Liquidity Ratios Analysis - Brazilian Stock Market (B3)

Balance Sheet Analysis for Investment Decision Support

Finance
R
Author

Sidney Bissoli

Published

January 5, 2025

Overview

This project demonstrates how to extract annual balance sheets from publicly traded companies listed on B3 (Brazilian stock exchange), and to calculate liquidity ratios (current, quick, immediate, and general) from this data, in order to inform investment decisions.

Methodology

Using R programming and financial data analysis techniques, this study focuses on four key liquidity ratios:

  • Current Ratio: Current assets / Current liabilities

  • Quick Ratio: (Current assets - Inventory) / Current liabilities

  • Immediate Ratio: Cash and equivalents / Current liabilities

  • General Ratio: (Current assets + Long-term assets) / (Current + Non-current liabilities)

Data Extraction and Preparation

# install pacman if it is not installed
if (!require("pacman")) install.packages("pacman")
Carregando pacotes exigidos: pacman
# install packages if they are not installed and load them
pacman::p_load(
  tidyverse,
  GetDFPData2,
  janitor,
  here,
  scales,
  ggthemes,
  plotly,
  DT,
  crosstalk,
  htmltools
  )
# store company name
cia_name <- "anima"

# get data
data_info <- get_info_companies()

# subset companies currently traded on B3
info_cleaned <- 
  data_info |>
  clean_names() |>
  rename(
    name_social     = denom_social,
    name_commercial = denom_comerc,
    registro        = sit_reg,
    code_cvm        = cd_cvm,
    sector          = setor_ativ,
    tipo            = tp_merc
    ) |>
  mutate(across(c(registro, tipo, sector), str_to_lower)) |>
  filter(registro == "ativo") |>
  filter(tipo == "bolsa") |>
  mutate(sector = str_remove_all(sector, ".*- "))

# store company sector
cia_sector <- 
  info_cleaned |>
  filter(str_detect(name_social, str_to_upper(cia_name))) |>
  pull(sector)

# store company codes from the same economic sector of company chosen
cvm_codes <- 
  info_cleaned |>
  filter(sector == cia_sector) |>
  pull(code_cvm)

# download balance sheet data
list_bp  <- 
  get_dfp_data(
    companies_cvm_codes = cvm_codes,
    type_docs           = c("BPA","BPP"),
    type_format         = "con",
    clean_data          = TRUE,
    use_memoise         = FALSE,
    cache_folder        = "gdfpd2_cache"
)

Data Processing and Liquidity Calculations

# convert list into data set and provide basic manipulations
data_bp <-
  list_bp |>
  bind_rows() |>
  clean_names() |>
  rename(
    cnpj        = cnpj_cia, 
    date        = dt_refer,
    name        = denom_cia,
    escala      = escala_moeda,
    value       = vl_conta,
    index       = cd_conta,
    description = ds_conta
    ) |>
  mutate(across(c(escala, description), str_to_lower)) |>
  mutate(value = case_when(
    escala == "mil" ~ value * 1000,
    TRUE            ~ value
  ))

# store company complete name
cia_name_complete <- 
  data_bp |>
  filter(str_detect(name, str_to_upper(cia_name))) |>
  distinct(name) |>
  pull(name)

# store variables of interest
variables_chosen <- c(
  "ativo circulante",
  "passivo circulante",
  "caixa e equivalentes de caixa",
  "ativo realizável a longo prazo",
  "passivo não circulante"
)

# store index for inventory (estoques)
index_chosen <- "1.01.04"

# convert data to wide format
data_wide <-
  data_bp |>
  filter(description %in% variables_chosen | index %in% index_chosen) |>
  select(date, name, description, value) |>
  mutate(description = case_when(
    str_detect(description, "caixa")              ~ "caixa",
    str_detect(description, "realizável")         ~ "at_realizavel",
    str_detect(description, "não circulante")     ~ "pa_nao_circ",
    str_detect(description, "ativo circulante")   ~ "at_circ",
    str_detect(description, "passivo circulante") ~ "pa_circ",
    TRUE                                          ~ description
  )) |>
  pivot_wider(names_from = description, values_from = value) |>
  group_by(date) |>
  mutate(n = n()) |>
  ungroup()

# store abbreviated variables
variables_abbreviated <- c(
  "at_circ",
  "pa_circ",
  "caixa",
  "at_realizavel",
  "pa_nao_circ",
  "estoques"
)

