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.

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Trend Following vs. Return Chasing

How can trend following (intrinsic or absolute or time series momentum) beat the market, while ostensibly similar return chasing transfers wealth from naive to smart investors? In their January 2016 paper entitled “Return Chasing and Trend Following: Superficial Similarities Mask Fundamental Differences”, Victor Haghani and Samantha McBride offer a plausible and testable definition of return chasing and explore its differences from trend following. They characterize trend followers as mechanical and decisive and return chasers as discretionary and slow moving. For quantitative comparison, they consider three long-only, no-leverage strategies:

  1. 50-50 (benchmark): 50% equities and 50% U.S. Treasury bills (T-bills), rebalanced monthly.
  2. Trend following: 100% stocks (T-bills) when real stock market return over the past year is greater than (less than) 2.5%.
  3. Return chasing: increase (decrease) exposure to stocks each month by 20% of however much real stock market return exceeds (falls short of) 2.5% over the past year, holding the balance in T-bills.

They test these strategies with Robert Shiller’s long-run U.S. stock market data spanning 1871 through 2015 and with separately specified Monte Carlo simulation (5,000 runs of 20 years based on weekly simulated prices). Using these two approaches, they find that: Keep Reading

Momentum Strategy and Trading Calendar Updates

We have updated the the monthly asset class ETF momentum winners and associated performance data at Momentum Strategy.

We have updated the Trading Calendar to incorporate data for January 2016.

Preliminary Momentum Strategy Update

The home page and “Momentum Strategy” now show preliminary asset class ETF momentum strategy positions for February 2016. Differences in past returns among the top places suggest that rankings are unlikely to change by the close. Only three of nine asset class proxies have positive past returns.

Mutual Fund Hot Hand Performance

A subscriber inquired about a “hot hand” strategy that each year picks the top performer from a family of diversified equity mutual funds (not including sector funds) and holds that winner the next year. To evaluate this strategy, we consider the Vanguard family of diversified equity mutual funds over the period during which SPDR S&P 500 (SPY) is available as a total return benchmark. We assume that there are no costs or holding period constraints/delay for switching from one fund to another. We also simplify calculations by assuming that end-of-month “hot hand” fund identification and fund switches occur simultaneously (in other words, we can accurately rank mutual funds one day before the end of the month). Using monthly total returns for SPY since inception and for Vanguard diversified equity mutual funds as available from Yahoo!Finance since December 1992, all through December 2015, we find that: Keep Reading

Stock Anomaly Momentum Strategy

Do U.S. stock return anomalies exhibit exploitable momentum? In their December 2016 paper entitled “Scaling Up Market Anomalies”, Doron Avramov, Si Cheng, Amnon Schreiber and Koby Shemer test momentum across stock return anomalies. Their investment universe consists of the long and short sides of 15 stock portfolios, each long (short) the top (bottom) tenth of stocks based on sorting by one of the following 15 variables: failure probability, O-Score, net stock issuance, composite equity issuance, total accruals, net operating assets, momentum, gross profitability, asset growth, return on assets, abnormal capital investment, standardized unexpected earnings, analyst dispersion, idiosyncratic volatility and book-to-market ratio. They each month rank the 15 anomaly portfolios by prior-month return and test an anomaly momentum strategy that is long (short) the long (short) sides of the top five winner (bottom five loser) portfolios. They also consider top three-bottom three and top four-bottom four long-short strategies. Their benchmark is the equally weighted combination of all 15 anomaly portfolios. Using daily and monthly data for a broad sample of U.S. common stocks during 1976 through 2013, they find that: Keep Reading

Simple Sector ETF Momentum Strategy Robustness/Sensitivity Tests

How sensitive is the performance of the “Simple Sector ETF Momentum Strategy” to selecting ranks other than winners and to choosing a momentum ranking interval other than six months? This strategy each month ranks the following nine sector exchange-traded funds (ETF) on past return and rotates to the strongest sector:

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)

Available data are so limited that sensitivity test results may mislead. With that reservation, we perform two robustness/sensitivity tests: (1) comparison of returns for all nine ranks of winner through loser based on a ranking interval of six months and a holding interval of one month (6-1); and, (2) comparison of winner returns for ranking intervals ranging from one to 12 months (1-1 through 12-1) and for a six-month lagged six-month ranking interval (12:7-1) per “Isolating the Decisive Momentum (Echo?)”, all with one-month holding intervals. Using monthly adjusted closing prices for the sector ETFs and SPDR S&P 500 (SPY) over the period December 1998 through December 2015 (205 months), we find that: Keep Reading

