Introduction

The goal of this project is to analyze the impacts to a balance sheet and income statement by changing accounting ratios such as the Current Ratio, the Debt to Equity Ratio, and the PE Ratio. Normally, these ratios are calculated from figures on the balance sheet and income statements. They are meant to give investors and related parties a quick and easy way to get a rough idea of the state of a business without having to delve through the entire balance sheet. However, analysts who use said ratios may be interested in learning how a balance sheet may change given changes to the various ratios. An example of this is any adverse macro-economic event whose impact on a firm’s balance sheet may be difficult to understand, though adjusting ratios to reflect such an adverse event may be easier. This project will look specifically at Bank of America’s balance sheets and income statements.

Gathering Data

The first step in this project involves gathering relevant data and formatting it in a way that is easy to do calculations with. For this project, balanace sheet and income sheet data comes from an api by financialmodelprep.com.To learn more about this api, please visit https://financialmodelingprep.com/developer/docs/. The R code used to access the api is shown below.

library(ggplot2)
library(data.table)
library(DT)
library(ggpubr)
library(tidyverse)
library(DT)
library(jtools)
library(kableExtra)
library(knitr)
options(scipen=999)

findTicker <- function(search){
  suppressMessages(library(gtools))
  suppressMessages(library(data.table))
  suppressMessages(library(readr))
  NYSE <- suppressWarnings(suppressMessages(read_csv("https://datahub.io/core/nyse-other-listings/r/nyse-listed.csv")))
  NASDAQ <- suppressWarnings(suppressMessages(read_csv("https://pkgstore.datahub.io/core/nasdaq-listings/nasdaq-listed_csv/data/7665719fb51081ba0bd834fde71ce822/nasdaq-listed_csv.csv")))
  AMEX <- suppressWarnings(suppressMessages(read_csv("https://old.nasdaq.com/screening/companies-by-name.aspx?letter=0&exchange=amex&render=download")))
  OTC <- suppressWarnings(suppressMessages(read_csv("http://www.sharecsv.com/dl/737aeb85295fd0668006d98fb59107ca/otcList.csv")))
  NYSE <- suppressWarnings(suppressMessages(data.table(Ticker = NYSE$`ACT Symbol`,Name = NYSE$`Company Name`)))
  NASDAQ <- suppressWarnings(suppressMessages(data.table(Ticker = NASDAQ$Symbol, Name = NASDAQ$`Company Name`)))
  AMEX <- suppressWarnings(suppressMessages(data.table(Ticker = AMEX$Symbol,Name = AMEX$Name)))
  OTC <- suppressWarnings(suppressMessages(data.table(Ticker = OTC$Symbol,Name = OTC$Description)))
  allTickers <- suppressWarnings(suppressMessages(data.table(smartbind(NYSE, NASDAQ,AMEX,OTC))))
  a <- suppressWarnings(suppressMessages(allTickers[allTickers$Name %ilike% search,])  )
  a <- rbind(a,suppressWarnings(suppressMessages(allTickers[allTickers$Ticker %ilike% search,])  ) )
  a
   
}


fxRates <- function(baseCurrency = "USD", compareAgainst = "JPY"){
  
  suppressMessages(library(httr))
  suppressMessages(library(jsonlite))
  suppressMessages(library(dplyr))
  suppressMessages(library(data.table))
  suppressMessages(library(bizdays))
  suppressMessages(library(lubridate))
  
  fxURL <- "https://financialmodelingprep.com/api/v3/forex"
  newFXURL <- paste0(fxURL,"/",baseCurrency,compareAgainst)
  
  jsonDwn <- suppressMessages(httr::GET(newFXURL))
  jsonText <- suppressMessages(httr::content(jsonDwn, as = "text"))
  jsonContent <- suppressMessages(jsonlite::fromJSON(jsonText))
  FXtable <- as.data.table(jsonContent)
  FXtable
}

geometricReturn <- function(vectorOfPrices,vectorOfReturns,vectorOfDates,annualizeDaily = FALSE,annualizeMonthly = FALSE ,annualizeQuarterly = FALSE){
  
  ##either vectorOfPrices or vectorOfReturns should be in date order by latest to oldest
  
  
  ##coerce
  if(!missing(vectorOfPrices)){
    vectorOfPrices <- as.numeric(vectorOfPrices)
  }
  if(!missing(vectorOfReturns)){
    vectorOfReturns <- as.numeric(vectorOfReturns)
  }
  vectorOfDates <- as.Date(vectorOfDates)
  
  ##order
   if(!missing(vectorOfPrices)){
    dat <- data.table(prices = vectorOfPrices, dates = vectorOfDates)
    dat <- dat[rev(order(as.Date(dat$dates))),]
  }
     if(!missing(vectorOfReturns)){
    dat <- data.table(prices = vectorOfReturns, dates = vectorOfDates)
    dat <- dat[rev(order(as.Date(dat$dates))),]
  }
  
  
  ##calc
  
  if(missing(vectorOfPrices)){
    returnsPlusOne <- dat$prices + 1
    multReturns <- prod(returnsPlusOne)
    retPwr <- multReturns ^ (1/(length(vectorOfReturns)+1))
    geoReturn <- retPwr - 1
  }
  
  if(missing(vectorOfReturns)){
    geoReturn <- ( (dat$prices[1] / dat$prices[length(vectorOfPrices)]) ^ (1 / length(vectorOfPrices) ) ) - 1
    
  }
 
  ## if given daily prices or returns and desired an anualized figure
  if(annualizeDaily == TRUE){
    geoReturn <- ( ( geoReturn + 1 ) ^ 253) - 1
    
  }
  ## if given monthly prices or returns and desired an anualized figure
  else if(annualizeMonthly == TRUE){
    geoReturn <- ( ( geoReturn + 1 ) ^ 12) - 1
    
  }
  ## if given quarterly prices or returns and desired an anualized figure
  else if(annualizeQuarterly == TRUE){
    geoReturn <- ( ( geoReturn + 1 ) ^ 4) - 1
    
  }
  
  return(geoReturn)
}

historicalFX <- function(baseCurrency = "USD",compareAgainst = "JPY",startDate = "2018-01-01", endDate = as.character(Sys.Date()) ){
  
  suppressMessages(library(httr))
  suppressMessages(library(jsonlite))
  suppressMessages(library(dplyr))
  suppressMessages(library(data.table))
  suppressMessages(library(bizdays))
  suppressMessages(library(lubridate))
  
  ##https://api.exchangeratesapi.io/history?start_at=2017-01-01&end_at=2018-09-01&?base=USD
  fxURL <- "https://api.exchangeratesapi.io/history?"
  newFXURL <- paste0(fxURL,"start_at=",startDate,"&end_at=",endDate,"&base=",baseCurrency)
  
  jsonDwn <- suppressMessages(httr::GET(newFXURL))
  jsonText <- suppressMessages(httr::content(jsonDwn, as = "text"))
  jsonContent <- suppressMessages(jsonlite::fromJSON(jsonText))
  fxRates <- rbindlist(jsonContent$rates,idcol = "date", fill = TRUE)
  finalFXtable <- data.table(date = fxRates$date, fxRates[,..compareAgainst])
  finalFXtable$date <- as.Date(finalFXtable$date)
  finalFXtable

}

historicalStocks <- function(ticker = "AAPL", api = "UX6D3HZK0BHUUGNW"){
  
  suppressMessages(library(httr))
  suppressMessages(library(jsonlite))
  suppressMessages(library(dplyr))
  suppressMessages(library(data.table))
  
  historicalURL <- paste0("https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=",ticker,"&outputsize=full&apikey=")
  jsonDwn <- suppressMessages(httr::GET(historicalURL, query = list(apikey = api) ))
  jsonText <- suppressMessages(httr::content(jsonDwn, as = "text"))
  jsonContent <- suppressMessages(jsonlite::fromJSON(jsonText))
  timeSeries <- suppressMessages(rbindlist(jsonContent$`Time Series (Daily)`))
  
  all_Dates <- as.Date.character(names(jsonContent$`Time Series (Daily)`))
 
  timeSeries$Date <- all_Dates
  timeSeries$ticker <- ticker
  
  timeSeries$`1. open` <- as.numeric(timeSeries$`1. open`)
  timeSeries$`2. high` <- as.numeric(timeSeries$`2. high`)
  timeSeries$`3. low` <- as.numeric(timeSeries$`3. low`)
  timeSeries$`4. close` <- as.numeric(timeSeries$`4. close`)
  timeSeries$`5. volume` <- as.numeric(timeSeries$`5. volume`)
  
  timeSeries
  
}

incomeStatement <- function(ticker = "AAPL" ){
  
  suppressMessages(library(httr))
  suppressMessages(library(jsonlite))
  suppressMessages(library(dplyr))
  suppressMessages(library(data.table))
  suppressMessages(library(bizdays))
  suppressMessages(library(lubridate))
  suppressMessages(library(formattable))
  
  balanceURL <- paste0("https://financialmodelingprep.com/api/v3/financials/income-statement/",ticker ,"?period=quarter")
  jsonDwn <- httr::GET(balanceURL) 
  jsonText <- httr::content(jsonDwn, as = "text")
  jsonContent <- jsonlite::fromJSON(jsonText)
  financial <- jsonContent$financials                       
  financial <- data.table(financial)
  financial[,2:30] <- financial[, lapply(.SD, accounting), .SDcols = 2:30]  
  financial$date <- as.Date(financial$date)
  financial
  
}

quarterlyBalanceSheet <- function(ticker = "AAPL" ){
  
  suppressMessages(library(httr))
  suppressMessages(library(jsonlite))
  suppressMessages(library(dplyr))
  suppressMessages(library(data.table))
  suppressMessages(library(bizdays))
  suppressMessages(library(lubridate))
  suppressMessages(library(formattable))
  
  balanceURL <- paste0("https://financialmodelingprep.com/api/v3/financials/balance-sheet-statement/",ticker ,"?period=quarter")
  jsonDwn <- httr::GET(balanceURL) 
  jsonText <- httr::content(jsonDwn, as = "text")
  jsonContent <- jsonlite::fromJSON(jsonText)
  financial <- jsonContent$financials                       
  financial <- data.table(financial)
  financial[,2:30] <- financial[, lapply(.SD, accounting), .SDcols = 2:30]  
  financial$date <- as.Date(financial$date)
  financial
  
}

stockSplits2 <- function(ticker = "MSFT"){
  
  suppressMessages(library(httr))
  suppressMessages(library(jsonlite))
  suppressMessages(library(dplyr))
  suppressMessages(library(data.table))
  suppressMessages(library(bizdays))
  suppressMessages(library(lubridate))
  suppressMessages(library(rvest))
  closeAllConnections()
  ticker <- as.character(ticker)
  ticker <- toupper(ticker)
  
  ##relevant function needed for later
  splitInParts <- function(string, size){
    pat <- paste0('(?<=.{',size,'})')
    strsplit(string, pat, perl=TRUE)
  }
  
  
  url <- "https://www.splithistory.com/?symbol="
  ##create unique url
  splitsURL <- paste0(url,ticker)
  
  ##scrape site
  splits <- try(suppressWarnings(read_html(splitsURL)))
  if("try-error" %in% class(splits)) return("no stock split history")
  
  txt <- splits %>% html_node("center") %>% html_text()
 
  beginningInt <- regexpr("Split History Table",txt)[[1]]
  endingInt <- gregexpr("Stock Splits",txt)[[1]]
  endingInt <- endingInt[which(endingInt > beginningInt)]
  endingInt <- min(endingInt)
  
  
  txt <- substr(txt,beginningInt,endingInt)
  txt <- gsub("[[:alpha:]]{4,}","",txt)
  txt <- gsub("[\\(\\)]","",txt)
  txt <- gsub("[\\)\\)]","",txt)
  txt <- gsub("\\s[[:alpha:]]{1,2}\\s","",txt)
  txt <- gsub("\\s","",txt)
  txt <- gsub("[[:alpha:]]{1}$","",txt)
  
  dateInts <- gregexpr("[[:digit:]]{2}/[[:digit:]]{2}/[[:digit:]]{4}",txt)[[1]]
  dateIntsEnd <- dateInts + 9
  d <- NULL
  for(i in 1:length(dateInts)){
    d <- rbind(d,substr(txt,dateInts[i],dateIntsEnd[i]))
  }
  dates <- data.table(dates = as.Date.character(d,format = "%m/%d/%Y"))
  splitEnds <- dateInts[2:length(dateInts)] - 1
  splitEnds <- append(splitEnds, nchar(txt))
  
  d <- NULL
  dateIntsEnd <- dateIntsEnd + 1
  for(i in 1:length(dateInts)){
    d <- rbind(d,substr(txt,dateIntsEnd[i],splitEnds[i]))
  }
  dates$splits <- d
  # 
  # splitData <- suppressWarnings(tstrsplit(dates$splits,"for"))
  # dates$OUT <- suppressWarnings(splitData[1])
  # dates$FOR <- suppressWarnings(splitData[2])
  # 
  # dates$ticker <- ticker
  
  closeAllConnections()
  if(nrow(dates) == 0){
    return(data.table("no stock split history"))
  }else if(is.na(dates[[1]])){
    return(data.table("no stock split history"))
  }
  else{
    splitData <- suppressWarnings(tstrsplit(dates$splits,"for"))
    dates$OUT <- suppressWarnings(splitData[1])
    dates$FOR <- suppressWarnings(splitData[2])
    
    dates$ticker <- ticker
    return (dates)
  }
  
  
}

The following is the raw data for Bank of America’s balance sheet and income statement sourced from financialmodelingprep.com.

library(knitr)
library(kableExtra)
BAC_Balance <- quarterlyBalanceSheet("BAC")
BAC_Income <- incomeStatement("BAC")

scroll_box(kable(BAC_Balance, caption = "Bank of America Balance Sheet Data") %>% kable_styling("bordered"), height = "500px", width = "1100px"  )

