Combining Industry Crowding and Momentum for Sector Allocation

Summary

This research framework measures how crowded equity industries are using price and trading activity. It groups candidate measures into trend-oriented indicators, supplementary volume and order-flow measures, and relative comparisons across industries. Examples include price deviation, moving-average dispersion, turnover, size-weighted CSAD, trade-size relationships, price-volume correlation, excess-return skew, and an industry’s share of trading volume.

The report argues that high crowding can compress future excess-return opportunity and weaken risk-adjusted prospects, without necessarily triggering an immediate trend reversal. Simply choosing the least crowded industries can also select falling sectors too early, so it proposes combining industry momentum with a moderate crowding level. Backtests from 2016 to 2022 report negative excess returns for groups of the most crowded industries versus an equal-weight industry benchmark, and positive relative performance for an equal-weight composite momentum-and-crowding portfolio. These are historical results; the summary supplies limited detail on implementation and does not establish out-of-sample robustness.

Key ideas

• Crowding can be estimated from price trends, trading activity, order flow, and cross-industry comparisons.
• High crowding may reduce the remaining excess-return opportunity without causing an immediate reversal.
• Low crowding alone can identify declining industries and lead to premature entry.
• The proposed allocation combines industry momentum with a crowding assessment.
• The reported historical tests compare crowded groups and a composite portfolio with an equal-weight industry benchmark.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.