Momentum Investing

Are trend following (intrinsic or time series momentum) and risk parity complementary multi-class portfolio construction approaches? In his October 2015 paper entitled “Trend-Following, Risk-Parity and the Influence of Correlations”, Nick Baltas compares performances of inverse volatility weighting and risk parity weighting as adapted to a long-short trend following strategy. Unlike volatility weighting, risk parity weighting incorporates asset return correlations, assigning higher (lower) weights to assets with lower (higher) average pairwise correlations with other assets. For both weighting schemes, portfolios are each month long (short) assets with positive (negative) past 12-month returns. Monthly inverse volatility weights derive from actual daily asset return volatilities over the past 90 trading days. Monthly risk parity weights derive from actual daily asset return volatilities and correlations over the past 90 trading days. Both weighting schemes target 10% portfolio volatility by each month applying overall leverage based on actual annualized volatility of an unleveraged trend following portfolio over the past 60 trading days divided by 10%. Using daily closing prices for the most liquid contract for each of 35 (6 energy, 10 commodity, 6 government bond, 6 currency exchange rate and 7 equity index) futures contract series as available during January 1987 through December 2013, he finds that: Keep Reading

Is there a simple way to improve the performance of conventional asset class target allocations by rotating to strength within classes based on Relative Strength Index (RSI)? In his September 2015 paper entitled “Momentum Investing and Asset Allocation”, Drew Knowles seeks to improve the performance of baseline asset class (equity, fixed income, hedge fund) allocations via dynamic intra-class rotation to strength based on RSI. His principal passive benchmark (50/30/20) allocates 50% to equities (S&P 500 Total Return Index), 30% to fixed income (Barclays U.S. Aggregate Index) and 20% to hedge funds (HFRI Fund Weighted Composite), apparently rebalanced annually. For dynamic rotation, he replaces the broad equity, fixed income and hedge fund indexes with, respectively, the apparently equally weighted Top 5 (of 10) S&P 500 sector indexes, Top 5 (of seven) fixed income style indexes and Top 5 (of eight) hedge fund style indexes based on 12-month RSI. He breaks ties in RSI by picking the index with higher return per unit of risk (compound annual growth rate divided by standard deviation of returns) over the same 12 months. Within each asset class, he tests four Top 5 reformation frequencies: annual, semi-annual, quarterly or monthly. He ignores rebalancing/reformation frictions and tax implications of trading. Using monthly data for the selected broad and sector/style indexes during 1991 through 2014, he finds that: Keep Reading

Does the rate of change of return momentum (return acceleration) usefully predict stock returns? In their August 2015 paper entitled “The Acceleration Effect and Gamma Factor in Asset Pricing”, Diego Ardila-Alvarez, Zalan Forro and Didier Sornette compare the effectiveness of return acceleration (difference between returns for the last six months and the preceding six months) and return momentum as stock return predictors. They devise and test an acceleration factor (which they call gamma) by each month ranking stocks into tenths (deciles) by acceleration and measuring the returns to a monthly reformed hedge portfolio that is long (short) the value-weighted decile with the highest (lowest) acceleration. They also test trading strategies that each month weight stocks according to the ratio of prior-month stock acceleration to the average prior-month acceleration of all stocks versus similarly constructed momentum strategies for 36 combinations of different: ranking intervals (3, 6 or 12 months); holding intervals (1, 3, 6 or 12 months); and, implementation delays (1, 3 or 6 months). Using monthly data for a broad sample of U.S. common stocks and monthly market, size, book-to-market and momentum risk factors during May 1963 through December 2013, they find that: Keep Reading

Is there a way to suppress the volatility and drawdowns of a mixed value and momentum stock strategy while retaining most of its benefit? In his September 2015 paper entitled “Learning to Play Offense and Defense: Combining Value and Momentum from the Bottom up, and the Top Down”, Mebane Faber examines the feasibility of a strategy that combines market valuation and market trend timing (defense) with a mixed value and momentum stock selection strategy (offense). Specifically:

For offense, he each month: (1) ranks stocks by each of price-to-earnings, price-to-book and earnings before interest and taxes-to-total enterprise value ratios and then re-ranks them by the average of the three separate value rankings; (2) ranks stocks by each of 3-month, 6-month and 12-month past returns and then re-ranks them by the average of the three separate momentum rankings; and, (3) forms an equally weighted portfolio of the top 100 value and top 100 momentum stocks and holds for three months (three overlapping portfolios).

For defense, he each month: (1) hedges half of the portfolio by shorting the S&P 500 Index if the long-term real earnings yield for the S&P 500 (inverse of the Cyclically Adjusted Price-Earnings ratio, CAPE or P/E10 as calculated by Robert Shiller, minus the most recently available actual 12-month U.S. inflation rate) is in the 20% of its lowest inception-to-date monthly values; and, (2) hedges half of the portfolio by shorting the S&P 500 Index if the index is below its 12-month simple moving average. 

