Momentum Investing
Do financial market prices reliably exhibit momentum? If so, why, and how can traders best exploit it? These blog entries relate to momentum investing/trading.
May 18, 2020 - Momentum Investing, Size Effect, Value Premium, Volatility Effects
Are there equity styles that tend to perform relatively well during and after stock market crashes? In their April 2020 paper entitled “Equity Styles and the Spanish Flu”, Guido Baltussen and Pim van Vliet examine equity style returns around the Spanish Flu pandemic of 1918-1919 and five earlier deep U.S. stock market corrections (-20% to -25%) in 1907, 1903, 1893, 1884 and 1873. They construct three factors by:
- Separating stocks into halves based on market capitalization.
- Sorting the big half only into thirds based on dividend yield as a value proxy, 36-month past volatility or return from 12 months ago to one month ago. They focus on big stocks to avoid illiquidity concerns for the small half.
- Forming long-only, capitalization-weighted factor portfolios that hold the third of big stocks with the highest dividends (HighDiv), lowest past volatilities (Lowvol) or highest past returns (Mom).
They also test a multi-style strategy combining Lowvol, Mom and HighDiv criteria (Lowvol+) and a size factor calculated as capitalization-weighted returns for the small group (Small). Using data for all listed U.S. stocks during the selected crashes, they find that: Keep Reading
April 3, 2020 - Momentum Investing, Value Premium
Do stock anomaly (factor premium) portfolios exhibit exploitable value and momentum? In their February 2020 paper entitled “Value and Momentum in Anomalies”, Deniz Anginer, Sugata Ray, Nejat Seyhun and Luqi Xu investigate exploitability of time variation in the predictive ability of 13 published U.S. stock accounting and price-based anomalies based on: (1) anomaly momentum (1-month premiums); and/or (2) anomaly value (adjusted average book-to-market ratios). Specifically, they each month:
- For each anomaly, form a value-weighted portfolio that is long (short) the tenth, or decile, of stocks with the highest (lowest) expected returns.
- For each long-short anomaly portfolio:
- Measure its value as last-year average book-to-market ratio minus its average of average book-to-market ratios over the previous five years.
- Measure its momentum as last-month return.
- Form a value portfolio of anomaly portfolios that holds the equal-weighted top seven based on value, rebalanced annually.
- Form a momentum portfolio of anomaly portfolios that holds the equal-weighted top seven based on momentum, rebalanced monthly.
- Form a combined value-momentum portfolio of anomaly portfolios that holds those in the top seven of both value and momentum, equal-weighted and rebalanced monthly.
Their benchmark is the equal-weighted, monthly rebalanced portfolio of all anomaly portfolios (1/N). Using data required to construct anomaly portfolios and monthly delisting-adjusted returns for U.S. common stocks excluding financial stocks and stocks priced under $1 during January 1975 through December 2014, they find that: Keep Reading
March 27, 2020 - Calendar Effects, Momentum Investing
Are some calendar months more likely to exhibit stock market continuation or reversal than others, perhaps due to seasonal or fund reporting effects? In other words, is intrinsic (times series or absolute) momentum an artifact of some months or all months? To investigate, we relate U.S. stock index returns for each calendar month to those for the preceding 3, 6 and 12 months. Using monthly closes of the S&P 500 Index since December 1927 and the Russell 2000 Index since September 1987, both through January 2020, we find that: Keep Reading
March 10, 2020 - Momentum Investing, Sentiment Indicators, Strategic Allocation, Value Premium
“Verification Tests of the Smart Money Indicator” reports performance results for a specific version of the Smart Money Indicator (SMI) stocks-bonds timing strategy, which exploits differences in futures and options positions in the S&P 500 Index, U.S. Treasury bonds and 10-year U.S. Treasury notes between institutional investors (smart money) and retail investors (dumb money). Do these sentiment-based results diversify those for the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS)? To investigate, we look at correlations of annual returns between variations of SMI (no lag between signal and execution, 1-week lag and 2-week lag) and each of SACEMS equal-weighted (EW) Top 3 and SACEVS Best Value. We then look at average gross annual returns, standard deviations of annual returns and gross annual Sharpe ratios for the individual strategies and for equal-weighted, monthly rebalanced portfolios of the three strategies. Using gross annual returns for the strategies during 2008 through 2019, we find that: Keep Reading
January 14, 2020 - Momentum Investing, Strategic Allocation, Technical Trading
How can investors suppress the downside of trend following strategies? In their July 2019 paper entitled “Protecting the Downside of Trend When It Is Not Your Friend”, flagged by a subscriber, Kun Yan, Edward Qian and Bryan Belton test ways to reduce downside risk of simple trend following strategies without upside sacrifice. To do so, they: (1) add an entry/exit breakout rule to a past return signal to filter out assets that are not clearly trending; and, (2) apply risk parity weights to assets, accounting for both their volatilities and correlations of their different trends. Specifically, they each month:
- Enter a long (short) position in an asset only if the sign of its past 12-month return is positive (negative), and the latest price is above (below) its recent n-day minimum (maximum). Baseline value for n is 200.
- Exit a long (short) position in an asset only if the latest price trades below (above) its recent n/2-day minimum (maximum), or the 12-month past return goes negative (positive).
- Assign weights to assets that equalize respective risk contributions to the portfolio based on both asset volatility and correlation structure, wherein covariances among assets adapt to whether an asset is trending up or down. They calculate covariances based on monthly returns from an expanding (inception-to-date) window with baseline 2-year half-life exponential decay.
- Impose a 10% annual portfolio volatility target.
