Momentum Investing

Can individual investors reliably extract excess returns from long-only stock momentum? In their April 2014 paper entitled “Profitable Momentum Trading Strategies for Individual Investors”, Bryan Foltice and Thomas Langer examine whether a long-only stock momentum portfolio holding the top one to 50 stocks outperforms the stock market on a net basis. Their approach avoids issues of shorting costs/feasibility and explores trade-offs between gross performance and trading frictions. Their baseline analysis: (1) ranks stocks based on six-month total return; (2) forms an equally weighted portfolio of the top 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40 and 50 stocks at the next close; (3) holds the portfolio for 12 months; and, (4) estimates net returns nine initial investment amounts ranging from $5,000 to $1,000,000. They approximate one-way trading friction a $10 commission plus a half bid-ask spread specified by range of market capitalization (0.21% to 0.75%) plus a relatively small SEC fee. For stocks retained from the prior portfolio, the bid-ask spread applies only to the change in position size from rebalancing to equal weight. Finally, they investigate whether employing overlapping portfolios of small numbers of stocks (forming portfolios more frequently than annually but still holding each portfolio for a year) suppresses volatility and improves risk-adjusted performance. They use buy-and-hold the S&P 500 Total Return Index as a benchmark. Using data for live and dead NYSE stocks with contemporaneous market capitalizations over $20 million during 1991 through 2010 (an average of 2,201 stocks per month), they find that: Keep Reading

As a follow-up to “Mutual Fund Hot Hand Performance Robustness Test”, a subscriber suggested testing a portfolio that each year holds the top two Fidelity diversified equity funds plus the top two Vanguard diversified equity funds from “Mutual Fund Hot Hand Performance” (four funds total). Such a portfolio should suppress volatility, particularly the effects of any outlier returns, while maintaining tax-friendly capital gains treatment. We extend that suggestion to consider the top three funds from each of the Fidelity and Vanguard sets. We use June-to-June annual returns starting 1993 with availability of SPDR S&P 500 (SPY) as a widely used and easily investable benchmark. The number of Fidelity (Vanguard) funds available for the initial ranking in 1993 is 23 (10), growing to 61 (49) by 2013. Using monthly total returns for SPY and the Fidelity and Vanguard diversified equity mutual funds as available from Yahoo!Finance during June 1993 through June 2014 (21 years), we find that: Keep Reading

In response to “Optimal Monthly Cycle for Simple Asset Class ETF Momentum Strategy?”, a subscriber asked about the optimal monthly cycle for “Simple Sector ETF Momentum Strategy”, which each month allocates all funds to the one of the following nine Select Sector Standard & Poor’s Depository Receipts (SPDR) exchange-traded funds (ETF) with the highest total return over the past six months :

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)

To investigate, we compare 21 variations of the strategy based on shifting the monthly return calculation cycle relative to trading days from the end of the month (EOM). For example, an EOM+5 cycle ranks assets based on closing prices five trading days after EOM each month. Using daily dividend-adjusted closes for the sector ETFs from mid-January 1999 through mid-July 2014 (about 186 months), we find that:

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Has the growing role of financial investors in commodities markets (financialization) weakened performance of widely used momentum and term structure investing strategies? In his July 2014 paper entitled “Strategies Based on Momentum and Term Structure in Financialized Commodity Markets”, Adam Zaremba investigates impacts of financialization of commodity markets on the profitability of momentum and term structure strategies. His base momentum strategy is each month long (short) the half of commodity futures with higher (lower) returns over the past month. His base term structure strategy is long (short) the half of commodity futures with the largest positive or backwardated (negative or contangoed) difference in prices between the nearest and next-nearest contracts. For each commodity futures series and each strategy, he performs double-sorts on strategy parameters and the level of financial investor (non-commercial trader) participation from Commitments of Traders (COT) reports to measure the effects of financialization on strategy performance. All portfolios are equally weighted and fully collateralized. Using monthly total returns for 26 commodity futures series as available and a broad commodities index, along with position data from COT reports, during 1986 through 2013, he finds that: Keep Reading

“Mutual Fund Hot Hand Performance” tests a “hot hand” strategy that each year picks the top performer from the Vanguard family of diversified equity mutual funds (not including sector funds) and holds that winner the next year. A subscriber suggested a robustness test using the Fidelity family of diversified equity mutual funds. To support the test, we select all Fidelity diversified U.S. and international equity mutual funds that bear no transaction fee, are open to new investors and have a history of at least three years. We consider the total return on the S&P 500 Index (with dividends estimated from Robert Shiller’s data) and SPDR S&P 500 (SPY) as benchmarks. As in the prior analysis of Vanguard funds, we pick end of June to end of the next June for annual return measurement intervals. To simplify analysis, we assume the “hot hand” mutual fund on the next-to-last trading day of June is the same as that for the end of June. We assume that there are no costs or holding period constraints/delay for switching from one fund to another. Using annual returns for the S&P 500 Index plus Shiller’s dividend data and annual returns for SPY and Fidelity diversified equity mutual funds as available from Yahoo!Finance during June 1980 through June 2014, we find that: Keep Reading

