Event Studies with Recorded Future

At Recorded Future we are harvesting events from online content. Given that event studies are a staple of financial analysis, it was really just a matter of time before we investigated our events in an event study framework. We've built this In R on top of our Web services API and will eventually be placing the code in our Google Code Site. I’ve got a number of posts about specific studies but wanted to put out some technical details as a stand alone posting. This post is really designed for people who want a look under the hood of how the event study results in other posts were found.

There is one nomenclature detail to address right away. The term “Event” in event studies may not be synonymous with individual “events” we observe in news data. The “Events” that are the basis of event studies are complex events defined from one or more observed news analytic “events” retrieved from online content and associated derived metrics. Going forward, the distinction between the two uses of the word event should be clear by context.

To do an event study, we start with a selected market basket of equities and a time range. Next, we define an event criteria and search our databases for instances of that event for the selected companies. Event definition is a creative step in which any of the content we collect can be used to define a complex event: sentiment, momentum, event type, event time, event attributes, source, novelty, etc. Once we’ve identified the events, we assign a date to them, generally either an event time for one of our observed events or a publication time.

After the events and dates are defined for a set of stocks, we can look at the market basket adjusted returns for those stocks -typically close of event day to close of the following day. The market basket adjusted return is simply the difference of the daily return for a specific company vs the unweighted average return of all of the stocks in the market basket for the day. Abnormal returns are these market adjusted returns associated with events and normal returns are the rest of the population of market adjusted returns not associated with events. By definition, the total population of abnormal and normal returns will average to zero. The purpose of the event study is to see if a specific approach to defining events yields event associated returns that are statistically different from zero.

We look at a number of statistical tests to make this assessment. We generally look at a collection of one and two sample t-tests as well as non-parametric versions of these tests. The one sample t-tests of the abnormal returns give an estimate of the significance of the abnormal returns when compared to zero. We also look at the one sample t-tests of all of the returns not associated with the event to ensure that these normal returns are still not statistically different than zero. The two-sample t-test between these groups can be used if the normal returns are non-zero for some reason. We look at the non-parametric versions of these t-tests as well to ensure that findings from the t-tests are not driven by outliers.

As well as returns, we can also look at abnormal volume and volatility associated with events. Daily volume is easily obtained from typical online Finance sites like Yahoo!. We estimate volatility as the log of the ratio of the daily high over the daily low for a stock. In order to put these volume and volatility measures on a comparable scale across companies, we normalize each daily value by subtracting the mean and dividing by the standard deviation of the value for the specific company over the trailing 240 trading days. The resulting value, or Z-score, is essentially the number of standard deviations a given daily volume or volatility is above or below the average level. While the average return is guaranteed to be zero across all companies for a given day, and thus across all days, this isn’t true for volatility or volume. In some cases there are overall market trends of these values both for individual days and over time. In these cases, it is the two sample t-tests that are the basis of findings of significant changes in volume and volatility.




There are several ways to visualize results from an event study. In a daily return chart, we set the event at time zero and look at the average returns across events for the individual days around the event time. In the example above we are looking at days after the event and we could easily look at days before as well. Alternatively, we can look at cumulative returns as well where we are summing the daily returns starting from a specific starting point. In the example below, we are looking at the cumulative returns starting from the first day after the event.

The dotted lines in the two graphs are the confidence limits for the associated data. In the daily return plot, it is only the 2nd day individual return that is statistically different from zero. In the cumulative returns plot, the total return becomes statistically different than zero at day two and continues to be significantly different than zero for the next three days.

There are a number of choices to make in doing these kinds of studies and this post simply lays out the approaches we’ve taken, Feel free to contact us directly if you have more detailed questions or comments.