Momentum Investing

Is it possible to predict serial correlation (autocorrelation) of stock returns, and thereby enhance reversal and momentum strategies. In the January 2014 version of his paper entitled “The Information Content of Option Prices Regarding Future Stock Return Serial Correlation”, Scott Murray investigates the relationship between the variance ratio (the ratio of realized to implied stock return variance, a measure of the variance risk premium) to stock return serial correlation. He calculates realized variance at the end of each month from daily log stock returns over the prior three months. He calculates implied variance at the end of each month as the square of the volatility implied by at-the-money 0.5 delta call and put options one month from expiration. He first measures the power of the variance ratio to predict stock return serial correlation. He then tests the effectiveness of this predictive power to enhance reversal and momentum stock trading. Using the specified return and option data for all U.S. stocks with listed options during January 1996 through December 2012, he finds that: Keep Reading

Do trend following strategies widely used by managed futures funds break down during financial crises? In the December 2013 version of their paper entitled “Is This Time Different? Trend Following and Financial Crises”, Mark Hutchinson and John O’Brien examine the effectiveness of trend following strategies as applied to futures contract series during and between financial crises. They define a financial crisis interval as the two to four years after the start of the crisis. They consider six global crises: (1) the Great Depression commencing 1929: (2) the 1973 oil crisis; (3) the third world debt crisis of 1981; (4) the crash of October 1987; (5) the bursting of the dot-com bubble in 2000; and, (6) the sub-prime/euro crisis commencing in 2007. They also consider eight regional crises during 1977 through 2000. They calculate momentum returns for each asset class by each month weighting constituent contract series proportionally to their excess return over the past one to 12 months and inversely to an estimate of their volatility based on lagged data. They include estimates of transaction costs proportional to the value traded that vary by asset class and time period. They also incorporate management and incentive fees (based on high water mark) of 2% and 20%, respectively. Using actual and modeled futures prices encompassing 21 equity indexes, 13 government bonds, nine currency exchange rates and 21 commodities (and contemporaneous risk-free rates) during January 1921 through June 2013, they find that: Keep Reading

Is there a tractable way to combine momentum investing with Modern Portfolio Theory (MPT)? In their December 2013 paper entitled “Tactical MPT and Momentum: the Modern Asset Allocation (MAA)”, Wouter Keller and Hugo van Putten present a tactical, simplified, long-only version of MPT that applies momentum to estimate future asset returns. Specifically, they:

1. Make MPT tactical by using short historical intervals to estimate future asset returns (rate of return, or absolute momentum), return volatilities (based on daily returns) and return correlations (based on daily returns), assuming that behaviors over a short historical interval will materially persist during the next month.
2. Exclude from the portfolio any assets with negative estimated returns (i.e., negative returns over the specified historical interval).
3. Simplify correlation calculations by relating daily historical returns for each asset to those for a single index (the equally weighted average returns for all assets) rather than to those for all other assets separately.
4. Dampen any errors in rapidly changing asset return, volatility and correlation estimates by “shrinking” them toward their respective averages across all assets in the universe, and dampen the predicted market volatility by “shrinking” it toward zero.

They reform the MAA portfolio monthly at the first close. Their baseline historical interval for estimation of all variables is four months (84 trading days). Their baseline shrinkage factor for all variables is 50%. Their benchmark is the equally weighted (EW) “market” of all assets, rebalanced monthly. They assume a one-way trading friction of 0.1%. They consider a range of portfolio performance metrics: annualized return, annual volatility, maximum drawdown, Sharpe ratio, Omega ratio and Calmar ratio. Using daily dividend-adjusted prices for assets allocated to nine universes (of seven to 130 assets, generally consisting of asset class proxy funds) during November 1997 through mid-November 2013, they find that: Keep Reading

Readers have proposed several hedging/shorting variations for “Simple Sector ETF Momentum Strategy Performance”, as follows: (1) buy the top and hedge with (short) the bottom sector based on past six-month return; (2) buy the top sector based on past six-month return and hedge it with a matched short position in the S&P 500 Index via ProShares Short S&P500 (SH); and, (3) buy the top (sell the bottom) sector when the S&P 500 Index is above (below) its 10-month simple moving average (SMA). The strategies apply to the following nine sector exchange-traded funds (ETF) defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR), all of which have trading data back to December 1998:

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)

Using monthly dividend-adjusted closing levels for the sector ETFs, SPDR S&P 500 (SPY), SH (as available) and the 3-month Treasury bill (T-bill) yield over the period December 1998 through December 2013 (182 months), we find that: Keep Reading

What market conditions make stock price momentum strategies crash? In the October 2013 version of their paper entitled “Momentum Profits, Market Cycles, and Rebounds: Evidence from Germany”, Martin Bohl and Marc-Gregor Czaja analyze performance statistics for both price and earnings momentum portfolios of German stocks across different market states. They use a stock price momentum strategy that each month ranks stocks based on returns from 12 months ago to one month ago and then constructs a winner (loser) portfolio as the equally weighted top (bottom) fifth of past performers. They use an earnings momentum strategy that each month ranks stocks based on normalized analyst forecast revisions (earnings forecast revision ratio, or ERR) over the past month and then constructs a winner (loser) portfolio as the equally weighted highest (lowest) fifth of ERRs. Using the value-weighted total return CDAX as a proxy for the German stock market, they specify market state retrospectively as bull (bear) when it generally advances (declines) over six or more consecutive months without any interim new high. They focus on a market rebounds, defined as the first three months after a switch from bear to bull state with high market volatility. Using monthly data for a broad sample of German common stocks tracked by at least three analysts during February 1987 through October 2012 (an average of 171 stocks per month), they find that: Keep Reading

