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Momentum Investing Strategy (Strategy Overview)

Allocations for September 2023 (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.

Classic Paper: Physical Inventories and Commodity Futures Returns

We occasionally select for retrospective review an all-time “best selling” research paper from the past few years from the General Financial Markets category of the Social Science Research Network (SSRN). Here we summarize the June 2007 paper entitled “The Fundamentals of Commodity Futures Returns” (download count over 2,500) by Gary Gorton, Fumio Hayashi and Geert Rouwenhorst. Commodity futures are derivative, short-maturity claims on real assets. In this paper, the authors apply the theory of storage to investigate relationships between the physical inventories of these assets and the returns to traders in the associated commodity futures. Using monthly data for over 30 commodity futures and associated physical inventories as available between 1969 and 2006 and data from the weekly Commodity Futures Trading Commission Commitments of Traders (COT) reports, they conclude that: Keep Reading

Momentum and Contrarian Commodity Futures Returns

Do commodity futures exhibit short-term momentum and long-term reversion, as do stocks? In the August 2006 version of their paper entitled “Momentum Strategies in Commodity Futures Markets”, Joelle Miffre and Georgios Rallis examine the profitability of 32 momentum (short-term continuation) and 24 contrarian (long-term reversal) strategies in commodity futures markets. The momentum strategies buy (sell) recently outperforming (underperforming) commodity futures and hold resulting long-short portfolios up to 12 months. The contrarian strategies buy (sell) the commodity futures that underperformed (outperformed) in the distant past and hold resulting long-short portfolios for periods of two to five years. All strategies trade liquid futures contracts with nearby maturities involving 31 commodities, unimpeded by short-selling restrictions often encountered in equity markets. Using futures contract price data spanning 1/31/79-9/30/04, they conclude that: Keep Reading

Institutional Trading, Returns and Strength of Anomalies

Are there exploitable differences in returns for stocks with heavy versus light institutional trading activity? In his March 2008 paper entitled “Trader Composition and the Cross-Section of Stock Returns”, Tao Shu analyzes the impact of institutional trading activity on the returns of individual stocks and on the strength of the momentum effect, post earnings-announcement drift (PEAD), the value premium and the investment effect. He calculates institutional trading activity at a quarterly frequency by dividing the aggregate absolute change in reported institutional holdings of a stock by the contemporaneous total quarterly trading volume for the stock. Using holdings data as reported via SEC Form 13F and associated stock trading volume and return data for the period 1980-2005, he concludes that: Keep Reading

The Pervasiveness and Persistence of Momentum

Is the momentum effect pervasive across different equity markets and persistent through different time periods? The overview of Chapter 3 in “Global Investment Returns Yearbook 2008: Synopsis”, which summarizes annual work performed by by Elroy Dimson, Paul Marsh and Mike Staunton for ABN AMRO, provides “findings from the longest momentum study ever undertaken.” Applying a 12-1-1 strategy (rank returns over the past 12 months, wait one month and then hold for one month until rebalancing) to very long-run UK data and more recent data for each of 17 country stock markets, they conclude that: Keep Reading

Fama and French Dissect Anomalies

Which stock return anomalies are trustworthy, and which are not? In the June 2007 draft of their paper entitled “Dissecting Anomalies”, Eugene Fama and Kenneth French apply both sorts and regressions to examine the robustness of the momentum, net stock issuance, accruals, profitability and asset growth anomalies. They note that sorts on an anomaly variable offer a simple picture of how average returns vary, but microcaps (a few big stocks) can dominate the performance of a sort-based equal-weighted (value-weighted) hedge portfolio. In addition, sorts are ill-suited to determinations of: (1) the exact relationship between an anomaly variable and returns, and (2) relationships among anomalies. They note also that extreme behavior by microcaps and outliers generally can distort inference from regressions. Using a robust set of firm data for a broad set of U.S. stocks allocated to three size groups (microcap, small and big) over the period 1963-2005, they conclude that: Keep Reading

Combined Value-Momentum Tactical Asset Class Allocation

Are value and momentum anomalies reliably present across international asset classes? If so, can investors exploit them to generate abnormal returns? In the December 2007 version of their paper entitled “Global Tactical Cross-Asset Allocation: Applying Value and Momentum Across Asset Classes”, David Blitz and Pim van Vliet examine global tactical asset allocation strategies across a broad range of asset classes based on both value (asset yield or earnings yield) and momentum (both short-term and long-term). These strategies weight asset classes according to volatility, with higher (lower) weights assigned to classes with lower (higher) volatilities. Using price and yield data for 12 international asset classes spanning January 1985 through September 2007, they conclude that: Keep Reading

Trading Friction as a Momentum Killer

Are momentum trading strategies profitable after accounting for trading costs? In their August 2007 draft paper entitled “Low-Cost Momentum Strategies”, Xiafei Li, Chris Brooks and Joelle Miffre analyze the impact of transaction costs on the profitability of momentum strategies for UK stocks. They consider all combinations of 3-month, 6-month and 12 month ranking and holding periods. Using stock price data for 3,520 UK companies and separately for the constituents of the FTSE 100 index (large capitalization stock sample) and the Alternative Investment Market (AIM – small capitalization stock sample) over the period 1986-2005, they conclude that: Keep Reading

The 52-Week High as a Momentum Indicator for Individual Stocks

A reader notes and asks: “It is frequently said that stocks at 52-week highs are the most likely to outperform in the future. Is there any academic evidence to support this assertion?” In their October 2004 Journal of Finance article entitled “The 52-Week High and Momentum Investing”, Thomas George and Chuan-Yang Hwang examine the explanatory power of the 52-week high in the context of momentum investing. They compare the 52-week high as a momentum indicator to benchmark momentum strategies that employ six months of past returns to forecast six months of future returns. Using price data for a broad range of stocks over the period 1963-2001, they find that: Keep Reading

Loss of Momentum?

Has the focus of investors/traders (especially hedge funds) on stock return momentum, the persistence of outperformance and underperformance, killed the effect? In their March 2007 paper entitled “The Disappearance of Momentum”, Soosung Hwang and Alexandre Rubesam investigate trends in the momentum effect over a long period. Their baseline analysis examines sets of ten momentum-ranked portfolios formed on past five-month returns and held for six months, with an intervening month skipped. Using monthly return data for a large number of individual NYSE, AMEX and Nasdaq stocks over the period July 1926 through December 2005, they conclude that: Keep Reading

Follow the Leaders to Capture Short-term Abnormal Returns

Do investors/traders taking cues from the trades of top performers produce the momentum effect? In his December 2006 paper entitled “Follow the Leader: Peer Effects in Mutual Fund Portfolio Decisions”, Lukasz Pomorski investigates whether actively managed equity mutual funds tend to follow the stock trading leads of outperforming peers as the picks become known via the media and quarterly filings. He defines outperforming (leader) funds in two ways: (1) funds with alphas in the top 5% over the past two years, and (2) funds on the Forbes Honor Roll (high media exposure). He calculates overall leader activity in a stock based only on trades by leader funds with a position in the stock. Using mutual fund holdings and performance data for 1980-2003 (96 quarters), he finds that: Keep Reading

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