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Momentum Investing Strategy (Strategy Overview)

Allocations for August 2020 (Final)
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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.

Mutual Funds Successfully Exploiting Academic Research?

Can equity funds exploit widely accepted stock return anomalies? In their July 2013 paper entitled “Academic Knowledge Dissemination in the Mutual Fund Industry: Can Mutual Funds Successfully Adopt Factor Investing Strategies?”, Eduard Van Gelderen and Joop Huij investigate whether mutual funds that materially adopt investment strategies based on published asset pricing anomalies consistently outperform the stock market. They first use monthly regressions to measure degrees of use of six factor investing strategies (low-beta, small cap, value, momentum, short-term reversal and long-term reversion) across U.S. equity mutual funds. They then calculate market-adjusted returns to determine whether funds employing the strategies outperform those that do not and the market. Using monthly returns for 6,814 U.S. equity mutual funds, and contemporaneous monthly returns for the specified factors, during 1990 through 2010, they find that: Keep Reading

Stock Price Acceleration as a Momentum Investing Enhancement

Are winning (losing) stocks with the strongest upward (downward) acceleration the best bets for a momentum strategy? In their July 2013 paper entitled “Investor Attention, Visual Price Pattern, and Momentum Investing”, Li-Wen Chen and Hsin-Yi Yu investigate whether visually striking patterns of past prices tend to grab investor attention, induce overreaction and amplify the momentum effect. They first rank stocks into fifths (quintiles) based on past returns to identify winners and losers (with a skip-month between the ranking interval and portfolio formation to avoid reversals). They then regress daily returns of winners and losers versus time squared over the past 12 months, with a positive (negative) coefficient indicating a convex (concave) price trajectory curvature, and further sort winner and loser quintiles into fifths based on curvature. Intuitively, winners (losers) with convex, upward accelerating (concave, downward accelerating) price trajectories most strongly attract trader attention and most reliably exhibit price momentum. They test this intuition by each month forming nine momentum hedge portfolios that are:

  1. Long winners and short losers (traditional momentum approach).
  2. Long winners and short convex-shaped (decelerating) losers.
  3. Long winners and short concave-shaped (accelerating) losers.
  4. Long concave-shaped (decelerating) winners and short losers.
  5. Long convex-shaped (accelerating) winners and short losers.
  6. Long convex-shaped (accelerating) winners and short concave-shaped (accelerating) losers.
  7. Long concave-shaped (decelerating) winners and short convex-shaped (decelerating) losers.
  8. Long convex-shaped (accelerating) winners and short convex-shaped (decelerating) losers.
  9. Long concave-shaped (decelerating) winners and short (accelerating) concave-shaped losers.

Portfolios are equally weighted with baseline settings of a 12-month momentum ranking interval and a six-month holding interval (six overlapping portfolios in any month). Using monthly and daily prices and accounting data for a broad sample of U.S. common stocks, along with contemporaneous return factors and economic data, during January 1962 through December 2011, they find that: Keep Reading

Stock Price Momentum Over the Very Long Run

Is stock return momentum persistent over a very long sample? In their July 2013 paper entitled “212 Years of Price Momentum (The World’s Longest Backtest: 1801 – 2012)”, Christopher Geczy and Mikhail Samonov extend analysis of momentum in U.S. stock prices back to 1800. They measure a stock’s momentum as its return from 11 months ago to one month ago, with the skipped month avoiding any short-term reversal. They measure the momentum effect as the return for a portfolio that is each month long (short) the equally weighted third of stocks with the highest (lowest) momentum. They define excess return as the return above the market return. Because reliable shares outstanding data are unavailable, they define the market return as the equal-weighted (rather than value-weighted) average return for all stocks in the universe. They ignore dividends (also not reliably available). They define market state in terms of sign (up or down during the same interval used for stock momentum measurement) and duration (number of consecutive months up or down). Using monthly returns for a sample of publicly traded U.S. stocks during January 1800 through December 2012, with focus on the “new” data for 1800 through 1926, they find that: Keep Reading

Inside Intrinsic Momentum

A subscriber inquired whether the level of momentum (past return) for each asset in the “Momentum Strategy” indicates the level of future return for that asset, and whether extreme negative momentum supports shorting an asset. In other words, do each of these asset class proxies exhibit reliably exploitable intrinsic momentum? To investigate, we regress next-month return versus past five-month return for each of the following eight asset class exchange-traded funds (ETF):

