V F (VFC) Accumulated Expenses (2009 - 2026)
V F's (VFC) quarterly Accumulated Expenses came in at $839.2 million in Q2 2026, down 14.53% year-over-year from $981.9 million in Q2 2025, and down 17.01% quarter-over-quarter from $1.0 billion in Q1 2026.
V F (VFC) Accumulated Expenses (2009 - 2026) Analysis & Trends
V F has disclosed Accumulated Expenses across 18 years of filings, most recently posting $839.2 million for Q2 2026.
- In Q2 2026, Accumulated Expenses fell 14.53% year-over-year to $839.2 million; the TTM figure through Jun 2026 stood at $839.2 million (down 14.53% YoY), while the FY2026 annual figure was $1.0 billion, down 3.42% from the prior year.
- Accumulated Expenses came in at $839.2 million for Q2 2026 at V F, down from $1.0 billion in the prior quarter.
- In the past five years, Accumulated Expenses ranged from a high of $2.1 billion in Q1 2022 to a low of $839.2 million in Q2 2026.
- Average Accumulated Expenses over 5 years is $1.5 billion, with a median of $1.5 billion recorded in 2023.
- Year-over-year, Accumulated Expenses gained 23.58% in 2022 and decreased 22.65% in 2024.
- Over 5 years, Accumulated Expenses stood at $1.8 billion in 2022, then fell by 17.14% to $1.5 billion in 2023, then slipped by 3.86% to $1.5 billion in 2024, then climbed by 10.18% to $1.6 billion in 2025, then tumbled by 47.68% to $839.2 million in 2026.
- Per Business Quant data, the three most recent Accumulated Expenses figures were $839.2 million in Q2 2026, $1.0 billion in Q1 2026, and $1.6 billion in Q4 2025.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Lululemon Athletica | 10.45 Bn | 9.06 Bn | 1.46 Bn |
| 2 | Levi Strauss | 7.65 Bn | 6.67 Bn | 979.10 Mn |
| 3 | Gildan Activewear | 7.18 Bn | 6.91 Bn | 459.76 Mn |
| 4 | V F | 5.02 Bn | 4.35 Bn | 917.04 Mn |
| 5 | Kontoor Brands | 3.63 Bn | 3.57 Bn | 328.26 Mn |
| 6 | Pvh | 3.38 Bn | 2.42 Bn | 1.32 Bn |
| 7 | Columbia Sportswear | 2.91 Bn | 2.29 Bn | 358.43 Mn |
| 8 | Warby Parker | 2.78 Bn | 2.48 Bn | 136.46 Mn |
| 9 | Figs | 2.06 Bn | 1.76 Bn | 147.86 Mn |
| 10 | Under Armour | 2.04 Bn | 1.65 Bn | 593.83 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 27, 2026 | 839.25 Mn |
| Mar 28, 2026 | 1.01 Bn |
| Dec 27, 2025 | 1.60 Bn |
| Sep 27, 2025 | 1.54 Bn |
| Jun 28, 2025 | 981.93 Mn |
| Mar 29, 2025 | 1.05 Bn |
| Dec 28, 2024 | 1.46 Bn |
| Sep 28, 2024 | 1.49 Bn |
| Jun 29, 2024 | 1.20 Bn |
| Mar 30, 2024 | 1.32 Bn |
| Dec 30, 2023 | 1.51 Bn |
| Sep 30, 2023 | 1.49 Bn |
| Jul 1, 2023 | 1.55 Bn |
| Apr 1, 2023 | 1.67 Bn |
| Dec 31, 2022 | 1.83 Bn |
| Oct 1, 2022 | 1.80 Bn |
| Jul 2, 2022 | 1.61 Bn |
| Apr 2, 2022 | 1.92 Bn |
| Jan 1, 2022 | 2.06 Bn |
| Oct 2, 2021 | 1.84 Bn |
V F Accumulated Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=accumulated-expenses&ticker=VFC&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-expenses", "ticker": "VFC", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()
const res = await fetch("https://data.businessquant.com/historic?slug=accumulated-expenses&ticker=VFC&period=max&api_key=YOUR_API_KEY");
const data = await res.json();