Momentum Investing

What is the state of momentum investing? In their January 2011 paper entitled “Momentum”, Narasimhan Jegadeesh and Sheridan Titman summarize equity price momentum research and discuss explanations for the momentum anomaly. Specifically, they address equity momentum investing performance during 1990 through 2009, and the firm characteristics and market conditions that affect momentum returns. Based on a review of momentum research since 1990, their key points are: More…

Does a strategy of combining monthly individual stock return reversal with monthly industry momentum enhance results compared to the separate strategies. In their August 2011 paper entitled “One-month Individual Stock Return Reversals and Industry Return Momentum”, Marc Simpson, Emiliano Giudici and John Emery examine the relationship between individual stock return reversals and industry momentum by considering three strategies: (1) a conventional reversal strategy that each month buys (shorts) individual stock losers (winners); (2) a simple industry momentum strategy that each month buys (shorts) the previous month’s winning (losing) industry portfolio; and, (3) a combined reversal-industry momentum strategy that buys (shorts) the losing (winning) stocks within the previous month’s winning (losing) industry portfolio. Using monthly returns, SIC codes and the Fama-French definitions for ten industries over the period January 1931 through December 2010 (960 months) , they find that: More…

Based on results from “Simple Sector ETF Momentum Strategy Performance”, “Does the Turn-of-the-Month Effect Work for Sectors?” and “Long-term SMA and TOTM Combination Strategy”, a subscriber proposed: “Have you ever thought of combining the three? When SPY is above a long term average, buy the best performing sector ETF using the TOTM strategy.” To investigate, we consider the nine sector exchange-traded funds (ETF) defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR), all of which have trading data back to December 1998:

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)

We determine sector momentum based on total return over the past six months (6-1). We define bull-bear stock market state according to whether SPDR S&P 500 (SPY) is above-below its 200-day simple moving average (SMA). We define the turn-of-the-month (TOTM) as the eight-trading day interval from the close five trading days before the first trading day of a month to the close on the fourth trading day of the month. Using daily dividend-adjusted closes for the sector ETFs and SPY from 12/22/98 through 7/11/11 (151 months), we find that: More…

Is there a way to enhance momentum strategy performance by avoiding stocks about to enter post-momentum, long-term reversals? In the May 2011 version of their paper entitled “Momentum – Reversal Strategy”, Hsin-Yi Yu and Li-Wen Chen investigate momentum-reversal trading strategies that buy past winners and sell past losers while seeking to avoid stocks about to reverse. They devise two ways to measure the trend in past stock returns and thereby assess likelihood of return reversal. The comparison method compares geometric mean returns over the past 12 months and n<12 months, hypothesizing that both past winner and past loser stocks with accelerating momentum are more likely to reverse. The alternative convex-concave method graphs geometric mean returns over the past 1 to 12 months versus number of months, hypothesizing that a convex (concave) profile indicates a high (low) probability of reversal for past winners and low (high) probability of reversal for past losers. They test the effectiveness of these two trend measures via five alternative trading strategies that reform portfolios monthly based on different momentum-only and momentum-reversal criteria. Using monthly returns for all NYSE/AMEX and NASDAQ common stocks during 1965 through 2009 (22,421 stocks), they find that: More…

Does the size effect vary in a predictable way? In the May 2011 version of his paper entitled “Explaining the Dynamics of the Size Premium”, Valeriy Zakamulin investigates relationships between eight market/economic variables and the size effect in U.S. stocks to identify the best model of size effect variation. The eight variables are: (1) stock market return; (2) stock market dividend yield; (3) equity value premium; (4) stock return momentum; (5) default spread  (Moody’s BAA-AAA corporate bond yield spread); (6) Treasury bill yield; (7) U.S. Treasuries term premium  (30-year bond yield minus one-month bill yield); and, (8) inflation rate. He then tests the exploitability of the best model via a strategy that switches between small-capitalization and large-capitalization stocks out of sample based on inception-to-date historical data. Using annual data for the eight potentially predictive variables and annual and monthly data for the magnitude of the size effect among NYSE, AMEX and NASDAQ stocks as available over the period 1927 through 2009 (83 years), he finds that: More…

When implemented via exchange-traded funds (ETF), does an equity sector momentum strategy beat an equity style momentum strategy? How do these approaches compare to a geographic equity momentum strategy? In his paper entitled “Optimal Momentum”, runner-up for the 2011 Wagner Award presented by the National Association of Active Investment Managers, Gary Antonacci uses ETFs to compare style, sector and geographic momentum strategies. He uses a six-month ranking period to select the top two of six iShares value-growth-size ETFs, the top three of nine SPDR sector ETFs and the top two of four iShares region/country ETFs each month, with a 0.2% per fund switching friction. In addition, he experiments with adding short-term and intermediate-term Treasury ETFs and then gold to the geographic momentum ranking process. His benchmarks are the Russell 1000 ETF (IWB), the AQR Momentum Index (adjusted by debiting an estimated annual trading friction of 0.7%) and equally weighted portfolios of the ETF groups (rebalanced monthly). Using eight years of monthly ETF prices (2003 through 2010) and 34 years of related monthly index levels, he concludes that: More…

