earnings ratio (P/E). What story would a more direct analysis of demographics and stock market returns tell? To investigate, we: (1) collect historical U.S. age demographics; (2) construct an annual series of the ratio of middle-age cohort (ages 40–49) population to the old-age cohort (ages 60–69) population (designated M/O, similar to the metric described in “Demographic Headwind for U.S. Stock Market?”) to capture the joint behavior of presumed equity investors and equity disinvestors; and, (3) relate M/O to annual U.S. stock market returns. Using estimated annual (July 1) age demographics for 1900-2009, 2010 census age demographics, annual S&P 500 Index returns (June 30 to June 30) for 1950 through 2011, annual Dow Jones Industrial Average (DJIA) returns (June 30 – June 30) for 1929 through 2011 and annual Consumer Price Index (CPI) data (June) for 1913 through 2011, we find that: Keep Reading

Will disinvestment of the baby boom generation retard U.S. equities? In their August 2011 letter entitled “Boomer Retirement: Headwinds for U.S. Equity Markets?”, flagged by a reader, Zheng Liu and Mark Spiegel revisit the relationship between U.S. age demographics and U.S. equity valuation as indicated by the lagged price-earnings ratio (P/E). They calculate P/E based on year-end level of the S&P 500 Index adjusted for inflation and inflation-adjusted S&P 500 earnings over the prior 12 months. They specify a critical demographic metric, M/O, based on the ratio of the middle-age cohort (ages 40–49) to the old-age cohort (ages 60–69), epitomizing equity investors and equity disinvestors, respectively. Using annual data for 1954 through 2010, they find that: Keep Reading

Are financial market returns from extreme outlier days mostly good or bad for investors? Is the occurrence of such days usefully predictable? In his August 2011 paper entitled “Where the Black Swans Hide & The 10 Best Days Myth”, Mebane Faber examines the effects and predictability of daily market return outliers. Using daily returns for the broad U.S. stock market for September 1928 through December 2010 and shorter samples through 2010 for 15 other country stock markets (as in “The (Worldwide) Futility of Market Timing?”), he finds that: Keep Reading

When do simple moving averages (SMA) serve as useful trading rules? Do they exploit some hidden pattern in asset price behavior? In their July 2011 paper entitled “The Trend is not Your Friend! Why Empirical Timing Success is Determined by the Underlying’s Price Characteristics and Market Efficiency is Irrelevant “, flagged by a subscriber, Peter Scholz and Ursula Walther investigate the relationship between the performance of technical trend-following rules and the characteristics (statistics) of the target asset return series. They use timing rules based on SMAs of different intervals (5, 10, 20, 38, 50, 100 and 200 trading days) as examples of trend-following rules. They consider the effects on SMA rule performance of variations in four asset price series statstics: the first-order trend (drift); return autocorrelation (return persistence); volatility of returns; and, volatility autocorrelation (volatility persistence/clustering). Analyses are long-only and ignore trading frictions, dividends, return on cash and buffering tactics such as stop-loss. They use a robust array of risk and performance measures to compare SMA rule performance to a buy-and-hold approach. Using both simulated price series and ten years of daily prices (2000-2009) for 35 country stock market indexes, they find that: Keep Reading

Financial markets sometimes switch states (regimes), with key investment decision statistics (such as average return and volatility of returns) shifting dramatically for extended intervals. A simple example of financial market regimes is the designation of bull and bear stock market states, estimated (for example) by a broad index being above or below its long-interval simple moving average. What is the big picture on the concepts, estimation and application of regime changes in investing? In their June 2011 paper entitled “Regime Changes and Financial Markets”, Andrew Ang and Allan Timmermann review the basics of modeling regime switches and applying such models to asset allocation decisions. Drawing on prior theoretical and empirical research, they conclude that: Keep Reading