Bank of America Balance Sheet Data
date	Cash and cash equivalents	Short-term investments	Cash and short-term investments	Receivables	Total current assets	Property, Plant & Equipment Net	Goodwill and Intangible Assets	Long-term investments	Tax assets	Total non-current assets	Total assets	Short-term debt	Total current liabilities	Long-term debt	Total debt	Tax Liabilities	Deposit Liabilities	Total non-current liabilities	Total liabilities	Other comprehensive income	Retained earnings (deficit)	Total shareholders equity	Investments	Net Debt	Other Assets	Other Liabilities
2019-09-30	157,094,000,000.00000	444,594,000,000.00000	601,688,000,000.00000	52,560,000,000.0	654,248,000,000.00000	10,493,000,000.0	70,551,000,000.0	444,594,000,000.00000	0.0	1,772,082,000,000.000000	2,426,330,000,000.000000	3,458,000,000.0	3,458,000,000.0	243,405,000,000.00000	246,863,000,000.00000	0.0	1,392,836,000,000.000000	243,405,000,000.00000	2,157,943,000,000.000000	122,820,999,999.99	151,183,000,000.00000	268,387,000,000.00000	889,188,000,000.00000	89,769,000,000.00000	0.0	1,911,080,000,000.000000
2019-06-30	171,394,000,000.00000	446,075,000,000.00000	617,469,000,000.00000	53,329,000,000.0	670,798,000,000.00000	10,426,000,000.0	70,551,000,000.0	446,075,000,000.00000	0.0	1,725,094,000,000.000000	2,395,892,000,000.000000	2,403,000,000.0	2,403,000,000.0	238,011,000,000.00000	240,414,000,000.00000	0.0	1,375,093,000,000.000000	238,011,000,000.00000	2,124,484,000,000.000000	131,307,999,999.99	147,577,000,000.00000	271,408,000,000.00000	892,150,000,000.00000	69,020,000,000.00000	0.0	1,884,070,000,000.000000
2019-03-31	171,623,000,000.00000	440,674,000,000.00000	612,297,000,000.00000	53,496,000,000.0	665,793,000,000.00000	10,251,000,000.0	70,551,000,000.0	440,674,000,000.00000	0.0	1,711,371,000,000.000000	2,377,164,000,000.000000	1,895,000,000.0	1,895,000,000.0	233,929,000,000.00000	235,824,000,000.00000	0.0	1,379,337,000,000.000000	233,929,000,000.00000	2,110,154,000,000.000000	135,163,999,999.99	141,888,000,000.00000	267,010,000,000.00000	881,348,000,000.00000	64,201,000,000.00000	0.0	1,874,330,000,000.000000
2018-12-31	177,404,000,000.00000	441,753,000,000.00000	619,157,000,000.00000	65,814,000,000.0	684,971,000,000.00000	9,906,000,000.0	70,551,000,000.0	441,753,000,000.00000	17,595,000,000.0	1,669,536,000,000.000000	2,354,507,000,000.000000	1,648,000,000.0	3,845,000,000.0	229,340,000,000.00000	230,988,000,000.00000	2,197,000,000.0	1,381,476,000,000.000000	236,194,000,000.00000	2,089,182,000,000.000000	141,221,999,999.99	136,314,000,000.00000	265,325,000,000.00000	883,506,000,000.00000	53,584,000,000.00000	0.0	1,849,143,000,000.000000
2018-09-30	184,858,000,000.00000	446,107,000,000.00000	630,965,000,000.00000	56,962,000,000.0	687,927,000,000.00000	9,680,000,000.0	70,551,000,000.0	446,107,000,000.00000	0.0	1,650,906,000,000.000000	2,338,833,000,000.000000	1,789,000,000.0	1,789,000,000.0	234,100,000,000.00000	235,889,000,000.00000	0.0	1,345,649,000,000.000000	234,100,000,000.00000	2,076,675,000,000.000000	146,246,999,999.99	130,747,000,000.00000	262,158,000,000.00000	892,214,000,000.00000	51,031,000,000.00000	0.0	1,840,786,000,000.000000
2018-06-30	171,199,000,000.00000	438,269,000,000.00000	609,468,000,000.00000	57,813,000,000.0	667,281,000,000.00000	9,537,000,000.0	70,551,000,000.0	438,269,000,000.00000	0.0	1,624,389,000,000.000000	2,291,670,000,000.000000	3,396,000,000.0	3,396,000,000.0	226,595,000,000.00000	229,991,000,000.00000	0.0	1,309,691,000,000.000000	226,595,000,000.00000	2,027,454,000,000.000000	152,002,999,999.99	125,546,000,000.00000	264,216,000,000.00000	876,538,000,000.00000	58,792,000,000.00000	0.0	1,797,463,000,000.000000
2018-03-31	204,241,000,000.00000	426,837,000,000.00000	631,078,000,000.00000	58,127,000,000.0	689,205,000,000.00000	9,399,000,000.0	70,551,000,000.0	426,837,000,000.00000	0.0	1,639,273,000,000.000000	2,328,478,000,000.000000	2,284,000,000.0	2,284,000,000.0	232,256,000,000.00000	234,540,000,000.00000	0.0	1,328,664,000,000.000000	232,256,000,000.00000	2,062,254,000,000.000000	158,203,999,999.99	120,298,000,000.00000	266,224,000,000.00000	853,674,000,000.00000	30,299,000,000.00000	0.0	1,827,714,000,000.000000
2017-12-31	157,434,000,000.00000	440,130,000,000.00000	597,564,000,000.00000	61,623,000,000.0	659,187,000,000.00000	9,247,000,000.0	70,551,000,000.0	440,130,000,000.00000	19,462,000,000.0	1,622,047,000,000.000000	2,281,234,000,000.000000	1,494,000,000.0	3,267,000,000.0	227,402,000,000.00000	228,896,000,000.00000	1,773,000,000.0	1,309,545,000,000.000000	234,239,000,000.00000	2,014,088,000,000.000000	160,411,999,999.99	113,816,000,000.00000	267,146,000,000.00000	880,260,000,000.00000	71,462,000,000.00000	0.0	1,776,582,000,000.000000
2017-09-30	172,381,000,000.00000	439,209,000,000.00000	611,590,000,000.00000	55,855,000,000.0	667,445,000,000.00000	8,971,000,000.0	71,427,000,000.0	439,209,000,000.00000	0.0	1,616,451,000,000.000000	2,283,896,000,000.000000	1,904,000,000.0	1,904,000,000.0	228,666,000,000.00000	230,570,000,000.00000	0.0	1,284,417,000,000.000000	228,666,000,000.00000	2,011,437,000,000.000000	165,140,999,999.99	113,486,000,000.00000	272,459,000,000.00000	878,418,000,000.00000	58,189,000,000.00000	0.0	1,780,867,000,000.000000
2017-06-30	158,704,000,000.00000	434,517,000,000.00000	593,221,000,000.00000	59,342,000,000.0	652,563,000,000.00000	8,904,000,000.0	71,579,000,000.0	434,517,000,000.00000	0.0	1,601,966,000,000.000000	2,254,529,000,000.000000	1,572,000,000.0	1,572,000,000.0	223,923,000,000.00000	225,495,000,000.00000	0.0	1,262,980,000,000.000000	223,923,000,000.00000	1,983,542,000,000.000000	167,963,999,999.99	109,628,000,000.00000	270,987,000,000.00000	869,034,000,000.00000	66,791,000,000.00000	0.0	1,758,047,000,000.000000
2017-03-31	168,025,000,000.00000	428,045,000,000.00000	596,070,000,000.00000	59,534,000,000.0	655,604,000,000.00000	9,319,000,000.0	71,735,000,000.0	428,045,000,000.00000	0.0	1,592,097,000,000.000000	2,247,701,000,000.000000	1,041,000,000.0	1,041,000,000.0	221,385,000,000.00000	222,426,000,000.00000	0.0	1,272,141,000,000.000000	221,385,000,000.00000	1,979,548,000,000.000000	170,001,999,999.99	105,467,000,000.00000	268,153,000,000.00000	856,090,000,000.00000	54,401,000,000.00000	0.0	1,757,122,000,000.000000
2016-12-31	147,738,000,000.00000	430,731,000,000.00000	578,469,000,000.00000	58,759,000,000.0	637,228,000,000.00000	9,139,000,000.0	71,891,000,000.0	430,731,000,000.00000	28,371,000,000.0	1,550,474,000,000.000000	2,187,702,000,000.000000	2,024,000,000.0	2,899,000,000.0	216,823,000,000.00000	218,847,000,000.00000	875,000,000.0	1,260,934,000,000.000000	225,968,000,000.00000	1,920,862,000,000.000000	172,257,999,999.99	101,870,000,000.00000	266,840,000,000.00000	861,462,000,000.00000	71,109,000,000.00000	0.0	1,691,995,000,000.000000
2016-09-30	143,434,000,000.00000	434,914,000,000.00000	578,348,000,000.00000	54,116,000,000.0	632,464,000,000.00000	9,133,000,000.0	72,912,000,000.0	434,914,000,000.00000	0.0	1,562,850,000,000.000000	2,195,314,000,000.000000	1,055,000,000.0	1,055,000,000.0	225,136,000,000.00000	226,191,000,000.00000	0.0	1,232,895,000,000.000000	225,136,000,000.00000	1,925,231,000,000.000000	173,480,999,999.99	98,303,000,000.00000	270,083,000,000.00000	869,828,000,000.00000	82,757,000,000.00000	0.0	1,699,040,000,000.000000
2016-06-30	171,207,000,000.00000	411,949,000,000.00000	583,156,000,000.00000	58,150,000,000.0	641,306,000,000.00000	9,150,000,000.0	73,096,000,000.0	411,949,000,000.00000	0.0	1,545,303,000,000.000000	2,186,609,000,000.000000	1,860,000,000.0	1,860,000,000.0	229,617,000,000.00000	231,477,000,000.00000	0.0	1,216,091,000,000.000000	229,617,000,000.00000	1,919,540,000,000.000000	174,773,999,999.99	93,623,000,000.00000	267,069,000,000.00000	823,898,000,000.00000	60,270,000,000.00000	0.0	1,688,063,000,000.000000
2016-03-31	179,610,000,000.00000	400,311,000,000.00000	579,921,000,000.00000	56,838,000,000.0	636,759,000,000.00000	9,358,000,000.0	73,339,000,000.0	400,311,000,000.00000	0.0	1,548,739,000,000.000000	2,185,498,000,000.000000	1,482,000,000.0	1,482,000,000.0	232,849,000,000.00000	234,331,000,000.00000	0.0	1,217,261,000,000.000000	232,849,000,000.00000	1,922,722,000,000.000000	175,115,999,999.99	90,270,000,000.00000	262,776,000,000.00000	800,622,000,000.00000	54,721,000,000.00000	0.0	1,688,391,000,000.000000
2015-12-31	159,353,000,000.00000	407,005,000,000.00000	566,358,000,000.00000	58,312,000,000.0	624,670,000,000.00000	9,485,000,000.0	73,529,000,000.0	407,005,000,000.00000	32,195,000,000.0	1,519,646,000,000.000000	2,144,316,000,000.000000	1,325,000,000.0	2,420,000,000.0	236,764,000,000.00000	238,089,000,000.00000	1,095,000,000.0	1,197,259,000,000.000000	244,495,000,000.00000	1,888,111,000,000.000000	173,314,999,999.99	88,564,000,000.00000	256,205,000,000.00000	814,010,000,000.00000	78,736,000,000.00000	0.0	1,641,196,000,000.000000
2015-09-30	170,426,000,000.00000	391,651,000,000.00000	562,077,000,000.00000	63,443,000,000.0	625,520,000,000.00000	9,554,000,000.0	73,734,000,000.0	391,651,000,000.00000	0.0	1,527,486,000,000.000000	2,153,006,000,000.000000	1,869,000,000.0	1,869,000,000.0	237,288,000,000.00000	239,157,000,000.00000	0.0	1,162,009,000,000.000000	237,288,000,000.00000	1,897,101,000,000.000000	174,113,999,999.99	85,485,000,000.00000	255,905,000,000.00000	783,302,000,000.00000	68,731,000,000.00000	0.0	1,657,944,000,000.000000
2015-06-30	163,514,000,000.00000	392,379,000,000.00000	555,893,000,000.00000	64,505,000,000.0	620,398,000,000.00000	9,700,000,000.0	73,963,000,000.0	392,379,000,000.00000	0.0	1,528,636,000,000.000000	2,149,034,000,000.000000	1,841,000,000.0	1,841,000,000.0	243,414,000,000.00000	245,255,000,000.00000	0.0	1,149,560,000,000.000000	243,414,000,000.00000	1,897,375,000,000.000000	174,910,999,999.99	81,938,000,000.00000	251,659,000,000.00000	784,758,000,000.00000	81,741,000,000.00000	0.0	1,652,120,000,000.000000
