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Volatility Effects

Reward goes with risk, and volatility represents risk. Therefore, volatility means reward; investors/traders get paid for riding roller coasters. Right? These blog entries relate to volatility effects.

Best Measure of Investor Sentiment?

Is there a best measure of investor sentiment for predicting stock market returns? In his March 2016 paper entitled “Investor Sentiment and Stock Market Returns”, Lee Smales updates relationships between stock market/portfolio returns and five sentiment measures:

  1. CBOE Implied Volatility Index (VIX).
  2. Baker-Wurgler composite sentiment index (readily available only through 2012).
  3. American Association of Individual Investors (AAII) investor sentiment.
  4. University of Michigan Consumer Sentiment Index.
  5. Commitment of Traders (COT) reports from the Commodity Futures Trading Commission.

He controls for multiple economic and financial variables likely to be related to stock market returns (gross domestic product, industrial production, unemployment rate, consumer price index, Federal Funds target rate, term spread, credit spread and dividend yield). He also investigates economic recessions separately. Principal tests relate sentiment levels and changes in sentiment levels to S&P 500 Index and style/industry portfolio returns (from Kenneth French’s data library) at horizons of 1, 3, 6 and 12 months. Using monthly values of sentiment measures as available and monthly index/portfolio returns during January 1990 through December 2015, he finds that: Keep Reading

Exploiting VIX Futures Roll Return with Exchange-traded Products

Is the VIX futures roll yield (roll return) exploitable via exchange-traded products (ETPs) designed to track direct, levered or inverse VIX futures indexes? In their March 2016 paper entitled “VIX Exchange Traded Products: Price Discovery, Hedging and Trading Strategy”, Christoffer Bordonado, Peter Molnar and Sven Samdal test abilities of the seven most traded such ETPs (VXX, XIV, TVIX, UVXY, SVXY, VIXY and VXZ) to hedge the S&P 500 Index. They also propose a trading strategy designed to capture VIX futures roll yield that pairs VXX with SPDR S&P 500 Trust ETF (SPY) as a hedge and XIV with ProShares Short S&P 500 ETF (SH) as a hedge. The strategy:

  • Buys XIV+SH when nearest-month VIX futures cross above VIX by a specified relative threshold (+8%) and sells when they cross back below a threshold (+6%).
  • Buys VXX+SPY when nearest-month VIX futures cross below VIX by a specified relative threshold (-8%) and sells when they cross back above a threshold (-6%).

They test SH/SPY hedges ranging from 0% to 100% of associated XIV/VXX positions, in increments of 10% (with no rebalancing while positions are open). They assume brokerage fee 0.20% and bid-ask spread 0.15% (based on historical average) to estimate trading frictions. Using simulated daily data for the ETPs before their respective inceptions (ranging from January 2009 to October 2011) and actual daily data thereafter during late June 2006 through late April 2014, they find that: Keep Reading

Varying Risk Inversely with Recent/Expected Volatility

Can investors beat the market by avoiding high volatility and embracing low volatility? In the April 2016 version of their paper entitled “Volatility Managed Portfolios”, Alan Moreira and Tyler Muir test the performance of a simple volatility timing approach that lowers (raises) exposure to risky assets when volatility of recent returns for those assets is relatively high (low). Contrary to conventional wisdom, this volatility-managed strategy sells during panics like the Great Depression and 2008. Specifically, they construct portfolios that scale exposure to a stock factor portfolio or a currency carry trade by the inverse of expected variance. They consider widely used stock factor portfolios such as market, size, book-to-market, momentum, investment and profitability. They also consider a high-yield minus low-yield currency carry trade. They distinguish between a short-term trader, who responds the same way to all changes in volatility, and a long-term investor, who does not care about losses that predictably reverse (discount rate shocks) but does care about persistent losses (cash flow shocks). Their principal strategy scales risky holdings by the inverse of realized daily return variance over the past month, scaled up or down to have the same standard deviation as buy-and-hold. To estimate net performance, they consider trading frictions of 0.01%, 0.10% and 0.14%. Using daily and monthly factor portfolio returns from Kenneth French during 1926 or 1963 through 2015 and currency carry trade returns during 1983 through 2015, they find that: Keep Reading

Leveraging the U.S. Stock Market Based on SMA Rules

Can simple moving average (SMA) rules tell investors when it is prudent to leverage the U.S. stock market? In their March 2016 paper entitled “Leverage for the Long Run – A Systematic Approach to Managing Risk and Magnifying Returns in Stocks”, Michael Gayed and Charles Bilello augment conventional U.S. stock market SMA timing rules by adding leverage while in equities. Specifically, they test a Leverage Rotation Strategy (LRS) comprised of the following rules:

  • When the S&P 500 Total Return Index closes above its SMA, hold the index and apply 1.25X, 2X or 3X leverage to magnify returns.
  • When the S&P 500 Total Return Index closes below its SMA, switch to U.S. Treasury bills (T-bills) to manage risk.

