A news item may carry several relevant times: when an event occurred, when its source published it, when a data service distributed it and when a local system received it. Collapsing those into one “time” can distort a market-event study before the analysis begins.
Ask what each clock represents
Publication time describes when a source made an item public. Event time may describe when the underlying meeting, filing or economic release happened. Vendor time records a step in the provider’s distribution process; receipt time records when your own system observed it. These fields are related, but they are not interchangeable.
Create a data dictionary with the original field name, its documented meaning, its timezone and any known precision or limitations. Keep the raw timestamp as supplied alongside any normalized representation. If a provider cannot define a field clearly, record that gap rather than inferring a meaning from the label alone.
Normalize without losing the original
Use a consistent internal time standard for comparison, while preserving the original timestamp and stated timezone. Check daylight-saving transitions, date-only observations and events close to local market openings or closes. Cross-border research may compare sources and venues that use different local clocks, so the display convention should be explicit to the reader.
Time conversion cannot repair uncertain source timing. If a page is updated in place, a publication date may not reveal when each substantive change became visible. Record whether the timestamp describes first publication, latest update or an event’s effective date.
Plan for corrections and late arrivals
News and company disclosures can be corrected, updated or republished. Ask a data provider how it represents revisions, whether the original version remains available, and how duplicate or related items can be recognized. The answers matter if the research compares what was knowable at one point in time with what is known now.
A later correction should not silently overwrite the earlier observation in a point-in-time study. Keep a record of the version used and state whether the analysis follows the first distributed item, the latest version or another rule. The rule should follow the research question and should be applied consistently.
Check for timing bias
Suppose a hypothetical project asks whether public announcements coincide with unusual trading activity. If it sorts events by the time a local system ingested them, network or processing delays may be mistaken for market reaction. If it sorts by the latest corrected timestamp, it may use information that was not available to a researcher at the earlier moment.
This example does not imply a trading signal or performance result. It shows why a study should align the event’s timing definition with the claim it makes, then document exclusions, uncertain times and sensitivity checks.
Turn the rule into a repeatable method
Before collecting a large sample, inspect a small set of representative items across the expected markets and sources. Check field definitions, timezone handling, corrections, duplicates and session boundaries. Document the transformation logic and retain a handful of reviewed examples as a reference for future updates.
A clear timing protocol helps another researcher reproduce the dataset and understand its limits. In a concise report, a short table of timestamp definitions and a note on revised items can prevent a confident-looking chart from carrying a hidden assumption.
Name every clock in the dataset, preserve the original time fields and test how revisions or delivery delays affect the question.
An original methodological perspective from the Geteridian research concept. This article is educational and does not provide investment recommendations or report live market data.