2015-03-31	162,543,000,000.00000	383,989,000,000.00000	546,532,000,000.00000	63,716,000,000.0	610,248,000,000.00000	9,833,000,000.0	74,167,000,000.0	383,989,000,000.00000	0.0	1,533,297,000,000.000000	2,143,545,000,000.000000	2,893,000,000.0	2,893,000,000.0	237,858,000,000.00000	240,751,000,000.00000	0.0	1,153,168,000,000.000000	237,858,000,000.00000	1,893,357,000,000.000000	175,682,999,999.99	77,472,000,000.00000	250,188,000,000.00000	767,978,000,000.00000	78,208,000,000.00000	0.0	1,652,606,000,000.000000
2014-12-31	138,589,000,000.00000	380,461,000,000.00000	519,050,000,000.00000	61,845,000,000.0	580,895,000,000.00000	10,049,000,000.0	74,389,000,000.0	380,461,000,000.00000	37,830,000,000.0	1,523,639,000,000.000000	2,104,534,000,000.000000	2,697,000,000.0	3,765,000,000.0	243,139,000,000.00000	245,836,000,000.00000	1,068,000,000.0	1,118,936,000,000.000000	252,715,000,000.00000	1,861,063,000,000.000000	172,766,999,999.99	75,024,000,000.00000	243,471,000,000.00000	760,922,000,000.00000	107,247,000,000.00000	0.0	1,604,583,000,000.000000
2014-09-30	128,659,000,000.00000	368,124,000,000.00000	496,783,000,000.00000	67,092,000,000.0	563,875,000,000.00000	9,987,000,000.0	74,633,000,000.0	368,124,000,000.00000	0.0	1,559,738,000,000.000000	2,123,613,000,000.000000	2,418,000,000.0	3,418,000,000.0	250,115,000,000.00000	252,533,000,000.00000	1,000,000,000.0	1,111,981,000,000.000000	250,115,000,000.00000	1,884,932,000,000.000000	171,384,999,999.99	72,811,000,000.00000	238,681,000,000.00000	736,248,000,000.00000	123,874,000,000.00000	0.0	1,631,399,000,000.000000
2014-06-30	152,899,000,000.00000	352,883,000,000.00000	505,782,000,000.00000	65,475,000,000.0	571,257,000,000.00000	10,145,000,000.0	74,909,000,000.0	352,883,000,000.00000	0.0	1,599,300,000,000.000000	2,170,557,000,000.000000	45,873,000,000.0	45,873,000,000.0	257,071,000,000.00000	302,944,000,000.00000	0.0	1,134,329,000,000.000000	257,071,000,000.00000	1,933,146,000,000.000000	168,313,999,999.99	73,808,000,000.00000	237,411,000,000.00000	705,766,000,000.00000	150,045,000,000.00000	0.0	1,630,202,000,000.000000
2014-03-31	151,645,000,000.00000	249,981,000,000.00000	401,626,000,000.00000	64,135,000,000.0	465,761,000,000.00000	10,351,000,000.0	75,179,000,000.0	120,546,000,000.00000	0.0	1,684,090,000,000.000000	2,149,851,000,000.000000	51,409,000,000.0	51,409,000,000.0	254,785,000,000.00000	306,194,000,000.00000	0.0	1,133,650,000,000.000000	254,785,000,000.00000	1,917,963,000,000.000000	154,248,000,000.00	71,877,000,000.00000	231,888,000,000.00000	370,527,000,000.00000	154,549,000,000.00000	0.0	1,611,769,000,000.000000
2013-12-31	131,322,000,000.00000	268,795,000,000.00000	400,117,000,000.00000	59,448,000,000.0	459,565,000,000.00000	10,475,000,000.0	75,418,000,000.0	11,540,000,000.00000	43,841,000,000.0	1,642,708,000,000.000000	2,102,273,000,000.000000	45,999,000,000.0	49,067,000,000.0	249,674,000,000.00000	295,673,000,000.00000	3,068,000,000.0	1,119,271,000,000.000000	261,159,000,000.00000	1,869,588,000,000.000000	155,845,000,000.00	72,497,000,000.00000	232,685,000,000.00000	280,335,000,000.00000	164,351,000,000.00000	0.0	1,559,362,000,000.000000
2013-09-30	121,233,000,000.00000	234,896,000,000.00000	356,129,000,000.00000	60,065,000,000.0	416,194,000,000.00000	10,703,000,000.0	75,734,000,000.0	14,449,000,000.00000	0.0	1,710,459,000,000.000000	2,126,653,000,000.000000	40,769,000,000.0	40,769,000,000.0	255,331,000,000.00000	296,100,000,000.00000	0.0	1,110,118,000,000.000000	255,331,000,000.00000	1,894,371,000,000.000000	156,886,000,000.00	69,419,000,000.00000	232,282,000,000.00000	249,345,000,000.00000	174,867,000,000.00000	0.0	1,598,271,000,000.000000
2013-06-30	98,828,000,000.00000	240,154,000,000.00000	338,982,000,000.00000	67,526,000,000.0	406,508,000,000.00000	10,836,000,000.0	76,034,000,000.0	12,916,000,000.00000	0.0	1,716,812,000,000.000000	2,123,320,000,000.000000	46,470,000,000.0	46,470,000,000.0	262,480,000,000.00000	308,950,000,000.00000	0.0	1,080,783,000,000.000000	262,480,000,000.00000	1,892,288,000,000.000000	158,633,000,000.00	67,308,000,000.00000	231,032,000,000.00000	253,070,000,000.00000	210,122,000,000.00000	0.0	1,583,338,000,000.000000
2013-03-31	100,980,000,000.00000	261,850,000,000.00000	362,830,000,000.00000	70,981,000,000.0	433,811,000,000.00000	11,085,000,000.0	76,309,000,000.0	12,740,000,000.00000	0.0	1,741,008,000,000.000000	2,174,819,000,000.000000	42,148,000,000.0	42,148,000,000.0	279,641,000,000.00000	321,789,000,000.00000	0.0	1,095,183,000,000.000000	279,641,000,000.00000	1,937,526,000,000.000000	164,137,000,000.00	63,844,000,000.00000	237,293,000,000.00000	274,590,000,000.00000	220,809,000,000.00000	0.0	1,615,737,000,000.000000
2012-12-31	110,752,000,000.00000	286,906,000,000.00000	397,658,000,000.00000	71,467,000,000.0	469,125,000,000.00000	11,858,000,000.0	76,660,000,000.0	18,694,000,000.00000	48,558,000,000.0	1,740,849,000,000.000000	2,209,974,000,000.000000	30,731,000,000.0	34,408,000,000.0	275,585,000,000.00000	306,316,000,000.00000	3,677,000,000.0	1,105,261,000,000.000000	291,105,000,000.00000	1,973,018,000,000.000000	164,110,000,000.00	62,843,000,000.00000	236,956,000,000.00000	305,600,000,000.00000	195,564,000,000.00000	0.0	1,647,505,000,000.000000
2012-09-30	106,415,000,000.00000	305,949,000,000.00000	412,364,000,000.00000	66,341,000,000.0	478,705,000,000.00000	12,436,000,000.0	77,006,000,000.0	15,950,000,000.00000	0.0	1,687,457,000,000.000000	2,166,162,000,000.000000	35,291,000,000.0	35,291,000,000.0	286,534,000,000.00000	321,825,000,000.00000	0.0	1,063,307,000,000.000000	286,534,000,000.00000	1,927,556,000,000.000000	164,034,000,000.00	62,583,000,000.00000	238,606,000,000.00000	321,899,000,000.00000	215,410,000,000.00000	0.0	1,605,731,000,000.000000
2012-06-30	123,717,000,000.00000	300,049,000,000.00000	423,766,000,000.00000	71,458,000,000.0	495,224,000,000.00000	12,653,000,000.0	77,311,000,000.0	22,350,000,000.00000	0.0	1,665,630,000,000.000000	2,160,854,000,000.000000	39,019,000,000.0	39,019,000,000.0	301,848,000,000.00000	340,867,000,000.00000	0.0	1,035,225,000,000.000000	301,848,000,000.00000	1,924,879,000,000.000000	163,963,000,000.00	62,712,000,000.00000	235,975,000,000.00000	322,399,000,000.00000	217,150,000,000.00000	0.0	1,584,012,000,000.000000
2012-03-31	128,792,000,000.00000	297,040,000,000.00000	425,832,000,000.00000	74,358,000,000.0	500,190,000,000.00000	13,104,000,000.0	77,672,000,000.0	20,479,000,000.00000	0.0	1,681,259,000,000.000000	2,181,449,000,000.000000	39,254,000,000.0	39,254,000,000.0	354,912,000,000.00000	394,166,000,000.00000	0.0	1,041,311,000,000.000000	354,912,000,000.00000	1,948,950,000,000.000000	163,961,000,000.00	60,734,000,000.00000	232,499,000,000.00000	317,519,000,000.00000	265,374,000,000.00000	0.0	1,554,784,000,000.000000
2011-12-31	120,102,000,000.00000	276,151,000,000.00000	396,253,000,000.00000	66,999,000,000.0	463,252,000,000.00000	13,637,000,000.0	77,988,000,000.0	26,004,000,000.00000	47,211,000,000.0	1,665,794,000,000.000000	2,129,046,000,000.000000	35,698,000,000.0	39,901,000,000.0	372,265,000,000.00000	407,963,000,000.00000	4,203,000,000.0	1,033,041,000,000.000000	387,477,000,000.00000	1,898,945,000,000.000000	162,218,000,000.00	60,520,000,000.00000	230,101,000,000.00000	302,155,000,000.00000	287,861,000,000.00000	0.0	1,471,567,000,000.000000
2011-09-30	82,865,000,000.00000	324,267,000,000.00000	407,132,000,000.00000	89,302,000,000.0	496,434,000,000.00000	13,552,000,000.0	79,596,000,000.0	18,330,000,000.00000	0.0	1,723,194,000,000.000000	2,219,628,000,000.000000	33,869,000,000.0	33,869,000,000.0	398,965,000,000.00000	432,834,000,000.00000	0.0	1,041,353,000,000.000000	398,965,000,000.00000	1,989,376,000,000.000000	160,480,000,000.00	59,043,000,000.00000	230,252,000,000.00000	342,597,000,000.00000	349,969,000,000.00000	0.0	1,556,542,000,000.000000
2011-06-30	0.00000	0.00000	0.00000	0.0	0.00000	0.0	0.0	0.00000	0.0	0.000000	0.000000	0.0	0.0	0.00000	0.00000	0.0	0.000000	0.00000	0.000000	0.00	793,000,000.00000	0.00000	0.00000	0.00000	0.0	0.000000
2011-03-31	0.00000	0.00000	0.00000	0.0	0.00000	0.0	0.0	0.00000	0.0	0.000000	0.000000	0.0	0.0	0.00000	0.00000	0.0	0.000000	0.00000	0.000000	0.00	0.00000	0.00000	0.00000	0.00000	0.0	0.000000
2010-12-31	0.00000	0.00000	0.00000	0.0	0.00000	0.0	0.0	0.00000	0.0	0.000000	0.000000	0.0	0.0	0.00000	0.00000	0.0	0.000000	0.00000	0.000000	0.00	0.00000	0.00000	0.00000	0.00000	0.0	0.000000
2010-09-30	0.00000	0.00000	0.00000	0.0	0.00000	0.0	0.0	0.00000	0.0	0.000000	0.000000	0.0	0.0	0.00000	0.00000	0.0	0.000000	0.00000	0.000000	0.00	0.00000	0.00000	0.00000	0.00000	0.0	0.000000
2010-06-30	0.00000	0.00000	0.00000	0.0	0.00000	0.0	0.0	0.00000	0.0	0.000000	0.000000	0.0	0.0	490,083,000,000.00000	490,083,000,000.00000	0.0	0.000000	490,083,000,000.00000	490,083,000,000.000000	0.00	(4,447,000,000.00000)	(490,083,000,000.00000)	0.00000	490,083,000,000.00000	0.0	0.000000
2010-03-31	144,794,000,000.00000	316,020,000,000.00000	460,814,000,000.00000	83,636,000,000.0	544,450,000,000.00000	15,147,000,000.0	97,853,000,000.0	20,256,000,000.00000	0.0	1,794,250,000,000.000000	2,338,700,000,000.000000	85,406,000,000.0	85,406,000,000.0	511,653,000,000.00000	597,059,000,000.00000	0.0	976,102,000,000.000000	511,653,000,000.00000	2,108,877,000,000.000000	155,641,000,000.00	67,811,000,000.00000	229,823,000,000.00000	336,276,000,000.00000	452,265,000,000.00000	0.0	1,511,818,000,000.000000
2009-12-31	335,474,000,000.00000	NA	NA	81,996,000,000.0	NA	15,500,000,000.0	118,114,000,000.0	NA	0.0	NA	2,230,232,000,000.000000	NA	NA	NA	763,230,000,000.00000	0.0	991,611,000,000.000000	NA	1,998,788,000,000.000000	(5,619,000,000.00)	71,233,000,000.00000	231,444,000,000.00000	1,305,865,000,000.00000	NA	NA	NA
2009-09-30	363,165,000,000.00000	NA	NA	0.0	NA	15,373,000,000.0	116,574,000,000.0	NA	0.0	NA	2,251,043,000,000.000000	NA	NA	NA	768,146,000,000.00000	0.0	974,899,000,000.000000	NA	1,993,360,000,000.000000	(6,705,000,000.00)	76,881,000,000.00000	257,683,000,000.00000	1,270,158,000,000.00000	NA	NA	NA

