In bergant/finstr: Financial Statements in R

The purpose of finstr package is to create an environment for reproducible financial statement analysis. The package will not cover specific types of analysis (except in examples and package vignettes) but will provide a domain language to write them. With other functions in basic R and existing R packages users could store, share, reuse and reproduce the results of their analitic work.

For now it is offering:

1. Data structure for financial statements

Reading from data parsed by XBRL package
Statements in tidy format with accounting taxonomy concepts as columns
Encapsulates calculation hierarchy of variables
Default printing in transposed format and with visible hierarchy

2. Statement calculation validation

Calculation of higher order elements
Check if calculated values match original values

3. Merge statements

Merge different periods of equal statement type
Merge statements of a different type

4. Calculate and reveal

Custom financial ratio calculations definitions
Exposing data by rearranging the calculation hierarchy
Time lagged difference

Install finstr

To install finstr from github use install_github from devtools package:

library(devtools)
install_github("bergant/finstr")

library(dplyr)
library(tidyr)
library(finstr)
data(xbrl_data_aapl2013)
data(xbrl_data_aapl2014)

Get data

Use XBRL package to parse XBRL files. For example:

library(XBRL)
# parse XBRL (Apple 10-K report)
xbrl_url2014 <- 
  "http://edgar.sec.gov/Archives/edgar/data/320193/000119312514383437/aapl-20140927.xml"
xbrl_url2013 <- 
  "http://edgar.sec.gov/Archives/edgar/data/320193/000119312513416534/aapl-20130928.xml"
xbrl_data_aapl2014 <- xbrlDoAll(xbrl_url2014)
xbrl_data_aapl2013 <- xbrlDoAll(xbrl_url2013)

Prepare statements

With xbrl_get_statements convert XBRL data to statements object.

library(finstr)

st2013 <- xbrl_get_statements(xbrl_data_aapl2013)
st2014 <- xbrl_get_statements(xbrl_data_aapl2014)
st2014

Statements object is a list of several statement objects (ballance sheets, income and cash flow statements).

To get a single statement use statements object as a regular R list:

balance_sheet2013 <- st2013$StatementOfFinancialPositionClassified
balance_sheet2014 <- st2014$StatementOfFinancialPositionClassified
income2013 <- st2013$StatementOfIncome
income2014 <- st2014$StatementOfIncome
balance_sheet2014
tail(income2014, 2)

Validate statement calculation hierarchy

Recalculate higher order concepts from basic values and check for errors.

check <- check_statement(balance_sheet2014)
check

In case of error the numbers with errors will be presented along with elements:

check_statement(
  within(balance_sheet2014, InventoryNet <- InventoryNet * 2)
)

Validation returns all calculation results in a readable data frame. Lets check only operating income from income statement:

check <- check_statement(income2014, element_id = "OperatingIncomeLoss")
check
check$expression[1]
check$calculated / 10^6

Merge statements from different periods

Use merge function to create single financial statement data from two statements.

balance_sheet <- merge( balance_sheet2013, balance_sheet2014 )

The structure of merged balance sheets may differ if XBRL taxonomy changes. Function merge takes care of it by expanding the elements hierarchy to fit both statements. The values of any missing elements in different periods is set to 0.

To merge all statements from statements object use merge on statements objects:

# merge all statements
st_all <- merge( st2013, st2014 )
# check if balance sheets are merged:
balance_sheet <- st_all$StatementOfFinancialPositionClassified
balance_sheet$endDate

Merge different types of statements

If there are no matching elements between the two statements merge joins statements by matching their periods. For some financial ratio calculations the combined statement may be
a better starting point.

  merge.statement(
    st_all$StatementOfFinancialPositionClassified, 
    st_all$StatementOfIncome )

Calculate financial ratios

Statement object (in our case balance_sheet) is also a data frame object with statement elements as columns and time periods as rows. It is possible then to use statement as a data frame.

