Investing Research Articles
March 7, 2014 - Weekly Summary
Below is a weekly summary of our research findings for 3/3/14 through 3/7/14. 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.
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March 7, 2014 - Commodity Futures, Volatility Effects
A subscriber inquired about strategies for trading exchange-traded notes (ETN) constructed from near-term S&P 500 Volatility Index (VIX) futures: iPath S&P 500 VIX Short-Term Futures ETN (VXX) and VelocityShares Daily Inverse VIX Short-Term (XIV), available since 1/30/09 and 11/30/10, respectively. The managers of these securities buy and sell VIX futures daily to maintain a constant maturity of one month (long for VXX and short for XIV), continually rolling partial positions from the nearest term contract to the next nearest. We consider four potential predictors of the price behavior of these securities:
- The level of VIX, in case a high (low) level indicates a future decrease (increase) in VIX that might affect VXX and XIV.
- The change in VIX, in case there is some predictable reversion or momentum for VIX that might affect VXX and XIV.
- The term structure of VIX futures (roll return) underlying VXX and XIV, as measured by the percentage difference in settlement price between the nearest and next nearest VIX futures, indicating a price headwind or tailwind for a fund manager continually rolling from one to the other. Roll return is usually negative (contango), but occasionally positive (backwardation).
- The Volatility Risk Premium (VRP), estimated as the difference between VIX and the annualized standard deviation of daily S&P 500 Index returns over the past 21 trading days (multiplying by the square root of 250 to annualize), in case this difference between expectations and recent experience indicates the direction of future change in VIX.
We identify predictive power by relating daily VXX and XIV returns over the next 21 trading days to daily values of each indicator. Using daily levels of VIX, settlement prices for VIX futures contracts, levels of the S&P 500 Index and split-adjusted prices for VXX and XIV from inceptions of the ETNs through February 2014, we find that: Keep Reading
March 6, 2014 - Momentum Investing, Size Effect, Value Premium
Are there parallels at the country stock market level of the size, value and momentum effects observed for individual stocks? In their January 2014 paper entitled “Value, Size and Momentum across Countries”, Adam Zaremba and Przemysław Konieczka investigate country-level value, size and momentum premiums. They measure these factors at the country level as:
- Value (V): book-to-market ratio of country stocks aggregated via the weighting scheme used to construct the country stock index at the time of portfolio formation.
- Size (S): total market capitalization of country stocks at the time of portfolio formation.
- Long-Term Momentum (LTM): country index return during the 12 months before portfolio formation.
- Short-Term Momentum (STM): country index return during the month before portfolio formation.
They calculate these factors using either MSCI equity indexes (47 indexes available at the beginning of the sample period) or local stock indexes (only 24 indexes available at the beginning of the sample period). They measure the country-level premium for each factor as the return on an equally weighted portfolio that is each month long (short) the 30% of countries with the highest (lowest) expected returns for that factor. They fully collateralize short sides with reserves in the risk-free rate. They also calculate a total market return as the capitalization-weighted average return across all country markets. They perform calculations separately in U.S. dollars, euros and yen. Using monthly firm/stock data for listed stocks as available within 66 countries from the end of May 2000 through November 2013, and contemporaneous Fama-French model U.S. factors, they find that: Keep Reading
March 5, 2014 - Momentum Investing, Size Effect, Technical Trading, Value Premium
Does the variation of individual stock returns with liquidity support an investment style? In the January 2014 update of their paper entitled “Liquidity as an Investment Style”, Roger Ibbotson and Daniel Kim examine the viability and distinctiveness of a liquidity investment style and investigate the portfolio-level performance of liquidity in combination with size, value and momentum styles. They define liquidity as annual turnover, number of shares traded divided by number of shares outstanding. They hypothesize that stocks with relatively low (high) turnover tend to be near the bottom (top) of their ranges of expectation. Their liquidity style thus overweights (underweights) stocks with low (high) annual turnover. They define size, value and momentum based on market capitalization, earnings-to-price ratio (E/P) and past 12-month return, respectively. They reform test portfolios via annual sorts into four ranks (quartiles), with initial equal weights and one-year holding intervals. Using monthly data for the 3,500 U.S. stocks with the largest market capitalizations (re-selected each year) over the period 1971 through 2013, they find that: Keep Reading
March 4, 2014 - Technical Trading
A reader observed: “One of the problems with simple moving average (SMA) crossing rules is the churning from random price movements across the average. Lars Kestner proposes improvements to SMA crossing rules that signal:
- BUY when: (1) the close crosses over an SMA of the highs (rather than the closes); and, (2) the SMA of the closes is greater today than yesterday.
