One finding of “Identifying VXX/XIV Tendencies” is that shorting iPath S&P 500 VIX Short-Term Futures ETN (VXX), with crash protection, may be attractive. To investigate further, we consider applying a simple crash protection rule to three VXX shorting scenarios: (1) shorting an initial amount of VXX and letting this position ride indefinitely (Let It Ride); (2) shorting a fixed amount of VXX and resetting this fixed position monthly (Fixed Reset); and, (3) shorting an initial amount of VXX and adjusting the size of the short position monthly according to the prior-month gain or loss (Gain/Loss Adjusted). For comparison, we also test a more complex set of trend detection rules proposed by a subscriber. For tractability, we ignore trading frictions, costs of shorting and return on cash proceeds from shorting/gains. Using monthly closes for the S&P 500 Volatility Index (VIX) and both daily and monthly reverse split-adjusted closing prices for VXX from January 2009 through January 2014 (61 months), we find that: Keep Reading
March 12, 2014
March 11, 2014
“Identifying VXX/XIV Tendencies” finds that the Volatility Risk Premium (VRP), estimated as the difference between the current level of the S&P 500 implied volatility index (VIX) and the annualized standard deviation of S&P 500 Index daily returns over the previous 21 trading days (multiplying by the square root of 250 to annualize), may be a useful predictor of iPath S&P 500 VIX Short-term Futures ETN (VXX) and VelocityShares Daily Inverse VIX Short-term ETN (XIV) returns. Is there a way to exploit this predictive power? To investigate, we compare cumulative performance for: (1) buying and holding XIV; (2) timing XIV to avoid times when VRP is low; and, (3) timing XIV and VXX to exploit both high and low VRP conditions. Using daily closes for XIV, VXX, VIX and the S&P 500 Index from XIV inception (end of November 2010) through February 2014, we find that: Keep Reading
“Identifying VXX/XIV Tendencies” finds that S&P 500 implied volatility index (VIX) futures roll return, as measured by the percentage difference in settlement price between the nearest and next nearest VIX futures, may be a useful predictor of iPath S&P 500 VIX Short-term Futures ETN (VXX) and VelocityShares Daily Inverse VIX Short-term ETN (XIV) returns. Is there a way to exploit this predictive power? To investigate, we compare cumulative performance for: (1) buying and holding XIV; (2) timing XIV to avoid times when the roll return is positive; and, (3) timing XIV and VXX to exploit both negative and positive roll return conditions. Using daily closing prices for XIV and VXX and daily settlement prices for VIX futures from XIV inception (end of November 2010) through February 2014, we find that: Keep Reading
March 7, 2014
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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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
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
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
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