Momentum Investing

“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

The “Simple Asset Class ETF Momentum Strategy” each month allocates all funds to the one of the following eight asset class exchange-traded funds (ETF), or cash, with the highest total return over the past five months:

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)

“Simple Asset Class ETF Momentum Strategy Robustness/Sensitivity Tests” shows that, among uniform ranking intervals, five months is optimal. Citing the optimality of a three-month ranking interval in “Simple Debt Class Mutual Fund Momentum Strategy”, a subscriber inquired whether using a three-month ranking interval just for TLT might improve Simple Asset Class ETF Momentum Strategy performance. To investigate more generally, we compute net terminal values for 108 variations of the strategy by letting the ranking interval for each asset range from one to 12 months, while holding the ranking interval for all other assets at five months. In order to compare ranking intervals of different lengths, we use the average total return per month for ranking. For example, the average monthly total return for a five-month ranking interval is the five-month total return divided by five. Using monthly dividend-adjusted closes for the asset class proxies and the yield for Cash during July 2002 (or inception if not available then) through May 2014 (141 months), we find that:

Keep Reading

A subscriber suggested an alternative to the “Simple Asset Class ETF Momentum Strategy” that weights asset class ETFs according to five-month past return ranking (such as 35-25-20-10-4-3-2-1) rather than allocating all funds to the winner. Do the diversification benefits of this alternative outweigh the loss of momentum purity? To investigate, we return to the following eight asset class exchange-traded funds (ETF), plus cash:

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)

As one benchmark, we allocate all funds at the end of each month to the asset class ETF or cash with the highest total return over the past five months (5-1). As another benchmark, we maintain an equal-weighted (EW), monthly rebalanced portfolio of all nine asset classes. As alternatives, we test two momentum rank-weighted (RW), linearly-scaled combinations of all nine classes, one steep across ranks and one shallow. We also test EW combinations of the Top 5, Top 4, Top 3 and Top 2 momentum ranks. Using monthly adjusted closing prices for the asset class proxies and the yield for Cash over the period February 2006 (the earliest all ETFs are available) through May 2014 (100 months), we find that: Keep Reading

We have updated the detail at “Momentum Strategy” to incorporate the historical data changes described in “Simple Asset Class ETF Momentum Strategy Data Changes”. The principal effects are to decrease the performance of the Top 1 portfolio and increase the performance of the Top 2 portfolio.

Over the coming week, we will accordingly update much of the supporting research listed at “Momentum Strategy”.

Do individuals who actively reallocate funds within their pension accounts outperform passive counterparts? In the March 2014 update of their paper entitled “Individual Investor Activity and Performance”, Magnus Dahlquist, Jose Vicente Martinez and Paul Soderlind examine the activity and performance of individual participants in Sweden’s Premium Pension System. This system allows individual participants to reallocate among available mutual funds on a daily basis with no switching fees/impediments. Information about the 1,230 funds offered during the sample period includes type (fixed income, balanced, life-cycle and equity), return and risk measured at several horizons, fee and major holdings. Most are equity funds, about half of which invest primarily in international equities. The government assigns individuals who make no choice to a default fund. Using daily net returns, fund trades and demographics for 70,755 individuals (from a random draw of individuals in the system over the entire period) and contemporaneous returns for several benchmarks during September 2000 through May 2010, they find that: Keep Reading