Use Case: Dieselgate

knitr::opts_chunk$set(echo = TRUE)
Sys.setenv(event_study_api_key = "0245ed2e1d5011d8b87ba116f06e36ea")

Overview

Based on the Dieselgate scandal we want to show you how you are able to fetch data in R, perform an Event Study, and do some basic plots with our R package.

library(tidyquant)
library(dplyr)
library(readr)

We use the package tidyquant to fetch the automotive stock data from Yahoo Finance. As we cannot get the total volume size from these companies through Yahoo Finance API, we do not perform a Volume Event Study in this vignette.

Let's define the window from which we want to fetch the data of the German auto companies.

startDate <- "2014-05-01"
endDate <- "2015-12-31"

We focus us on the big five motor manufacturers in Germany, namely

# Firm Data
firmSymbols <- c("VOW.DE", "AUDVF", "PAH3.DE", "BMW.DE", "MBG.DE")
firmNames <- c("VW preferred", "Audi", "Porsche Automobil Hld", "BMW", "Daimler")
firmSymbols %>% 
  tidyquant::tq_get(from = startDate, to = endDate) %>% 
  dplyr::mutate(date = format(date, "%d.%m.%Y")) -> firmData
knitr::kable(head(firmData), pad=0)

As a reference market, we choose the DAX.

# Index Data
indexSymbol <- c("^GDAXI")
indexName <- c("^GDAXI")
indexSymbol %>% 
  tidyquant::tq_get(from = startDate, to = endDate) %>% 
  dplyr::mutate(date = format(date, "%d.%m.%Y")) -> indexData
knitr::kable(head(indexData), pad=0)

After we have fetched all the data, we prepare the data files for the API call, as described in the introductory vignette. We design in this step already the volume data for later purposes.

# Price files for firms and market
firmData %>% 
  dplyr::select(symbol, date, adjusted) %>% 
  readr::write_delim(file      = "02_firmDataPrice.csv", 
                     delim     = ";", 
                     col_names = F)

indexData %>% 
  dplyr::select(symbol, date, adjusted) %>% 
  readr::write_delim(file      = "03_marketDataPrice.csv", 
                     delim     = ";", 
                     col_names = F)

# Volume files for firms and market
firmData %>% 
  dplyr::select(symbol, date, volume) %>% 
  readr::write_delim(file      = "02_firmDataVolume.csv", 
                     delim     = ";", 
                     col_names = F)

indexData %>% 
  dplyr::select(symbol, date, volume) %>% 
  readr::write_delim(file      = "03_marketDataVolume.csv", 
                     delim     = ";", 
                     col_names = F)

Finally, we have to prepare the request file. The parameters for this Event Study are:

You can find details of the request file format in the introductory vignette.

group <- c(rep("VW Group", 3), rep("Other", 2))
request <- cbind(c(1:5), firmSymbols, rep(indexName, 5), rep("18.09.2015", 5), group, rep(-10, 5), rep(10, 5), rep(-11, 5), rep(250, 5))
request %>% 
  as.data.frame() %>% 
  readr::write_delim("01_requestFile.csv", delim = ";", col_names = F)

Perform Event Studies: Abnormal Return, Volume, and Volatility

After the preparation steps, we are now able to start the calculations. We use the GARCH(1, 1) model in all types of Event Studies.

Abnormal Return Event Study

The following lines perform the Event Study using our generated data. Our package places the results per default in the results folder.

event_study_api_key = Sys.getenv("event_study_api_key")

library(EventStudy)
est <- EventStudyAPI$new()
est$authentication(apiKey = event_study_api_key)

# get & set parameters for abnormal return Event Study
# we use a garch model and csv as return
# Attention: fitting a GARCH(1, 1) model is compute intensive
esaParams <- EventStudy::ARCApplicationInput$new()
esaParams$setResultFileType("csv")
esaParams$setBenchmarkModel("garch")

dataFiles <- c("request_file" = "01_requestFile.csv",
               "firm_data"    = "02_firmDataPrice.csv",
               "market_data"  = "03_marketDataPrice.csv")

# check data files, you can do it also in our R6 class
checkFiles(dataFiles)

# now let us perform the Event Study
est$performEventStudy(estParams     = esaParams, 
                      dataFiles     = dataFiles, 
                      downloadFiles = T)

After the analysis, you may continue your work in your toolset or parse the result files.

estParser <- ResultParser$new()
analysis_report = estParser$get_analysis_report("results/analysis_report.csv")
ar_result = estParser$get_ar("results/ar_results.csv", analysis_report)
aar_result = estParser$get_aar("results/aar_results.csv")

Now, you can use the downloaded CSV (or your preferred data format) files in your analysis. While creating the arEventStudy object, we merge information from the request and result files.

knitr::kable(head(ar_result$ar_tbl))

The averaged abnormal return (aar) data.frame has the following shape:

knitr::kable(head(aar_result$aar_tbl))
knitr::kable(head(aar_result$statistics_tbl))
aar_result$plot_test_statistics()
aar_result$plot(ci_statistics = "Patell Z")
aar_result$plot_cumulative()

Abnormal Volatility Event Study

event_study_api_key = Sys.getenv("event_study_api_key")
est <- EventStudyAPI$new()
est$authentication(apiKey = event_study_api_key)

# get & set parameters for abnormal return Event Study
esaParams <- EventStudy::AVyCApplicationInput$new()
esaParams$setResultFileType("csv")

est$performEventStudy(estParams     = esaParams, 
                      dataFiles     = dataFiles,
                      downloadFiles = T)
estParser <- ResultParser$new()
analysis_report = estParser$get_analysis_report("results/analysis_report.csv")
ar_result = estParser$get_ar("results/ar_results.csv", analysis_report)
aar_result = estParser$get_aar("results/aar_results.csv")
aar_result$plot_cumulative()

Abnormal Volatility Event Study

event_study_api_key = Sys.getenv("event_study_api_key")
est <- EventStudyAPI$new()
est$authentication(apiKey = event_study_api_key)

# get & set parameters for abnormal return Event Study
esaParams <- EventStudy::AVyCApplicationInput$new()
esaParams$setResultFileType("csv")

est$performEventStudy(estParams     = esaParams, 
                      dataFiles     = dataFiles,
                      downloadFiles = T)


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EventStudy documentation built on Sept. 21, 2022, 9:10 a.m.