Momentum Investing

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

What is the best way to combine styles (smart betas) in one portfolio? In their June 2016 paper entitled “Long-Only Style Investing: Don’t Just Mix, Integrate”, Shaun Fitzgibbons, Jacques Friedman, Lukasz Pomorski and Laura Serban compare two approaches to long-only combined equity style investing:

1. Mixed portfolio – simply picks stocks from single-style portfolios.
2. Integrated portfolio – first combines single-style rankings into an overall score for each stock and then builds a portfolio based on top overall scores.

They focus on combining momentum stocks (highest return from 12 months ago to one month ago) and value stocks (high book-to-market ratio). They first employ simulated data to illustrate differences in stock selection between the two approaches. They then compare net performances for equally weighted, monthly rebalanced mixed and integrated combinations of liquid global stocks. Using monthly data for large-capitalization stocks from developed markets (roughly the MSCI World Index components) during February 1993 through December 2015, they find that: Keep Reading

Does timing of factor premiums work? In his June 2016 paper entitled “My Factor Philippic”, Clifford Asness addresses three critiques of the exploitability of stock factor premiums:

1. Most factors are currently very overvalued (expected premiums are small), perhaps because of crowded bets on them.
2. Factor portfolios may therefore crash.
3. In fact, increasing factor valuations account for most of the historical premium (there are no essential premiums).

He considers five long-short factors: (1) value based on book-to-price ratio (B/P); (2) value based on sales-to-price ratio (S/P); (3) momentum (total return from 12 months ago to one month ago); (4) profitability (gross profits-to-assets); and, (5) betting-against-beta (long leveraged low-beta assets and short high-beta assets). He calculates each factor premium as average return to a capitalization-weighted portfolio that is each month long (short) the third of large-capitalization U.S. stocks with the best (worst) expected returns based on that factor. He estimates the time-varying valuation of a factor via a value spread, the ratio of the capitalization-weighted B/P (or S/P) of the long side of the factor portfolio to that of its short side. He tests a simple factor timing strategy that holds no position if the factor’s value spread is at its historical median and scales linearly up (down) to a 100% (-100%) position in the factor portfolio as the factor’s value spread increases to its 95th (decreases to its 5th) historical percentile. The initial look-back interval is 20 years (such that testing begins in 1988), expanding as more data become available. Using the specified factor premium data for January 1968 through January 2016, he finds that: Keep Reading

Is nearness to 52-week highs or lows informative about future stock returns? In their June 2016 paper entitled “Nearness to the 52-Week High and Low Prices, Past Returns, and Average Stock Returns”, Li-Wen Chen and Hsin-Yi Yu examine the power of extreme price levels (52-week highs and lows) to predict stock returns, and whether any such predictive power is distinct from the momentum effect. They focus on the left (right) tail of nearness to 52-week low (high), because these stocks may attract the most investor attention. They determine 52-week highs and lows with monthly data. Specifically, they each month form value-weighted portfolios that are:

1. Long the bottom 10% and short the top 90% of stocks sorted on nearness to 52-week low.
2. Long the top 10% and short the bottom 90% of stocks sorted on nearness to 52-week high.
3. For comparison, long the top 10% and short bottom 10% based on returns from 12 months ago to one month ago (momentum strategy).

Using monthly prices (ignoring dividends) for a broad sample of non-financial common U.S. stocks and monthly factor portfolio returns during July 1962 through December 2014, they find that: Keep Reading