scroll_box(kable(BAC_Income, caption = "Bank of America Income Statement Data") %>% kable_styling("bordered"), height = "500px", width = "1100px" )

Bank of America Income Statement Data
date	Revenue	Revenue Growth	Gross Profit	R&D Expenses	SG&A Expense	Operating Expenses	Operating Income	Interest Expense	Earnings before Tax	Income Tax Expense	Net Income	Preferred Dividends	Net Income Com	EPS	EPS Diluted	Weighted Average Shs Out	Weighted Average Shs Out (Dil)	Dividend per Share	Gross Margin	EBITDA Margin	EBIT Margin	Profit Margin	Free Cash Flow margin	EBITDA	EBIT	Consolidated Income	Earnings Before Tax Margin	Net Profit Margin
2019-09-30	22,807,000,000.0	(0.01199965343961190)	0.0	1,163,000,000.0	7,779,000,000.0	8,942,000,000.0	6,859,000,000.0	5,729,000,000.0	6,859,000,000.0	1,082,000,000.0	5,777,000,000.0	505,000,000.0	5,777,000,000.0	0.57	0.56	9,516,200,000.0	9,565,700,000.0	0.17343215865000	0.0	0.31994563072700	0.30074100057000	0.25329942561500	(0.2500109615470)	7,297,000,000.0	6,859,000,000.0	5,777,000,000.0	0.30074100057	0.253299425615
2019-06-30	23,084,000,000.0	0.00347765605981568	0.0	1,157,000,000.0	7,972,000,000.0	9,129,000,000.0	8,959,000,000.0	6,035,000,000.0	8,959,000,000.0	1,611,000,000.0	7,348,000,000.0	239,000,000.0	7,348,000,000.0	0.74	0.75	9,624,000,000.0	9,672,400,000.0	0.14680947851600	0.0	0.40690521573400	0.38810431467700	0.31831571651400	(0.2913273262870)	9,393,000,000.0	8,959,000,000.0	7,348,000,000.0	0.388104314677	0.318315716514
2019-03-31	23,004,000,000.0	0.01178747361013370	0.0	1,164,000,000.0	8,249,000,000.0	9,413,000,000.0	8,767,000,000.0	5,795,000,000.0	8,767,000,000.0	1,456,000,000.0	7,311,000,000.0	442,000,000.0	7,311,000,000.0	0.71	0.70	9,725,900,000.0	9,787,300,000.0	0.14876421485000	0.0	0.39927838636800	0.38110763345500	0.31781429316600	0.6564510519910	9,185,000,000.0	8,767,000,000.0	7,311,000,000.0	0.381107633455	0.317814293166
2018-12-31	22,736,000,000.0	(0.00180006146551346)	0.0	0.0	7,735,000,000.0	7,735,000,000.0	8,698,000,000.0	5,532,000,000.0	8,698,000,000.0	1,420,000,000.0	7,278,000,000.0	239,000,000.0	7,278,000,000.0	0.71	0.70	10,096,500,000.0	10,236,900,000.0	0.14379353124500	0.0	0.46767241379300	0.38256509500400	0.32010907811400	(0.6123328641800)	10,633,000,000.0	8,698,000,000.0	7,278,000,000.0	0.382565095004	0.320109078114
2018-09-30	22,777,000,000.0	0.00743066920252997	0.0	0.0	7,721,000,000.0	7,721,000,000.0	8,994,000,000.0	5,095,000,000.0	8,994,000,000.0	1,827,000,000.0	7,167,000,000.0	466,000,000.0	7,167,000,000.0	0.67	0.66	10,177,500,000.0	10,317,900,000.0	0.14508766318700	0.0	0.39544277121700	0.39487202002000	0.31465952495900	0.8607806120210	9,007,000,000.0	8,994,000,000.0	7,167,000,000.0	0.39487202002	0.314659524959
2018-06-30	22,609,000,000.0	(0.02231351351351350)	0.0	0.0	7,944,000,000.0	7,944,000,000.0	8,498,000,000.0	4,719,000,000.0	8,498,000,000.0	1,714,000,000.0	6,784,000,000.0	318,000,000.0	6,784,000,000.0	0.63	0.63	10,251,700,000.0	10,389,900,000.0	0.11722923223500	0.0	0.37936220089300	0.37586801716100	0.30005749922600	(0.2882922729890)	8,577,000,000.0	8,498,000,000.0	6,784,000,000.0	0.375868017161	0.300057499226
2018-03-31	23,125,000,000.0	0.13158152280289701	0.0	0.0	8,480,000,000.0	8,480,000,000.0	8,394,000,000.0	3,991,000,000.0	8,394,000,000.0	1,476,000,000.0	6,918,000,000.0	428,000,000.0	6,918,000,000.0	0.63	0.62	10,322,400,000.0	10,472,700,000.0	0.11811662704000	0.0	0.36454054054100	0.36298378378400	0.29915675675700	1.7450378378400	8,430,000,000.0	8,394,000,000.0	6,918,000,000.0	0.362983783784	0.299156756757
2017-12-31	20,436,000,000.0	(0.06424286826319890)	0.0	0.0	7,289,000,000.0	6,816,000,000.0	6,405,000,000.0	3,604,000,000.0	6,405,000,000.0	3,885,000,000.0	2,520,000,000.0	286,000,000.0	2,520,000,000.0	0.21	0.21	10,195,646,000.0	10,778,428,000.0	0.11680738601200	0.0	0.30906243883300	0.31341749853200	0.12331180270100	(0.3448815815230)	6,316,000,000.0	6,405,000,000.0	2,520,000,000.0	0.313417498532	0.123311802701
2017-09-30	21,839,000,000.0	(0.04336589425730430)	0.0	0.0	7,483,000,000.0	7,634,000,000.0	7,866,000,000.0	3,496,000,000.0	7,866,000,000.0	2,279,000,000.0	5,587,000,000.0	465,000,000.0	5,587,000,000.0	0.50	0.48	10,103,386,000.0	10,820,425,000.0	0.11681611397000	0.0	0.35747973808300	0.36018132698400	0.25582673199300	0.9373597692200	7,807,000,000.0	7,866,000,000.0	5,587,000,000.0	0.360181326984	0.255826731993
2017-06-30	22,829,000,000.0	0.02611470693994970	0.0	0.0	7,712,000,000.0	7,872,000,000.0	8,377,000,000.0	3,160,000,000.0	8,377,000,000.0	3,108,000,000.0	5,269,000,000.0	361,000,000.0	5,269,000,000.0	0.49	0.46	10,056,111,000.0	10,868,431,000.0	0.06873117196030	0.0	0.36738359104600	0.36694555171100	0.23080292610300	0.4304174514870	8,387,000,000.0	8,377,000,000.0	5,269,000,000.0	0.366945551711	0.230802926103
2017-03-31	22,248,000,000.0	0.11295647823912000	0.0	0.0	9,158,000,000.0	9,320,000,000.0	6,565,000,000.0	2,652,000,000.0	6,565,000,000.0	1,709,000,000.0	4,856,000,000.0	502,000,000.0	4,856,000,000.0	0.43	0.41	10,099,557,000.0	10,914,815,000.0	0.06935527537570	0.0	0.29463322545800	0.29508270406300	0.21826681050000	(0.5774002157500)	6,555,000,000.0	6,565,000,000.0	4,856,000,000.0	0.295082704063	0.2182668105
2016-12-31	19,990,000,000.0	(0.16011932271753301)	0.0	0.0	7,338,000,000.0	6,784,000,000.0	6,055,000,000.0	2,573,000,000.0	6,055,000,000.0	1,359,000,000.0	4,696,000,000.0	361,000,000.0	4,696,000,000.0	0.43	0.40	10,284,147,000.0	11,035,657,000.0	0.06959259426060	0.0	0.28379189594800	0.30290145072500	0.23491745872900	(0.4180090045020)	5,673,000,000.0	6,055,000,000.0	4,696,000,000.0	0.302901450725	0.234917458729
2016-09-30	23,801,000,000.0	0.16683008138052699	0.0	0.0	7,704,000,000.0	7,885,000,000.0	9,470,000,000.0	2,409,000,000.0	9,470,000,000.0	3,172,000,000.0	6,298,000,000.0	503,000,000.0	6,298,000,000.0	0.56	0.54	10,312,878,000.0	11,046,807,000.0	0.06970339936240	0.0	0.48262678038700	0.39788244191400	0.26461073064200	0.0220999117684	11,487,000,000.0	9,470,000,000.0	6,298,000,000.0	0.397882441914	0.264610730642
2016-06-30	20,398,000,000.0	0.04540795407954080	0.0	0.0	7,722,000,000.0	7,908,000,000.0	5,929,000,000.0	2,455,000,000.0	5,929,000,000.0	1,697,000,000.0	4,232,000,000.0	361,000,000.0	4,232,000,000.0	0.38	0.35	10,296,652,000.0	11,079,939,000.0	0.04675115991160	0.0	0.22732620845200	0.29066575154400	0.20747132071800	0.7497303657220	4,637,000,000.0	5,929,000,000.0	4,232,000,000.0	0.290665751544	0.207471320718
2016-03-31	19,512,000,000.0	0.05436074786555710	0.0	0.0	8,852,000,000.0	9,039,000,000.0	3,699,000,000.0	2,524,000,000.0	3,699,000,000.0	1,019,000,000.0	2,680,000,000.0	457,000,000.0	2,680,000,000.0	0.21	0.21	10,339,731,000.0	11,100,067,000.0	0.04657629544040	0.0	0.12623001230000	0.18957564575600	0.13735137351400	0.5557093070930	2,463,000,000.0	3,699,000,000.0	2,680,000,000.0	0.189575645756	0.137351373514
2015-12-31	18,506,000,000.0	(0.10521226187022500)	0.0	0.0	7,535,000,000.0	6,903,000,000.0	3,824,000,000.0	2,897,000,000.0	3,824,000,000.0	1,121,000,000.0	2,703,000,000.0	330,000,000.0	2,703,000,000.0	0.23	0.22	10,462,282,000.0	11,213,992,000.0	0.04637064124890	0.0	0.17848265427400	0.20663568572400	0.14606073705800	0.2956338484820	3,303,000,000.0	3,824,000,000.0	2,703,000,000.0	0.206635685724	0.146060737058
2015-09-30	20,682,000,000.0	(0.06488221729891031)	0.0	0.0	7,829,000,000.0	8,036,000,000.0	6,069,000,000.0	2,496,000,000.0	6,069,000,000.0	1,561,000,000.0	4,508,000,000.0	441,000,000.0	4,508,000,000.0	0.39	0.37	10,483,466,000.0	11,234,125,000.0	0.04628753908290	0.0	0.27903490958300	0.29344357412200	0.21796731457300	1.6875543951300	5,771,000,000.0	6,069,000,000.0	4,508,000,000.0	0.293443574122	0.217967314573
2015-06-30	22,117,000,000.0	0.04315630600886710	0.0	0.0	7,890,000,000.0	8,102,000,000.0	7,519,000,000.0	2,644,000,000.0	7,519,000,000.0	2,199,000,000.0	5,320,000,000.0	330,000,000.0	5,320,000,000.0	0.48	0.45	10,503,379,000.0	11,252,417,000.0	0.04656777295050	0.0	0.38997151512400	0.33996473301100	0.24053895193700	(0.3594972193340)	8,625,000,000.0	7,519,000,000.0	5,320,000,000.0	0.339964733011	0.240538951937
2015-03-31	21,202,000,000.0	0.13228304405874500	0.0	0.0	9,614,000,000.0	9,827,000,000.0	4,742,000,000.0	2,512,000,000.0	4,742,000,000.0	1,385,000,000.0	3,357,000,000.0	382,000,000.0	3,357,000,000.0	0.28	0.27	10,518,790,000.0	11,266,511,000.0	0.04677579420990	0.0	0.20620696160700	0.22365814545800	0.15833411942300	(0.2212998773700)	4,372,000,000.0	4,742,000,000.0	3,357,000,000.0	0.223658145458	0.158334119423
2014-12-31	18,725,000,000.0	(0.11712009052760600)	0.0	0.0	7,693,000,000.0	6,985,000,000.0	4,310,000,000.0	2,517,000,000.0	4,310,000,000.0	1,260,000,000.0	3,050,000,000.0	312,000,000.0	3,050,000,000.0	0.26	0.26	10,527,818,000.0	10,584,535,000.0	0.04960066738880	0.0	0.12090787717000	0.23017356475300	0.16288384512700	(0.0166622162884)	2,264,000,000.0	4,310,000,000.0	3,050,000,000.0	0.230173564753	0.162883845127
2014-09-30	21,209,000,000.0	(0.02473904446590330)	0.0	0.0	8,039,000,000.0	8,273,000,000.0	431,000,000.0	2,639,000,000.0	431,000,000.0	663,000,000.0	(232,000,000.0)	238,000,000.0	(232,000,000.0)	(0.04)	(0.04)	10,531,688,000.0	10,587,841,000.0	0.00311678273219	0.0	0.04979018341270	0.02032156160120	(0.01093875241640)	(0.0212174077043)	1,056,000,000.0	431,000,000.0	(232,000,000.0)	0.0203215616012	-0.0109387524164
2014-06-30	21,747,000,000.0	(0.03629353895240630)	0.0	0.0	8,306,000,000.0	8,541,000,000.0	2,795,000,000.0	2,928,000,000.0	2,795,000,000.0	504,000,000.0	2,291,000,000.0	256,000,000.0	2,291,000,000.0	0.19	0.19	10,539,769,000.0	10,599,641,000.0	0.03396341442130	0.0	0.15730905412200	0.12852347450200	0.10534786407300	0.2197084655350	3,421,000,000.0	2,795,000,000.0	2,291,000,000.0	0.128523474502	0.105347864073
2014-03-31	22,566,000,000.0	0.05016753536857780	0.0	0.0	9,749,000,000.0	9,988,000,000.0	(681,000,000.0)	2,850,000,000.0	(681,000,000.0)	(405,000,000.0)	(276,000,000.0)	238,000,000.0	(276,000,000.0)	(0.05)	(0.05)	10,560,518,000.0	10,560,518,000.0	0.03257415971450	0.0	(0.00230435167952)	(0.03017814411060)	(0.01223078968360)	1.0069573694900	(52,000,000.0)	(681,000,000.0)	(276,000,000.0)	-0.0301781441106	-0.0122307896836
2013-12-31	21,488,000,000.0	(0.00195076637250348)	0.0	0.0	7,987,000,000.0	8,253,000,000.0	8,180,000,000.0	2,926,000,000.0	16,172,000,000.0	406,000,000.0	3,439,000,000.0	256,000,000.0	3,439,000,000.0	0.30	0.28	10,731,165,000.0	11,491,418,000.0	0.03141474794490	0.0	0.41148548026800	0.38067758749100	0.16004281459400	0.5581720029780	8,842,000,000.0	8,180,000,000.0	3,439,000,000.0	0.752606105733	0.160042814594
2013-09-30	21,530,000,000.0	(0.05266863202358430)	0.0	0.0	8,310,000,000.0	8,580,000,000.0	2,497,000,000.0	3,116,000,000.0	0.0	2,348,000,000.0	2,497,000,000.0	279,000,000.0	2,497,000,000.0	0.21	0.20	10,764,216,000.0	11,523,649,000.0	0.03141366072500	0.0	0.14653971203000	0.11597770552700	0.11597770552700	0.3821179749190	3,155,000,000.0	2,497,000,000.0	2,497,000,000.0	0.0	0.115977705527
2013-06-30	22,727,000,000.0	(0.02026124067767380)	0.0	0.0	8,531,000,000.0	8,805,000,000.0	4,012,000,000.0	3,276,000,000.0	0.0	1,486,000,000.0	4,012,000,000.0	441,000,000.0	4,012,000,000.0	0.33	0.32	10,787,357,000.0	11,549,693,000.0	0.04086688711120	0.0	0.20627447529400	0.17653011836100	0.17653011836100	2.0296563558800	4,688,000,000.0	4,012,000,000.0	4,012,000,000.0	0.0	0.176530118361
2013-03-31	23,197,000,000.0	0.24314040728831701	0.0	0.0	9,891,000,000.0	10,167,000,000.0	1,483,000,000.0	3,437,000,000.0	0.0	501,000,000.0	1,483,000,000.0	373,000,000.0	1,483,000,000.0	0.10	0.10	10,798,975,000.0	11,154,778,000.0	0.04321018311610	0.0	0.09354657929900	0.06393068069150	0.06393068069150	1.1410096133100	2,170,000,000.0	1,483,000,000.0	1,483,000,000.0	0.0	0.0639306806915
2012-12-31	18,660,000,000.0	(0.08654787546504800)	0.0	0.0	8,300,000,000.0	8,609,000,000.0	(1,904,000,000.0)	3,647,000,000.0	(1,904,000,000.0)	(2,636,000,000.0)	732,000,000.0	365,000,000.0	732,000,000.0	0.04	0.03	10,746,028,000.0	10,840,854,000.0	0.04353900532190	0.0	(0.06259378349410)	(0.10203644158600)	0.03922829581990	(1.3884780278700)	(1,168,000,000.0)	(1,904,000,000.0)	732,000,000.0	-0.102036441586	0.0392282958199
2012-09-30	20,428,000,000.0	(0.07010196649672250)	0.0	0.0	8,431,000,000.0	8,746,000,000.0	1,110,000,000.0	4,038,000,000.0	1,110,000,000.0	770,000,000.0	340,000,000.0	373,000,000.0	340,000,000.0	(0.01)	0.00	10,735,461,000.0	10,826,503,000.0	0.04350435223640	0.0	0.09114940278050	0.05433718425690	0.01664382220480	0.4551595848830	1,862,000,000.0	1,110,000,000.0	340,000,000.0	0.0543371842569	0.0166438222048
2012-06-30	21,968,000,000.0	(0.01391507316635250)	0.0	0.0	8,729,000,000.0	9,050,000,000.0	3,147,000,000.0	4,444,000,000.0	3,147,000,000.0	684,000,000.0	2,463,000,000.0	365,000,000.0	2,463,000,000.0	0.20	0.19	10,714,881,000.0	11,509,945,000.0	0.04196371051300	0.0	0.17798616169000	0.14325382374400	0.11211762563700	0.8355790240350	3,910,000,000.0	3,147,000,000.0	2,463,000,000.0	0.143253823744	0.112117625637
2012-03-31	22,278,000,000.0	(0.10486981677917100)	0.0	0.0	10,188,000,000.0	10,507,000,000.0	719,000,000.0	4,615,000,000.0	719,000,000.0	66,000,000.0	653,000,000.0	325,000,000.0	653,000,000.0	0.03	0.03	10,651,367,000.0	10,761,917,000.0	0.04488047993680	0.0	0.06760032318880	0.03227399227940	0.02931142831490	(0.7003770535950)	1,506,000,000.0	719,000,000.0	653,000,000.0	0.0322739922794	0.0293114283149
2011-12-31	24,888,000,000.0	(0.64104709021417705)	0.0	0.0	8,761,000,000.0	9,126,000,000.0	315,000,000.0	4,712,000,000.0	1,718,000,000.0	441,000,000.0	1,991,000,000.0	407,000,000.0	1,991,000,000.0	0.16	0.16	10,142,625,000.0	10,254,824,000.0	0.04631966379920	0.0	0.04660880745740	0.01265670202510	0.07999839279970	0.9902362584380	1,160,000,000.0	315,000,000.0	1,991,000,000.0	0.0690292510447	0.0799983927997
2011-09-30	69,335,000,000.0	(53.44704992435700319)	0.0	0.0	28,204,000,000.0	29,348,000,000.0	(545,000,000.0)	16,908,000,000.0	(1,948,000,000.0)	(2,117,000,000.0)	(545,000,000.0)	954,000,000.0	(545,000,000.0)	(0.15)	(0.15)	10,095,859,000.0	10,095,859,000.0	0.12510079627700	0.0	0.03021561981680	(0.00786038797144)	(0.00786038797144)	0.5746736857290	2,095,000,000.0	(545,000,000.0)	(545,000,000.0)	-0.0280954784741	-0.00786038797144
2011-06-30	(1,322,000,000.0)	(3.39059674502711994)	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.00	0.00	0.0	0.0	0.00000000000000	0.0	0.00000000000000	0.00000000000000	0.00000000000000	0.0000000000000	0.0	0.0	0.0	-0.0	-0.0
2011-03-31	553,000,000.0	(1.42181540808542994)	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.00	0.00	0.0	0.0	0.00000000000000	0.0	0.00000000000000	0.00000000000000	0.00000000000000	0.0000000000000	0.0	0.0	0.0	0.0	0.0
2010-12-31	(1,311,000,000.0)	(3.29999999999999982)	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.00	0.00	0.0	0.0	0.00000000000000	0.0	0.00000000000000	0.00000000000000	0.00000000000000	0.0000000000000	0.0	0.0	0.0	-0.0	-0.0
2010-09-30	570,000,000.0	(1.01862075724412993)	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.00	0.00	0.0	0.0	0.00000000000000	0.0	0.00000000000000	0.00000000000000	0.00000000000000	0.0000000000000	0.0	0.0	0.0	0.0	0.0
2010-06-30	(30,611,000,000.0)	(1.95752134880666007)	0.0	0.0	(9,158,000,000.0)	(9,604,000,000.0)	(4,389,000,000.0)	(6,130,000,000.0)	(4,389,000,000.0)	(1,207,000,000.0)	(3,182,000,000.0)	(348,000,000.0)	(3,182,000,000.0)	(0.28)	(0.28)	0.0	0.0	0.00000000000000	0.0	0.17643984188700	0.14337983078000	0.10394956061500	0.7799810525630	(5,401,000,000.0)	(4,389,000,000.0)	(3,182,000,000.0)	0.14337983078	0.103949560615
2010-03-31	31,969,000,000.0	0.27488435157122298	0.0	0.0	9,158,000,000.0	9,604,000,000.0	4,389,000,000.0	6,130,000,000.0	4,389,000,000.0	1,207,000,000.0	3,182,000,000.0	348,000,000.0	3,182,000,000.0	0.28	0.28	9,177,468,000.0	10,005,254,000.0	0.04497636941550	0.0	0.16894491538700	0.13728924896000	0.09953392348840	0.7468485094940	5,401,000,000.0	4,389,000,000.0	3,182,000,000.0	0.13728924896	0.0995339234884
2009-12-31	25,076,000,000.0	(0.03683502976762050)	25,076,000,000.0	0.0	15,420,000,000.0	16,385,000,000.0	8,691,000,000.0	0.0	(1,419,000,000.0)	(1,225,000,000.0)	(194,000,000.0)	5,002,000,000.0	(5,196,000,000.0)	(0.68)	(0.68)	8,650,759,836.0	8,644,257,000.0	0.01000000000000	1.0	(0.01600000000000)	(0.05660000000000)	(0.20700000000000)	1.2500000000000	(406,000,000.0)	(1,419,000,000.0)	(194,000,000.0)	-0.0566	-0.0077
2009-09-30	26,035,000,000.0	(0.20562030878135101)	26,035,000,000.0	0.0	15,202,000,000.0	16,306,000,000.0	9,729,000,000.0	0.0	(1,976,000,000.0)	(975,000,000.0)	(1,001,000,000.0)	1,240,000,000.0	(2,241,000,000.0)	(0.26)	(0.26)	8,651,594,786.0	8,633,834,000.0	0.01000000000000	1.0	(0.03400000000000)	(0.07590000000000)	(0.08600000000000)	0.8686000000000	(880,000,000.0)	(1,976,000,000.0)	(1,001,000,000.0)	-0.0759	-0.0384
2009-06-30	32,774,000,000.0	0.60580000000000001	32,774,000,000.0	0.0	15,675,000,000.0	17,020,000,000.0	15,754,000,000.0	0.0	2,379,000,000.0	(845,000,000.0)	3,224,000,000.0	805,000,000.0	2,419,000,000.0	0.33	0.33	7,241,515,000.0	7,269,518,000.0	0.01000000000000	1.0	0.10600000000000	0.07260000000000	0.07400000000000	0.5878000000000	3,486,000,000.0	2,379,000,000.0	3,224,000,000.0	0.0726	0.0984

