Momentum Investing

Does a simple volatility-based risk management approach substantially enhance performance of a Betting-Against-Beta (BAB) strategy (long stocks with low market beta and short stocks with high market beta)? In their November 2016 paper entitled “Managing the Risk of the ‘Betting-Against-Beta’ Anomaly: Does It Pay to Bet Against Beta?”, Pedro Barroso and Paulo Maio examine a BAB risk management strategy that each month weights assets by a volatility target (12% annualized) divided by daily realized strategy volatility over the previous 21 trading days. For comparison, they apply this risk management approach also to other factor strategies based on their respective daily returns. Using daily and monthly BAB returns from AQR and momentum and factor model returns from Kenneth French covering a broad sample of U.S. stocks during July 1963 through December 2015, they find that: Keep Reading

Is there pervasive yield momentum among U.S. corporate bonds? In their November 2016 paper entitled “Is Momentum Spanned Over Corporate Bonds of Different Ratings?”, Hai Lin, Chunchi Wu and Guofu Zhou investigate whether momentum exists in all segments of the U.S. corporate bond market. Their approach to momentum measurement is unconventional, involving cross-sectional regression of bond returns on multiple simple moving averages (SMA) of bond yields. They call their result “trend momentum” to distinguish it from conventional momentum based on simple past return. Specifically, they each month:

1. Calculate yield SMAs over the last 1, 3, 6, 12, 24, 36, 48 and 60 months for each bond.
2. Regress returns for all bonds on respective prior-month yield SMAs to generate correlations (betas) between returns and past yield SMAs, thereby dynamically determining relative importance of yield SMA measurement intervals.
3. Calculate expected (for next month) yield SMA betas as average calculated betas over the past 12 months.
4. Estimate expected return (for next month) for each bond based on current yield SMAs and expected yield SMA betas.
5. Rank bonds based on expected returns into fifths (quintiles) or tenths (deciles).
6. Calculate gross trend momentum factor return as the difference in average (equal-weighted) actual returns between quintiles/deciles with the highest and lowest expected returns.

Using yields, returns, ratings and other characteristics for a broad sample of U.S. corporate bonds during January 1973 through September 2015, they find that: Keep Reading

Does adjusting leverage based on lagged strategy volatility protect an industry momentum strategy from crashes? In their September 2016 paper entitled “Risk-Managed Industry Momentum and Momentum Crashes”, Klaus Grobys, Joni Ruotsalainen and Janne Aijo investigate the profitability of risk-managed industry momentum strategies. Their asset universe consists of the 49 Fama-French value-weighted industry portfolios. They focus on a conventional momentum strategy that each month takes equally weighted long positions in past winners (top eight industries) and short positions in past losers (bottom eight industries) based on cumulative returns from 12 months ago to one month ago (12-2). They also analyze 6-2 and 12-7 variations to determine whether more recent or older past returns drive results. For risk management, they forecast next-month momentum strategy volatility based on past strategy volatility calculated based on daily returns over the past one, three or six months. They apply the volatility forecasts to determine the portfolio leverage required to target constant 12% annualized volatility. Using monthly and daily returns for the 49 industries during July 1926 through September 2014, they find that: Keep Reading

Can stop-loss rules solve the stock momentum crash problem? In the September 2016 update of their paper entitled “Taming Momentum Crashes: A Simple Stop-loss Strategy”, Yufeng Han, Guofu Zhou and Yingzi Zhu test the effectiveness of a somewhat complex stop-loss rule in limiting the downside risk of a stock momentum strategy. Each month, they rank stocks into tenths (deciles) based on cumulative returns over the past six months, with the top (bottom) decile designated as winners (losers). After a skip-month, they form an equal-weighted or value-weighted portfolio that is long (short) the winners (losers) and hold for one month, except: during the holding month, when any winner (loser) stock in the portfolio falls below (rises above) the portfolio formation price by a basic stop-loss percentage threshold, they next day issue a stop-loss limit order at 1.5 times the threshold. For example, if the basic stop-loss threshold is 15%, the limit order represents an adjusted stop-loss level of 22.5%. If this order does not execute the next day and the original stop-loss threshold is still breached (not still breached) at the close, they sell at the close (repeat the process for that stock daily until the end of the month). They assume funds from any liquidations earn the U.S. Treasury bill (T-bill) yield for the balance of the month. They consider basic stop-loss thresholds of 10%, 15% and 20%. Using daily closes, highs and lows and monthly market capitalizations for a broad sample of U.S. common stocks, daily T-bill yield and monthly Fama-French three-factor (market, size, book-to-market) model returns during January 1926 through December 2013, they find that: Keep Reading

Do spot price trends drive commodity futures momentum strategies? In their August 2016 paper entitled “Momentum and Mean-Reversion in Commodity Spot and Futures Markets”, Denis Chaves and Vivek Viswanathan investigate the reasons for the success of cross-sectional (relative) momentum strategies and failure of cross-sectional mean reversion strategies in the commodity futures markets. They specify commodity valuation as the ratio of current price to average price ratio over the past 120 months (P/A). They specify commodity price trend as cumulative return over measurement intervals ranging from the last month to the last 66 months. Using two independent sets of 25 (with liquid futures) and 21 (without liquid futures) commodity spot price series as available since 1946 and one set of 27 commodity futures price series as available since 1965, all through 2014, they find that: Keep Reading

