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bergant / finstr Public
📈 Financial statements in R
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Go to fileThe 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 it anables users to store, share, reuse and reproduce the results of their analitic work. For now it is offering: 1. Data structure for financial statements
2. Statement calculation validation
3. Merge statements
4. Calculate and reveal
To install finstr from github use install_github from devtools package:
library(devtools) install_github("bergant/finstr")
Use XBRL package to parse XBRL files. For example:
library(XBRL) # parse XBRL (Apple 10-K report) xbrl_url2014 "https://www.sec.gov/Archives/edgar/data/320193/000119312514383437/aapl-20140927.xml" xbrl_url2013 "https://www.sec.gov/Archives/edgar/data/320193/000119312513416534/aapl-20130928.xml" old_o options(stringsAsFactors = FALSE) xbrl_data_aapl2014 xbrlDoAll(xbrl_url2014) xbrl_data_aapl2013 xbrlDoAll(xbrl_url2013) options(old_o)
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 #> Financial statements repository #> From To Rows Columns #> StatementOfIncome 2012-09-29 2014-09-27 3 15 #> StatementOfOtherComprehensiveIncome 2012-09-29 2014-09-27 3 14 #> StatementOfFinancialPositionClassified 2013-09-28 2014-09-27 2 33 #> StatementOfCashFlowsIndirect 2012-09-29 2014-09-27 3 33
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 #> Financial statement: 2 observations from 2013-09-28 to 2014-09-27 #> Element 2014-09-27 2013-09-28 #> Assets = 231839 207000 #> + AssetsCurrent = 68531 73286 #> + CashAndCashEquivalentsAtCarryingValue 13844 14259 #> + AvailableForSaleSecuritiesCurrent 11233 26287 #> + AccountsReceivableNetCurrent 17460 13102 #> + InventoryNet 2111 1764 #> + DeferredTaxAssetsNetCurrent 4318 3453 #> + NontradeReceivablesCurrent 9759 7539 #> + OtherAssetsCurrent 9806 6882 #> + AvailableForSaleSecuritiesNoncurrent 130162 106215 #> + PropertyPlantAndEquipmentNet 20624 16597 #> + Goodwill 4616 1577 #> + IntangibleAssetsNetExcludingGoodwill 4142 4179 #> + OtherAssetsNoncurrent 3764 5146 #> LiabilitiesAndStockholdersEquity = 231839 207000 #> + Liabilities = 120292 83451 #> + LiabilitiesCurrent = 63448 43658 #> + AccountsPayableCurrent 30196 22367 #> + AccruedLiabilitiesCurrent 18453 13856 #> + DeferredRevenueCurrent 8491 7435 #> + CommercialPaper 6308 0 #> + DeferredRevenueNoncurrent 3031 2625 #> + LongTermDebt 28987 16960 #> + OtherLiabilitiesNoncurrent 24826 20208 #> + CommitmentsAndContingencies 0 0 #> + StockholdersEquity = 111547 123549 #> + CommonStocksIncludingAdditionalPaidInCapital 23313 19764 #> + RetainedEarningsAccumulatedDeficit 87152 104256 #> + AccumulatedOtherComprehensiveIncomeLossNetOfTax 1082 -471 tail(income2014, 2) #> Financial statement: 2 observations from 2013-09-28 to 2014-09-27 #> Element 2014-09-27 2013-09-28 #> NetIncomeLoss = 39510 37037 #> + IncomeLossFromContinuingOperationsBefore. = 53483 50155 #> + OperatingIncomeLoss = 52503 48999 #> + GrossProfit = 70537 64304 #> + SalesRevenueNet 182795 170910 #> - CostOfGoodsAndServicesSold 112258 106606 #> - OperatingExpenses = 18034 15305 #> + ResearchAndDevelopmentExpense 6041 4475 #> + SellingGeneralAndAdministrativeExpense 11993 10830 #> + NonoperatingIncomeExpense 980 1156 #> - IncomeTaxExpenseBenefit 13973 13118
Recalculate higher order concepts from basic values and check for errors.
check check_statement(balance_sheet2014) check #> Number of errors: 0 #> Number of elements in errors: 0
In case of error the numbers with errors will be presented along with elements:
check_statement( within(balance_sheet2014, InventoryNet InventoryNet * 2) ) #> Number of errors: 2 #> Number of elements in errors: 1 #> #> Element: AssetsCurrent = + CashAndCashEquivalentsAtCarryingValue + AvailableForSaleSecuritiesCurrent + AccountsReceivableNetCurrent + InventoryNet + DeferredTaxAssetsNetCurrent + NontradeReceivablesCurrent + OtherAssetsCurrent #> date original calculated error #> 3 2013-09-28 7.3286e+10 7.5050e+10 -1.764e+09 #> 4 2014-09-27 6.8531e+10 7.0642e+10 -2.111e+09
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 #> Number of errors: 0 #> Number of elements in errors: 0 check$expression[1] #> [1] "+ GrossProfit - OperatingExpenses" check$calculated / 10^6 #> [1] 55241 48999 52503
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 #> [1] "2012-09-29" "2013-09-28" "2014-09-27"
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 )
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
With dplyr package we can use mutate , select or transmute functions:
library(dplyr) balance_sheet %>% transmute( date = endDate, CurrentRatio = AssetsCurrent / LiabilitiesCurrent ) #> date CurrentRatio #> 1 2012-09-29 1.495849 #> 2 2013-09-28 1.678639 #> 3 2014-09-27 1.080113
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 ) #> date Current_Ratio Quick_Ratio #> 1 2012-09-29 1.50 1.04 #> 2 2013-09-28 1.68 1.23 #> 3 2014-09-27 1.08 0.67
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:
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 ) #> date DaysSalesOutstanding #> 1 2012-09-29 NA #> 2 2013-09-28 25.66 #> 3 2014-09-27 30.51
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.