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)

Using monthly dividend-adjusted closing prices for the asset class proxies over the period July 2002 (or inception if not available then) through June 2013 (maximum 132 months), we find that: Keep Reading

Intrinsic Momentum Framed as Stop-loss/Re-entry Rules

Do asset classes generally exhibit enough price momentum to make stop-loss and re-entry rules effective for timing them? In his June 2013 paper entitled “Assessing Stop-loss and Re-entry Strategies”, Joachim Klement analyzes four stop-loss and re-entry rule pairs for six regional stock market indexes, a U.S. real estate investment trust (REIT) index, a commodity index and spot gold. Specifically, he tests:

  1. Fast out-fast in (most effective when there are multiple brief corrections): Exit (re-enter) when the cumulative loss (gain) over the past 3 (3) months exceeds some specified threshold. 
  2. Fast out-slow in (most effective during a downward or sideways trend): Exit (re-enter) when the cumulative loss (gain) over the past 3 (12) months exceeds some specified threshold.
  3. Slow out-fast in (most effective during an upward trend with intermittent crashes): Exit (re-enter) when the cumulative loss (gain) over the past 12 (3) months exceeds some specified threshold.
  4. Slow out-slow in (most effective when momentum is weak and transaction costs are high): Exit (re-enter) when the cumulative loss (gain) over the past 12 (12) months exceeds some specified threshold.

He tests ranges of stop-loss and re-entry decision thresholds. Because asset class return volatilities differ, he scales these thresholds to the annual standard deviation of returns for each asset class. He assumes a constant exit/re-entry trading friction of 0.25% and zero return on cash. For relevant tests, he defines a secular bull (bear) market as an extended subperiod of positive returns significantly above long-term average (negative or zero real returns). Using monthly asset class index returns as available during January 1970 through April 2013 in local currencies when applicable, he finds that: Keep Reading

Short-term Currency Exchange Rate Momentum

Do currency exchange rates exhibit short-term momentum? In the April 2013 version of their paper entitled “Is There Momentum or Reversal in Weekly Currency Returns?”, Ahmad Raza, Ben Marshall and Nuttawat Visaltanachoti investigate whether exchange rate movements over the past one to four weeks persist over the next one to four weeks. They test these 16 alternative strategies (four look-back intervals times four holding intervals) by each week buying (selling) the fifth of available currencies that have appreciated (depreciated) the most against the U.S. dollar. Using weekly and monthly spot and forward prices for 63 emerging and developed market currencies versus the U.S. dollar as available during October 1997 through December 2011, they find that: Keep Reading

Extracting Strategic Benefits from a Commodities Allocation

Can commodities still be useful for portfolio diversification, despite their recent poor aggregate return, high volatility and elevated return correlations with other asset classes? In the May 2013 version of their paper entitled “Strategic Allocation to Commodity Factor Premiums”, David Blitz and Wilma de Groot examine the performance and diversification power of the commodity market portfolio and of alternative commodity momentum, carry and low-risk (low-volatility) portfolios. They define the commodity market portfolio as the S&P GSCI (production-weighted aggregation of six energy, seven metal and 11 agricultural commodities). The commodity long-only (long-short) momentum portfolio is each month long the equally weighted 30% of commodities with the highest returns over the past 12 months (and short the 30% of commodities with the lowest returns). The commodity long-only (long-short) carry portfolio is each month long the equally weighted 30% of commodities with the highest annualized ratios of nearest to next-nearest futures contract price (and short the 30% of commodities with the lowest ratios). The commodity long-only (long-short) low-risk portfolio is each month long the equally weighted 30% of commodities with the lowest daily volatilities over the past three years (and short the 30% of commodities with the highest volatilities). They also consider a combination that equally weights the commodity momentum, carry and low-risk portfolios. For comparison to U.S. stocks, they use returns of long-only, equally weighted “big-momentum” and “big-value” (comparable to commodity carry) stock portfolios from Kenneth French, and a similarly constructed “big-low-risk” stock portfolio. For comparison with bonds, they use the total return of the JP Morgan U.S. government bond index. For all return series and allocation strategies, they ignore trading frictions. Using daily and monthly futures index levels and contract prices for the 24 commodities in the S&P GSCI as available during January 1979 through June 2012, along with contemporaneous returns for a broad sample of U.S. stocks, they find that: Keep Reading

Optimal Quality and Value Combination?

