Introducing edgarWebR

Get Data

First, we’ll fetch a bunch of 10-K and 10-Q filings for our given company using company_filings(). To make sure we’re going far enough back we’ll take a peak at the tail of our results

ticker <- "EA"

filings <- company_filings(ticker, type = "10-", count = 100)
initial_count <- nrow(filings)
# Specifying the type provides all forms that start with 10-, so we need to
# manually filter.
filings <- filings[filings$type == "10-K" | filings$type == "10-Q", ]

Note that explicitly filtering caused us to go from 100 to 97 rows.

filings$md_href <- paste0("[Link](", filings$href, ")")
knitr::kable(tail(filings)[, c("type", "filing_date", "accession_number", "size",
                               "md_href")],
             col.names = c("Type", "Filing Date", "Accession No.", "Size", "Link"),
             digits = 2,
             format.args = list(big.mark = ","))

We’ve received filings dating back to 1995 which seems good enough for our purposes, so next we’ll get the filing information for each filing.

So far we’ve done everything in base R, but now we’ll use some useful functions from dplyr and purrr to make things a bit easier.

# this can take a while - we're fetching ~100 html files!
filing_infos <- map_df(filings$href, filing_information)

filings <- bind_cols(
                     filings[, !(names(filings) %in% names(filing_infos))],
                     filing_infos)
filings$filing_delay <- filings$filing_date - filings$period_date

# Take a peak at the data
knitr::kable(head(filings) %>% select(type, filing_date, period_date,
                                      filing_delay, documents, bytes) %>%
             mutate(filing_delay = as.numeric(filing_delay)),
             col.names = c("Type", "Filing Date", "Period Date", "Delay",
                           "Documents", "Size (B)"),
             digits = 2,
             format.args = list(big.mark = ","))

Basic Analysis

Now our data is arranged, we can run some summary statistics and plot using ggplot2.

knitr::kable(filings %>%
             group_by(type) %>% summarize(
               n = n(),
               avg_delay = as.numeric(mean(filing_delay)),
               median_delay = as.numeric(median(filing_delay)),
               avg_size = mean(bytes / 1024),
               avg_docs = mean(documents)
             ),
             col.names = c("Type", "Count", "Avg. Delay", "Median Delay",
                           "Avg. Size", "Avg. Docs"),
             digits = 2,
             format.args = list(big.mark = ","))

No surprise, yearly filings (10-K’s) are larger and take more time than quarterly filings (10-K’s). Lets see how the times are distributed:

ggplot(filings, aes(x = factor(type), y = filing_delay)) +
  geom_violin() + geom_jitter(height = 0, width = 0.1) +
  labs(x = "Filing Date", y = "Filing delay (days)")
#> Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.

We can also examine how the filing delay has changed over time:

ggplot(filings, aes(x = filing_date, y = filing_delay, group = type, color = type)) +
  geom_point() + geom_line() +
  labs(x = "Filing Type", y = "Filing delay (days)")
#> Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.

Whether because of some internal change or change to SEC rules, the time between the end of the fiscal year and the 10-K has dropped quite a bit, though there is still a bit of variation.

An interesting extension would be to compare the filing delays to the company’s stock price, overall market performance and other companies to see if there are particular drivers to the pattern.

ggplot(filings, aes(x = filing_date, y = bytes / 1024, group = type, color = type)) +
  geom_point() + geom_line() +
  labs(x = "Filing Type", y = "Filing Size (KB)")

The jump in size ~2010 is the requirement for inclusion of financial datafiles starting, but there is still interesting variations.