Accounting Ratios Being Analyzed

Accounting ratio data such as current ratios¹, debt-equity ratio², and PE ratio³ comes from web-scraping macrotrends.com. See below the code used to scrape the data.

Current Ratio:

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

The Current Ratio is a metric used to measure a company’s short-term liquidity with respect to its available assets and pending liablities. A lower Current Ratio (under 1) may indicate that a liquidity problem within the company, whereas a high Current Ratio (above 3) indicates idle assets that are not being efficiently used to generate revenues.

Debt to Equity Ratio:

$D/E\:Ratio = \frac{Total\:Liabilities}{Total\:Shareholders'\:Equity}$

The Debt to Equity Ratio is a metric used to measure how much of the company’s operations are being funded by financing means. The Debt to Equity Ratio reflects the ability of shareholder equity to cover all outstanding debts in the event of a business downturn. A high debt/equity ratio is associated with high risk as it means a company has taken on a large amount of debt vs it’s own cash to fund growth. Businesses who engage in this method to fund growth must be sure that the returns of their growth are greater than the cost of financing, else it would have not been worth it to take on debt.

Price to Earnings Ratio:

$P/E\:Ratio = \frac{Market\:Value\:per\:Share}{Earnings\:per\:Share}$

The Price to Earning Ratio is the ratio for valueing a company that measures current share price relative to its Earnings per Share. The P/E ratio is useful for comparing a company’s shares to others on a level field, taking into account the varying number of shares outstanding for different companies. Companies that have no earnings or are losing money have no P/E ratio.

library(rvest)
library(readr)

url <- "https://www.macrotrends.net/stocks/charts/BAC/bank-of-america/current-ratio"

data <- url %>%
    html() %>%
      html_nodes("#main_content") %>%
        html_text()
        
startInt <- regexpr("Bank Of America Current Ratio Historical Data", data)

endInt <- regexpr("Sector", data)

txt <- substr(data,startInt-45,endInt)

rm(data)
rm(startInt)
rm(endInt)
rm(url)

fileConn2 <- file("output2.txt")
writeLines(txt,fileConn2)
close(fileConn2)
rm(txt)

dat1 <- read_table("./output2.txt")
dat1 <- data.table(dat1)
dat1 <- dat1[!1:7,]
dat2 <- data.table(Dates = dat1[seq(1,249,by = 4),] , `Current Assets` = dat1[seq(2,249,by = 4),] , `Current Liabilities` = dat1[seq(3,249,by = 4),], `Current Ratio` = dat1[seq(4,249,by = 4),] )

rm(dat1)

colnames(dat2) <- as.character(dat2[1,])
dat2 <- dat2[-1,]
dat2 <- dat2[-c(61,62),]

rm(fileConn2)

currentRatio <- dat2
rm(dat2)

scroll_box(kable(currentRatio, caption = "Bank of America Current Ratio") %>% kable_styling("striped"), height = "700px", width = "500px" )

Bank of America Current Ratio
Date	Current Assets	Current Liabilities	Current Ratio
2019-12-31	$1,712.20B	$1,890.18B	0.91
2019-09-30	$1,725.78B	$1,876.51B	0.92
2019-06-30	$1,693.17B	$1,848.09B	0.92
2019-03-31	$1,683.01B	$1,839.89B	0.92
2018-12-31	$1,673.85B	$1,821.90B	0.92
2018-09-30	$1,642.68B	$1,806.39B	0.91
2018-06-30	$1,599.42B	$1,767.25B	0.91
2018-03-31	$1,646.59B	$1,796.10B	0.92
2017-12-31	$1,590.10B	$1,752.39B	0.91
2017-09-30	$1,594.93B	$1,750.99B	0.91
2017-06-30	$1,573.44B	$1,724.74B	0.91
2017-03-31	$1,569.18B	$1,721.74B	0.91
2016-12-31	$1,499.30B	$1,665.57B	0.90
2016-09-30	$1,516.62B	$1,656.61B	0.92
2016-06-30	$1,526.18B	$1,642.36B	0.93
2016-03-31	$1,537.69B	$1,648.81B	0.93
2015-12-31	$1,486.62B	$1,612.90B	0.92
2015-09-30	$1,510.93B	$1,613.95B	0.94
2015-06-30	$1,505.32B	$1,610.38B	0.94
2015-03-31	$1,501.26B	$1,603.27B	0.94
2014-12-31	$1,471.36B	$1,571.02B	0.94
2014-09-30	$1,499.53B	$1,590.58B	0.94
2014-06-30	$1,558.71B	$1,637.43B	0.95
2014-03-31	$1,551.74B	$1,626.27B	0.95
2013-12-31	$1,515.80B	$1,582.51B	0.96
2013-09-30	$1,538.92B	$1,594.47B	0.97
2013-06-30	$1,509.56B	$1,581.28B	0.96
2013-03-31	$1,536.78B	$1,610.06B	0.95
2012-12-31	$1,551.67B	$1,651.42B	0.94
2012-09-30	$1,517.07B	$1,589.65B	0.95
2012-06-30	$1,523.68B	$1,571.52B	0.97
2012-03-31	$1,542.24B	$1,544.87B	1.00
2011-12-31	$1,499.79B	$1,467.16B	1.02
2011-09-30	$1,537.43B	$1,531.11B	1.00
2011-06-30	$1,582.53B	$1,558.07B	1.02
2011-03-31	$1,578.97B	$1,555.72B	1.02
2010-12-31	$1,558.46B	$1,532.32B	1.02
2010-09-30	$1,631.78B	$1,568.65B	1.04
2010-06-30	$1,652.23B	$1,577.83B	1.05
2010-03-31	$1,616.34B	$1,550.30B	1.04
2009-12-31	$1,506.48B	$1,509.61B	1.00
2009-09-30	$1,486.56B	$1,484.45B	1.00
2009-06-30	$1,509.69B	$1,500.76B	1.01
2009-03-31	$1,541.94B	$1,565.08B	0.99
2008-12-31	$1,236.66B	$1,341.89B	0.92
2008-09-30	$1,262.69B	$1,385.96B	0.91
2008-06-30	$1,175.02B	$1,326.48B	0.89
2008-03-31	$1,194.28B	$1,348.22B	0.89
2007-12-31	$1,245.10B	$1,349.01B	0.92
2007-09-30	$1,142.30B	$1,235.76B	0.92
2007-06-30	$1,112.29B	$1,204.15B	0.92
2007-03-31	$1,071.35B	$1,196.79B	0.90
2006-12-31	$1,036.39B	$1,162.13B	0.89
2006-09-30	$980.33B	$1,162.48B	0.84
2006-06-30	$979.42B	$1,169.66B	0.84
2006-03-31	$908.45B	$1,106.94B	0.82
2005-12-31	$897.04B	$1,074.42B	0.84
2005-09-30	$846.95B	$1,035.41B	0.82
2005-06-30	$840.66B	$1,033.27B	0.81
2005-03-31	$825.43B	$999.59B	0.83

library(rvest)
library(readr)

url <- "https://www.macrotrends.net/stocks/charts/BAC/bank-of-america/debt-equity-ratio"

data <- url %>%
    html() %>%
      html_nodes("#main_content") %>%
        html_text()
        
startInt <- regexpr("Bank Of America Debt/Equity Ratio Historical Data", data)

endInt <- regexpr("Sector", data)

txt <- substr(data,startInt-45,endInt)

rm(data)
rm(startInt)
rm(endInt)
rm(url)

fileConn2 <- file("output2.txt")
writeLines(txt,fileConn2)
close(fileConn2)
rm(txt)

dat1 <- read_table("./output2.txt")
dat1 <- data.table(dat1)
dat1 <- dat1[!1:7,]
dat1 <- na.omit(dat1)
dat2 <- data.table(Dates = dat1[seq(1,249,by = 4),] , `Long Term Debt` = dat1[seq(2,249,by = 4),] , `Shareholder's Equity` = dat1[seq(3,249,by = 4),], `Debt to Equity Ratio` = dat1[seq(4,249,by = 4),] )

rm(dat1)

colnames(dat2) <- as.character(dat2[1,])
dat2 <- dat2[-1,]
dat2 <- dat2[-c(61,62),]

rm(fileConn2)

