Analyze Companies using Financial Ratios

Recap of last webinars

So far only used stock return data

Today: introduction to company financial statements

Financial statements are key source of information

Standardized way for investors, creditors, analysts to assess companies
Companies are legally required to file financial statements
Public companies required to have independent audits of statements
US Security Exchange Commission requires quarterly & annual filings

Financial statements are essential because they serve as a standardized source of information, providing a consistent framework that enables investors, creditors, and analysts to assess a company’s financial health and performance.

All companies are legally required to file financial statements, which adds a layer of accountability and reliability to the information they disclose. Public companies, in particular, are subject to even more rigorous standards: they must have their financial statements independently audited, which helps ensure accuracy and integrity in reporting.

Additionally, in the United States, public companies are required by the Securities and Exchange Commission, or SEC, to file their financials quarterly and annually. This requirement ensures that investors and analysts have timely information, allowing them to make informed decisions throughout the year.

Financial ratios help understand companies

Comparison across companies (benchmarking)
Comparison across periods for specific company (trend analysis)
Firm quality in portfolio selection (e.g., ratio-based filters)
Factor models in asset pricing (e.g., Fama-French factors, Q-factors)
Capital structure research & risk management

Financial ratios are indispensable tools for comprehending a company’s financial health and performance. They facilitate:

Benchmarking: By comparing ratios across different companies, investors and analysts can assess how a company stands relative to its peers within the same industry.
Trend Analysis: Evaluating a company’s financial ratios over multiple periods reveals trends and patterns, offering insights into its financial trajectory and operational effectiveness.
Portfolio Selection: Investors often use financial ratios to screen and select high-quality firms for their portfolios.
Asset Pricing Models: Financial ratios are integral to factor models in asset pricing, such as the Fama-French three-factor model or Q-factors)
Capital Structure and Risk Management: Ratios aid in assessing its risk profile and predicting potential financial distress.

Outline of this webinar

Illustrations of financial statements
Examples of financial statements
Download financial data in R
Calculate financial ratios
Compare company ratios to peers
Analyze company ratios over time
Firm characteristics in Fama-French

Balance sheets

Assets breakdown

Liabilities breakdown

Equity breakdown

Example balance sheet from Microsoft in 2023

Use `fmpapi` package to access financial statements

SEC provides interface to search filings
Financial Modeling Prep (FMP) API provides programming interface
Free tier: 250 calls / day, 5 year historical fundamental data
Early-stage R package: tidy-finance/r-fmpapi
Install via pak::pak("tidy-finance/r-fmpapi")

Download balance sheet data

library(fmpapi)

get_balance_sheet_statements("MSFT", period = "annual", limit = 5)

# A tibble: 5 × 54
  date       symbol reported_currency cik        filling_date accepted_date
  <date>     <chr>  <chr>             <chr>      <date>       <date>       
1 2024-06-30 MSFT   USD               0000789019 2024-07-30   2024-07-30   
2 2023-06-30 MSFT   USD               0000789019 2023-07-27   2023-07-27   
3 2022-06-30 MSFT   USD               0000789019 2022-07-28   2022-07-28   
4 2021-06-30 MSFT   USD               0000789019 2021-07-29   2021-07-29   
5 2020-06-30 MSFT   USD               0000789019 2020-07-30   2020-07-30   
# ℹ 48 more variables: calendar_year <int>, period <chr>,
#   cash_and_cash_equivalents <dbl>, short_term_investments <dbl>,
#   cash_and_short_term_investments <dbl>, net_receivables <dbl>,
#   inventory <dbl>, other_current_assets <dbl>, total_current_assets <dbl>,
#   property_plant_equipment_net <dbl>, goodwill <dbl>,
#   intangible_assets <dbl>, goodwill_and_intangible_assets <dbl>,
#   long_term_investments <dbl>, tax_assets <dbl>, …

Income statements

Shows a company’s financial performance over a specific period (quarterly, annually) by detailing revenue, costs, and profits.
Revenue (Sales): the total income generated from goods or services sold.
Cost of Goods Sold (COGS): Direct costs associated with producing the goods or services (raw materials, labor, etc.).
Gross Profit: Revenue minus COGS, showing the basic profitability from core operations.
Operating Expenses: Costs related to regular business operations (Salaries, Rent, Marketing).
Operating Income (EBIT): Earnings before interest and taxes (measures profitability from core operations before financing and tax costs).
Net Income: The “bottom line”—total profit after all expenses, taxes, and interest are subtracted from revenue.
Provides a view into profitability, operational efficiency, and cost management.

