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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.

Doing Momentum with Style (ETFs)

“Beat the Market with Hot-Anomaly Switching?” concludes that “a trader who periodically switches to the hottest known anomaly based on a rolling window of past performance may be able to beat the market. Anomalies appear to have their own kind of momentum.” Does momentum therefore work for style-based exchange-traded funds (ETF)? To investigate, we apply a simple momentum strategy to the following six ETFs that cut across market capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

We test a simple Top 1 strategy that allocates all funds each month to the one style ETF with the highest total return over a set momentum measurement (ranking or lookback) interval. We focus on the baseline ranking interval from “Simple Asset Class ETF Momentum Strategy”, but test sensitivity of findings to ranking intervals ranging from one to 12 months. As benchmarks, we consider an equally weighted and monthly rebalanced combination of all six style ETFs (EW All), buying and holding S&P Depository Receipts (SPY), and holding SPY when the S&P 500 Index is above its 10-month simple moving average and U.S. Treasury bills (T-bills) when the index is below its 10-month simple moving average (SPY:SMA10). We consider the performance metrics used in “Momentum Strategy (SACEMS)”. Using monthly dividend-adjusted closing prices for the style ETFs and SPY, monthly levels of the S&P 500 index and monthly yields for 3-month T-bills during August 2001 (limited by IWS and IWP) through May 2018 (202 months, ), we find that:

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Intrinsic Momentum or SMA for Avoiding Crashes?

A subscriber suggested comparing intrinsic momentum (IM), also called absolute momentum and time series momentum, to simple moving average (SMA) as alternative signals for equity market entry and exit. To investigate across a wide variety of economic and market conditions, we measure the long run performances of entry and exit signals from IM over past intervals of one to 12 months (IM1 through IM12) and SMAs ranging from 2 to 12 months (SMA2 through SMA12. We consider two cases for IM signals: (1) in stocks (cash) when past return is positive (negative); and, (2) in stocks (cash) when average monthly past return is above (below) the average monthly risk-free rate, proxied by the 3-month U.S. Treasury bill (T-bill) yield, over the same measurement interval. The rule for SMAs is: in stocks (cash) when current level is above (below) the SMA. Using monthly T-bill yield and monthly level of the Dow Jones Industrial Average (DJIA) during January 1934 through April 2018 (over 84 years), we find that: Keep Reading

Stock Market Continuation and Reversal Months?

Are some calendar months more likely to exhibit stock market continuation or reversal than others, perhaps due to seasonal or fund reporting effects? In other words, is intrinsic (times series or absolute) momentum an artifact of some months or all months? To investigate, we relate U.S. stock index returns for each calendar month to those for the preceding 3, 6 and 12 months. Using monthly closes of the S&P 500 Index since December 1949 (using the January 1950 open) and the Russell 2000 Index since September 1987, both through April 2018, we find that: Keep Reading

Intrinsic (Time Series) Momentum Does Not Really Exist?

Does rigorous re-examination of time series (intrinsic or absolute) asset return momentum confirm its statistical and economic significance? In their April 2018 paper entitled “Time-Series Momentum: Is it There?”, Dashan Huang, Jiangyuan Li, Liyao Wang and Guofu Zhou conduct a three-stage review of evidence for predictability of next-month returns based on past 12-month returns for a broad set of asset futures/forwards:

  1. They first run a time series regression of monthly returns versus past 12-month returns for each asset to check predictability for individual assets.
  2. They then run pooled time series regressions for asset returns scaled by respective volatilities as done in prior research, overall and by asset class, noting that pooled regressions can inflate conventional t-statistics and thereby incorrectly reject the null hypothesis. To correct for this predictability inflation, they apply three kinds of bootstrapping simulations.
  3. Finally, they consider a simple alternative explanation of the profitability of an intrinsic momentum strategy tested in prior research that each month buys (sells) assets with positive (negative) past 12-month returns, with the portfolio weight for each asset 40% divided by its past annualized volatility (asset-level target volatility 40%).