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) #> date Gross_Margin Operating_Margin Net_Margin #> 1 2011-09-24 0.40 0.31 0.24 #> 2 2012-09-29 0.44 0.35 0.27 #> 3 2013-09-28 0.38 0.29 0.22 income2014 %>% calculate(calculations = profit_margins, digits = 2) #> date Gross_Margin Operating_Margin Net_Margin #> 1 2012-09-29 0.44 0.35 0.27 #> 2 2013-09-28 0.38 0.29 0.22 #> 3 2014-09-27 0.39 0.29 0.22
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:
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" ) #> Financial statement: 3 observations from 2012-09-29 to 2014-09-27 #> Element 2014-09-27 2013-09-28 2012-09-29 #> Assets = 231839 207000 176064 #> + Current.Assets 68531 73286 57653 #> + Noncurrent.Assets 163308 133714 118411 #> LiabilitiesAndStockholdersEquity = 231839 207000 176064 #> + Current.Liabilities 63448 43658 38542 #> + Noncurrent.Liabilities 56844 39793 19312 #> + Stockholders.Equity 111547 123549 118210
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" ) #> Financial statement: 3 observations from 2012-09-29 to 2014-09-27 #> Element 2014-09-27 2013-09-28 2012-09-29 #> Assets = 231839 207000 176064 #> + Tangible.Assets 223081 201244 170705 #> + Intangible.Assets 8758 5756 5359 #> LiabilitiesAndStockholdersEquity = 231839 207000 176064 #> + Liabilities 120292 83451 57854 #> + Stockholders.Equity 111547 123549 118210
To calculate lagged difference for entire statement use diff function. The result is statement of changes between successive years:
diff(balance_sheet) #> Financial statement: 2 observations from 2013-09-28 to 2014-09-27 #> Element 2014-09-27 2013-09-28 #> Assets = 24839 30936 #> + AssetsCurrent = -4755 15633 #> + CashAndCashEquivalentsAtCarryingValue -415 3513 #> + AvailableForSaleSecuritiesCurrent -15054 7904 #> + AccountsReceivableNetCurrent 4358 2172 #> + InventoryNet 347 973 #> + DeferredTaxAssetsNetCurrent 865 870 #> + NontradeReceivablesCurrent 2220 -223 #> + OtherAssetsCurrent 2924 424 #> + AvailableForSaleSecuritiesNoncurrent 23947 14093 #> + PropertyPlantAndEquipmentNet 4027 1145 #> + Goodwill 3039 442 #> + IntangibleAssetsNetExcludingGoodwill -37 -45 #> + OtherAssetsNoncurrent -1382 -332 #> LiabilitiesAndStockholdersEquity = 24839 30936 #> + Liabilities = 36841 25597 #> + LiabilitiesCurrent = 19790 5116 #> + AccountsPayableCurrent 7829 1192 #> + AccruedLiabilitiesCurrent 4597 2442 #> + DeferredRevenueCurrent 1056 1482 #> + CommercialPaper 6308 0 #> + DeferredRevenueNoncurrent 406 -23 #> + LongTermDebt 12027 16960 #> + OtherLiabilitiesNoncurrent 4618 3544 #> + CommitmentsAndContingencies 0 0 #> + StockholdersEquity = -12002 5339 #> + CommonStockValue 0 -16422 #> + RetainedEarningsAccumulatedDeficit -17104 2967 #> + AccumulatedOtherComprehensiveIncomeLossNetOfTax 1553 -970 #> + CommonStocksIncludingAdditionalPaidInCapital 3549 19764
The only way to visualize a balance sheet is by exposing a limited number of values. The first step is then to aggregate a balance sheet by selected concepts. We can use expose to specify these groups of elements. For example:
bs_simple 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" )
library(htmlTable) print(bs_simple, html = TRUE, big.mark = ",", dateFormat = "%Y")
| 2014 | 2013 | 2012 | |
|---|---|---|---|
| Assets | 231,839 | 207,000 | 176,064 |
| Current Assets | 68,531 | 73,286 | 57,653 |
| Noncurrent Assets | 163,308 | 133,714 | 118,411 |
| Liabilities And Stockholders Equity | 231,839 | 207,000 | 176,064 |
| Current Liabilities | 63,448 | 43,658 | 38,542 |
| Noncurrent Liabilities | 56,844 | 39,793 | 19,312 |
| Stockholders Equity | 111,547 | 123,549 | 118,210 |
Using ggplot2 package we can plot a simplified balance sheet:
library(ggplot2) plot_double_stacked_bar(bs_simple)
Another option is to group by faceting balance sheet side instead of date:
plot_double_stacked_bar(bs_simple, by_date = FALSE)
Using proportional form we reveal the changes in balance sheet structure:
bs_simple_prop proportional(bs_simple) plot_double_stacked_bar(bs_simple_prop)