DebtToEquityRatio <- dat2
rm(dat2)

scroll_box(kable(DebtToEquityRatio, caption = "Bank of America Debt To Equity Ratio") %>% kable_styling("striped") , height = "700px", width = "500px" )

Bank of America Debt To Equity Ratio
Date	Long Term Debt	Shareholder’s Equity	Debt to Equity Ratio
2019-12-31	$240.86B	$241.41B	1.00
2019-09-30	$243.41B	$244.78B	0.99
2019-06-30	$238.01B	$246.72B	0.97
2019-03-31	$233.93B	$244.68B	0.96
2018-12-31	$229.39B	$243.00B	0.94
2018-09-30	$234.10B	$239.83B	0.98
2018-06-30	$226.60B	$241.04B	0.94
2018-03-31	$232.26B	$241.55B	0.96
2017-12-31	$227.40B	$244.82B	0.93
2017-09-30	$228.67B	$250.14B	0.91
2017-06-30	$223.92B	$245.77B	0.91
2017-03-31	$221.39B	$242.93B	0.91
2016-12-31	$216.82B	$240.98B	0.90
2016-09-30	$225.14B	$244.86B	0.92
2016-06-30	$229.62B	$241.85B	0.95
2016-03-31	$232.85B	$238.43B	0.98
2015-12-31	$236.76B	$233.90B	1.01
2015-09-30	$237.29B	$233.63B	1.02
2015-06-30	$243.41B	$229.39B	1.06
2015-03-31	$237.86B	$227.92B	1.04
2014-12-31	$243.14B	$224.16B	1.09
2014-09-30	$250.12B	$220.77B	1.13
2014-06-30	$257.07B	$222.57B	1.16
2014-03-31	$254.79B	$218.54B	1.17
2013-12-31	$249.67B	$219.33B	1.14
2013-09-30	$255.33B	$218.97B	1.17
2013-06-30	$262.48B	$216.79B	1.21
2013-03-31	$279.64B	$218.51B	1.28
2012-12-31	$275.59B	$218.19B	1.26
2012-09-30	$286.53B	$219.84B	1.30
2012-06-30	$301.85B	$217.21B	1.39
2012-03-31	$354.91B	$213.71B	1.66
2011-12-31	$372.27B	$211.70B	1.76
2011-09-30	$398.97B	$210.77B	1.89
2011-06-30	$426.66B	$205.61B	2.08
2011-03-31	$434.44B	$214.31B	2.03
2010-12-31	$448.43B	$211.69B	2.12
2010-09-30	$478.86B	$212.39B	2.26
2010-06-30	$490.08B	$215.18B	2.28
2010-03-31	$511.65B	$211.86B	2.42
2009-12-31	$438.52B	$194.24B	2.26
2009-09-30	$456.29B	$198.84B	2.30
2009-06-30	$447.19B	$196.49B	2.28
2009-03-31	$440.75B	$166.27B	2.65
2008-12-31	$268.29B	$139.35B	1.93
2008-09-30	$257.71B	$136.89B	1.88
2008-06-30	$206.61B	$138.54B	1.49
2008-03-31	$202.80B	$139.00B	1.46
2007-12-31	$197.51B	$142.39B	1.39
2007-09-30	$185.48B	$135.11B	1.37
2007-06-30	$169.32B	$132.90B	1.27
2007-03-31	$152.56B	$132.01B	1.16
2006-12-31	$146.00B	$132.42B	1.10
2006-09-30	$137.74B	$132.77B	1.04
2006-06-30	$129.06B	$127.57B	1.01
2006-03-31	$123.18B	$129.16B	0.95
2005-12-31	$100.85B	$101.26B	1.00
2005-09-30	$99.89B	$100.99B	0.99
2005-06-30	$96.89B	$100.27B	0.97
2005-03-31	$98.76B	$98.25B	1.01

library(rvest)
library(readr)

url <- "https://www.macrotrends.net/stocks/charts/BAC/bank-of-america/pe-ratio"

data <- url %>%
    html() %>%
      html_nodes("#main_content") %>%
        html_text()
        
startInt <- regexpr("Bank Of America PE Ratio Historical Data", data)

endInt <- regexpr("Sector", data)

txt <- substr(data,startInt-45,endInt)

rm(data)
rm(startInt)
rm(endInt)
rm(url)

fileConn2 <- file("output2.txt")
writeLines(txt,fileConn2)
close(fileConn2)
rm(txt)

dat1 <- read_table("./output2.txt")
dat1 <- data.table(dat1)
dat1 <- dat1[!1:7,]
dat1 <- na.omit(dat1)
dat2 <- data.table(Dates = dat1[seq(1,249,by = 4),] , `Stock Price` = dat1[seq(2,249,by = 4),] , `TTM Net EPS` = dat1[seq(3,249,by = 4),], `PE Ratio` = dat1[seq(4,249,by = 4),] )

rm(dat1)

colnames(dat2) <- as.character(dat2[1,])
dat2 <- dat2[-1,]

dat2 <- dat2[-(55:62),]

rm(fileConn2)

PERatios <- dat2
rm(dat2)

scroll_box(kable(PERatios, caption = "Bank of America Price to Earning Ratio") %>% kable_styling("striped") , height = "700px", width = "500px" )

Bank of America Price to Earning Ratio
Date	Stock Price	TTM Net EPS	PE Ratio
2020-02-14	34.85		12.72
2019-12-31	35.22	$2.74	12.85
2019-09-30	29.17	$2.70	10.80
2019-06-30	29.00	$2.80	10.36
2019-03-31	27.59	$2.69	10.26
2018-12-31	24.64	$2.61	9.44
2018-09-30	29.29	$2.11	13.88
2018-06-30	27.89	$1.93	14.45
2018-03-31	29.55	$1.76	16.79
2017-12-31	28.98	$1.55	18.70
2017-09-30	24.77	$1.75	14.15
2017-06-30	23.60	$1.68	14.05
2017-03-31	22.87	$1.58	14.47
2016-12-31	21.36	$1.38	15.48
2016-09-30	15.07	$1.26	11.96
2016-06-30	12.72	$1.22	10.43
2016-03-31	12.92	$1.31	9.86
2015-12-31	16.02	$1.37	11.69
2015-09-30	14.79	$1.34	11.04
2015-06-30	16.11	$0.96	16.78
2015-03-31	14.52	$0.70	20.74
2014-12-31	16.83	$0.38	44.28
2014-09-30	15.99	$0.42	38.07
2014-06-30	14.37	$0.63	22.81
2014-03-31	16.07	$0.76	21.14
2013-12-31	14.54	$1.01	14.39
2013-09-30	12.88	$0.75	17.17
2013-06-30	11.99	$0.55	21.80
2013-03-31	11.35	$0.42	27.02
2012-12-31	10.81	$0.25	43.23
2012-09-30	8.21	$0.37	22.20
2012-06-30	7.60	$0.95	8.00
2012-03-31	8.88	$-0.14	0.00
2011-12-31	5.15	$-0.00	0.00
2011-09-30	5.66	$-0.31	0.00
2011-06-30	10.12	$-1.66	0.00
2011-03-31	12.30	$-0.49	0.00
2010-12-31	12.30	$-0.38	0.00
2010-09-30	12.07	$-0.82	0.00
2010-06-30	13.23	$-0.31	0.00
2010-03-31	16.42	$-0.25	0.00
2009-12-31	13.85	$-0.09	0.00
2009-09-30	15.55	$0.03	518.24
2009-06-30	12.12	$0.44	27.55
2009-03-31	6.26	$0.83	7.54
2008-12-31	12.88	$0.62	20.78
2008-09-30	31.36	$1.15	27.27
2008-06-30	20.98	$1.82	11.53
2008-03-31	32.66	$2.38	13.72
2007-12-31	34.95	$3.31	10.56
2007-09-30	41.99	$4.42	9.50
2007-06-30	40.32	$4.78	8.44
2007-03-31	41.62	$4.69	8.87
2006-12-31	43.08	$4.60	9.37

Data Transformations

In order to analyze all the data, it is important to aggregate it together into a format that makes analysis convenient while also preserving all relevant meta-data. The following code merges the balance sheet, income statement, and various accounting ratio data into a single dataset that makes analyzing the variables against each other easy. Primarily, the various datasets are being matched to each other by using the date as a “primary key” for matching. Earlier data prior to 2011 was omitted to due data sufficiency reasons.

BAC_balance_sht <- quarterlyBalanceSheet("BAC")
##merge current ratio
colnames(currentRatio) <- c("date","crr assets","Crr liabilities", "current Ratio")
currentRatio$date <- as.Date.character(currentRatio$date)
BAC_balance_sht <- merge.data.table(BAC_balance_sht,currentRatio, by.x = "date", by.y = "date", all.x = TRUE, all.y = FALSE)
BAC_balance_sht$`crr assets` <- gsub("B","",BAC_balance_sht$`crr assets`)
BAC_balance_sht$`Crr liabilities` <- gsub("B","",BAC_balance_sht$`Crr liabilities`)
BAC_balance_sht$`crr assets` <- gsub("[[:punct:]]","",BAC_balance_sht$`crr assets`)
BAC_balance_sht$`Crr liabilities` <- gsub("[[:punct:]]","",BAC_balance_sht$`Crr liabilities`)
BAC_balance_sht$`crr assets` <- as.numeric(BAC_balance_sht$`crr assets`) * 10000000
BAC_balance_sht$`Crr liabilities` <- as.numeric(BAC_balance_sht$`Crr liabilities`) * 10000000
BAC_balance_sht$`crr assets` <- accounting(BAC_balance_sht$`crr assets`)
BAC_balance_sht$`Crr liabilities` <- accounting(BAC_balance_sht$`Crr liabilities`)
BAC_balance_sht$`current Ratio` <- as.numeric(BAC_balance_sht$`current Ratio`)

##merge debt to equity
colnames(DebtToEquityRatio) <- c("date","longTermDebt","shareholdersEquity", "debtToEquity")
DebtToEquityRatio$date <- as.Date.character(DebtToEquityRatio$date)
BAC_balance_sht <- merge.data.table(BAC_balance_sht,DebtToEquityRatio, by.x = "date", by.y = "date", all.x = TRUE, all.y = FALSE)
BAC_balance_sht$`longTermDebt` <- gsub("B","",BAC_balance_sht$`longTermDebt`)
BAC_balance_sht$`shareholdersEquity` <- gsub("B","",BAC_balance_sht$`shareholdersEquity`)
BAC_balance_sht$`longTermDebt` <- gsub("[[:punct:]]","",BAC_balance_sht$`longTermDebt`)
BAC_balance_sht$`shareholdersEquity` <- gsub("[[:punct:]]","",BAC_balance_sht$`shareholdersEquity`)
BAC_balance_sht$`longTermDebt` <- as.numeric(BAC_balance_sht$`longTermDebt`) * 10000000
BAC_balance_sht$`shareholdersEquity` <- as.numeric(BAC_balance_sht$`shareholdersEquity`) * 10000000
BAC_balance_sht$`longTermDebt` <- accounting(BAC_balance_sht$`longTermDebt`)
BAC_balance_sht$`shareholdersEquity` <- accounting(BAC_balance_sht$`shareholdersEquity`)
BAC_balance_sht$`debtToEquity` <- as.numeric(BAC_balance_sht$`debtToEquity`)

##merge pe ratio

PERatios$Date <- as.Date.character(PERatios$Date)
BAC_balance_sht <- merge.data.table(BAC_balance_sht,PERatios, by.x = "date", by.y = "Date", all.x = TRUE, all.y = FALSE)
BAC_balance_sht$`PE Ratio` <- as.numeric(BAC_balance_sht$`PE Ratio`)
BAC_balance_sht$`Stock Price` <- as.numeric(BAC_balance_sht$`Stock Price`)
BAC_balance_sht$`TTM Net EPS` <- gsub("\\$", "", BAC_balance_sht$`TTM Net EPS`)
BAC_balance_sht$`TTM Net EPS` <- as.numeric(BAC_balance_sht$`TTM Net EPS`)

##omitting earlier data as some info is missing
BAC_balance_sht <- BAC_balance_sht[-(1:8),]

#removing some variables as are irrelevant
BAC_balance_sht[,c("Inventories","Payables","Deferred revenue","Other Assets") := NULL]

#pulling in income statement
BAC_income_statement <- incomeStatement("BAC")
BAC_income_statement[,c("Cost of Revenue","Gross Profit","R&D Expenses","Net Income - Non-Controlling int", "Net Income - Discontinued ops", "Gross Margin") := NULL]

##merging income statement with balance sheet for convenience

BAC_All_Statement <- merge.data.table(BAC_balance_sht,BAC_income_statement,by = "date", all.x = TRUE, all.y = FALSE)
BAC_All_Statement$`Earnings Before Tax Margin` <- as.numeric(BAC_All_Statement$`Earnings Before Tax Margin`)
BAC_All_Statement$`Net Profit Margin` <- as.numeric(BAC_All_Statement$`Net Profit Margin`)

##kable(BAC_All_Statement, caption = "Bank of America Consolidated Accounting Data") %>% kable_styling("condensed")
formatCurrency(datatable(BAC_All_Statement,
          extensions = 'Buttons', 
          options = list(
    scrollX = TRUE,
    dom = 'Bfrtip',
    buttons = c('copy', 'print')
  )
                 ), columns = colnames(BAC_All_Statement)[-which(names(BAC_All_Statement) %in% c("date", "current Ratio", "debtToEquity", "PE Ratio"))],

  )

Correlation Matrix

When treating each entry on the balance sheet as a seperate variable and each ratio as a variable, there are 60 variables. It is important to understand the relationships between all the different variables. Below is a correlation matrix heat map that provides a visualization on how different variables are correlated with each other.