Does time series (intrinsic or absolute) momentum work across asset classes prior to the Great Moderation (secular decline in interest rates)? In their August 2016 paper entitled “Trend Following: Equity and Bond Crisis Alpha”, Carl Hamill, Sandy Rattray and Otto Van Hemert test several time series momentum portfolios as applied to groups of bonds, commodities, currencies and equity indexes as far back as 1960. They consider 10 developed country equity indexes, 11 developed country government bond series, 25 agricultural/energy/metal futures series and nine U.S. dollar currency exchange rate series. They calculate return momentum for each asset as the weighted sum of its past monthly returns (up to 11 months) divided by the normalized standard deviation of those monthly returns. They then divide each signal again by volatility and apply a gearing factor to specify a 10% annual volatility target for each holding. Within each of equity index, bond and currency groups, they weight components equally. Within commodities, they weight agriculture, energy and metal sectors equally after weighting individual commodities equally within each sector. They report strategy performance based on excess return, roughly equal to real (inflation-adjusted) return. They commence strategy performance analyses in 1960 to include an extreme bond bear market. Using monthly price series that dovetail futures/forwards from inception with preceding spot (cash) data as available starting as early as January 1950 and as late as April 1990, all through 2015, they find that: Keep Reading

Are the widely used stock characteristic/factor sorting practices of ranked fifth (quintile) or ranked tenth (decile) portfolios optimal in terms of interpretative power? In their August 2016 paper entitled “Characteristic-Sorted Portfolios: Estimation and Inference”, Matias Cattaneo, Richard Crump, Max Farrell and Ernst Schaumburg formalize the portfolio sorting process. Specifically, they describe how to choose the number of quantile portfolios best suited to source data via a trade-off between variability of outputs and effects of data abnormalities (such as outliers). They illustrate implications of the procedure for the:

• Size effect – each month sorting stocks by market capitalization and measuring the difference in value-weighted average next-month returns between small stocks and large stocks.
• Momentum effect – each month sorting stocks by cumulative return from 12 months ago to one month ago and measuring the difference in value-weighted average next-month returns between past winners and past losers.

Using monthly data for a broad sample of U.S. common stocks during January 1927 through December 2015, they find that: Keep Reading

How can investors integrate global asset class diversification, pre-eminent factor premiums and crash protection? In his July 2016 paper entitled “The Trinity Portfolio: A Long-Term Investing Framework Engineered for Simplicity, Safety, and Outperformance”, Mebane Faber summarizes a portfolio combining these three principles, as follows:

1. Global diversification: Include U.S. stocks, non-U.S. developed markets stocks, emerging markets stocks, corporate bonds, 30-year U.S. Treasury bonds, 10-year foreign government bonds, U.S. Treasury Inflation-Protected Securities (TIPS), commodities, gold and Real Estate Investment Trusts (REIT) .
2. Value/momentum screens: For U.S. stocks, each month first rank stocks by value and momentum metrics and then pick those with the highest average ranks. For non-U.S. stocks, each month pick the cheapest overall markets. For bonds, each month pick those with the highest yields.
3. Trend following for crash avoidance: For each asset each month, hold the asset (cash) if its price is above (below) its 10-month SMA at the end of the prior month.

The featured “Trinity” portfolio allocates 50% to a sub-portfolio based on principles 1 and 2 and 50% to a sub-portfolio based on principles 1, 2 and 3. Using monthly returns for the specified asset classes during 1973 through 2015, he finds that: Keep Reading

Is recent weakness in the stock return momentum anomaly, perhaps representing market adaptation to widespread anomaly exploitation, permanent or transitory? In their July 2016 paper entitled “Where Has the Trend Gone? An Update on Momentum Returns in the U.S. Stock Market”, Steven Dolvin and Bryan Foltice explore recent profitability of stock return momentum trading in the U.S. market. They each month rank stocks into tenths (deciles) based on cumulative return over the past six months and measure returns of equally weighted decile portfolios over the next 12 months (designating the strategy “6/12”), resulting in overlapping portfolios. To assess trends in momentum strategy performance, they examine average performance of decile portfolios during three subperiods: 1986-2006, 2007-2015 and 2010-2015. For robustness, they repeat some tests for 6/6, 3/6, and 3/12 ranking/holding intervals. Using monthly returns for a broad sample of U.S. stocks during July 1985 through December 2015, they find that: Keep Reading

Do global equity market behaviors support the hypothesis that intermediate-term momentum drives stock prices beyond fundamental values, thereafter driving long-term reversion? In their June 2016 paper entitled “Overreaction and the Cross-Section of Returns: International Evidence”, Douglas Blackburn and Nusret Cakici investigate whether long-term reversion is evident in global equity markets allocated to four regions: North America, Europe, Japan and Asia. They define long-term (LT) stock return as cumulative return over the last three years. They define momentum, or short-term (ST) stock return, as return from 12 months ago to one month ago. They measure LT and ST return effects in each region based on average monthly returns of hedge portfolios that are each month long (short) the equal-weighted or value-weighted ranked fifth (quintile) of stocks with highest (lowest) LT or ST returns. They designate these portfolios winners-minus-losers (WML). They examine a size effect by calculating hedge portfolio returns separately for big stocks (the largest stocks, comprising 90% of market capitalization) and small stocks (the rest) within each region. They test interactions of LT reversion with each of ST momentum, book-to-market ratio and size via hedge portfolios constructed from independent double sorts. Using monthly excess returns (in U.S. dollars relative to the U.S. Treasury bill yield) and characteristics for all stocks in the MSCI World Index, past and present, encompassing 23 developed equity markets during 1993 through 2014, they find that: Keep Reading