Lets calculate current ratio which is defined by

$$ Current Ratio = \frac{Current Assets}{Current Liabilities} $$

With dplyr package we can use mutate, select or transmute functions:

library(dplyr)

balance_sheet %>% transmute(
  date = endDate, 
  CurrentRatio = AssetsCurrent / LiabilitiesCurrent
)

By using finstr::calculate function we can achieve the same result but don't have to handle the date field and there is a rounding parameter. Lets calculate for example two ratios:

balance_sheet %>% calculate( digits = 2,

    Current_Ratio = AssetsCurrent / LiabilitiesCurrent,

    Quick_Ratio =  
      ( CashAndCashEquivalentsAtCarryingValue + 
          AvailableForSaleSecuritiesCurrent +
          AccountsReceivableNetCurrent
        ) / LiabilitiesCurrent

)

If we need a period average value we can use a lag function. For example, to calculate DSO (days sales outstanding) over longer periods the average of account receivable is compared to net sales.

We will use the formula for yearly preiods:

$$ DSO = \frac{Average Accounts Receivable}{Sales Revenue} \times 365 $$

In this case we need to connect two type of statements: balance sheets and income statements. With matching reporting periods it can be accomplished with joining two data frames:

merge(balance_sheet, st_all$StatementOfIncome ) %>% calculate( digits = 2,

    .AccountReceivableLast = lag(AccountsReceivableNetCurrent),
    .AccountReceivableAvg = (.AccountReceivableLast + AccountsReceivableNetCurrent)/2,

    DaysSalesOutstanding = .AccountReceivableAvg / SalesRevenueNet * 365 
  )

The leading dot instructs the calculate function to hide the value. In our case only DaysSalesOutstanding is selected in final result. Use digits parameter to control rounding.

Reusing calculations

When running same calculation for different statements, define the calculation with calculation and call calculate with argument calculations:

# define calculation
profit_margins <- calculation(

  Gross_Margin = 
    (SalesRevenueNet -  CostOfGoodsAndServicesSold) / SalesRevenueNet,

  Operating_Margin =
    OperatingIncomeLoss / SalesRevenueNet,

  Net_Margin = 
    NetIncomeLoss / SalesRevenueNet

)

# run profit margins for two different statements
income2013 %>% calculate(calculations = profit_margins, digits = 2)
income2014 %>% calculate(calculations = profit_margins, digits = 2)

Rearranging statement hierarchy

Calculations gives us freedom to use any formula with any data from financial statements. Most of the time this is not necessary as we can get useful information just by regrouping calculation hierarchy.

There are many additional reasons why is rearranging statements useful step before actual calculations:

We can avoid errors in formulas with many variables
Accounting taxonomies do change and using many formulas on original statement is harder to support than using custom hierarchy for analysis starting point
When sharing analysis it is much easier to print 6 values instead of 30

To rearrange the statement to simple 2-level hierarchy use expose function.

expose( balance_sheet,

  # Assets
  `Current Assets` = "AssetsCurrent",
  `Noncurrent Assets` = other("Assets"),

  # Liabilites and equity
  `Current Liabilities` = "LiabilitiesCurrent",
  `Noncurrent Liabilities` = other(c("Liabilities", "CommitmentsAndContingencies")),
  `Stockholders Equity` = "StockholdersEquity"
)

Balance sheet stays divided by Assets and Liabilities and Equity. For the second level we are exposing current assets from noncurrent and similar is done for the liabilities. We choose to separate equity.

Function expose expects a list of vectors with element names. Function other helps us identify elements without enumerating every single element. Using other reduces a lot of potential errors as the function "knows" which elements are not specified and keeps the balance sheet complete.

Sometimes it is easier to define a complement than a list of elements. In this case we can use the %without% operator. Lets expose for example tangible and then intangible assets:

expose( balance_sheet,

  # Assets
  `Tangible Assets` = 
    "Assets" %without% c("Goodwill", "IntangibleAssetsNetExcludingGoodwill"),
  `Intangible Assets` = other("Assets"),

  # Liabilites and equity
  `Liabilities` = c("Liabilities", "CommitmentsAndContingencies"),
  `Stockholders Equity` = "StockholdersEquity"
)