Is the variation of individual stock returns with liquidity a sound investment foundation? In the April 2011 version of their paper entitled “Liquidity as an Investment Style”, Roger Ibbotson, Zhiwu Chen and Wendy Hu examine the viability and distinctiveness of a liquidity investment style and investigate the portfolio-level performance of liquidity in combination with size, value and momentum investment styles. They define liquidity as annual turnover, number of shares traded divided by number of shares outstanding, a metric fairly independent of market capitalization. They hypothesize that stocks with relatively low (high) turnover tend to be near the bottom (top) of their ranges of expectation. Their liquidity style thus overweights (underweights) stocks with lower (higher) annual turnover. Using monthly data for the 3,500 U.S. stocks with the largest market capitalizations (with some screening for price, market capitalization, stock type and data availability) over the period 1972-2010, they find that: More…

“The Industry 52-week High Effect” summarizes findings that the 52-week high effect, the future outperformance (underperformance) of stocks currently near their respective 52-week highs (lows), is stronger and more consistent for 20 industries than for individual stocks. Do findings apply to equity sectors that are somewhat broader than the 20 industries? Specifically, might such a strategy outperform past six-month return when applied to the following nine sector exchange-traded funds (ETF) defined by the Select Sector Standard & Poor’s Depository Receipts (SPDR), all of which have trading data back to December 1998:

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 check, we consider three strategies based on closeness of each sector ETF to its 12-month high, defined as ratio of monthly close to highest monthly close over the prior 12 months. The three strategies are to: (1) allocate all funds each month to the sector ETF closest to its 12-month high at the end of the preceding month (12MH-1); (2) allocate all funds each month to the sector ETF closest to its 12-month high at the end of the month before the preceding month (12MH-1;1); and, (3) allocate all funds each quarter to the sector ETF closest to its 12-month high at the end of the month before the end of the quarter (12MH-3;1). Strategy (2) addresses the concern that a sector ETF surging toward a 12-month might experience some reversion the next month, and strategy (3) addresses the concern (based on the methodology in “The Industry 52-week High Effect”) that the effect materializes over several months. For comparison, we include the strategy of monthly allocation to the sector ETF with the highest total return over the past six months (6-1). Using monthly dividend-adjusted closing prices for the nine sector ETFs and S&P Depository Receipts (SPY) over the period December 1998 through March 2011 (148 months), we find that: More…

Are 52-week highs and lows useful equity price momentum indicators at the industry level? In their March 2011 paper entitled “Industry Information and the 52-Week High Effect”, Xin Hong, Bradford Jordan and Mark Liu compare the 52-week high effect for industries to that for individual stocks. This effect consists of the future outperformance (underperformance) of stocks currently near their respective 52-week highs (lows). Using monthly closes and rolling 52-week (intraday) highs for all stocks listed on NYSE, AMEX and NASDAQ and 20 value-weighted industry indexes constructed from SIC codes for these firms over the period July 1963 through 2009, they find that: More…

Does aggregate investor sentiment affect the strength of well-known U.S. stock return anomalies? In their January 2011 paper entitled “The Short of It: Investor Sentiment and Anomalies”, Robert Stambaugh, Jianfeng Yu and Yu Yuan explore the interaction of aggregate investor sentiment with 11 cross-sectional stock return anomalies. Their approach reflects expectations that: (1) overpricing of stocks is more common than underpricing due to short-sale constraints; and, (2) a high sentiment level amplifies overpricing. Specifically, they consider the effect of investor sentiment on hedge portfolios that are long (short) the highest(lowest)-performing) value-weighted deciles of stocks sorted on: financial distress (two measures), net stock issuance, composite equity issuance, total accruals, net operating assets, momentum, gross profit-to-assets, asset growth, return-on-assets and investment-to-assets. They use a long-run sentiment index derived from principal component analysis of six sentiment measures: trading volume as measured by NYSE turnover; the dividend premium; the closed-end fund discount; the number of and first-day returns on Initial Public Offerings; and, the equity share in new issues. They measure anomaly alphas relative to the three-factor model (adjusting for market, size, book-to-market). Using monthly sentiment and stock return anomaly data as available over the period July 1965 through January 2008, they find that: More…