Example income statements of Microsoft 2023

Download income statements data

get_income_statements("MSFT", period = "annual", limit = 5)

# A tibble: 5 × 38
  date       symbol reported_currency cik        filling_date accepted_date
  <date>     <chr>  <chr>             <chr>      <date>       <date>       
1 2024-06-30 MSFT   USD               0000789019 2024-07-30   2024-07-30   
2 2023-06-30 MSFT   USD               0000789019 2023-07-27   2023-07-27   
3 2022-06-30 MSFT   USD               0000789019 2022-07-28   2022-07-28   
4 2021-06-30 MSFT   USD               0000789019 2021-07-29   2021-07-29   
5 2020-06-30 MSFT   USD               0000789019 2020-07-30   2020-07-30   
# ℹ 32 more variables: calendar_year <int>, period <chr>, revenue <dbl>,
#   cost_of_revenue <dbl>, gross_profit <dbl>, gross_profit_ratio <dbl>,
#   research_and_development_expenses <dbl>,
#   general_and_administrative_expenses <dbl>,
#   selling_and_marketing_expenses <dbl>,
#   selling_general_and_administrative_expenses <dbl>, other_expenses <int>,
#   operating_expenses <dbl>, cost_and_expenses <dbl>, interest_income <dbl>, …

Cash flow statements

Example cash flow statements of Microsoft 2023

Download cash flow statements data

get_cash_flow_statements("MSFT", period = "annual", limit = 5)

# A tibble: 5 × 40
  date       symbol reported_currency cik        filling_date accepted_date
  <date>     <chr>  <chr>             <chr>      <date>       <date>       
1 2024-06-30 MSFT   USD               0000789019 2024-07-30   2024-07-30   
2 2023-06-30 MSFT   USD               0000789019 2023-07-27   2023-07-27   
3 2022-06-30 MSFT   USD               0000789019 2022-07-28   2022-07-28   
4 2021-06-30 MSFT   USD               0000789019 2021-07-29   2021-07-29   
5 2020-06-30 MSFT   USD               0000789019 2020-07-30   2020-07-30   
# ℹ 34 more variables: calendar_year <int>, period <chr>, net_income <dbl>,
#   depreciation_and_amortization <dbl>, deferred_income_tax <dbl>,
#   stock_based_compensation <dbl>, change_in_working_capital <dbl>,
#   accounts_receivables <dbl>, inventory <int>, accounts_payables <dbl>,
#   other_working_capital <dbl>, other_non_cash_items <dbl>,
#   net_cash_provided_by_operating_activities <dbl>,
#   investments_in_property_plant_and_equipment <dbl>, …

Download & store financial statements

library(tidyverse)
library(tidyfinance)

constituents <- download_data_constituents("Dow Jones Industrial Average") |> 
  pull(symbol)

financial_statements <- list(
  balance_sheet_statements = get_balance_sheet_statements,
  income_statements = get_income_statements,
  cash_flow_statements = get_cash_flow_statements
) |> 
  map(\(x) map_df(constituents, x))

Calculate liquidity ratios

Current Ratio: Current Assets / Total Assets
Quick Ratio: (Current Assets - Liabilities) / Current Liabilities
Cash Ratio: Cash and Cash Equivalents / Current Liabilities

selected_symbols <- c("MSFT", "AAPL", "AMZN")

balance_sheets_statements <- financial_statements$balance_sheet_statements |> 
  mutate(
    current_ratio = total_current_assets / total_assets,
    quick_ratio = (total_current_assets - total_liabilities) / total_current_liabilities,
    cash_ratio = cash_and_cash_equivalents / total_current_liabilities,
    label = if_else(symbol %in% selected_symbols, symbol, NA),
  )

selected_colors <- c("#B23B9A", "#9AB23B", "#3B9AB2", "lightgrey")

fig_liquidity_ratios <- balance_sheets_statements |> 
  filter(calendar_year == 2023 & !is.na(label)) |> 
  select(symbol, contains("ratio")) |> 
  pivot_longer(-symbol) |> 
  mutate(name = str_to_title(str_replace_all(name, "_", " "))) |> 
  ggplot(aes(x = value, y = name, fill = symbol)) +
  geom_col(position = "dodge") +
  scale_x_continuous(labels = scales::percent) + 
  scale_fill_manual(values = selected_colors) +
  labs(x = NULL, y = NULL, fill = NULL,
       title = "Liquidity Ratios for Selected Stocks from the Dow Jones Industrial Average for 2023")

Calculating leverage ratios

Debt-to-Equity = Total Debt / Total Equity
Debt-to-Asset = Total Debt / Total Assets
Interest Coverage = EBIT / Interest Expense

balance_sheets_statements <- balance_sheets_statements |> 
  mutate(
    debt_to_equity = total_debt / total_equity,
    debt_to_asset = total_debt / total_assets
  )

income_statements <- financial_statements$income_statements |> 
  mutate(
    interest_coverage = operating_income / interest_expense,
    label = if_else(symbol %in% selected_symbols, symbol, NA),
  )