Their asset sample consists of 55 contract series spanning commodity futures (24), equity index futures (9), government bond futures (13) and currency forwards (9). They construct returns for an asset by each day calculating excess return for the nearest or next-nearest contract and compounding to compute monthly excess return. Using daily excess returns for the 55 contract series during January 1985 through December 2015, they find that: Keep Reading

Interaction of Short-term Stock Momentum/Reversal and Share Turnover

Do informed (noise) traders drive short-term stock return momentum (reversal)? In their April 2018 paper entitled “Short-term Momentum”, Mamdouh Medhat and Maik Schmeling investigate interaction of short-term momentum/reversal and recent share turnover for U.S. and international stocks. They define share turnover as prior-month trading volume divided by number of shares outstanding. Specifically, they consider four portfolios:

  1. Conventional short-term reversal: Each month go long (short) the value-weighted tenth, or decile, of stocks with the lowest (highest) prior-month returns.
  2. Conventional momentum: Each month go long (short) the value-weighted decile of stocks with the highest (lowest) returns from 12 months ago to one month ago.
  3. Modified short-term reversal (short-term reversal*): Each month go long (short) the value-weighted decile of stocks with the lowest (lowest) share turnovers within in the presorted decile of stocks with the lowest (highest) prior-month returns. [Long and short sides are reversed from those in the paper so that the expected portfolio return is positive.] 
  4. Short-term momentum: Each month go long (short) the value-weighted decile of stocks with the highest (highest) share turnovers within in the presorted decile of stocks with the highest (lowest) prior-month returns.

In other words, they pick stocks for portfolios 3 and 4 by first sorting into deciles based on prior-month return and then sorting each of these deciles into nested deciles sorted based on share turnover. Using data for a broad sample of U.S. common stocks since July 1962 and common stocks in 22 developed markets since January 1993, both through December 2016, they find that: Keep Reading

Simple Volatility-Payout-Momentum Stock Strategy

Is there an easy way for investors to capture jointly the most reliable stock return factor premiums? In their March 2018 paper entitled “The Conservative Formula: Quantitative Investing Made Easy”, Pim van Vliet and David Blitz propose a stock selection strategy based on low return volatility, high net payout yield and strong price momentum. Specifically, at the end of each quarter they:

  1. Segment the then-current 1,000 largest stocks into 500 with the lowest and 500 with the highest 36-month return volatilities.
  2. Within each segment, rank stocks based on total net payout yield (NPY), calculated as dividend yield minus change in shares outstanding divided by its 24-month moving average.
  3. Within each segment, rank stocks based on return from 12 months ago to one month ago (with the skip-month intended to avoid return reversals).
  4. Within the low-volatility segment, average the momentum and NPY ranks for each stock and equally weight the top 100 to reform the Conservative Formula portfolio.
  5. Within the high-volatility segment, average the momentum and NPY ranks for each stock and equally weight the bottom 100 to reform the Speculative Formula portfolio.

Limiting the stock universe to the top 1,000 based on market capitalization suppresses liquidity risk. Limiting screening parameters to three intensely studied factors that require no accounting data mitigates data snooping and data availability risks. They focus on the 1,000 largest U.S. stocks to test a long sample, but also consider the next 1,000 U.S. stocks (mid-caps) and the 1,000 largest stocks from each of Europe, Japan and emerging markets. They further examine: (1) sensitivity to economic conditions doe the long U.S. sample; and, (2) impact of trading frictions in the range 0.1%-0.3% for developed markets and 0.2%-0.6% for emerging markets. Using quarterly prices, dividends and shares outstanding for the contemporaneously largest 1,000 U.S. stocks since 1926, European and Japanese stocks since 1986 and emerging markets stocks since 1991, all through 2016, they find that:

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Not the Simplest Asset Class ETF Momentum Strategy

Does adding international equity exposure and an escape to “cash” enhance performance of a relative momentum strategy that switches between stock and U.S. Treasury bond exchange-traded funds (ETF)? In his February 2018 paper entitled “Simple and Effective Market Timing with Tactical Asset Allocation Part 2 – Choices”, Lewis Glenn updates and considers two extensions to a strategy summarized in “Simplest Asset Class ETF Momentum Strategy?” that each month holds SPDR S&P 500 (SPY) or iShares Barclays 20+ Year Treasury Bond (TLT) depending on which has the higher total return over the last three months. Specifically, the three strategies are:

  1. Pair Switching (PS) – the original strategy as described above.
  2. Quint Switching (QS) – adds iShares MSCI EAFE (EFA), PowerShares QQQ (QQQ) and iShares MSCI Emerging Markets (EEM) to the asset universe, each month picking the top performer.
  3. Quint Switching Filtered (QSF) – modifies QS by adding a rule that if any of SPY, TLT, EFA, QQQ and EFA have non-positive returns over the lookback interval, switch to iShares Barclays 7-10 Year Treasury (IEF) . 