Does adding fundamental firm quality metrics to refine stock sorts based on traditional value ratios, book-to-market ratio (B/M) and earnings-to-price ratio (E/P), improve portfolio performance? In his 2013 paper entitled “The Quality Dimension of Value Investing”, Robert Novy-Marx tests combination strategies to determine which commonly used quality measures most enhance the performance of value ratios. He considers such quality metrics as Piotroski’s FSCORE, earnings accrualsgross profitability (GP) and return on invested capital (ROIC). His general test approach is to reform capitalization-weighted portfolios annually from stocks sorted at the end of each June according to value ratios and quality metrics for the previous calendar year. He uses the 1000 largest (2000 next largest) stocks by market capitalization to represent large (small) stocks. He considers both long-only (long the top 30%) and long-short (long the top 30% and short the bottom 30%) portfolios. He also considers the incremental benefit of incorporating stock price momentum based on return over the previous 11 months with a skip-month (11-1) into stock selection. He estimates trading frictions based on calculated turnover and effective bid-ask spreads. Using stock prices and associated firm fundamentals during July 1963 through December 2011, he finds that: Keep Reading

Intrinsic Momentum Across Asset Classes

Is intrinsic (time series) momentum effective in managing risk across asset classes? In his April 2013 paper entitled “Absolute Momentum: a Simple Rule-Based Strategy and Universal Trend-Following Overlay”, Gary Antonacci examines an intrinsic (absolute or time-series) momentum strategy that each month holds a risky asset (U.S. Treasury bills) when the return on the risky asset over the preceding 12 months is greater (less) than the contemporaneous yield on U.S. Treasury bills. He applies the strategy separately to eight risky asset classes: two equity indexes (MSCI US and MSCI EAFE); three bond/credit classes constructed from Barclay’s Capital Long U.S. Treasury, Intermediate U.S. Treasury, U.S. Credit, U.S. High Yield Corporate, U.S. Government & Credit and U.S. Aggregate Bond indexes; the FTSE NAREIT U.S. Real Estate Index; the S&P GSCI; and, spot gold based on the London PM fix. He also evaluates intrinsic momentum strategy performance for a 60%-40% MSCI US-Long U.S. Treasury portfolio and a portfolio consisting of five equally weighted assets, both rebalanced monthly. He assumes a friction of 0.2% for switching between a risky asset and U.S. Treasury bills (T-bill). Using monthly total returns for the eight asset classes as available and 90-day T-bills yields during January 1973 through December 2012, he finds that: Keep Reading

Intrinsic Value and Momentum Across (Futures) Asset Classes

Do time series carry (intrinsic value) and time series momentum (intrinsic momentum) strategies work across asset classes? What drives their returns, and how do they interact? In the January 2013 very preliminary version of their paper entitled “The Returns to Carry and Momentum Strategies: Business Cycles, Hedge Fund Capital and Limits to Arbitrage”, Jan Danilo Ahmerkamp and James Grant examine intrinsic value strategy and intrinsic momentum strategy returns for 55 worldwide futures contract series spanning equities, bonds, currencies, commodities and metals, including the effects of business cycle/economic conditions and institutional ownership. They study futures (rather than spot/cash) markets to minimize trading frictions and avoid shorting constraints. They calculate futures contract returns relative to the nearest-to-maturity futures contract (not spot/cash market) price. The momentum signal is lagged 12-month cumulative raw return. The carry (value) signal is the lagged 12-month average normalized price difference between second nearest-to-maturity and nearest contracts. They test strategies that are each month long (short) contracts with positive (negative) value or momentum signals. They also test a combination strategy that is long (short) contracts with both value and momentum signals positive (negative). For comparability of assets, they weight contract series within multi-asset portfolios by inverse volatility, estimated as the average absolute value of daily returns over the past three months. Their benchmark is a long-only portfolio of all contracts weighted by inverse volatility. Using daily settlement prices for the nearest and second nearest futures contracts of the 55 series (10 equities, 12 bonds, 17 commodities, nine currencies and seven metals) as available during 1980 through 2012, they find that: Keep Reading

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