Volatility Effects

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

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

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

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

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

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

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

Are higher even moments of asset return distributions useful predictors of future returns? In the September 2015 version of her paper entitled “A Low-Risk Strategy based on Higher Moments in Currency Markets”, Claudia Zunft explores an adaptive currency trading strategy that exploits the predictive power of higher even moments of forward currency exchange rate returns. The strategy is each month long (short) the equally weighted fifth, or quintile, of currencies with the lowest (highest) higher even return moments relative to recent past levels. For each currency, she first computes 13 even daily return moments over the last month (versus the U.S. dollar) ranging from 4 to 100 and then subtracts from these moments their respective average monthly values over lookback intervals of 12, 24, 36, 48 and 60 months and inception-to-date. From the resulting 78 combinations of moments and lookback intervals, she each month selects the combination with the highest average excess portfolio return over the last three months. For comparison, she also tests long-short quintile carry trade (high interest rate currencies minus low interest rate currencies) and momentum (high prior-month return currencies minus low prior month currencies) portfolios. Using bid, ask and mid-quote spot and forward contract (maturities up to a year) exchange rates versus the U.S. dollar for 20 of the most liquid developed and emerging market currencies as reliably available during December 1989 through October 2014, she finds that: Keep Reading

Is there a “trick” to good results for risk parity backtests? In their April 2014 brief research paper entitled “The Risks of Risk Parity”, the Brandes Institute examines the sustainability of a critical performance driver for the risk parity asset allocation approach. This approach weights asset classes such that their expected contributions to overall portfolio risk (volatility) are equal, generally by shifting conventional portfolio weights substantially from equities to bonds. Using hypothetical portfolio performance during 1994 through 2013 and bond yield data during 1871 through 2013, they find that: Keep Reading

How can investors best exploit research showing that low-beta (high-beta) stocks tend to outperform (underperform)? In their August 2015 paper entitled “Low-Beta Investment Strategies”, Olaf Kornz and Laura-Chloe Kuntz test 32 “zero-cost” (long-short) strategies designed to exploit the beta anomaly as applied to S&P 500 stocks. The strategies derive from three choices: (1) length of the rolling window used to calculate stock and market index betas (one, three, six or 12 months of daily returns); (2) portfolio holding period (12 months or three months); and, (3) portfolio tilt method (four alternatives). The four portfolio tilt alternatives are:

• Long Low-Beta/Short Index – long the equally weighted 50 stocks with the lowest betas, offset by a short position in the S&P 500 Index. 
• Long-Short Low-High Beta Equal Weight – equal dollar values for a long side of the equally weighted 50 stocks with the lowest betas and a short side of the equally weighted 50 stocks with highest betas, plus a small position in the market to offset any long-short beta mismatch.
• Long-Short Low-High Beta Matched – a long side of the equally weighted 50 stocks with the lowest betas and a short side of the equally weighted 50 stocks with highest betas, in different dollar amounts determined to produce a portfolio beta of one.
• Short High-Beta/Long Index – short the equally weighted 50 stocks with the highest betas, offset by a long position in the S&P 500 Index.

Portfolio tilt alternatives also include positions in 1-month U.S. Treasury bill futures to satisfy an exact zero-cost assumption. The authors form portfolios monthly, such that there are 12 (four) overlapping portfolios with 12-month (3-month) holding periods. They also test the ability of the betas of aggregate high-beta stocks and aggregate low-beta stocks to predict market returns. Using daily returns for the S&P 500 index and its component stocks during September 1988 through October 2014, they find that: Keep Reading