Do Industries Explain Momentum?

Source: Moskowitz, T. J. & Grinblatt, M. (1999). Journal of Finance 54(4), 1249–1290.

TL;DR

Industries themselves exhibit strong momentum: buying the past-winner industries and shorting the

past-loser industries earns roughly 0.4–0.5% per month. Strikingly, once you control for industry

momentum, much of the individual-stock momentum of Jegadeesh & Titman (1993) weakens — a large part

of "stock momentum" is really momentum in the stock's industry.

What anomaly it documents

Past industry returns predict future industry returns over horizons of one to twelve months: winning

industries keep winning and losing industries keep losing in the near term. The effect is

cross-sectional (long winners / short losers across industries) and is largest at short horizons,

decaying and eventually reversing over multi-year windows. The authors show industry momentum is

distinct from, and partly subsumes, individual-stock momentum, size, value, and the cross-sectional

dispersion in expected returns.

How to construct it

Universe / building blocks: value-weighted industry portfolios (the paper uses 20 industries

built from CRSP; the Ken-French 12- or 49-industry portfolios are the standard public proxy).

Sorting variable: trailing industry return, commonly the past 6 months, skipping the most

recent week/month to avoid bid-ask bounce and short-term reversal.

Portfolio: long the top-tertile (or top-3) industries, short the bottom; equal-weight the legs.

Rebalancing: monthly; one-month holding period.

ConvexPi replication: the 12 Ken-French industry portfolios, ranked on the trailing 12-month

return skipping the most recent month, long the top 3 and short the bottom 3, rebalanced monthly.

Evidence and replication

Period	Sharpe / return	Source
IS (1963–1995, 1-month industry momentum)	~0.43%/month, highly significant	this paper
OOS (post-1999, ConvexPi 12-industry version)	Sharpe 0.31 (vs 0.47 pre-1999)	ConvexPi benchmark

Industry momentum survives out of sample with roughly a third of its in-sample Sharpe lost — a

milder decay than the size or value premia, consistent with cross-sectional momentum more broadly

remaining one of the more robust anomalies (McLean & Pontiff, 2016).

Why it might work

Slow information diffusion: industry-wide news (commodity prices, regulation, demand shocks) is

incorporated gradually across the sector, so recent industry returns predict near-term returns.

Behavioural underreaction to sector fundamentals, plus delayed sector rotation by investors.

Risk-based readings are weaker here than for value; the effect looks more like mispricing.

Limitations and risks

Turnover and transaction costs: monthly rebalancing of concentrated sector bets is costly,

though cheaper than single-name momentum.

Crash risk: like all momentum, vulnerable to sharp reversals after market bottoms (e.g. 2009).

Industry definitions matter: results shift with the number and construction of industries.

Crowding: widely known since publication; sector-rotation products may have compressed the edge.

Key references

Jegadeesh, N. & Titman, S. (1993) — Returns to Buying Winners and Selling Losers — Journal of Finance

Moskowitz, T. & Grinblatt, M. (1999) — Do Industries Explain Momentum? — Journal of Finance

Grundy, B. & Martin, J. S. (2001) — Understanding the Nature of the Risks and the Source of the Rewards to Momentum Investing — Review of Financial Studies

Asness, C., Moskowitz, T. & Pedersen, L. (2013) — Value and Momentum Everywhere — Journal of Finance

Daniel, K. & Moskowitz, T. (2016) — Momentum Crashes — Journal of Financial Economics

Reference replication on ConvexPi

An open, verified replication of this strategy is maintained at convexpi/replications. It recomputes the strategy from underlying building blocks and scores it out of sample (the McLean & Pontiff test):

Period	Annualized Sharpe
In-sample (pre-1999)	+0.47
Out-of-sample (≥ 1999)	+0.31
Last 10 years	+0.24

Verdict: alive. Run it on live data in Colab · view the code