- SELL when the close crosses below an SMA of the lows (rather than the closes).
These rules create a self-adaptive band around the SMA to identify true trends rather then noise, while retaining most of the responsiveness of daily measurements.” Do these buffered SMA crossing rules outperform pure rules that simply buy (sell) on crossovers (crossunders) based on daily closes? To check, we compare the terminal values from pure and buffered rules for a 200-day SMA (SMA200) applied to both the Dow Jones Industrial Average (DJIA) and its exchange traded fund (ETF) proxy, SPDR Dow Jones Industrial Average (DIA). Using daily highs, lows and closes for DJIA since October 1928 and DIA since January 1998, both through early February 2014, and the contemporaneous 3-month Treasury bill yield as the return on cash, we find that: Keep Reading
March 3, 2014 - Calendar Effects
“Intraday U.S. Stock Market Behavior” examines behavior of the S&P 500 Index at 15-minute intervals over the trading day during each of 2007 (bullish year) and 2008 (bearish year), finding slight tendencies for market weakness during mid-afternoon and market volatility at the beginning and the end of the trading day. Does recent data confirm these findings? To investigate, we calculate average cumulative returns and standard deviations of returns for both the S&P 500 Index and SPDR S&P 500 (SPY) measured at 5-minute intervals during the trading day over the last six months. Using 5-minute levels/prices for the S&P 500 Index and for SPY during 9:30-16:00 over the period August 2012 through February 2014, we find that: Keep Reading
March 3, 2014 - Momentum Investing
Does a stronger stock price trend, up or down, indicate a bigger momentum effect? In their February 2014 paper entitled “Trend Salience, Investor Behaviors and Momentum Profitability”, Paul Docherty and Gareth Hurst test variations of a conventional stock momentum strategy that consider both past returns and rate of change of past returns relative to other stocks. Specifically, each year they reform a universe of the 500 stocks listed on the Australian Stock Exchange with the largest market capitalizations. Then each month, they rank stocks in the current universe based on past cumulative returns, designating the top fifth (quintile) as winners and bottom quintile as losers. They then further categorize each winner (loser) stock as salient if the ratio of its geometric mean return over the past 3, 6 or 9 months to its geometric mean return over the past 12 months is greater (less) than the quintile median of this ratio. Finally, they each month form equally weighted momentum and salience portfolios (with a skip-month between ranking and portfolio formation) and hold for overlapping intervals of 3, 6, 9 or 12 months. These portfolios include:
- Conventional momentum: long (short) the winners (losers).
- Salient momentum: long (short) salient winners (salient losers).
- Non-salient momentum: long (short) non-salient winners (non-salient losers).
Using monthly return data for the specified Australian stocks during January 1992 through December 2011, they find that: Keep Reading
February 28, 2014 - Calendar Effects, Fundamental Valuation, Momentum Investing
We have updated the S&P 500 Market Models summary as follows:
- Extended Market Models regressions/rolled projections by one month based on data available through February 2014.
- Updated Market Models backtest charts and the market valuation metrics map based on data available through February 2014.
We have updated the Trading Calendar to incorporate data for February 2014.
We have updated the the monthly asset class momentum winners and associated performance data at Momentum Strategy.
February 28, 2014 - Weekly Summary
Below is a weekly summary of our research findings for 2/24/14 through 2/28/14. 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.
Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list.
February 28, 2014 - Momentum Investing
The home page and “Momentum Strategy” now show preliminary asset class momentum strategy positions for March 2014. Differences in past returns between the first and second places and the third and fourth places are small enough that they could change order by the close.