Total Debt USD (Historical) Formula in Excel

Get precise historical insights into a company's total debt expressed in USD. By using the Total Debt USD (Historical) formula in Excel with MarketXLS, you can quickly make informed decisions about a company's financial leverage over different time periods.

Understanding Total Debt USD (Historical)

  • Purpose and Use Cases
    The Total Debt USD (Historical) formula helps analysts and investors understand a company's debt profile over time, including short-term and long-term debt obligations.
  • Key Benefits
    • Quickly assess a company’s historical leverage.
    • Compare total debt across different years and quarters.
    • Evaluate risk and financial stability for investment decisions.
  • When to Use
    Use this formula when performing fundamental analysis, screening for highly leveraged companies, or creating historical trend charts in Excel.

Syntax and Parameters

=hf_Total_Debt_USD(Symbol, year, [quarter], [TTM])
Parameter Description Required Example
Symbol The security symbol (stock, index, option, or crypto). Yes "MSFT", "^SPX", "@MSFT 110122C00020000", "BTCUSD:DEFAULT"
year The fiscal year or special reference (e.g., "2022", "lq", "ly", "lt" and their offsets like "lq-1"). Yes "2022", "lq", "ly-1"
quarter The calendar quarter ranging from 1 to 4. If blank, defaults to 1. No "2"
TTM Use "TTM" for trailing twelve months calculation. If blank, annual/quarterly data is returned. No "TTM"

Return Value:
• Returns a numeric value representing the total debt in USD for the specified symbol and period. Returns "NA" if data is not available or if the symbol is invalid.

?? Note: This function requires an active subscription to historical fundamental data in MarketXLS.

Examples and Usage

Below are some common ways to use the Total Debt USD (Historical) formula. These examples demonstrate how easy it is to vary the arguments for deeper insights:

  1. Basic year-based lookup:

    =hf_Total_Debt_USD("MSFT", 2022)

    Retrieves Microsoft’s total debt in USD for the year 2022.

  2. Specifying year and quarter:

    =hf_Total_Debt_USD("MSFT", 2022, 2)

    Returns Microsoft’s total debt for the second calendar quarter of 2022.

  3. Adding trailing twelve months:

    =hf_Total_Debt_USD("MSFT", 2022, 3, "TTM")

    Gives the trailing twelve months total debt for Microsoft from calendar quarter 3 of 2022.

  4. Using last and previous periods:
    • Last quarter:

    =hf_Total_Debt_USD("MSFT", "lq")

    • Last quarter minus one:

    =hf_Total_Debt_USD("MSFT", "lq-1")

    • Last year:

    =hf_Total_Debt_USD("MSFT", "ly")

    • Last year minus one:

    =hf_Total_Debt_USD("MSFT", "ly-1")

    • Last twelve months:

    =hf_Total_Debt_USD("MSFT", "lt")

    • Previous last twelve months:

    =hf_Total_Debt_USD("MSFT", "lt-1")

? Pro Tip: Combine TTM with any valid quarter or use “lq” to always get the most updated trailing data.

Common Questions

  1. What happens if I enter an invalid symbol?

    • The formula returns "NA". Double-check the symbol or ensure your data subscription includes that security.
  2. Why do I get “NA” even when the symbol is correct?

    • Historical data might be unavailable for that period or symbol. Confirm your subscription or adjust the year/quarter values.
  3. How can I speed up multiple calculations?

    • Use the function on small ranges or limit recalculations. MarketXLS caches data, but large spreadsheets with many calls can slow down recalculations.
  4. Does this work with non-standard date references?

    • Yes, you can pass strings like "lq", "ly", "lt" (and their offsets). Generate dynamic references with any Excel date function if needed.
  5. Is a MarketXLS subscription required?

    • Yes. You need a valid subscription that includes access to historical fundamentals data for the function to return results.

By mastering the Total Debt USD (Historical) formula, you can effortlessly review a company’s debt structure over various historical periods right within Excel. Adjust the parameters to fit your exact needs, and combine this metric with other historical fundamentals for more comprehensive analysis.