Momentum trading, buying winning assets and selling losing assets, is a most popular trading strategy. It relies on sluggish market adjustment, allowing the trader to follow best-informed investors before the more inert part of the market does. Herding simply means that market participants imitate each others’ actions. Herding accelerates and potentially exaggerates market adjustments. The more quickly the herd moves, the harder it becomes to follow informed leaders profitably. In a large agile herd, sluggish adjustment gives way to frequent overreaction. Momentum strategies fail. This suggests that popularity and commoditization of momentum strategies (and trend-following) are ultimately self-defying. Conditioning momentum strategies on the estimated degree of herding should produce superior investment returns.

Yan, Zhipeng, Yan Zhao, and Libo Sun (2012), “Industry Herding and Momentum” and Lansdorp, Simon and Willem Jellema (2013), “Momentum from Underreaction”, Robeco White Paper.

The below are excerpts from the papers. Emphasis and cursive text have been added.

Herding and momentum

“Herd behavior, or the tendency of individuals in a group to ‘follow the trend,’ has frequently been observed in equity markets. Herd behavior in investors leads to a convergence of action.” [Yan/Zhao/Sun]

See post on rational informational herding here.

“The momentum effect is the tendency of stocks that performed well in the past months to continue to do well in the following period and vice versa for stocks with a poor performance” [Landsdorp/Jellema]

“[In the simplest case momentum means that] stocks that have previously exhibited positive returns (winners) continue to outperform stocks that have previously exhibited negative returns (losers).” [Yan/Zhao/Sun]

More generally, there are two types of momentum strategies, time-series and cross-section momentum strategies. The time-series momentum is called trend-following. It assumes that assets with a current positive price trend will continue to have a future positive trend and assets with a current negative price trend will continue to have a future negative trend. A cross-section momentum is called a winners-minus-losers strategy. It assumes that the current winners will continue to outperform the current losers in the future. View post on basic theory of momentum strategies here.

Why herding is the death of momentum

“Uninformed traders can become informed by imitating the observed movement in the market. In that way, herd behavior in investors may help impound information about fundamentals into asset prices …Herd behavior in investors helps move asset prices toward fundamentals, enhances market efficiency, and reduces the momentum effect.” [Yan/Zhao/Sun]

“If herding can help impound fundamental news into asset prices and enhance market efficiency, then investor herding will accelerate the rate of movement to efficiency, so stocks with a low level of herding will exhibit the momentum effect more than stocks with a high level of herding.”

Identifying the source of a stock’s momentum, i.e. over- or under-reaction, matters, because it gives insights into the expected risk and sustainability of a strategy profiting from that stock’s momentum. A momentum strategy that buys a winner stock which suffers from overreaction causes upward price pressure on that stock, makes the price drift even further away from its intrinsic value and hence aggravates the mispricing. Sooner or later though, the market will realize the stock price deviates from its intrinsic value and the price of the stock will revert towards its fundamental value. Hence, a momentum strategy that invests in stocks whose momentum is driven by overreaction is exposed to the risk of reversals in stock prices.” [Landsdorp/Jellema]

Empirical support

“Our empirical findings show that acting in a herd, investors help move asset prices toward fundamentals, enhance market efficiency, and reduce the momentum effect…We find that a low level of herding significantly enhances stock price momentum.” [Yan/Zhao/Sun]

“As a dispersion measure, either the cross-sectional standard deviation or the cross-sectional absolute deviation [of single stock returns with respect to the industry mean return] indicates a high level of herding when its value is low. In other words, when stocks in the same industry move in tandem, or herd, the dispersion is small. Some industries, such as computers, pharmaceutical products, and computer software, have relatively low levels of herding. On the other hand, the utilities industry consistently has a very high level of herding. Investors may consistently go long some industries and short other industries, in which case the herding-momentum strategy is just an industry bet in disguise…The herding level is calculated using the previous 1-month returns.” [Yan/Zhao/Sun]

Winner industries with a low level of herding generate higher subsequent returns than those with a high level of herding. Loser industries with a low level of herding generate lower subsequent returns than those with a high level of herding. We conclude that the herd effect plays an important role in the momentum effect.” [Yan/Zhao/Sun]

“The fact that the momentum effect is more significant when the herding level is low is consistent with the notion that herd behavior helps incorporate news of fundamentals into asset prices…This lends support to the… strand of herding theories—herding will accelerate the rate of movement to market efficiency.” [Yan/Zhao/Sun]

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Ralph Sueppel is founder and director of SRSV, a project dedicated to socially responsible macro trading strategies. He has worked in economics and finance for over 25 years for investment banks, the European Central Bank and leading hedge funds. At present, he is head of research and quantitative strategies at Macrosynergy Partners.