A High-Frequency Volatility Factor for Measuring Information Flow

Summary

This research summary proposes an information-distribution uniformity factor, UID, based on the idea that uneven arrivals of information create changes in share-price volatility. It uses intraday stock data to calculate daily high-frequency volatility, then derives a cross-sectional stock-selection signal from the size of volatility changes. The hypothesis is that relatively steady information flow corresponds to smaller volatility shifts, while abrupt shifts indicate stronger information shocks.

For Chinese A-shares over 2014 through July 2020, the summary reports negative average monthly IC and Rank IC, alongside long-short portfolio results for a five-group sort. It also reports that the signal retained predictive power after controlling for common styles and industries, though performance was lower. These are historical backtest statistics presented in an abstract; the underlying report is not reproduced here, so implementation and validation details cannot be assessed. The authors caution that future markets may differ and single-factor returns can vary substantially.

Key ideas

• The UID factor aims to infer information-flow regularity from changes in intraday volatility.
• It is constructed from minute-level data and daily high-frequency volatility estimates.
• The summary reports historical stock-selection and long-short results for Chinese A-shares.
• The signal retained some reported efficacy after common style and industry effects were removed.
• The evidence is historical and the provided summary omits detailed methods and validation procedures.

Tags

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