Momentum Investing

Are stock and bond markets mutually reinforcing with respect to time series (intrinsic or absolute return) momentum? In their December 2016 paper entitled “Cross-Asset Signals and Time Series Momentum”, Aleksi Pitkajarvi, Matti Suominen and Lauri Vaittinen examine a strategy that times each of country stock and government bond (constant 5-year maturity) indexes based on past returns for both. Specifically:

• For stocks, they each month take a long (short) position in a country stock index if past returns for both the country stock and government bond indexes are positive (negative). If past stock and bond index returns have different signs, they take no position.
• For bonds, they each month take a long (short) position in a country government bond index if past return for bonds is positive (negative) and past return for stocks is negative (positive). If past stock and bond index returns have the same sign, they take no position.

They call this strategy cross-asset time series momentum (XTSMOM). For initial strategy tests, they consider past return measurement (lookback) and holding intervals of 1, 3, 6, 9, 12, 24, 36 or 48 months. For holding intervals longer than one month, they average monthly returns for overlapping positions. For most analyses, they focus on lookback interval 12 months and holding interval 1 month. For a given lookback and holding interval combination, they form a diversified XTSMOM portfolio by averaging all positions for all countries. They measure excess returns relative to one-month U.S. Treasury bills. They employ the MSCI World Index and the Barclays Capital Aggregate Bond Index as benchmarks. Using monthly stock and government bond total return indexes for 20 developed countries as available during January 1980 through December 2015, they find that: Keep Reading

Do supply chain (trade network) dynamics explain intermediate-term momentum in industry stock returns? In their December 2016 paper entitled “Feedback Loops in Industry Trade Networks and the Term Structure of Momentum Profits”, Ali Sharifkhani and Mikhail Simutin examine whether industry trading network activities create feedback that induces intermediate-term autocorrelation (echo) in associated stock returns. They apply graph theory to quantify supply-demand relationships within industry trade networks and strength of feedback loops that connect each of 49 industries to itself. They then relate network feedback strength to intermediate-term momentum (industry return from 12 months ago to seven months ago) and short-term momentum (industry return from six months ago to two months ago) for each industry as follows:

1. Each month, sort the 49 industries into thirds (terciles) by current trade network feedback strength.
2. Calculate the value-weighted average return of stocks within each industry.
3. Within each feedback strength tercile, form a hedge portfolio that is long (short) the equal-weighted fifth, or quintile, of industries with the highest (lowest) past returns over each of the two specified momentum measurement intervals.
4. Calculate average next-month return for each feedback strength-momentum double-sorted hedge portfolio.

Using industry input-output network trade data as issued (partly every five years and partly annual) and monthly industry component stock returns/capitalizations for 49 U.S. industries since 1972, and related analyst coverage data since 1984, all through December 2014, they find that: Keep Reading

Does the credit premium, measured by the difference in returns between U.S. corporate bonds and duration-matched U.S. Treasuries, exhibit momentum? In his December 2016 paper entitled “Momentum in the Cross-Section of Corporate Bond Returns”, Jeroen van Zundert tests for momentum of the volatility-adjusted credit premium among U.S. corporate bonds via the following methodology:

1. Acquire the monthly total credit premium of each corporate bond as the difference in total (coupon-reinvested) returns between the bond and a duration-matched U.S. Treasury instrument.
2. For each bond, divide cumulative total credit premium over the last six months by standard deviation of monthly credit premiums over the last 12 months (something like a Sharpe ratio).
3. After inserting a skip-month, sort all bonds on this metric into tenths (decile portfolios), with each bond weighted by the inverse of its volatility.
4. Hold each portfolio for six months, computing an overall monthly return as the average for portfolios formed within the last six months.
5. Calculate volatility-adjusted credit premium momentum as the gross difference in performance between the top (winner) and bottom (loser) decile portfolios.

To estimate portfolio alphas, he adjusts for six factors (equity market, equity size, equity value, equity momentum, bond term and default risk). In robustness tests, he considers past return measurement and holding intervals of one, three, nine and 12 months. Using total credit premiums, trading volumes and characteristics for a broad sample of U.S. investment grade and high yield corporate bonds during January 1994 through December 2015, he finds that: Keep Reading

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