Does the stock momentum anomaly interact with the quarterly financial cycle? In his August 2014 paper entitled “Seasonal Patterns in Momentum and Reversal in the U.S. Stock Market: The Consequences of Tax-Loss Sales and Window Dressing”, David Brown examines whether tax-loss selling and window dressing at the ends of calendar quarters affect U.S. stock momentum strategy returns. Each month, he ranks stocks by returns over the last 12 months, skipping the last month to avoid reversal, and then forms a momentum hedge portfolio that is long (short) the capitalization-weighted tenth of stocks with the highest (lowest) past returns, making the long and short sides of the portfolio equal in magnitude. He then measures how this portfolio performs by calendar month to check for end-of-quarter effects. He also investigates whether the level of capital losses among stocks in the portfolio affects performance. Using monthly returns for NYSE, AMEX and NASDAQ common stocks, along with contemporaneous risk-free rates and Fama-French model risk factor returns, during January 1927 through December 2013, he finds that: Keep Reading
September 15, 2014
Is the capitalization-weighted market portfolio a lame benchmark? In his August 2014 paper entitled “It’s Easy to Beat the Market”, Moshe Levy tests the perception that it is hard to beat a capitalization-weighted portfolio and therefore that an index so weighted is a challenging benchmark. Specifically, he compares the gross risk-adjusted performance of a capitalization-weighted buy-and-hold portfolio to those of 1,000 random-weighted (normalized to 100%) buy-and-hold portfolios of the same stocks.To ensure liquidity, he restricts the portfolios to the 500 U.S. stocks with the largest market capitalizations at the beginning of 1998. If a stock is delisted during the sample period due to merger/acquisition or bankruptcy, he sets its weight to zero at that point and renormalizes residual portfolios to 100% [per an email exchange with the author]. He focuses on Sharpe ratio and terminal value of an initial investment as key performance metrics. He ignores trading frictions, arguing that no trading is involved other than initial purchases at portfolio formation and reinvestment of dividends. Using daily total (dividend-reinvested) returns for the specified stocks and the contemporaneous 30-day U.S. Treasury bill yield as the risk-free rate during January 1998 through December 2012, he finds that: Keep Reading
September 12, 2014
Below is a weekly summary of our research findings for 9/8/14 through 9/12/14. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.
September 12, 2014
What is the best estimate of the Equity Risk Premium (ERP), the return in excess of the risk-free rate required as compensation for the risk of holding equity? In his August 2014 paper entitled “A History of the Equity Risk Premium and its Estimation”, Basil Copeland summarizes recent ERP estimates and explains how the historical equity return can overstate ERP in terms of unanticipated (anomalous) capital gains. He further describes the behavior of historical and expected ERP during 1872 through 2013 and estimates ERP for 2014 through 2023. He discusses ERP estimation issues such as geometric mean versus arithmetic mean and top-down versus bottom-up forecasts. Using data from Shiller for 1871-1959 and from Damodaran for 1960-2013, he finds that: Keep Reading
Do financial markets adapt to widespread use of an indicator, such as Bollinger Bands, thereby extinguishing its informativeness? In the August 2014 version of their paper entitled “Popularity versus Profitability: Evidence from Bollinger Bands”, Jiali Fang, Ben Jacobsen and Yafeng Qin investigate the effectiveness of Bollinger Bands as a stock market trading signal before and after its introduction in 1983. They focus on bands defined by 20 trading days of prices to create the middle band and two standard deviations of these prices to form upper and lower bands. They consider two trading strategies based on Bollinger Bands:
- Basic volatility breakout, which generates buy (sell) signals when price closes outside the upper (lower) band.
- Squeeze refinement of volatility breakout, which generates buy (sell) signals when band width drops to a six-month minimum and price closes outside the upper (lower) band.
