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Refining A-Share Money-Flow Factors with Order-Size Rescaling

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Summary

This report develops Chinese equity money-flow signals using tick data to reconstruct order sizes. It argues that conventional fixed cutoffs miss meaningful institutional activity, tests 44 absolute amount thresholds, and finds that lower cutoffs can better capture informed trading, particularly in smaller stocks. It then adjusts buy-sell imbalance through daily cross-sectional regression against returns, aiming to remove price-move effects that obscure the signal. The resulting NIR_MOD factor is reported to outperform the unadjusted version; a broader CNIR factor combines conventional large, extra-large, and medium orders for easier lower-frequency use.

The report presents factor tests, subgroup comparisons, correlations, turnover, and portfolio analyses, reporting stronger results in smaller-cap universes and limited incremental value from active-versus-passive trade labels. These are historical findings, not guarantees: the study is focused on Chinese equities, some tick-data analyses are limited to Shenzhen listings, and the reported backtests have market, period, implementation, and transaction-cost limitations. The document also notes a notable drawdown and weaker February results.

Key ideas

  • Tick-level order information can support a more informative large-versus-small order classification than standard fixed cutoffs.
  • Regressing money-flow imbalance on returns is used to reduce the influence of contemporaneous price moves.
  • The report finds stronger factor results at lower order-size thresholds, especially among smaller-cap stocks.
  • The CNIR factor combines order categories to make the tick-data insight easier to apply at lower frequency.
  • Active-trade labels did not provide clear additional factor information in the reported tests.
  • The historical backtests include substantial drawdowns and may not generalize beyond their Chinese equity samples.

Tags

China marketsEquitiesFactor investingMarket microstructureBacktestingStatistics

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