e.l.f. Beauty (ELF) Total Liabilities (2015 - 2026)
e.l.f. Beauty (ELF) posted Total Liabilities of $1.29 billion for fiscal Q1 2027 (quarter ended Jun 30, 2026), up 153.3% from $510.04 million a year earlier and up 2.2% from the prior quarter.
e.l.f. Beauty (ELF) Total Liabilities (2015 - 2026) Analysis & Trends
At the end of FY2026 (ended Mar 31, 2026), e.l.f. Beauty's Total Liabilities came in at $1.26 billion, up 159.3% from FY2025.
- Annual Total Liabilities has increased for four consecutive fiscal years, with a five-year compound annual growth rate of 42.1% (FY2021 to FY2026).
- In prior fiscal years, e.l.f. Beauty's Total Liabilities was $487.32 million in FY2025 (+0.1%), $486.68 million in FY2024 (+163.7%), $184.58 million in FY2023 (+1.3%) and $182.2 million in FY2022 (-16.3%).
- The fiscal Q1 2027 figure stands as the highest quarterly Total Liabilities in data going back to fiscal Q4 2019.
- On a year-over-year basis, Total Liabilities has increased in each of the last 14 quarters, with growth averaging 87.7% over the last eight quarters.
- The strongest year-over-year quarter for Total Liabilities in the past five years was fiscal Q3 2024, with growth of 189.6%; the weakest was fiscal Q1 2023, with a decline of 16.4%.
- According to Business Quant data, Total Liabilities for the three prior fiscal quarters was $1.26 billion (Q4 2026), $1.16 billion (Q3 2026) and $1.18 billion (Q2 2026).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Procter & Gamble | 384.13 Bn | 335.69 Bn | 11.60 Bn | 77.80 Bn |
| 2 | Colgate Palmolive | 76.99 Bn | 71.47 Bn | 3.23 Bn | 16.10 Bn |
| 3 | Estee Lauder Companies | 40.05 Bn | 27.14 Bn | 2.81 Bn | 15.93 Bn |
| 4 | Kenvue | 37.02 Bn | 32.73 Bn | 2.31 Bn | 16.51 Bn |
| 5 | Kimberly Clark | 36.07 Bn | 32.01 Bn | 1.44 Bn | 14.65 Bn |
| 6 | Church & Dwight | 25.86 Bn | 24.25 Bn | 749.40 Mn | 4.85 Bn |
| 7 | Clorox | 12.14 Bn | 10.40 Bn | 770.00 Mn | 7.71 Bn |
| 8 | e.l.f. Beauty | 6.86 Bn | 5.68 Bn | 370.22 Mn | 1.34 Bn |
| 9 | Reynolds Consumer Products | 5.08 Bn | 4.77 Bn | 236.00 Mn | 2.78 Bn |
| 10 | Bath & Body Works | 4.61 Bn | 1.81 Bn | 692.00 Mn | 6.21 Bn |
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | 1.34 Bn |
| Jun 30, 2026 | 1.29 Bn |
| Mar 31, 2026 | 1.26 Bn |
| Dec 31, 2025 | 1.16 Bn |
| Sep 30, 2025 | 1.18 Bn |
| Jun 30, 2025 | 510.04 Mn |
| Mar 31, 2025 | 487.32 Mn |
| Dec 31, 2024 | 497.45 Mn |
| Sep 30, 2024 | 509.32 Mn |
| Jun 30, 2024 | 501.24 Mn |
| Mar 31, 2024 | 486.68 Mn |
| Dec 31, 2023 | 494.06 Mn |
| Sep 30, 2023 | 230.51 Mn |
| Jun 30, 2023 | 186.18 Mn |
| Mar 31, 2023 | 184.58 Mn |
| Dec 31, 2022 | 170.62 Mn |
| Sep 30, 2022 | 185.18 Mn |
| Jun 30, 2022 | 180.41 Mn |
| Mar 31, 2022 | 182.20 Mn |
| Dec 31, 2021 | 185.48 Mn |
e.l.f. Beauty Total Liabilities 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=total-liabilities&ticker=ELF&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "ELF", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()
const res = await fetch("https://data.businessquant.com/historic?slug=total-liabilities&ticker=ELF&period=max&api_key=YOUR_API_KEY");
const data = await res.json();