Do the behaviors of the most widely accepted stock market factors (size, book-to-market or value, and momentum) vary with the economic trend? In the June 2014 version of their paper entitled “Macroeconomic Determinants of Cyclical Variations in Value, Size and Momentum premium in the UK”, Golam Sarwar, Cesario Mateus and Natasa Todorovic examine differences in the sensitivities of UK equity market size, value and momentum factor returns (premiums) to changes in broad and specific economic variables. They define the broad economic state each month as upturn (downturn) when the OECD Composite Leading Indicator for the UK increases (decreases) that month. They also consider contributions of six specific variables to economic trend: GDP growth; unexpected inflation (change in CPI); interest rate (3-month UK Treasury bill yield); term spread (10-year UK Treasury bond yield minus 3-month UK Treasury bill yield); credit spread (Moody’s U.S. BBA yield minus 10-year UK government bond yield); and, money supply growth. They lag economic variables by one or two months to align their releases with stock market premium measurements. Using monthly UK size, value and momentum factors and economic data during July 1982 through December 2012, they find that: Keep Reading

Can investors exploit monthly persistence in the value premium for U.S. stocks? In his February 2014 paper entitled “Exploiting Factor Autocorrelation to Improve Risk Adjusted Returns”, Kevin Oversby investigates whether investors can exploit the fact that the Fama-French model high-minus-low (HML) value factor exhibits positive monthly autocorrelation (persistence). The HML factor derives from the difference in performance between portfolios of stocks with high and low book-to-market ratios. Prior published research indicates that the value premium concentrates in small firms, so he focuses on stocks with market capitalizations below the NYSE median. His test strategies each month invest in capitalization-weighted small value (small growth or small momentum) Fama-French portfolios when the prior-month sign of the HML factor is positive (negative). The strategies additionally retreat to a risk-free asset (such as U.S. Treasury bills) if the prior-month return for the test strategy is negative. Using HML factor values and monthly portfolio returns for small value, small growth and small momentum Fama-French portfolios, he finds that: Keep Reading

Do commonly used indicators reliably predict stock size, value and momentum strategy returns? In the June 2014 version of his paper entitled “A Comprehensive Look at Size, Value and Momentum Return Predictability”, Afonso Januario examines the abilities of 17 fundamental and technical indicators and indicator combinations to anticipate returns for these three factors. He defines factor portfolios based on market capitalization (size), book-to-market ratio (value) and return from 12 months ago to one month ago (momentum), reformed monthly, as follows:

1. Size = (SmallValue+SmallNeutral+SmallGrowth)/3 – (BigValue+BigNeutral+BigGrowth)/3
2. Value = (SmallValue+BigValue)/2 – (SmallGrowth+BigGrowth)/2
3. Momentum = (SmallWinners+BigWinners)/2 – (SmallLosers+BigLosers)/2

He selects the 17 indicators (such as book-to-market ratio, dividend yield, earnings-price ratio, return on equity, lagged return, short interest and implied volatility) from prior published research on predictive variables. He measures indicator values each month as the averages only for stocks in long or short sides (and the spread between them) of each of the above three factor portfolios. He applies linear regressions at monthly and annual frequencies to determine whether an indicator is more effective than the historical average factor portfolio return in predicting future factor portfolio returns. Using relevant sets of data for a broad sample of relatively liquid U.S. stocks from initial set availability (ranging from 1950 to 1995) through 2012, he finds that: Keep Reading

“Sticky Winner Asset Class ETF Momentum Strategy” tests whether limiting the trading of the “Simple Asset Class ETF Momentum Strategy” by holding onto the winner until it drops out of the top three boosts performance of the latter by reducing trading and thereby suppressing trading frictions. A subscriber proposed a more precise approach to limit trading: continue holding a past winner until it loses to a new winner by a significant margin. To investigate whether this approach (Buffered Winner) works, we compare it to the original strategy (Winner), which allocates all funds at the end of each month to the asset class exchange-traded fund (ETF) or cash with the highest total return over the last five months, as applied to the following nine assets:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

Using monthly adjusted closing prices for the asset class proxies and the yield for Cash over the period July 2002 (or inception if not available then) through June 2014 (144 months), we find that: Keep Reading

Is there a practical way to apply momentum investing in a Modern Portfolio Theory (MPT) framework? In his June 2014 paper entitled “Momentum, Markowitz, and Smart Beta”, Wouter Keller constructs a long-only, unleveraged Modern Asset Allocation (MAA) model in three steps

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

He reforms the MAA portfolio monthly at the first close. His baseline historical interval for estimation of all variables is four months (84 trading days). His baseline shrinkage factor for all variables is 50%. His principal benchmark is the equally weighted (EW) “market” of all assets, rebalanced monthly. He assumes one-way trading friction of 0.1%. He considers a range of portfolio performance metrics: annualized return, annual volatility, maximum drawdown, turnover, Sharpe ratio, Omega ratio and Calmar ratio. Using daily dividend-adjusted prices for assets allocated to three universes (10 exchange-traded funds [ETF], 35 ETFs and 104 U.S. stocks/bonds) during December 1997 through December 2013, he finds that: Keep Reading