Are momentum and trend-following strategies effective in tactical asset allocation to European equity sectors and countries? In the July 2013 version of their paper entitled “European Equity Investing Through the Financial Crisis: Can Risk Parity, Momentum or Trend Following Help to Reduce Tail Risk?”, Andrew Clare, James Seaton, Peter Smith and Steve Thomas apply momentum and trend-following strategies to portfolios of European sector and country indexes. Specifically, they consider three long-only sets of portfolios, as follows:

1. Simple momentum: the equal-weighted top 8 or top 4 sectors or countries ranked by simple total return over the previous 1, 3, 6 or 12 months, or over the interval from 2 to 6 months ago, or the interval from 7 through 12 months ago.
2. Risk-adjusted momentum: The inverse volatility-weighted top 8 or top 4 sectors and/or countries ranked over the same intervals by risk-adjusted returns (with both weighting and risk-adjusted returns based on daily returns over the past 120 days).
3. Risk-adjusted momentum with SMA10: move positions in the risk-adjusted momentum portfolios to 3-month U.S. Treasury bills whenever the current value of the STOXX 600 Index is below its 10-month simple moving average (SMA10). 

They ignore trading frictions involved in strategy implementations. Using monthly total returns in U.S. dollars for 19 European equity sector and 15 European country indexes during 1988 through 2011, they find that: Keep Reading

How do value and momentum interact with each other and with size, economic and liquidity factors worldwide? In the November 2013 version of their paper entitled “Size, Value, and Momentum in Developed Country Equity Returns: Macroeconomic and Liquidity Exposures”, Nusret Cakici and Sinan Tan address this question for developed markets. They use long-short, factor-sorted portfolios to measure size, value and momentum premiums. They consider future Gross Domestic Product (GDP) growth and future consumption growth as economic factors. They consider both funding liquidity (a potential indicator of investor margin cost, focusing on the difference between interbank lending rate and short-term deposit yield) and stock market liquidity (the estimated cost of trading stocks). Using monthly stock returns, firm accounting data and economic data for 23 developed countries during January 1990 through March 2012, they find that: Keep Reading

Is simple momentum the secret sauce of Managed Futures funds? In their 2013 paper entitled “Demystifying Managed Futures”, Brian Hurst, Yao Ooi and Lasse Pedersen examine how well simple trend-following strategies based on time series (intrinsic or absolute) momentum explain the performance of Managed Futures funds. Their simple intrinsic momentum strategy goes long (short) a contract series with a positive (negative) return relative to the risk-free rate over 1-month, 3-month and 12-month look-back intervals. They apply the strategy to a liquid universe of 24 commodity futures, 9 equity futures, 13 government bond futures and 12 currency forwards. They adopt a simple diversification weighting that targets 40% annualized volatility for each position. They rebalance the diversified portfolio weekly at the Friday close based on data from the Thursday close. They ignore rebalancing/roll frictions. Using daily and weekly prices for 58 futures contract and currency forward series during January 1985 through June 2012, they find that: Keep Reading

Has Modern Portfolio Theory failed to deliver over the past decade because users employ long-term averages for expected returns, volatilities and correlations that do not respond to changing market environments? Do short-term estimates of these key inputs work better? In their May 2012 paper entitled “Adaptive Asset Allocation: A Primer”, Adam Butler, Michael Philbrick and Rodrigo Gordillo backtest a progression of strategies culminating in an Adaptive Asset Allocation (AAA) strategy that incorporates return predictability from relative momentum (last 120 trading days, about six months), volatility predictability from recent volatility (last 60 trading days) and pairwise correlation predictability from recent correlations (last 250 trading days). Their tests employ nine asset class indexes (U.S. stocks, European stocks, Japanese stocks, U.S. real estate investment trusts (REIT), International REITs, intermediate-term U.S. Treasuries, long-term U.S. Treasuries and commodities) and a spot gold price series. They reform portfolios monthly based on evolving return, volatility and correlation forecasts. They ignore trading frictions as negligible for “intelligent retail or institutional investors” via mutual funds or Exchange Traded Funds. Using daily returns for the nine indexes and spot gold) to test six strategies during January 1995 through March 2012, they find that: Keep Reading

Does purifying stock return rankings of any dependence on Fama-French three-factor model risk factors enhance momentum strategy performance? In an update of their August 2009 paper entitled “Residual Momentum”, David Blitz, Joop Huij and Martin Martens suppress exposures of a conventional stock momentum strategy to market, size and book-to-market ratio risk factors by ranking stocks on residual returns instead of total returns. They calculate the residual return for each stock each month as the error term from a regression of its total returns versus the three risk factors over the past 36 months (excluding stocks without 36-month histories). For a total return momentum benchmark, they rank stocks each month based on total return over the last 12 months, excluding the most recent month. For residual return momentum, they rank stocks each month based on residual returns divided by their respective standard deviations over the past 12 months, excluding the most recent month. For both strategies, they measure the momentum effect as the average gross return on hedge portfolios that are long (short) the equally weighted tenth of stocks with the highest (lowest) past returns. They focus on a one-month holding interval, but also consider 3-month, 6-month and 12-month holding intervals (with overlapping portfolios). Using monthly returns for a broad sample of U.S. common stocks and contemporaneous three-factor returns during January 1926 through December 2009, they find that: Keep Reading