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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.

Investor Access to Factor Premiums via Funds

Are widely accepted equity factor exposures available in fact to investors via “smart beta” mutual funds and exchange-traded funds (ETF)? In their May 2020 paper entitled “Smart Beta Made Smart”, Andreas Johansson, Riccardo Sabbatucci and Andrea Tamoni test effectiveness of individual U.S. equity mutual funds and ETFs and combinations of these funds for exploiting several major equity risk factors (value, size, profitability and momentum). After assembling a sample of funds with names that indicate smart beta strategies, they iteratively (annually for size, value and profitability and daily for momentum):

  1. Apply a double-regression to each fund to identify those that are actually “closet” market index funds.
  2. Refine factor exposures of each true smart beta fund based on actual fund holdings.
  3. Construct separately for institutional and retail investors tradable long-side (mutual funds and ETFs) and short-side (ETFs only) risk factors via value-weighted combinations of the 10 funds with the strongest exposures to each factor.

Using daily, monthly, and quarterly data for U.S. equity mutual funds and ETFs with (1) names indicating smart beta strategies, (2) at least one year of returns and (3)assets over $1 billion, data for their individual component U.S. stocks and specified factor returns during January 2003 through May 2019, they find that: Keep Reading

Mitigating Impact of Price Turning Points on Trend Following

Is there a way to mitigate adverse impact of price trajectory turning points (trend changes) on performance of intrinsic (absolute or time series) momentum strategies? In their May 2020 paper entitled “Breaking Bad Trends”, Ashish Garg, Christian Goulding, Campbell Harvey and Michele Mazzoleni measure impact of turning points on time series momentum strategy performance across asset classes. They define a turning point as a month for which slow (12-month or longer lookback) and fast (3-month or shorter lookback) momentum signals disagree on whether to buy or sell. They test a dynamic strategy to mitigate trend change impact based on turning points defined by disagreement between 12-month (slow) and 2-month (fast) momentum signals. Specifically, their dynamic strategy each month:

  1. For each asset, measures slow and fast momentum as averages of monthly excess returns over respective lookback intervals.
  2. Specifies the trend condition for each asset as: (1) Bull (slow and fast signals both non-negative); (2) Correction (slow non-negative and fast negative); Bear (slow and fast both negative); and, Rebound (slow negative and fast non-negative). For Bull and Bear (Correction and Rebound) conditions, next-month return is the same (opposite in sign) for slow and fast signals.
  3. After trend changes (Corrections and Rebounds separately), empirically determines with at least 48 months of historical data optimal weights for combinations of positions based on slow and fast signals.

They compare performance of this dynamic strategy with several conventional (static) time series momentum strategies, with each competing strategy retrospectively normalized to 10% test-period volatility. They test strategies on 55 futures, forwards and swaps series spanning four asset classes, with returns based on holding the nearest contract and rolling to the next at expiration. Using monthly returns for futures, forwards and swaps for 12 equity indexes, 10 bond indexes, 24 commodities and 9 currency pairs as available during January 1971 through December 2019, they find that:

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Reliability and Exploitability of U.S. Stock Market Trends

Does the U.S. stock market exhibit reliable and exploitable trends as measured by intrinsic (absolute or time series) momentum? In their April 2020 paper entitled “Time Series Momentum in the US Stock Market: Empirical Evidence and Theoretical Implications”, Valeriy Zakamulin and Javier Giner examine evidence of time series momentum in the excess returns (relative to the risk-free rate) of the S&P Composite Index. Their approach involves autocorrelations of multi-month (not monthly) excess returns. They then use simulations modeled with actual index return statistics to; (1) assess potential profitability of long-only and long-short time series momentum strategies; and, (2) estimate the optimal lookback interval. Using monthly total returns for the S&P Composite Index and the monthly risk-free rate represented by the U.S. Treasury bill (T-bill) yield during January 1857 through December 2018, they find that: Keep Reading

SACEMS at Weekly and Biweekly Frequencies

A subscriber asked for an update on whether weekly or biweekly (every two weeks) measurement of asset class momentum works better than monthly measurement as used in “Simple Asset Class ETF Momentum Strategy (SACEMS)” (SACEMS). Do higher measurement frequencies respond more efficiently to market turns? To investigate, we compare performances of strategies based on monthly, weekly and biweekly frequencies with comparable lookback intervals. For this comparison, we align weekly and biweekly results with monthly results, though they differ somewhat due to mismatches between ends of weeks and ends of months. We consider portfolios of past ETF winners based on Top 1 and on equally weighted (EW) Top 2 and Top 3. Using weekly dividend-adjusted closing prices for the asset class proxies per baseline SACEMS and the yield for Cash during February 2006  through April 2020, we find that: Keep Reading

Best Stock Portfolio Styles During and After Crashes

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:

  1. Separating stocks into halves based on market capitalization.
  2. 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.
  3. 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

Exploiting Stock Anomaly Value and Momentum

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

Stock Market Continuation and Reversal Months?

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

Combining the Smart Money Indicator with SACEMS and SACEVS

“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

Reducing Downside Risk of Trend Following Strategies

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

Handling Reversals in Price Trend Direction

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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