Simple Sector ETF Momentum Strategy

Do simple momentum trading strategies applied to major U.S. stock market sectors outperform reasonable benchmarks? To investigate, we apply three simple momentum strategies to the nine sector exchange-traded funds (ETF) defined by the Select Sector 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)

The three strategies are: (1) allocate all funds at the end of each month to the sector ETF with the highest total return over the past six months (6-1); (2) allocate all funds at the end of each month to the sector ETF with the highest total return over the six months ending the prior month (6-1;1), hypothesizing that the skip-month avoids short-term reversals; and, (3) more cautiously, allocate all funds at the end of each month either to the sector ETF with the highest total return over the past six months or to cash depending on whether the S&P 500 Index is above or below its 10-month simple moving average (6-1;SMA10). A six-month ranking period is intuitively large enough to gauge sector momentum but small enough to react to changes in business conditions that might favor one sector over others. Using monthly dividend-adjusted closing prices for the sector ETFs, the S&P 500 index, 3-month Treasury bills (T-bills) and S&P Depository Receipts (SPY) over the period December 1998 through December 2015 (205 months), we find that: Keep Reading

Combining SMA Crash Protection and Momentum in Asset Allocation

Does asset allocation based on both trend following via a simple moving average (SMA) and return momentum work well? In the July 2015 update of their paper entitled “The Trend is Our Friend: Risk Parity, Momentum and Trend Following in Global Asset Allocation”, Andrew Clare, James Seaton, Peter Smith and Stephen Thomas examine the effectiveness of trend following based on SMAs and momentum screens in forming portfolios across and within asset classes. They consider five asset classes: developed equity markets (24 component country indexes); emerging equity markets (16 component country indexes); bonds (19 component country indexes); commodities (23 component commodity indexes); and, real estate (13 country REIT indexes). They compare equal weight and risk parity (proportional to inverse 12-month volatility) strategic allocations. They define trend following as buying (selling) an asset when its price moves above (below) a moving average of 6, 8, 10 or 12 months. They consider both simple momentum (12-month lagged total return) and volatility-adjusted momentum (dividing by standard deviation of monthly returns over the same 12 months) for momentum screens. They ignore trading frictions, exclude shorting and assume monthly trend/momentum calculations and associated trade executions are coincident. Using monthly total returns in U.S. dollars for the five broad value-weighted asset class indexes and for the 95 components of these indexes during January 1993 through March 2015, along with contemporaneous 3-month Treasury bill yields as the return on cash, they find that: Keep Reading

Trend Factor and Future Stock Returns

Does the information in short, intermediate and long stock price trends combined by relating multiple simple moving averages (SMA) to future returns usefully predict stock returns? In the September 2015 update of their paper entitled “A Trend Factor: Any Economic Gains from Using Information over Investment Horizons?”, Yufeng Han and Guofu Zhou examine a trend factor that simultaneously captures short, intermediate and long stock price trends. Specifically, at the end of each month for each sampled stock, they:

  1. Calculate SMAs over the past 3, 5, 10, 20, 50, 100, 200, 400, 600, 800 and 1,000 trading days.
  2. Normalize SMAs by dividing by the final close.
  3. Regress monthly SMAs against next-month stock returns to estimate historical linear coefficients for all SMAs.
  4. Predict the return for the stock next month based on average SMA coefficients for the past 12 months applied to the most recent set of SMAs.

They define the trend factor as the average monthly gross return for a portfolio that is each month long (short) the equally weighted fifth (quintile) of stocks with the highest (lowest) expected returns. Using daily prices and associated stock/firm characteristics for a broad sample of U.S. common stocks during January 1926 through December 2014, they find that: Keep Reading

SACEMS-SACEVS Mutual Diversification

Are the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) mutually diversifying. To check, we relate monthly returns for the SACEVS and the SACEMS exchange-traded fund (ETF) selections and look at the performance of an equally weighted portfolio of the two strategies, rebalanced monthly (50-50). Specifically, we consider: SACEVS Best Value paired with SACEMS Top 1; and, SACEVS Weighted paired with SACEMS Equally Weighted (EW) Top 3. Using monthly gross returns for SACEVS Best Value and SACEMS Top 1 since January 2003 and for SACEVS Weighted and SACEMS EW Top 3 since August 2006, all through November 2015, we find that: Keep Reading

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Current Momentum Winners

ETF Momentum Signal
for February 2016 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
11.5% 11.9%
Top 3 ETFs SPY
12.4% 6.5%
Strategy Overview
Current Value Allocations

ETF Value Signal
for February 2016 (Final)

Cash

IEF

LQD

SPY

The asset with the highest allocation is the holding of the Best Value strategy.
Gross Compound Annual Growth Rates
(Since September 2002)
Best Value Weighted 60-40
12.4% 9.4% 7.6%
Strategy Overview
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