library(knitr)
library(kableExtra)
library(scales)
library(ggplot2)
library(RColorBrewer)
library(plotly)
library(ggrepel)
correlationMatrix <- cor(BAC_All_Statement[,2:60])
# heatmap(correlationMatrix,Colv = NA, Rowv = NA ,scale="column", cexRow=0.8, col= colorRampPalette(brewer.pal(8, "Blues"))(25))

 correlationMatrix2 <- data.table(correlationMatrix,keep.rownames = TRUE)
 
 correlationMatrix2 <- melt.data.table(correlationMatrix2, id.vars = 1,measure.vars = 2:60)
 
 ggplotly(ggplot(correlationMatrix2, aes(rn, variable) ) +
    geom_tile(aes(fill = value)) + 
    geom_text(aes(label = round(value, 2))) +
    scale_fill_gradient(low = "white", high = "lightblue") 
    )

The following are variables with strongly positively correlated relationships with each other.

k <- which(correlationMatrix > 0.80, arr.ind = TRUE)

positiveRelations <- data.table( variable1 = rownames(correlationMatrix)[k[,1]], variable2 = colnames(correlationMatrix)[k[,2]], correlation = correlationMatrix[k])
##omit correlations of 1

positiveRelations <- positiveRelations[!correlation == 1,]

positiveRelations <- positiveRelations[!duplicated(positiveRelations$correlation),]
setorder(positiveRelations, -correlation)

datatable(positiveRelations)

Correlations that involve the accouning ratios we are interested in include the following. There appears to be NO strongly positive relations between the price to earning ratio and any other element on the balance sheet or income statement.

positiveCurrentRatioCorrel <- positiveRelations[variable1 %like% "current Ratio",]

kable(positiveCurrentRatioCorrel, caption = "Positive Correlations with the Current Ratio") %>% kable_styling("striped")

Positive Correlations with the Current Ratio
variable1	variable2	correlation
current Ratio	Total debt	0.9314173
current Ratio	Long-term debt	0.9008346
current Ratio	Net Debt	0.8988680
current Ratio	Total non-current liabilities	0.8977222
current Ratio	Goodwill and Intangible Assets	0.8889521
current Ratio	Property, Plant & Equipment Net	0.8646112

positiveDebtToEquityCorrel <- positiveRelations[variable1 %like% "debtToEquity",]

kable(positiveDebtToEquityCorrel, caption = "Positive Correlations with the Debt to Equity Ratio") %>% kable_styling("striped")

Positive Correlations with the Debt to Equity Ratio
variable1	variable2	correlation
debtToEquity	Long-term debt	0.9907085
debtToEquity	longTermDebt	0.9907026
debtToEquity	Total non-current liabilities	0.9877031
debtToEquity	Total debt	0.9811738
debtToEquity	Net Debt	0.9532704
debtToEquity	Property, Plant & Equipment Net	0.9401397
debtToEquity	current Ratio	0.9337540
debtToEquity	Goodwill and Intangible Assets	0.8746961
debtToEquity	Receivables	0.8304065

To dive further into the relationships between ratios and the variables they are positively correlated to, it helps to plot them on a graph to visualize the relationship. First, a look at variables positively correlated with the Current Ratio.

Linear Regression Plots Against Current Ratio; Positive Correlations

CurrentRatioData <- data.table(Date = BAC_All_Statement$date, `current Ratio` = as.numeric(BAC_All_Statement$`current Ratio`), `Total debt` = BAC_All_Statement$`Total debt`, `Long-term debt` = BAC_All_Statement$`Long-term debt`, `Net Debt` = BAC_All_Statement$`Net Debt`, `Total non-current liabilities` = BAC_All_Statement$`Total non-current liabilities`, `Goodwill and Intangible Assets` = BAC_All_Statement$`Goodwill and Intangible Assets`, Property = BAC_All_Statement$`Property, Plant & Equipment Net`)

ggplot(CurrentRatioData) +
 aes(x = `current Ratio`, y = `Total debt`) +
 geom_point(size = 3L, colour = "#4292c6") +
 scale_y_continuous(labels = comma) +
 geom_smooth(method = "lm") +
 stat_cor(label.x = 0.9, label.y = 140000000000) +
 stat_regline_equation(label.x = 0.9, label.y = 170000000000)  +
 ggthemes::theme_economist_white() +
 ggtitle("Total Debt is Highly Correlated with Current Ratio", subtitle = "This is because Total Liabilities is a part of the equation for the current ratio;\nthus when estimating a future current ratio, \nwe should expect a direct impact on all liabilities including debt.")

CurrentRatioDataMelt <- melt.data.table(CurrentRatioData, measure.vars = c("Total debt", "Long-term debt", "Net Debt", "Total non-current liabilities"))


library(dplyr)
library(ggplot2)

CurrentRatioDataMelt %>%
 filter(!(`Goodwill and Intangible Assets` %in% c("79,596,000,000.0", 
"77,988,000,000.0", "77,672,000,000.0", "77,311,000,000.0", "77,006,000,000.0", "76,660,000,000.0", 
"76,309,000,000.0", "76,034,000,000.0", "75,734,000,000.0", "75,418,000,000.0", "75,179,000,000.0", 
"74,909,000,000.0", "74,633,000,000.0", "74,389,000,000.0", "74,167,000,000.0", "73,963,000,000.0", 
"73,734,000,000.0", "73,529,000,000.0", "73,339,000,000.0", "73,096,000,000.0", "72,912,000,000.0", 
"71,891,000,000.0", "71,735,000,000.0", "71,579,000,000.0", "71,427,000,000.0", "70,551,000,000.0"
)) & !is.na(`Goodwill and Intangible Assets`)) %>%
 filter(!(Property %in% c("13,552,000,000.0", 
"13,637,000,000.0", "13,104,000,000.0", "12,653,000,000.0", "12,436,000,000.0", "11,858,000,000.0", 
"11,085,000,000.0", "10,836,000,000.0", "10,703,000,000.0", "10,475,000,000.0", "10,351,000,000.0", 
"10,145,000,000.0", "9,987,000,000.0", "10,049,000,000.0", "9,833,000,000.0", "9,700,000,000.0", 
"9,554,000,000.0", "9,485,000,000.0", "9,358,000,000.0", "9,150,000,000.0", "9,133,000,000.0", 
"9,139,000,000.0", "9,319,000,000.0", "8,904,000,000.0", "8,971,000,000.0", "9,247,000,000.0", 
"9,399,000,000.0", "9,537,000,000.0", "9,680,000,000.0", "9,906,000,000.0", "10,251,000,000.0", 
"10,426,000,000.0", "10,493,000,000.0")) & !is.na(Property)) %>%
 ggplot() +
 aes(x = `current Ratio`, y = value) +
 geom_point(size = 2.5, colour = "#31688e") +
 geom_smooth(method = "lm") +
 labs(title = "Current Ratio Against All Types of Liabilities", subtitle = "As expected, the Current Ratio is strongly correlated to all types of liabilities. The reasoning to this is eluded to above.") +
 ggthemes::theme_economist_white() +
 facet_wrap(vars(variable), scales = "free_y") + scale_y_continuous(labels = comma) + stat_cor(label.x = 0.9, label.y = 140000000000) + stat_regline_equation(label.x = 0.9, label.y = 170000000000)

When looking at positive correlations of variables with the Debt-to-Equity ratio, we see many of the same varibles as seen in with the Current Ratio. It is also important to note a couple things. First, that the current ratio and debt-to-equity ratio variables are themselves strongly postively correlated. Second, all the relations that have we have looked at thusfar have been statistically signficant. Because of this, when attempting to estimate impacts on a balance sheet based on changes to one of these accounting ratios, it maybe best to omit one in favour of another.

Linear Regression Plots Against Debt-to-Equity Ratio; Positive Correlations

DebtEquityData <- data.table(Date = BAC_All_Statement$date,
                             `DebtEquityRatio` = as.numeric(BAC_All_Statement$debtToEquity),
                             `Long-term debt` = as.numeric(BAC_All_Statement$`Long-term debt`),
                             `Total debt` = as.numeric(BAC_All_Statement$`Total debt`),
                             `Net Debt` = as.numeric(BAC_All_Statement$`Net Debt`),
                             Receivables = as.numeric(BAC_All_Statement$Receivables),
                             `Total non-current liabilities` = as.numeric(BAC_All_Statement$`Total non-current liabilities`),
                             `Goodwill and Intangible Assets` = as.numeric(BAC_All_Statement$`Goodwill and Intangible Assets`),
                             Property = as.numeric(BAC_All_Statement$`Property, Plant & Equipment Net`))

DebtEquityDataMelt <- melt.data.table(DebtEquityData, measure.vars = c("Long-term debt", "Total debt","Long-term debt","Net Debt", "Receivables", "Total non-current liabilities", "Goodwill and Intangible Assets", "Property"))

ggplot(DebtEquityDataMelt) +
 aes(x = DebtEquityRatio, y = value) +
 geom_point(size = 1.5, colour = "#0c4c8a") +
 geom_smooth(method = "lm") +
 labs(title = "Positively Correlated Variables with Debt-to-Equity Ratio", subtitle = "There are strong positive correlations between the D/E Ratio and Debt. This is because similar to the Current Ratio, debt on the balance sheet factors into the calculation \n for the D/E Ratio.") +
 theme_light() +
 facet_wrap(vars(variable), scales = "free") + scale_y_continuous(labels = comma) + stat_cor(label.x = 0.9, label.y = 140000000000) + stat_regline_equation(label.x = 0.9, label.y = 170000000000)

Negative Correlations

In addition to looking at positively correlated relations, it is also important to look at negatively correlated relations.

k <- which(correlationMatrix < -0.80, arr.ind = TRUE)

negativeRelations <- data.table( variable1 = rownames(correlationMatrix)[k[,1]], variable2 = colnames(correlationMatrix)[k[,2]], correlation = correlationMatrix[k])
##omit correlations of 1

negativeRelations <- negativeRelations[!correlation == 1,]

negativeRelations <- negativeRelations[!duplicated(negativeRelations$correlation),]
setorder(negativeRelations, -correlation)

datatable(negativeRelations)

negativeCurrentRatioCorrel <- negativeRelations[variable1 %like% "current Ratio",]

kable(negativeCurrentRatioCorrel, caption = "Negative Correlations with the Current Ratio") %>% kable_styling("striped")

Negative Correlations with the Current Ratio
variable1	variable2	correlation
current Ratio	Deposit Liabilities	-0.8165375
current Ratio	Long-term investments	-0.8170001
current Ratio	Investments	-0.8178709
current Ratio	Total shareholders equity	-0.8515319

negativeDebtToEquityCorrel <- negativeRelations[variable1 %like% "debtToEquity",]

kable(negativeDebtToEquityCorrel, caption = "Negative Correlations with the Debt to Equity Ratio") %>% kable_styling("striped")

Negative Correlations with the Debt to Equity Ratio
variable1	variable2	correlation
debtToEquity	shareholdersEquity	-0.8353062

The Debt-to-Equity ratio is only negatively correlated with one other variable. Given this, in addition to the fact that the positive correlations are similar to that of the current ratio and that this project is more interested in impacts on the balanace sheet based on liquidity events, it seems appropriate to omit the Debt-to Equity ratio and focus more on the Current Ratio.

Just as before, it helps to plot variables that appear to be negatively correlated with the Current Ratio to visualize the relationship.

Linear Regression Plots Against Current Ratio; Negative Correlations

negativeCurrentRatioData <- data.table(Date = BAC_All_Statement$date, `current Ratio` = BAC_All_Statement$`current Ratio`, `Deposit Liabilities` = BAC_All_Statement$`Deposit Liabilities`, Investments = BAC_All_Statement$Investments, `Total shareholders equity` = BAC_All_Statement$`Total shareholders equity`)

negativeCurrentRatioDataMelt <- melt.data.table(negativeCurrentRatioData, measure.vars = c("Deposit Liabilities", "Investments", "Total shareholders equity"))

ggplot(negativeCurrentRatioDataMelt) +
 aes(x = `current Ratio`, y = value, size = Date) +
 geom_point(colour = "#0c4c8a") +
 geom_smooth(method = "lm") +
 geom_smooth(span = 1L) +
 labs(title = "Strong Negative Correlations with Current Ratio", subtitle = "Unlike what was seen with the positive correlated plots;\nwith the variables that are negatively correlated, the  trends seen are less consistant. There are inconsistencies\nwhere we see some variables rise despite an increase in the Current Ratio. The P-values are still very low, indicating a statistically significant relationship.\nThough it may be interesting to look at other types of regression such as exponential. There also appears to be a relation with the date as more recent data\nis associated with higher levels of Liabilities, Investments, and Shareholders' Equity. That is most probably an indicator of Bank of America expanding it's business.") +
 ggthemes::theme_gdocs() +
 facet_wrap(vars(variable), scales = "free") + scale_y_continuous(labels = comma) + stat_cor(label.x = 0.9, label.y = 130000000000) + stat_regline_equation(label.x = 0.9, label.y = 190000000000)

The exploratory analysis helps determine which variables on the balance sheet will be effected by movements by the current ratio and by how much. All the regressions have low P-Values indicating statistically significant results. The R-Squared values for Total Debt, Long-Term Debt, and Total non-current liabilities are above 80%, indicating that movements in the Current Ratio are able to explain at least 80% of the movements in the dependent variables. For Goodwill, Property, Deoposit Liabilities, Lont-term Investments, Investments, and Total shareholder’s equity, the R-Squared is under 80%, indicating that including other variables in the regression may be needed to explain more of the movement.

