Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for May 2022 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for May 2022 (Final)
1st ETF 2nd ETF 3rd ETF

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.

Stock Option Momentum and Seasonality

Do options of individual stocks exhibit momentum and seasonality patterns? In their November 2020 paper entitled “Momentum, Reversal, and Seasonality in Option Returns”, Christopher Jones, Mehdi Khorram and Haitao Mo investigate momentum and seasonality effects for options on U.S. common stocks. They focus on performance of straddles, combining a put and a call with the same strike price and expiration date. They balance needs for liquidity and sample size by requiring positive open interest during the holding period but not the momentum calculation interval. Specifically, on each monthly option expiration date, they:

  1. Form two straddles from near-the-money options expiring next month for each for each stock: (1) the pair with call delta closest to 0.5 for calculating momentum; and, (2) the pair with call delta closest to 0.5 and with positive open interest for both the put and the call when selected for calculating momentum portfolio return.
  2. Construct from these pairs zero-delta straddles using bid-ask midpoints as prices and calculate monthly straddle excess returns relative to the 1-month Treasury bill yield. This process generates about 1,600 straddles per month with average monthly excess return -5.6% and very large standard deviations.
  3. Calculate momentum as average monthly excess return over a specified lookback interval (rather than cumulative return, to suppress effects of return outliers).
  4. Rank straddle returns into equal-weighted fifths (quintiles) based on momentum and calculate average return for each quintile and for a portfolio that is long the top quintile and short the bottom quintile.

Using end-of-day open interest and bid-ask quotes for call and put options on U.S. common stocks from OptionMetric and trading data for underlying stocks during January 1996 through June 2019, they find that: Keep Reading

Sector Breadth as Market Return Indicator

Does breadth of equity sector performance predict overall stock market return? To investigate, we relate next-month stock market return to sector breadth (number of sectors with positive past returns) over lookback intervals ranging from 1 to 12 months. We consider the following nine sector exchange-traded funds (ETF) offered as Standard & Poor’s Depository Receipts (SPDR):

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

We use SPDR S&P 500 (SPY) to represent the overall stock market. Using monthly dividend-adjusted returns for SPY and the sector ETFs during December 1998 through September 2020, we find that: Keep Reading

Breaking Asset Ranking Systems into Pairs

Is there a better way to identify attractive and unattractive assets than simply ranking them? In the August 2020 version of their paper entitled “Decoding Systematic Relative Investing: A Pairs Approach”, Christian Goulding, Campbell Harvey and Alex Pickard examine a long-short strategy that periodically reforms a portfolio by evaluating all possible pairs within an asset universe based on:

  1. High positive signal-future return correlation for each asset on its own in a pair.
  2. Low (or negative) signal correlation between assets in the pair.
  3. Low (or negative) signal-future return correlations between one asset and the other in the pair.

They use these three inputs to calculate a (somewhat complex) composite score for each pair. Among pairs with the highest composite scores, the member with the higher (lower) signal goes to the long (short) side of the portfolio. They assess usefulness of the three conditions and the composite score using a momentum signal calculated as average past monthly return over a specified lookback interval minus its inception-to-date mean and divided by its inception-to-date standard deviation. They split their sample roughly in half and use the first half for detection of profitable pair strategies and the second half to measure out-of-sample performance. They further test an explicit tactical allocation strategy using a 12-month momentum lookback interval, a rolling 10-year monthly composite score and a scheme that weights the top four asset pairs according to respective composite scores. As a benchmark, they use a comparable conventional relative momentum strategy that simply ranks assets on momentum signal. Using monthly returns for 13 broad asset-class indexes encompassing equities, bonds, real estate investment trusts (REIT) and commodities (78 possible pairs) as available through May 2020, they find that:

Keep Reading

GDX Instead of GLD in SACEMS?

Subscribers have asked whether substituting Market Vectors Gold Miners ETF (GDX) for SPDR Gold Shares (GLD) as a proxy for gold improves the performance of the Simple Asset Class ETF Momentum Strategy (SACEMS)? To check, we backtest the strategy twice using either GLD or GDX to represent gold, and then compare results. Using dividend-adjusted closing prices for SACEMS asset class proxies and the yield for Cash during June 2006 (per tracked SACEMS) through September 2020, we find that: Keep Reading

Size as Catalyst for Value and Momentum

The conventional size (market capitalization) premium is notoriously weak since discovery almost 40 years ago. Does this poor live track record mean it is useless to investors? In their September 2020 paper entitled “Settling the Size Matter”, David Blitz and Matthias Hanauer examine whether the size premium is exploitable as a standalone anomaly or in combination with other anomalies. They consider six versions of a size factor from prior research, as follows:

  1. Adjusted for value – average of three small-cap stock portfolios minus average of three big-cap stock portfolios after sorting for book-to-market ratio.
  2. Adjusted for value, investment and profitability – average of nine small-cap stock portfolios minus average of nine big-cap stock portfolios after separately sorting on the other three factors.
  3. Adjusted for profitability – average of three small-cap stock portfolios minus average of three big-cap stock portfolios after sorting for profitability.
  4. Adjusted for quality – average of three small-cap stock portfolios minus average of three big-cap stock portfolios after sorting for quality.
  5. Adjusted for quality beta – average of three small-cap stock portfolios minus average of three big-cap stock portfolios after sorting for quality beta.
  6. Adjusted for size, investment and return on equity – average of nine small-cap stock portfolios minus average of nine big-cap stock portfolios after separately sorting on the other three factors.

All factor portfolio segments are capitalization-weighted, and all returns are in U.S. dollars. They consider regressions (implying long-short implementations) and long-only sides of these factors. They also consider size factor definitions that do not overweight size inputs, as do those above. Using data required by these definitions for U.S. stocks since July 1963 (or January 1967 for some inputs) and for international stocks since July 1990 (or July 1993 for some inputs), all through December 2019, they find that: Keep Reading

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:

Keep Reading

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

Daily Email Updates
Filter Research
  • Research Categories (select one or more)