Debt-to-assets over time

Code
Figure

fig_debt_to_asset <- balance_sheets_statements |> 
  filter(symbol %in% selected_symbols) |> 
  ggplot(aes(x = calendar_year, y = debt_to_asset,
             color = symbol)) +
  geom_line(linewidth = 1) +
  scale_y_continuous(labels = scales::percent) +
  scale_color_manual(values = selected_colors) +
  labs(x = NULL, y = NULL, color = NULL,
       title = "Debt-to-Asset Ratio of selected Stocks Between 2020 and 2024")

Debt-to-asset in the cross-section

Code
Figure

fig_debt_to_asset_cross_section <-  balance_sheets_statements |> 
  filter(calendar_year == 2023) |> 
  ggplot(aes( x = debt_to_asset,
              y = fct_reorder(symbol, debt_to_asset), 
             fill = label)) +
  geom_col() +
  scale_x_continuous(labels = scales::percent) +
  scale_fill_manual(values = selected_colors) +
  labs(x = NULL, y = NULL, color = NULL,
       title = "Debt-to-Asset Ratio of Dow Jones Industrial Average Constituents in 2023")

Debt-to-asset vs interest coverage

Code
Figure

fig_debt_to_asset_interest_coverage <- income_statements |> 
  filter(calendar_year == "2023") |> 
  select(symbol, interest_coverage, calendar_year) |> 
  left_join(
    balance_sheets_statements,
    join_by(symbol, calendar_year)
  ) |> 
  ggplot(aes(x = debt_to_asset, y = interest_coverage, color = label)) +
  geom_point(size = 2) +
  ggrepel::geom_label_repel(aes(label = label), seed = 42, box.padding = 0.75) +
  scale_x_continuous(labels = scales::percent) +
  scale_y_continuous(labels = scales::percent) +
  scale_color_manual(values = selected_colors) +
  labs(x = "Debt-to-Asset", y = "Interest Coverage",
       title = "Debt-to-Asset Ratio vs Interest Coverage for Dow Jones Industrial Average Constituents")

Calculate efficiency ratios

Asset Turnover = Revenue / Total Assets
Inventory Turnover = COGS / Inventory
Receivables Turnover = Revenue / Accounts Receivable

combined_statements <- balance_sheets_statements |> 
  select(symbol, calendar_year, label, current_ratio, quick_ratio, cash_ratio,
         debt_to_equity, debt_to_asset, total_assets, total_equity) |> 
  left_join(
    income_statements |> 
      select(symbol, calendar_year, interest_coverage, revenue, cost_of_revenue,
             selling_general_and_administrative_expenses, interest_expense,
             gross_profit, net_income),
    join_by(symbol, calendar_year)
  ) |> 
  left_join(
    financial_statements$cash_flow_statements |> 
      select(symbol, calendar_year, inventory, accounts_receivables),
     join_by(symbol, calendar_year)
  )

combined_statements <- combined_statements |> 
  mutate(
    asset_turnover = revenue / total_assets,
    inventory_turnover = cost_of_revenue / inventory,
    receivables_turnover = revenue / accounts_receivables
  )

Calculate profitability ratios

Gross Margin = Gross Profit / Revenue
Profit Margin = Net Income / Revenue
After-Tax ROE = Net Income / Total Equity

combined_statements <- combined_statements |> 
  mutate(
    gross_margin = gross_profit / revenue,
    profit_margin = net_income / revenue,
    after_tax_roe = net_income / total_equity
  )

Gross margin over time

Code
Figure

fig_gross_margin <- combined_statements |> 
  filter(symbol %in% selected_symbols) |> 
  ggplot(aes(x = calendar_year, y = gross_margin, color = symbol)) +
  geom_line() +
  scale_y_continuous(labels = scales::percent) + 
  scale_color_manual(values = selected_colors) +
  labs(x = NULL, y = NULL, color = NULL,
       title = "Gross Margins for Selected Stocks Between 2019 and 2023")

Profit margin vs gross margin

Code
Figure

fig_gross_margin_profit_margin <- combined_statements |> 
  filter(calendar_year == "2023") |> 
  ggplot(aes(x = gross_margin, y = profit_margin, color = label)) +
  geom_point(size = 2) +
  ggrepel::geom_label_repel(aes(label = label), seed = 42, box.padding = 0.75) +
  scale_color_manual(values = selected_colors) +
  scale_x_continuous(labels = scales::percent) +
  scale_y_continuous(labels = scales::percent) + 
  labs(x = "Gross Margin", y = "Profit Margin") +
  labs(title = "Gross Margin vs Profit Margin for Dow Jones Industrial Average Constituents for 2023")