Regression Summary Statistics against Current Ratio

Variable	Correlation	Y-Intercept	Coefficient	P-Value	R-squared
Total Debt	0.93	-1,467,424,955,946	1,852,647,112,531	0.00000000000000375	0.8675
Long-Term Debt	0.90	-1,018,755,301,301	1,357,651,095,640	0.000000000000917	0.8115
Net Debt	0.90	-2,258,443,522,255	2,537,431,899,110	0.00000000000123	0.808
Total non-current liabilities	0.90	-1,040,439,653,047	1,383,407,829,263	0.00000000000145	0.8059
Goodwill and Intangible Assets	0.89	-2,767,212,737	81,534,409,952	0.00000000000487	0.7902
Property, Plant & Equipment Net	0.86	-27,462,295,138	40,303,800,502	0.0000000000882	0.7476
Deposit Liabilities	-0.82	4,236,378,079,434	-3,239,048,470,669	0.00000000688164	0.6667
Long-term investments	-0.82	5,324,441,695,960	-5,365,783,439,854	0.00000000664	0.6675
Investments	-0.82	7,563,665,709,199	-7,356,349,851,632	0.000000006207	0.6582
Total shareholders equity	-0.85	690,155,140,607	-466,810,511,299	0.00000000033476732	0.7251

Other Correlations

Though the current ratio itself only significantly impacts 9 other variables on the balance sheet, those 9 variables may effect other variables on the balance sheet as well. Below is a table of correlations for the auxiliary variables.

auxPositiveRelations <- positiveRelations[variable1 %in% c("Total Debt", "Long-Term Debt","Net Debt", "Total non-current liabilities", "Goodwill and Intangible Assets" , "Property, Plant & Equipment Net") | variable2 %in% c("Total Debt", "Long-Term Debt","Net Debt", "Total non-current liabilities", "Goodwill and Intangible Assets" , "Property, Plant & Equipment Net"),]

auxPositiveRelations <- auxPositiveRelations[variable1 != "Current Ratio" | variable2 != "Current Ratio", ]

kable(auxPositiveRelations, caption = "Positive Correlations with the Variables that are Positively Correlated with Current Ratio") %>% kable_styling("striped")

Positive Correlations with the Variables that are Positively Correlated with Current Ratio
variable1	variable2	correlation
longTermDebt	Total non-current liabilities	0.9943523
Total non-current liabilities	Long-term debt	0.9943493
debtToEquity	Total non-current liabilities	0.9877031
Net Debt	Total debt	0.9741387
Total non-current liabilities	Total debt	0.9598042
debtToEquity	Net Debt	0.9532704
Total debt	Property, Plant & Equipment Net	0.9404925
debtToEquity	Property, Plant & Equipment Net	0.9401397
Total non-current liabilities	Property, Plant & Equipment Net	0.9386269
longTermDebt	Property, Plant & Equipment Net	0.9374526
Long-term debt	Property, Plant & Equipment Net	0.9374477
Net Debt	Long-term debt	0.9239168
longTermDebt	Net Debt	0.9238992
Net Debt	Total non-current liabilities	0.9231342
Net Debt	Property, Plant & Equipment Net	0.9206659
Net Debt	Goodwill and Intangible Assets	0.9161831
current Ratio	Net Debt	0.8988680
current Ratio	Total non-current liabilities	0.8977222
current Ratio	Goodwill and Intangible Assets	0.8889521
Total debt	Goodwill and Intangible Assets	0.8783257
debtToEquity	Goodwill and Intangible Assets	0.8746961
current Ratio	Property, Plant & Equipment Net	0.8646112
Net Debt	Total current liabilities	0.8243514
Net Debt	Short-term debt	0.8193955
Net Debt	Receivables	0.8164714
Total non-current liabilities	Goodwill and Intangible Assets	0.8164448
Long-term debt	Goodwill and Intangible Assets	0.8156820
longTermDebt	Goodwill and Intangible Assets	0.8156473
Goodwill and Intangible Assets	Property, Plant & Equipment Net	0.8020187
Goodwill and Intangible Assets	Receivables	0.8009194

auxNegativeRelations <- negativeRelations[variable1 %in% c("Total Debt", "Long-Term Debt","Net Debt", "Total non-current liabilities", "Goodwill and Intangible Assets" , "Property, Plant & Equipment Net") | variable2 %in% c("Total Debt", "Long-Term Debt","Net Debt", "Total non-current liabilities", "Goodwill and Intangible Assets" , "Property, Plant & Equipment Net"),]

auxNegativeRelations <- auxNegativeRelations[variable1 != "Current Ratio" | variable2 != "Current Ratio", ]

kable(auxNegativeRelations, caption = "Negative Correlations with the Variables that are Negatively Correlated with Current Ratio") %>% kable_styling("striped")

Negative Correlations with the Variables that are Negatively Correlated with Current Ratio
variable1	variable2	correlation
Net Debt	Deposit Liabilities	-0.8096540
Net Debt	Short-term investments	-0.8114304
Long-term investments	Property, Plant & Equipment Net	-0.8168505
Net Income	Goodwill and Intangible Assets	-0.8233511
EBIT Margin	Goodwill and Intangible Assets	-0.8278289
Profit Margin	Goodwill and Intangible Assets	-0.8310122
Operating Income	Goodwill and Intangible Assets	-0.8312241
EPS	Goodwill and Intangible Assets	-0.8379777
EPS Diluted	Goodwill and Intangible Assets	-0.8391685
Goodwill and Intangible Assets	Cash and cash equivalents	-0.8453159
Net Debt	Total current assets	-0.8533579
Net Debt	Total shareholders equity	-0.8554443
Goodwill and Intangible Assets	Short-term investments	-0.8574609
Net Debt	Cash and short-term investments	-0.8745239
Goodwill and Intangible Assets	Total current assets	-0.8766236
Long-term investments	Goodwill and Intangible Assets	-0.8789820
shareholdersEquity	Net Debt	-0.8791396
Investments	Goodwill and Intangible Assets	-0.8826940
Net Debt	Investments	-0.8874466
Net Debt	Cash and cash equivalents	-0.8916782
Crr liabilities	Goodwill and Intangible Assets	-0.8916919
Goodwill and Intangible Assets	Cash and short-term investments	-0.8954594
Net Debt	Long-term investments	-0.9031995
Total shareholders equity	Goodwill and Intangible Assets	-0.9073629
TTM Net EPS	Goodwill and Intangible Assets	-0.9088636
Other Liabilities	Goodwill and Intangible Assets	-0.9152228
Retained earnings (deficit)	Goodwill and Intangible Assets	-0.9279262
shareholdersEquity	Goodwill and Intangible Assets	-0.9397745
Stock Price	Goodwill and Intangible Assets	-0.9439021
Deposit Liabilities	Goodwill and Intangible Assets	-0.9617530

From a quick look at what variables are correlated with variables that are strongly correlated with the Current Ratio, we can see that for the most part, these variable are correlated to each other. For example, Net Debt is correlated with Desposit Liabilities and Short-term investments; however we already have current ratio explaining all three of these variables.

Filtering out variables that have already been explained.

auxPositiveRelations2 <- positiveRelations[variable1 %in% c("Total Debt", "Long-Term Debt","Net Debt", "Total non-current liabilities", "Goodwill and Intangible Assets" , "Property, Plant & Equipment Net") & !(variable2 %in% c("Total Debt", "Long-Term Debt","Net Debt", "Total non-current liabilities", "Goodwill and Intangible Assets" , "Property, Plant & Equipment Net", "Long-term debt", "Total debt", "Total current liabilities")),]


kable(auxPositiveRelations2, caption = "Positive Correlations with Variable Left Unexplained") %>% kable_styling("striped")

Positive Correlations with Variable Left Unexplained
variable1	variable2	correlation
Net Debt	Short-term debt	0.8193955
Net Debt	Receivables	0.8164714
Goodwill and Intangible Assets	Receivables	0.8009194

auxNegativeRelations2 <- negativeRelations[variable1 %in% c("Total Debt", "Long-Term Debt","Net Debt", "Total non-current liabilities", "Goodwill and Intangible Assets" , "Property, Plant & Equipment Net") & !(variable2 %in% c("Total Debt", "Long-Term Debt","Net Debt", "Total non-current liabilities", "Goodwill and Intangible Assets" , "Property, Plant & Equipment Net" , "Deposit Liabilities" , "Total shareholders equity","Investments", "Long-term investments", "Total current assets")),]

kable(auxNegativeRelations2, caption = "Negative Correlations with Variable Left Unexplained") %>% kable_styling("striped")

Negative Correlations with Variable Left Unexplained
variable1	variable2	correlation
Net Debt	Short-term investments	-0.8114304
Goodwill and Intangible Assets	Cash and cash equivalents	-0.8453159
Goodwill and Intangible Assets	Short-term investments	-0.8574609
Net Debt	Cash and short-term investments	-0.8745239
Net Debt	Cash and cash equivalents	-0.8916782
Goodwill and Intangible Assets	Cash and short-term investments	-0.8954594

Plotting Other Correlations

According to the above table, Net Debt might be able to be used to explain Short-term debt, Receivables, Short-term investments, and Cash and cash equivalents. Once again, plotting the variables against net Debt will help gain a better picture of the relationship.

library(kableExtra)

NetDebtData <- data.table(`Date` = BAC_All_Statement$date, `Net Debt` = BAC_All_Statement$`Net Debt`, 
                          `Short-term debt` = BAC_All_Statement$`Short-term debt`,
                          `Receivables` = BAC_All_Statement$Receivables,
                          `Short-term investments` = BAC_All_Statement$`Short-term investments`,
                          `Cash and cash equivalents` = BAC_All_Statement$`Cash and cash equivalents` )

NetDebtDataMelt <- melt.data.table(NetDebtData, measure.vars = c("Short-term debt",
                                                                 "Receivables",
                                                                 "Short-term investments",
                                                                 "Cash and cash equivalents"))

ggplot(NetDebtDataMelt) +
 aes(x = `Net Debt`, y = value) +
 geom_point(size = 2.5, colour = "#cb181d") +
 labs(title = "Net Debt Used to Model Other Unexplained Variables", subtitle = "There appears to be a large degree of variance") +
 ggthemes::theme_gdocs() +
 facet_wrap(vars(variable), scales = "free") + scale_y_continuous(labels = comma) + stat_cor(label.x = 0.9, label.y = 140000000000) + stat_regline_equation(label.x = 0.9, label.y = 170000000000)

netDebtvsRec <- lm(as.numeric(NetDebtData$Receivables) ~ as.numeric(NetDebtData$`Net Debt`)   )

#summ(netDebtvsRec)

netDebtvsShortinv <- lm(as.numeric(NetDebtData$`Short-term investments` ) ~ as.numeric(NetDebtData$`Net Debt`)   )

#summ(netDebtvsShortinv)

netDebtvsshortDebt <- lm(as.numeric(NetDebtData$`Short-term debt` ) ~ as.numeric(NetDebtData$`Net Debt`)   )

#summ(netDebtvsshortDebt)

netDebtvscash <- lm(as.numeric(NetDebtData$`Cash and cash equivalents` ) ~ as.numeric(NetDebtData$`Net Debt`)   )

#summ(netDebtvscash)

Regression Summary Statistics; Other Variables

Reveivables ~ Net Debt Regression

MODEL INFO:
Observations:	33
Dependent Variable:	as.numeric(NetDebtData$Receivables)
Type:	OLS linear regression

MODEL FIT:
F(1,31) = 61.99	p = 0.00
R² = 0.67
Adj. R² = 0.66

Standard errors: OLS

	Est.	S.E. t	val.	p
(Intercept)	53459394607.95	1396772704.16	38.27	0.00
as.numeric(NetDebtData$`Net Debt`)	0.07	0.01	7.87	0.00

Short-Term Investments ~ Net Debt Regression

MODEL INFO:
Observations:	33
Dependent Variable:	as.numeric(NetDebtData$`Short-term investments`)
Type:	OLS linear regression

MODEL FIT:
F(1,31) = 59.75	p = 0.00
R² = 0.66
Adj. R² = 0.65

Standard errors: OLS

	Est.	S.E. t	val.	p
(Intercept)	460321453654.93	13842095442.22	33.26	0.00
as.numeric(NetDebtData$`Net Debt`)	-0.73	0.09	-7.73	0.00

Short Term Debt ~ Net Debt Regression

MODEL INFO:
Observations:	33
Dependent Variable:	as.numeric(NetDebtData$`Short-term debt`)
Type:	OLS linear regression

MODEL FIT:
F(1,31) = 63.34	p = 0.00
R² = 0.67
Adj. R² = 0.66

Standard errors: OLS

	Est.	S.E. t	val.	p
(Intercept)	-7856897412.45	3575031897.74	-2.20	0.04
as.numeric(NetDebtData$`Net Debt`)	0.19	0.02	7.96	0.00

Cash and Cash Equivalents ~ Net Debt Regression

MODEL INFO:
Observations:	33
Dependent Variable:	as.numeric(NetDebtData$`Cash and cash equivalents`)
Type:	OLS linear regression

MODEL FIT:
F(1,31) = 120.29	p = 0.00
R² = 0.80
Adj. R² = 0.79

Standard errors: OLS

	Est.	S.E. t	val.	p
(Intercept)	186934091713.61	4212738770.38	44.37	0.00
as.numeric(NetDebtData$`Net Debt`)	-0.31	0.03	-10.97	0.00

After analyzing the linear statistics for the various regressions of correlated variables against Net Debt, all values appear to be statistically significant. Furthermore, one variable; Cash and Cash Equivalents, has an R-Squared of almost 80%. This means that movements in Net Debt can explain approximately 80% of the movement in Cash and Cash Equivalents. Considering the fact that Net Debt able to be estimated using the Current Ratio, we are able to indirectly approximate Cash and Cash Equivalents using the Current Ratio.

Conclusion

With the limited data available to this project as well as the limited scope (only looking at three ratios), we see that we can estimate certain elements of the balance sheet with a reasonable degree of accuracy using the Current Ratio. Furthermore, variables that can be estimated using the current ratio can further be used to estimate other variables on the balance sheet. Given more ratios and metrics, more aspects of the balance sheet can be estimated resulting in a fully estimated balance sheet. This is useful because it is often easier to consider the impacts on a business from adverse business conditions and inclimate economic environment in terms of metrics and ratios. From that point, building a theoretical balance sheet that could exist in a simulated or theoretical senario gives risk analysts foresight on potential risks to the business.

Bank of America Balance Sheet Impact Analysis

Hossain

2/15/2020

Introduction

Gathering Data

Accounting Ratios Being Analyzed

Data Transformations

Correlation Matrix

Linear Regression Plots Against Current Ratio; Positive Correlations

Linear Regression Plots Against Debt-to-Equity Ratio; Positive Correlations

Negative Correlations

Linear Regression Plots Against Current Ratio; Negative Correlations

Regression Summary Statistics against Current Ratio

Other Correlations

Plotting Other Correlations

Regression Summary Statistics; Other Variables

Reveivables ~ Net Debt Regression

Standard errors: OLS

Short-Term Investments ~ Net Debt Regression

Standard errors: OLS

Short Term Debt ~ Net Debt Regression

Standard errors: OLS

Cash and Cash Equivalents ~ Net Debt Regression

Standard errors: OLS

Conclusion