The overall portfolio can therefore be 100% long “offense” stocks, 50% hedged or market neutral. He does not account for costs of portfolio reformations or hedging. Using monthly total returns for all NYSE stocks in the top 60% of market capitalizations, monthly levels of the S&P 500 Total Return Index and monthly values of CAPE during 1964 through 2014, he finds that: Keep Reading

Are there exploitable size and momentum effects among international stocks? In their August 2015 paper entitled “Size and Momentum Profitability in International Stock Markets”, Peter Schmidt, Urs Von Arx, Andreas Schrimpf, Alexander Wagner and Andreas Ziegler examine the size effect and the interplay between size and momentum strategies via long-short stock portfolios in 23 countries. They measure stock size as market capitalization and consider several ways of measuring the difference in average returns and four-factor (market, size, book-to-market, momentum) alphas between value-weighted portfolios of small stocks and big stocks. They measure stock momentum as return from 12 months ago to one month ago, with a skip-month between ranking and value-weighted portfolio formation. They assess net portfolio performance in three ways: (1) imposing estimated trading frictions (0.3%-0.4% for small stocks and 0.15% for big stocks); (2) calculating the maximum trading frictions an investor could bear; and, (3) calculating U.S. dollar trading volume for each portfolio. Using stock data for the U.S. during 1985 through 2012 and for 22 other countries mostly during 1991 through 2012, they find that: Keep Reading

Does a stock momentum strategy selecting only persistent winners and losers work better than a conventional strategy that includes one-month wonders? In their August 2015 paper entitled “Persistency of the Momentum Effect: The Role of Consistent Winners and Losers”, Hong-Yi Chen, Pin-Huang Chou and Chia-Hsun Hsieh examine stock momentum persistence as a condition for momentum portfolio construction. They define the momentum of a stock as persistent if it appears in the top or bottom tenth (decile) of ranking interval returns for at least two consecutive months. They determine what kinds of stocks tend to exhibit momentum persistence. They also investigate whether restricting momentum portfolios to persistent winners and losers improves performance compared to a conventional momentum portfolio. While considering several ranking intervals, they focus on six months. Using firm accounting information (lagged at least six months), stock trading data and quarterly institutional holdings for a broad sample of U.S. common stocks during 1980 through 2011, they find that: Keep Reading

What indicator works best to mitigate stock momentum strategy crashes? In his March 2015 paper entitled “Momentum Crash Management”, Mahdi Heidari compares performances of seven indicators for avoiding conventional stock momentum strategy crashes: (1) prior-month market return; (2) change in prior-month market return: (3) market volatility (standard deviation of 52 weekly returns); (4) dispersion (variance) of daily returns across all stocks; (5) market illiquidity (aggregate impact of trading on price); (6) momentum volatility (standard deviation of momentum strategy returns the past six months); and, (7) change in momentum volatility. The conventional strategy is each month long (short) the value-weighted tenth of stocks with the highest (lowest) returns from 12 months ago to one month ago. For each of the competing indicators, he invests in the conventional momentum strategy (cash) when the indicator is below (within) the top 10% of its values over the past five years. He uses portfolio turnover to compare implementation costs. Using data for a broad sample of relatively liquid U.S. stocks during January 1926 through December 2013, he finds that: Keep Reading

Can investors refine and exploit the upward bias of overnight stock returns? In the July 2015 version of her paper entitled “Night Trading: Lower Risk but Higher Returns?”, Marie-Eve Lachance presents a way of sorting stocks by strength of overnight return bias and investigates gross and net profitability of associated overnight-only investment strategies. Specifically, she each month regresses daily overnight returns on total returns over the past year to measure an Overnight Bias Parameter (OBP) for each stock. She then forms portfolios based on monthly OBP sorts, focusing on the portfolio of stocks with significantly positive OBPs. She estimates trading frictions by: (1) assuming market-on-open and market-on-close trades, avoiding bid-ask spreads; and, (2) estimating broker charges from the lowest fees available in the U.S. in 2014. Using daily overnight (close-to-open) and intraday (open-to-close) total returns, trading data and characteristics for a broad sample of reasonably liquid U.S. stocks during 1995 through 2014, she finds that: Keep Reading

Do dual-sorts of country stock market predictive factors add value to single-sorts? In the July 2015 version of his paper entitled “Combining Equity Country Selection Strategies” Adam Zaremba first re-examines earnings-price ratio (E/P), momentum (return from 12 months ago to one month ago), skewness (based on the last 24 monthly returns) and turnover ratio (average monthly turnover for the past 12 months) as country stock market predictive factors. He then investigates whether combined sorts on two factors outperform single-factor sorts. For each individual factor, he sorts country stock markets into fifths (quintiles) and measures the factor premium as the difference in returns between the highest and lowest quintiles. He focuses on market capitalization weighting within quintiles but considers equal and liquidity (average turnover) weighting schemes as robustness checks. For dual sorts, he computes combined ranking as the average of component factor rankings and then forms quintile portfolios. Using monthly total returns adjusted for local dividend tax rates in U.S. dollars for 78 existing and discontinued country stock indexes (primarily MSCI) during 1999 through March 2015, he finds that: Keep Reading

Has (hypothetical) equity factor investing worked as well in recent years as indicated in past studies? In his July 2015 paper entitled “Factor Investing Revisited”, David Blitz updates his prior study quantifying the performance of allocations to U.S. stocks based on three factor premiums: (1) value (high book-to-market ratio); (2) momentum (high return from 12 months ago to one month ago); and, (3) low-volatility (low standard deviation of total returns over the last 36 months). He considers two additional factor allocations: (4) operating profitability (high return on equity); and, (5) investment (low asset growth). He specifies each factor portfolio as the 30% of U.S. stocks with market capitalizations above the NYSE median that have the highest expected returns, reformed monthly for momentum and low-volatility and annually for the other factors. He considers both equal-weighted and value-weighted portfolios for each factor. He also summarizes recent research on the role of small-capitalization stocks, factor timing, long-only versus long-short portfolios, applicability to international stocks and applicability to other asset classes. Using value, momentum, profitability and investment factor portfolio returns from Kenneth French’s library and low-volatility portfolio returns as constructed from a broad sample of U.S. stocks during July 1963 through December 2014, he finds that: Keep Reading