Using PMI Correlations to Classify Cyclical Industries and Rotate Sectors

Summary

This report proposes classifying equity sectors by how their excess returns relate to economic conditions. It questions existing Chinese cyclical and noncyclical sector indexes, then measures lead-lag correlations between China Securities Index industry returns and the Purchasing Managers’ Index (PMI). The reported examples include positive relationships for nonbank financials, petrochemicals, basic chemicals, and nonferrous metals, and negative relationships for pharmaceuticals and utilities.

Using the revised classifications, the authors describe a long-only sector rotation strategy and a long-short timing strategy, with timing signals from Xingye Securities’ quantitative fundamental model. The summary reports annualized returns of 17% for rotation versus 12% for equal weighting, and 15% for the long-short strategy; it also gives return-to-volatility figures. These are backtest results as summarized in the document. It does not provide the detailed methodology, test period, trading costs, or robustness checks, so the figures alone do not establish out-of-sample performance.

Key ideas

• The report classifies sectors by measuring cross-period correlations between industry excess returns and PMI.
• It identifies several sectors with positive economic-cycle relationships and others with negative relationships.
• The revised cyclical and noncyclical groupings are used to construct sector indexes and rotation strategies.
• The authors describe both a long-only rotation approach and a long-short timing approach.
• The reported performance comes from backtests, and the excerpt omits details needed to assess robustness.

Tags

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