Their benchmark is a simpler strategy that uses only past 12-month return for trend signals and inverse volatility weighting with annual volatility target 40% for each asset. Their asset universe consists of 66 futures/forwards. They roll futures to next nearest contracts on the first day of the expiration month. They calculate returns to currency forwards using spot exchange rates adjusted for carry. Using daily prices for 23 commodity futures, 13 equity index futures, 11 government bond futures and 19 developed and emerging markets currency forwards as available during August 1959 through December 2017, they find that: Keep Reading
December 26, 2019 - Momentum Investing
What is the best way to balance crash protection and false alarms for intrinsic, also called absolute or time series, momentum strategies that are long (short) an asset when its return over a specified past interval is positive (negative)? In their November 2019 paper entitled “Momentum Turning Points”, Ashish Garg, Christian Goulding, Campbell Harvey and Michele Mazzoleni investigate blending slow and fast intrinsic momentum signals with various weights on each (adding to one) to identify the best way to handle reversals in trend direction. They specify a slow (fast) signal as that derived from past 12-month (1-month) excess return. They define four market states: (1) Bull (slow and fast signals both non-negative); (2) Correction (slow signal non-negative and fast signal negative); (3) Bear (slow and fast signals both negative); and, (4) Rebound (slow signal negative and fast signal non-negative). They first consider static weights in increments of 25% for slow and fast signals. They then consider a dynamic strategy with slow and fast signal weights that differ for Correction and Rebound states as identified with monthly data. They test usefulness of the dynamic strategy by optimizing weights with historical returns and then evaluating performance of these weights out-of-sample. While focusing on the U.S. stock market, they test robustness of findings across other developed country equity markets. Using monthly excess returns for the U.S. value-weighted stock market since July 1926 and for 10 other developed stock markets since February 1980, all through December 2018, they find that:
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December 20, 2019 - Animal Spirits, Individual Investing, Momentum Investing
Is retail trading a reliable driver of U.S. stock momentum? In his November 2019 paper entitled “Retail Trading and Momentum Profitability”, Douglas Chung investigates interactions across stocks between current proportion of retail trading and future momentum returns. Specifically, for each month and for each of two recent stock samples, he:
- Sorts stocks into fifths (quintiles) by current proportion of retail trading.
- Within each proportion-of-retail-trading quintile:
- Sorts stocks into sub-quintiles by return from 12 months ago to one month ago.
- Calculates average next-month returns for an equal-weighted momentum portfolio that is long (short) the sub-quintile of stocks with the highest (lowest) past returns. He also considers other portfolio weighting schemes.
- Measures alphas of these returns based on various widely accepted single-factor and multi-factor models of stock returns.
He next tests whether proportion of retail trading relates to a gambling motive (lottery trading) by constructing a stock lottery index from inverse of stock price, idiosyncratic volatility, idiosyncratic skewness and recent maximum daily return. In other words, he examines whether the lottery index value for a stock is a proxy for its proportion of retail trading. Using daily data for all NYSE retail orders during March 2004 through December 2014, for small NYSE trades of U.S. common stocks (a proxy for retail trading) during January 1993 through July 2000 and for lottery index inputs during 1940 through 2016, he finds that: Keep Reading
December 17, 2019 - Equity Premium, Momentum Investing, Value Premium, Volatility Effects
Do both the long and short sides of portfolios used to quantify widely accepted equity factors benefit investors? In their November 2019 paper entitled “When Equity Factors Drop Their Shorts”, David Blitz, Guido Baltussen and Pim van Vliet decompose and analyze gross performances of long and short sides of U.S. value, momentum, profitability, investment and low-volatility equity factor portfolios. The employ 2×3 portfolios, segmenting first by market capitalization into halves and then by selected factor variables into thirds. The extreme third with the higher (lower) expected return constitutes the long (short) side of a factor portfolio. When looking at just the long (short) side of factor portfolios, they hedge market beta via a short (long) position in liquid derivatives on a broad market index. Using monthly returns for the specified 2×3 portfolios during July 1963 through December 2018, they find that:
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December 6, 2019 - Animal Spirits, Equity Premium, Fundamental Valuation, Momentum Investing
Which equity factors from among those included in the most widely accepted factor models are really important? In their October 2019 paper entitled “Winners from Winners: A Tale of Risk Factors”, Siddhartha Chib, Lingxiao Zhao, Dashan Huang and Guofu Zhou examine what set of equity factors from among the 12 used in four models with wide acceptance best explain behaviors of U.S. stocks. Their starting point is therefore the following market, fundamental and behavioral factors:
They compare 4,095 subsets (models) of these 12 factors models based on: Bayesian posterior probability; out-of-sample return forecasting performance; gross Sharpe ratios of the optimal mean variance factor portfolio; and, ability to explain various stock return anomalies. Using monthly data for the selected factors during January 1974 through December 2018, with the first 10 (last 12) months reserved for Bayesian prior training (out-of-sample testing), they find that: Keep Reading
November 18, 2019 - Momentum Investing
Do U.S. equity exchange-traded funds (ETF) exhibit long-term momentum? In their October 2019 paper entitled “ETF Momentum”, Frank Li, Melvyn Teo and Chloe Yang investigate future performance of U.S. equity ETFs sorted on multi-year past returns. Each month starting August 2004, they:
- Sort selected ETFs into tenths (deciles) based on returns over the past two, three or four years, with focus on three years.
- Reform an equal-weighted (EW) or value-weighted (VW) portfolio that is long (short) the decile with the highest (lowest) past returns, with focus on value-weighted.
They then evaluate performances of deciles and long-short portfolios based on raw return, 4-factor (adjusting for market, size, book-to-market and momentum) alpha and 5-factor (replacing momentum with profitability and investment) alpha. Using monthly returns, market capitalizations and net asset values for all U.S. equity ETFs with capitalizations greater than $20 million and share price greater than one dollar during August 2000 through June 2018, they find that: Keep Reading