Intraday Event Studies

High-frequency event study analysis with minute-level precision and non-parametric significance tests.

Overview

Intraday event studies analyze market reactions at sub-daily frequency, capturing price movements within minutes or seconds of an event. This is critical when events have immediate, short-lived effects that daily data would miss.

The R package v0.40.0 provides IntradayEventStudyTask with POSIXct timestamp support and a dedicated non-parametric significance test.

When to Use Intraday Analysis

  • Scheduled announcements: Earnings releases, Fed rate decisions, macro data
  • Flash events: Flash crashes, circuit breakers, sudden halts
  • High-frequency trading: Algorithmic trading responses
  • Precise timing: When you know the exact timestamp of the event
  • Short-lived effects: Reactions that are absorbed within hours

IntradayEventStudyTask

library(EventStudy)

# Data must include POSIXct timestamps
intraday_task <- IntradayEventStudyTask$new(
  data = intraday_data,        # minute-level price data
  event_time = "2020-03-15 14:30:00",
  estimation_window = c(-120, -11),  # in minutes
  event_window = c(-5, 5)            # in minutes
)

model <- MarketModel$new()
model$fit(intraday_task)

Data Requirements

  • Timestamps: POSIXct format with timezone information
  • Frequency: Typically 1-minute or 5-minute intervals
  • Market hours: Data should cover trading hours only
  • Synchronization: Event and market data must be time-aligned

Non-Parametric Significance Test

The package implements the Rinaudo & Saha (2014) non-parametric test specifically designed for intraday event studies:

# Non-parametric test for intraday data
stats <- IntradayNonParametricTest$new()
stats$compute(intraday_task)

This test is preferred over standard parametric tests because:

  • Intraday returns often exhibit stronger non-normality
  • Microstructure effects (bid-ask bounce, price discreteness) violate parametric assumptions
  • The test is robust to irregular spacing of observations

Window Design for Intraday Studies

Window Duration Purpose
Estimation 60–120 minutes Calibrate normal return model
Pre-event gap 5–10 minutes Buffer to avoid information leakage
Event window 5–30 minutes Capture the market reaction

Best Practices

  1. Align timestamps across stock and market data carefully
  2. Exclude non-trading hours from the analysis
  3. Account for market microstructure effects
  4. Use robust tests given the non-normality of intraday returns
  5. Consider bid-ask bounce when using transaction-level data

References

  • Rinaudo, J.B. & Saha, A. (2014). A non-parametric test for event studies with intraday data.
  • Barclay, M.J. & Litzenberger, R.H. (1988). Announcement effects of new equity issues and the use of intraday price data. Journal of Financial Economics, 21(1), 71-99.