For all strategies, he includes 0.1% switching frictions for each buy and sell action. He focuses on compound annual growth rate (CAGR) and maximum drawdown (DDDmax) as key strategy performance metrics. He considers momentum ranking (lookback) intervals of 1 to 5 months to determine the optimal interval for the two strategy extensions. Using monthly dividend-adjusted closes of the specified funds during April 2004 through January 2018, he finds that:

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Technical Trading of Equity Factor Premiums

Do technical trend trading/intrinsic momentum strategies work for widely used equity factors such as size (small minus big market capitalizations), value (high minus low book-to-market ratios), profitability (robust minus weak), investment (conservative minus aggressive) and momentum (winners minus losers)? In their January 2018 paper entitled “What Goes up Must Not Come Down – Time Series Momentum in Factor Risk Premiums”, Maximilian Renz investigates time variation and trend-based predictability of these five factors and the market factor. He first constructs price series for the six long-short factor portfolios. He then considers seven rules based on a short simple moving average (SMA) crossing above (bullish) or below (bearish) a long SMA measured in trading days: SMA(1, 20), SMA(1, 40), SMA(1, 120), SMA(1, 180), SMA(1, 240), SMA(20, 180) and SMA(20, 240). He also considers two intrinsic (absolute or time series) momentum rules based on change in price over the past 180 or 240 trading days (positive bullish and negative bearish). Motivated by prior research by others, he focuses on SMA(1, 180), daily price crossing its 180-day SMA. He measures trend-based statistical predictability of factor premiums and investigates economic value via a strategy that levers factor exposures between 0 and 1.5 using trend-based signals. Finally, he examines whether incorporating trend information improves accuracies of 1-factor (market), 3-factor (adding size and value) and 5-factor (further adding profitability and investment) models of stock returns. Using daily returns for the six selected U.S. stock market equity factors and for 30 industries during July 1963 through December 2015, he finds that: Keep Reading

Industry Rotation Based on Advanced Regression Techniques

Can advanced regression techniques identify monthly cross-industry lead-lag return relationships that usefully indicate an industry rotation strategy? In their January 2018 paper entitled “Dynamic Return Dependencies Across Industries: A Machine Learning Approach”, David Rapach, Jack Strauss, Jun Tu and Guofu Zhou examine dynamic relationships between past and future returns (lead-lag) across 30 U.S. industries. To guard against overfitting the data, they employ a machine learning regression approach that combines a least absolute shrinkage and selection operator (LASSO) and ordinary least squares (OLS). Their approach allows each industry’s return to respond to lagged returns of all 30 industries. They assess economic value of findings via a long-short industry rotation hedge portfolio that is each month long (short) the fifth, or quintile, of industries with the highest (lowest) predicted returns for the next month based on inception-to-date monthly calculations. They consider three benchmark hedge portfolios based on: (1) historical past average returns of the industries; (2) an OLS-only approach; and, (3) a cross-sectional, or relative, momentum approach that is each month long (short) the quintile of industries with the highest (lowest) returns over the past 12 months. Using monthly returns  for 30 value-weighted U.S. industry groups during 1960 through 2016, they find that:

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Preliminary Momentum Strategy and Value Strategy Updates

The home page“Simple Asset Class ETF Momentum Strategy” (SACEMS) and “Simple Asset Class ETF Value Strategy” (SACEVS) now show preliminary positions for February 2018. For SACEMS, past returns for the first and second positions and for the third and fourth positions are close, such that rankings could change by the close. For SACEVS, allocations are unlikely to change by the close.

An anomaly in the source data surfaced this month. Returns for December 2017 for dividend-paying ETFs changed between the end of December 2017 and the end of January 2018. It appears that data available as of the December market close did not account for dividend ex-dates during December. This anomaly has two implications:

  1. December 2017 returns previously reported for SACEMS and SACEVS (and alternatives using dividend paying ETFs) were too low. We are correcting these returns.
  2. More seriously, incorporation of December 2017 dividends causes a change in the SACEMS top three winners for December 2017, which we determine based on total returns. Since the historical SACEMS performance we present is based on fully updated backtests, the data anomaly introduces a disconnect between backtest and live portfolio performances. In this case, the backtest performs better than a live portfolio. If this issue recurs, we will consider other data management approaches.

Recall the prior data instability reported in “Simple Asset Class ETF Momentum Strategy Data Changes”. Over the long run, data instability issues may cancel with respect to live portfolio performance.

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