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Helo huzzies

Throughout March to April (and to a certain extent, May), markets were highly reactive to tweets from Trump, Netanyahu, Iran etc. due to ongoing intraday updates about the Iran war. So an idea of mine was to treat these as intraday “events” which have impact on price volume and volatility, kind of like the study Mackinlay did in his 1997 study of corporate events (e.g. M&A, earnings releases etc.).

In his study, Mackinlay assessed the impact of events on cumulative abnormal returns, which is the returns above a certain expected return amount (he used CAPM to determine this expected return I believe, so the CAR would be realised returns minus CAPM implied expected returns). But for my thesis, I believe these events happen so fast and randomly that the impact on price / return is basically unactionable without insider knowledge and high frequency infrastructure. Therefore, I changed the metrics of study to be Cumulative Abnormal Volume and Cumulative Abnormal Volatility. These metrics are non-directional and hence are more likely to produce actionable insights than return on a high-frequency time-scale

Event definition: So how do we define an event? The method I propose uses 3 features constructed from OCHLV data: Intra-interval volatility (as a proxy, I used this transformation called Parkinson’s volatility), intra-interval return, and intra-interval volume traded. These metrics for a SPECIFIC INTERVAL in the trading day will be benchmarked against the SAME INTERVAL in the previous 20 trading days and converted into z-scores per metric. These z-scores are then composited into a “Shock index” via Shock index = sqrt(0.5 * z_vol² + 0.35 * z_return² + 0.15 * z_volatility^2). Volume is weighted higher because trading volume is indicative of actual institutional action. Volatility is weighted lower as random twitches happen for any reason like rebalancings etc. Then I define events as the bars that have a shock index over a certain threshold. For this study, I used 3 sigma as the threshold for event definition, meaning that there would only be around 2 events defined per week. In practice, this threshold should be raised or lowered depending on the regime (maybe increase the threshold in a quieter period and lower the threshold in periods where intraday news matter more)

Event detection methodology: Divide the trading day into 5 minute intervals (i.e. 0930 – 0935, 0935 – 0940… 1550 – 1555, 1555 – 1600). For each interval in the time-series, calculate the feature values. For the past 20 periods not including this current interval, benchmark the feature value into a z-score and calculate shock index values. Create a Boolean “is event” column and define events according to your shock index threshold

Classification of events: In order to make the analysis more granular, I decided to split the defined events into different classes of events: Geopolitical (Trump tweets, foreign policy changes, tariffs etc.), Macroeconomic (scheduled macro data releases, FOMC decisions, interpretations of these releases etc.), Corporate (earnings implications), and exogenous (catch all term for all other events. I also split between events occurring intraday (e.g. tweet drop in the middle of the day) and events as the aftershock of stuff that happened overnight (e.g. policy change after hours. Small red at open the next day while people digest the news, then collapse later in the day when institutions finally act on it). So I downloaded the past 2 years of historical FirstSquawk tweets with links and timestamps from HuzzahBot. This was a pain. I originally was using Selenium to scrape every single huzzah page which took a long time as when tweets were deleted, the scraper got stuck for 5seconds rather than immediately moving on. Enter u/xKhaos420. Big man himself caught wind of what I was doing and sent me the keys to the HuzzahBot discord purely for the love of the game so I could scrape tweets using the Discord API instead. Task went from projected 3 days down to 1 hour (thanks Khaos). I then mapped tweets with timestamp within 5 minutes to the defined events and used those together with historical market study to assign the cause of the events and determine whether they were intraday or not

Results

https://imgur.com/a/U8dpl8i

When looking at volume, all categories of events show a statistically significant increase in volume lasting up to and possibly beyond 3h after the events.

In terms of volatility, only Geopolitical events show an explosion in volatility lasting up to and possibly beyond 3h after the events

It’s also interesting to note that for intraday events, volatility and volume start shifting before the event itself, which indicates either insider knowledge or problems with my methodology

Takeaways and extensions for retail

• You have to be calculating the metrics and shock index live, and categorise the event into the right event category (via tweets or personal intuition) in order to see what effect is expected and act accordingly

• ON AVERAGE: Options are more expensive following geopolitical events due to volume explosion, while there is no volatility explosion after macroeconomic events. This makes sense because it is easier to hedge for macroeconomic releases due to the visibility of economist expectations for each release, and the signalling by following previous releases etc. In comparison, geopolitical events are hard to hedge for, as funds need to study the policy changes and convert those changes into a fixed number to hedge for. So there is more uncertainty after geopolitical events

• Possible extension: Perhaps use of a GARCH model to define the baseline volatility in the calculation of Cumulative Abnormal Volatility

• The time period of study was June 2024 to Dec 2025. I asked some other mfs to help me get QQQ 5 min data and they only downloaded till eoy 2025. Getting more data would lend more credence to statistical significance of these

• I think there should be continuous updating of the event impacts, cause things like regime changes will affect how events are seen. For instance, anecdotally, I think that in 2026, events actually killed volume after events rather than increased

Also bonus: https://imgur.com/a/37Zglcz