# calculate sector totals
data_total <- 
  data_wide |>
  select(-name) |>
  group_by(date) |>
  mutate(across(any_of(variables_abbreviated), sum, na.rm = TRUE)) |>
  ungroup() |>
  mutate(name = str_to_upper("total"), .after = date) |>
  distinct()

# combine company data with sector averages
data_subset <-
  data_wide |>
  bind_rows(data_total) |>
  mutate(name = str_to_lower(name)) |>
  filter(str_detect(name, cia_name) | name == "total") |>
  mutate(name = str_to_upper(name))

# calculate liquidity ratios
data_multiplos <- 
  data_subset |> 
  mutate(
    liq_corrente = at_circ / pa_circ,
    liq_seca     = (at_circ - coalesce(estoques, 0)) / pa_circ,
    liq_imediata = caixa / pa_circ,
    liq_geral    = (at_circ + at_realizavel) / (pa_circ + pa_nao_circ)
  ) |>
  select(date, name, starts_with("liq"), n)
# order levels for classification
levels <- c(
  "adequate", 
  "regular", 
  "concerning", 
  "critical"
  )

# prepare data for visualization with classification
data_classification <- 
  data_multiplos |>
  group_by(date) |>
  filter(n() == 2) |>
  ungroup() |>
  arrange(date) |>
  pivot_longer(cols = starts_with("liq"), names_to = "variable") |>
  mutate(classification = case_when(
    variable == "liq_corrente" & value < 1                   ~ "critical",
    variable == "liq_corrente" & value >= 1   & value < 1.2  ~ "concerning",
    variable == "liq_corrente" & value >= 1.2 & value < 1.5  ~ "regular",
    variable == "liq_corrente" & value >= 1.5                ~ "adequate",
    variable == "liq_seca"     & value < .8                  ~ "critical",
    variable == "liq_seca"     & value >= .8  & value <=1    ~ "concerning",
    variable == "liq_seca"     & value > 1                   ~ "adequate",
    variable == "liq_imediata" & value < .15                 ~ "critical",
    variable == "liq_imediata" & value >= .15 & value <=.3   ~ "concerning",
    variable == "liq_imediata" & value > .3                  ~ "adequate",
    variable == "liq_geral"    & value < 1                   ~ "critical",
    variable == "liq_geral"    & value >= 1   & value < 1.2  ~ "concerning",
    variable == "liq_geral"    & value >= 1.2 & value < 1.5  ~ "regular",
    variable == "liq_geral"    & value >= 1.5                ~ "adequate",
  )) |>
  mutate(classification = str_to_title(classification)) |>
  mutate(classification = factor(classification, levels = str_to_title(levels))) |>
  mutate(year = year(date), .after = date)

# nest data by liquidity type
data_nested <- 
  data_classification |>
  nest(data = -variable)

Interactive Liquidity Analysis Dashboard

Summary Table

Key Findings

Company Performance vs. Sector Average

The analysis reveals important insights about ANIMA HOLDING S.A. liquidity position:

  1. Current Ratio Trends: Shows the company’s ability to meet short-term obligations

  2. Quick Ratio Analysis: Excludes inventory to provide a more conservative liquidity measure

  3. Immediate Ratio: Reveals cash position strength for urgent obligations

  4. General Ratio: Provides comprehensive liquidity assessment including long-term assets

Classification System

The visualizations use a color-coded classification system:

  • 🟢 Adequate (green): Strong liquidity position

  • 🔵 Regular (blue): Acceptable liquidity levels

  • 🟠 Concerning (orange): Attention required

  • 🔴 Critical (red): Immediate attention needed

Investment Implications

This liquidity analysis provides valuable insights for:

  • Portfolio managers seeking companies with solid financial foundations

  • Individual investors evaluating investment safety and company stability

  • Financial analysts comparing companies within the same economic sector

  • Risk managers assessing short-term financial risk exposure

Technical Implementation

Data Sources: B3 (Brazilian Stock Exchange) official financial reports
Analysis Tools: R (tidyverse, GetDFPData2, ggplot2, plotly)
Methodology: Balance sheet analysis with sector benchmarking
Interactivity: Dynamic plots with hover information and classification tooltips

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This analysis demonstrates the integration of financial data extraction, statistical analysis, and interactive visualization techniques using R programming. The complete methodology can be adapted for analyzing any publicly traded company on B3.