They assess the popularity (and presumed level of use) of Bollinger Bands over time based on a search of articles from U.S. media in the Factiva database. They evaluate the predictive power of Bollinger Bands across their full sample sample and three subsamples: before 1983, 1983 through 2001, and after 2001. Using daily levels of 14 major international stock market indexes (both the Dow Jones Industrial Average and the S&P 500 Index for the U.S.) from initial availabilities (ranging from 1885 to 1971) through March 2014, they find that: Keep Reading
How much performance improvement comes from rebalancing a stocks-bonds portfolio, and what specific rebalancing approach works best? In their August 2014 paper entitled “Testing Rebalancing Strategies for Stock-Bond Portfolios Across Different Asset Allocations”, Hubert Dichtl, Wolfgang Drobetz and Martin Wambach investigate the net performance implications of different rebalancing approaches and different rebalancing frequencies on portfolios of stocks and government bonds with different weights and in different markets. With buy-and-hold as a benchmark, they consider three types of rebalancing rules: (1) strict periodic rebalancing to target weights; (2) threshold rebalancing, meaning periodic rebalancing to target weights if out-of-balance by 3% or more; and, (3) range rebalancing, meaning periodic rebalancing to plus (minus) 3% of target weights if above (below) target weights by more than 3%. They consider annual, quarterly and monthly rebalancing frequencies. They use 30 years of broad U.S., UK and German stock market, bond market and risk-free returns to construct simulations with 10-year investment horizons. Their simulation approach preserves most of the asset class time series characteristics, including stocks-bonds correlations. They assume round-trip rebalancing frictions of 0.15% (0.10% for stocks and 0.05% for bonds). Using monthly returns for country stock and bonds markets and risk-free yields during January 1982 through December 2011 to generate 100,000 simulated 10-year return paths, they find that: Keep Reading
Does focus on nearest-expiration contracts in commodity futures momentum strategies leave money on the table? In their May 2014 paper entitled “Exploiting Commodity Momentum Along the Futures Curves”, Wilma De Groot, Dennis Karstanje and Weili Zhou investigate commodities futures momentum strategies that consider all available contract expirations. They hypothesize that a broadened contract universe could increase roll yield, reduce volatility and lower portfolio turnover. Their generic benchmark strategy each month buys (sells) the equally weighted half of commodities with the highest (lowest) 12-month returns using nearest-expiration contracts. They consider three alternatives to the generic strategy:
- Optimal-roll momentum: each month ranks commodities in the same way as the generic strategy, but buys the most backwardated contract for each winner commodity and sells the most contangoed contract for each loser commodity from among contracts with expirations up to 12 months.
- All-contracts momentum: each month first select for each commodity the contract expiration with the strongest and weakest momentum. Then rank the commodities based on these contracts and buy (sell) the equally weighted half with the highest (lowest) momentum.
- Low-turnover roll momentum: modify the optimal-roll momentum strategy by holding each position until it is about to expire or until it switches sides (long-to-short or short-to-long), whichever occurs first.
They assume fully collateralized portfolios, such that total monthly return for each position is change in month-end settlement price plus the risk-free interest rate (U.S. Treasury bill yield) earned by the collateral. They focus on changes in settlement prices (excess returns). They consider several ways of estimating trading frictions. Using daily and monthly prices of S&P GSCI components during January 1990 through September 2011 (initially 18 commodity series growing to all 24 by July 1997), they find that: Keep Reading
A subscriber asked which exchange-traded fund (ETF) asset class proxies make the best safe havens for the U.S. stock market as proxied by the S&P 500 Index. To investigate, we consider the the following 12 ETFs as potential safe havens:
Utilities Select Sector SPDR ETF (XLU)
SPDR Dow Jones REIT ETF (RWR)
iShares 20+ Year Treasury Bond (TLT)
iShares 7-10 Year Treasury Bond (IEF)
iShares 1-3 Year Treasury Bond (SHY)
iShares Core US Aggregate Bond (AGG)
iShares TIPS Bond (TIP)
SPDR Gold Shares (GLD)
PowerShares DB Commodity Tracking ETF (DBC)
United States Oil (USO)
iShares Silver Trust (SLV)
PowerShares DB G10 Currency Harvest ETF (DBV)
We consider three ways of testing these ETFs as safe havens for the U.S. stock market based on daily, weekly and monthly return measurement intervals:
- Contemporaneous return correlation with the S&P 500 Index during all market conditions.
- Return/performance during S&P 500 Index bear markets as specified by the index being below its 200-day/40-week/10-month simple moving average (SMA) for the prior measurement interval.
- Return/performance during S&P 500 Index bear markets as specified by the index being in drawdown from a prior high-water mark by more than some percentage (baseline -10%) for the prior measurement interval.
Using daily, weekly and monthly dividend-adjusted closing prices for the 12 ETFs from their respective inceptions through July 2014, and contemporaneous daily, weekly and monthly levels of the S&P 500 Index from 10 months before the earliest ETF inception through July 2014, we find that: Keep Reading