Momentum has strong roots in academic research. Alfred Cowles and Herbert Jones published the first scientific momentum study in 1937. They compiled stock performance statistics from 1920 through 1935 and found the strongest stocks during the preceding year remained strong the following year.
Momentum research languished after Cowles and Jones. Until behavioral finance caught on in the 1980s, the efficient market hypothesis had a firm grip on academic finance. Under efficient market theory, all information is accounted for, and one should not expect to do better than the market itself.
Based on more recent evidence, momentum and other trend-following methods have gained acceptance among academics and practitioners.
Behavioral finance challenges these assumptions and provides logical reasons momentum investors could earn consistently high profits:
Anchoring bias – investors are slow to react to new information and over-rely on past data
Confirmation bias – investors ignore information that is contrary to their prior beliefs
Disposition effect – investors sell winners too soon and hold on to losers too long
Herding – buying begets more buying so that trends persist and overextend
With behavioral finance to support it logically, momentum research took a giant leap forward in 1993 with the publication of “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency” by Jegadeesh and Titman. This seminal study used modern analytic methods to validate the findings of Cowles and Jones.
Since the seminal research of Jegadeesh and Titman in the early 1990s, momentum has been one of the most heavily researched finance topics. Continuing research has firmly established momentum as an anomaly that works well in nearly all markets, including equities, stock indices, currencies, commodities, real estate, and bonds. Out-of-sample research has shown momentum to be effective from the early 1800s until now. Even though momentum is most often applied to individual stocks, research shows momentum works in all areas and is best when applied to geographically diversified stock indices.
The premier market anomaly is momentum. Stocks with low returns over the past year tend to have low returns for the next few months, and stocks with high past returns tend to have high future returns.
–Fama & French
Investors today mostly use momentum the same way Cowles and Jones discovered it by looking at relative momentum and individual stocks. Relative momentum looks at the performance of one asset versus other assets. But there is also absolute momentum that uses an asset’s past performance to infer future performance. Absolute momentum can reduce downside exposure as well as enhance returns. The best approach is to use both types of momentum together. That is what dual momentum is all about.
Most applications of momentum today use relative momentum and ignore absolute momentum. To remedy this situation, we developed a publicly available simple model called Global Equities Momentum (GEM). It applies relative strength and trend to geographically diversified stock market indices. We also have more advanced proprietary models covering a broader mix of assets and additional selection methods. Our proprietary models take momentum to a higher level by using more advanced trend identification algorithms, all of which has at least 100 years of academic research validation. These models also add other proven investment criteria, such as mean reversion, market structure, and Intermarket relationships. We license our proprietary models to family offices, foundations, private investment partnerships, and selected investment advisors. See our proprietary models’ performance page for more information.