They focus on a conventional 200-day SMA (SMA200), but include some tests with shorter measurement intervals to gauge robustness. They ignore costs of switching between stocks and T-bills. They apply targeted leverage daily with an assumed 1% annual cost of leverage, approximating current expense ratios for the largest leveraged exchange-traded funds (ETF) that track the S&P 500 Index. Using daily closes of the S&P 500 Total Return Index and T-bill yields during October 1928 through October 2015, they find that: Keep Reading

Breaking Down Smart Beta

What kinds of smart beta work best? In their January 2016 paper entitled “A Taxonomy of Beta Based on Investment Outcomes”, Sanne De Boer, Michael LaBella and Sarah Reifsteck compare and contrast smart beta (simple, transparent, rules-based) strategies via backtesting of 12 long-only smart beta stock portfolios. They assign these portfolios to a framework that translates diversification, fundamental weighting and factor investing into core equity exposure and style investing (see the figure below). They constrain backtests to long-only positions, relatively investable/liquid stocks and quarterly rebalancing, treating developed and emerging markets separately. Backtest outputs address gross performance, benchmark tracking accuracy and portfolio turnover. Using beta-related data for developed market stocks during 1979 through 2014 and emerging market stocks during 2001 through 2014, they find that: Keep Reading

Distinguishing Low-volatility from Value

Is outperformance of low-volatility stocks just a manifestation of the value premium (outperformance of stocks with high book-to-market ratios compared to stocks with low book-to-market ratios)? In his February 2016 paper entitled “The Value of Low Volatility”, David Blitz examines the interaction of the value premium with returns of long-only portfolios of low-volatility U.S. stocks over various sample periods. His low-volatility portfolios consist of the 30% of stocks with the lowest standard deviations of monthly total returns during the preceding 36 months, reformed monthly. He considers large and small stocks separately, delineated by median NYSE market capitalization, either value-weighted or equal-weighted. Using monthly data for a broad sample of U.S. stocks and the value premium during 1926 through 2014, he finds that: Keep Reading

Magnitude and Evolution of ETF Trading Frictions

Are exchange-traded funds (ETF) efficient from a trading frictions perspective? In the October 2015 version of their paper entitled “ETF Liquidity”, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti examine magnitude of trading frictions, best liquidity metric, time-variation in liquidity time and liquidity-return relationship across a large number of ETFs. Their universe consists of 870 ETFs: 571 equity (411 U.S. and 160 international),  83 bond, 17 commodity, 19 currency, 25 real estate, and 155 “other” that involve leveraged or short exposures to any asset classes. They employ the Dow Jones Industrial Average ETF (DIA) to relate ETF liquidity to underlying asset liquidity. They consider three trading friction metrics: (1) effective spread (twice the absolute difference between natural logarithms of price and bid-ask midpoint); (3) quoted spread (ask minus bid divided by midpoint); and, (3) price impact (five-minute changes in midpoint). Using tick and daily data for the selected ETFs and DIA components during 1996 through 2014, they find that: Keep Reading

Simple Volatility Harvesting?

Findings in “Add Stop-gain to Asset Class Momentum Strategy?” suggest that systematic capture of upside volatility may enhance the base strategy. Does this conclusion hold for a simpler application to a single liquid asset over a longer sample period? To investigate, we apply a stop-gain rule to SPDR S&P 500 (SPY) that: (1) exits SPY if its intra-month return exceeds a specified threshold; and, (2) re-enters SPY at the end of the month. We also look at a corresponding stop-loss rule. Using monthly unadjusted highs, lows and closes (for stop-gain and stop-loss calculations) and dividend-adjusted closes (for return calculations) for SPY during February 1993 through November 2015 (275 months), we find that:

Keep Reading

Combining Trend Following and Risk Parity across Asset Classes

Are trend following (intrinsic or time series momentum) and risk parity complementary multi-class portfolio construction approaches? In his October 2015 paper entitled “Trend-Following, Risk-Parity and the Influence of Correlations”, Nick Baltas compares performances of inverse volatility weighting and risk parity weighting as adapted to a long-short trend following strategy. Unlike volatility weighting, risk parity weighting incorporates asset return correlations, assigning higher (lower) weights to assets with lower (higher) average pairwise correlations with other assets. For both weighting schemes, portfolios are each month long (short) assets with positive (negative) past 12-month returns. Monthly inverse volatility weights derive from actual daily asset return volatilities over the past 90 trading days. Monthly risk parity weights derive from actual daily asset return volatilities and correlations over the past 90 trading days. Both weighting schemes target 10% portfolio volatility by each month applying overall leverage based on actual annualized volatility of an unleveraged trend following portfolio over the past 60 trading days divided by 10%. Using daily closing prices for the most liquid contract for each of 35 (6 energy, 10 commodity, 6 government bond, 6 currency exchange rate and 7 equity index) futures contract series as available during January 1987 through December 2013, he finds that: Keep Reading

When Low-beta Minus High-beta Works

Is there a way to predict when beta anomaly arbitrage (long low-beta stocks and short high-beta stocks will work? In the August 2015 version of their paper entitled “The Booms and Busts of Beta Arbitrage”, Shiyang Huang, Dong Lou and Christopher Polk investigate the power of a metric that measures beta arbitrage activity to predict associated returns. They measure beta via regressions of daily stock returns over the past 12 months versus daily market returns (contemporaneous and five lags to smooth unrelated effects). Each month, they rank stocks by beta and reform a hedge portfolio that is long (short) the value-weighted tenth, or decile, of stocks in the lowest (highest) betas. They measure beta arbitrage activity as the average pairwise correlation of weekly three-factor alphas (adjusting for market, size and book-to-market factors) of stocks in the lowest beta decile over the past 52 weeks. They then measure average hedge portfolio alphas for different ranges (ranked fifths, or quintiles) of beta arbitrage activity by month over long holding intervals. Using daily returns and other data for a broad sample of U.S. common stocks during 1970 through 2010, they find that: Keep Reading

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