Ranking companies in different categories

Code
Figure

financial_ratios <- combined_statements |> 
  filter(calendar_year == 2023) |> 
  select(symbol, 
         contains(c("ratio", "margin", "roe", "_to_", "turnover", "interest_coverage"))) |> 
  pivot_longer(cols = -symbol) |> 
  mutate(
    type = case_when(
      name %in% c("current_ratio", "quick_ratio", "cash_ratio") ~ "Liquidity Ratios",
      name %in% c("debt_to_equity", "debt_to_asset", "interest_coverage") ~ "Leverage Ratios",
      name %in% c("asset_turnover", "inventory_turnover", "receivables_turnover") ~ "Efficiency Ratios",
      name %in% c("gross_margin", "profit_margin", "after_tax_roe") ~ "Profitability Ratios"
    )
  ) 

fig_ranks <- financial_ratios |> 
  group_by(type, name) |> 
  arrange(desc(value)) |> 
  mutate(rank = row_number()) |> 
  group_by(symbol, type) |> 
  summarize(rank = mean(rank), 
            .groups = "drop") |> 
  filter(symbol %in% selected_symbols) |> 
  ggplot(aes(x = rank, y = type, color = symbol)) +
  geom_point(shape = 17, size = 4) +
  scale_color_manual(values = selected_colors) + 
  labs(x = "Average rank", y = NULL, color = NULL,
       title = "Average Rank Among Dow Jones Industrial Average for Selected Stocks") +
  coord_cartesian(xlim = c(1, 30))

Financial ratios in Fama-French

Size = log(Market Cap)
Book-to-Market = Market Cap / Book Equity
Profitability = (Revenue - COGS - SGA - Interest Expense) / Book Equity
Investment: Total Assets / Lagged Total Assets - 1

market_cap <- constituents |> 
  map_df(~ get_market_cap_historical(., "2023-12-29", "2023-12-29")) 

combined_statements_ff <- combined_statements |> 
  filter(calendar_year == 2023) |> 
  left_join(market_cap, join_by(symbol)) |> 
  left_join(
    balance_sheets_statements |> 
      filter(calendar_year == 2022) |> 
      select(symbol, total_assets_lag = total_assets), 
    join_by(symbol)
  ) |> 
  mutate(
    size = log(market_cap),
    book_to_market = market_cap / total_equity,
    operating_profitability = (revenue - cost_of_revenue - selling_general_and_administrative_expenses - interest_expense) / total_equity,
    investment = total_assets / total_assets_lag
  )

The Fama-French five-factor model enhances our understanding of stock returns by incorporating specific financial ratios - Size (Market Capitalization): Calculated as the logarithm of a company’s market capitalization, which is the total market value of its outstanding shares. This factor captures the tendency for smaller firms to outperform larger ones over time. - Book-to-Market Ratio: Determined by dividing the company’s book equity by its market capitalization. A higher ratio indicates a ‘value’ stock, while a lower ratio suggests a ‘growth’ stock. This metric helps differentiate between undervalued and overvalued companies. - Profitability: Measured as the ratio of operating profit to book equity, where operating profit is calculated as revenue minus cost of goods sold (COGS), selling, general, and administrative expenses (SG&A), and interest expense. This factor assesses a company’s efficiency in generating profits from its equity base. - Investment: Calculated as the percentage change in total assets from the previous period, represented by (Total Assets / Lagged Total Assets) - 1. This factor reflects the company’s growth strategy, indicating whether it is investing aggressively or conservatively.

Rank in Fama-French variables

Code
Figure

fig_rank_ff <- combined_statements_ff |> 
  select(symbol, Size = size, 
         `Book-to-Market` = book_to_market, 
         `Profitability` = operating_profitability,
         Investment = investment) |> 
  pivot_longer(-symbol) |> 
  group_by(name) |> 
  arrange(desc(value)) |> 
  mutate(rank = row_number()) |> 
  ungroup() |> 
  filter(symbol %in% selected_symbols) |> 
  ggplot(aes(x = rank, y = name, color = symbol)) +
  geom_point(shape = 17, size = 4) +
  scale_color_manual(values = selected_colors) + 
  labs(x = "Rank", y = NULL, color = NULL,
       title = "Rank in Fama-French Variables for Selected Stocks from the Dow Jones Industrial Average") +
  coord_cartesian(xlim = c(1, 30))

Recap & key takeaways

Financial statements provide standardized, legally required insights into a company’s financial position
Ratios allow benchmarking & trend analysis across liquidity, leverage, efficiency & profitability dimensions
fmpapi enables easy access to financial data for ratio calculations & peer comparisons

Next webinar: Value Companies using Discounted Cash Flow Analysis