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Event studies

July 2023

1 TASK 1: Exploring the relationship between Google’s restruc-ture and its share price

In the folder ’Tutorial 2: Event studies’, there are three files. For the first exercise, we are going to use two of them.

1. First upload "goog_orig.csv" up to Posit (or save in a folder if using desktop R studio).

2. Then copy the code in ’Google_base’ into an R editor.

TASK 1A: The first thing is to recreate the analysis from the online lecture using the code in the Google_base file. The only thing you need to add is some code to read in the "goog_orig.csv" file using the write.csv command (remember this from last week).

TASK 1B: Let’s change the prediction model for Y(0) [the counterfactual]. Let’s assume that our best guess of Google’s return without the restructure is its mean daily return before the event. HINT: When you regress Y on a constant, the constant represents the mean value of Y. To do this in R lm(Y 1, data = )

TASK 1C: Let’s see if any of Google’s employees traded on their advanced knowledge of the re-structure in the week before 10 August 2015. What needs to change to carry out this analysis? Default back to the original market model to predict Y0 [the counterfactual].

2 Task 2: Tiger’s bad day

Read this article for the background to the event:

https://www.irishtimes.com/sport/golf/10-years-on-the-night-tiger-woods-hit-a-fire-hydrant-and-his-world-turned-upside-down-1.4097028

Tiger Woods was sponsored by Nike. Our research question is: Did Tiger’s antics on 27 Novem-ber 2009 affect Nike’s share price?

In the tutorial folder there is a csv file called: ’Nikedata’. Can you repurpose your Google code to see if Nike’s share price was lower over the 28 days following Tiger’s crash? You will need to add one thing to the code after you read in the dataset (I’ve called the dataset ’NK’)

names(NK)[1]<-"Date"

This just makes sure the first column is called Date

NK$Date<-as.Date(NK$Date,’%d/%m/%Y’)

This just tells R to treat the Date column as a date.