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Price, Volume, and Spread Dynamics After Extreme Intraday Moves

Article arXiv papers · Author: A. G. Zawadowski et al.
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Summary

The study tracks price, trading volume, and bid-ask spreads following unusually large 15-minute price changes in stocks listed on the NYSE and NASDAQ. It examines how these market measures evolve after the event, with attention to whether sharp moves are followed by continued pressure or a reversal.

The authors report evidence of behavioral overreaction after extreme moves. Volatility rises sharply around the event and then declines following a power-law pattern with an exponent near 0.4, a faster decay than volatility's usual autocorrelation. The excerpt does not describe the sample period, event definitions beyond the 15-minute horizon, statistical procedures, or the detailed paths of volume and spreads. Its findings therefore support a short-horizon account of post-event dynamics, but provide too little information to assess robustness or trading profitability.

Key ideas

  • Extreme 15-minute price changes on NYSE and NASDAQ stocks are followed by behavioral overreaction.
  • Volatility increases sharply around the event and subsequently decays.
  • The reported volatility decay follows a power law with an exponent of approximately 0.4.
  • This post-event decay is faster than the autocorrelation pattern of volatility.
  • The study also examines subsequent volume and bid-ask spread behavior, though the excerpt gives no detailed results for either.

Tags

EquitiesVolatilityMarket microstructureStatistics
Full text
# Large price changes on small scales


# Large price changes on small scales









In this study we examine the evolution of price, volume, and the bid-ask spread after extreme 15 minute intraday price changes on the NYSE and the NASDAQ. We find that due to strong behavioral trading there is an overreaction. Furthermore we find that volatility which increases sharply at the event decays according to a power law with an exponent of approximately 0.4, i.e., much faster than the autocorrelation function of volatility.

Shown in full with attribution under the source's licence. Licence: abstract CC0

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