Codexis (CDXS) Total Liabilities (2010 - 2026)
Codexis (CDXS) posted Total Liabilities of $87.1 million for Q2 2026, up 5.4% from $82.62 million a year earlier and up 1.1% from the prior quarter.
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Analysis
Codexis (CDXS) Total Liabilities (2010 - 2026) Analysis & Trends
At the end of FY2025, Codexis' Total Liabilities came in at $97.27 million, up 18.5% from FY2024.
- Annual Total Liabilities shows a five-year compound annual growth rate of 13.5% (FY2020 to FY2025).
- In prior years, Codexis' Total Liabilities was $82.08 million in FY2024 (+64.3%), $49.95 million in FY2023 (-52.7%), $105.6 million in FY2022 (+28.8%) and $81.99 million in FY2021 (+59.1%).
- Quarterly Total Liabilities has run from a low of $49.95 million in Q4 2023 to a high of $105.6 million in Q4 2022 over five years.
- On a year-over-year basis, Total Liabilities has increased in each of the last eight quarters, with growth averaging 20.0% over the last eight quarters.
- The strongest year-over-year quarter for Total Liabilities in the past five years was Q4 2024, with growth of 64.3%; the weakest was Q4 2023, with a decline of 52.7%.
- According to Business Quant data, Total Liabilities for the three prior quarters was $86.14 million (Q1 2026), $97.27 million (Q4 2025) and $85.42 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 250.84 Bn | 229.99 Bn | 4.88 Bn | 60.49 Bn |
| 2 | Abbott Laboratories | 174.73 Bn | 145.82 Bn | 7.27 Bn | 57.45 Bn |
| 3 | Danaher | 159.85 Bn | 143.67 Bn | 3.61 Bn | 39.78 Bn |
| 4 | Intuitive Surgical | 146.79 Bn | 126.35 Bn | 1.96 Bn | 2.58 Bn |
| 5 | Medtronic | 114.54 Bn | 80.41 Bn | 6.34 Bn | 42.46 Bn |
| 6 | Stryker | 105.67 Bn | 91.79 Bn | 4.50 Bn | 23.94 Bn |
| 7 | Boston Scientific | 63.93 Bn | 58.94 Bn | 3.85 Bn | 20.04 Bn |
| 8 | Becton Dickinson | 50.12 Bn | 47.27 Bn | 2.32 Bn | 26.32 Bn |
| 9 | Edwards Lifesciences | 50.06 Bn | 34.17 Bn | 1.35 Bn | 3.14 Bn |
| 10 | Codexis | 124.29 Mn | -132.76 Mn | 11.40 Mn | 87.10 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 87.10 Mn |
| Mar 31, 2026 | 86.14 Mn |
| Dec 31, 2025 | 97.27 Mn |
| Sep 30, 2025 | 85.42 Mn |
| Jun 30, 2025 | 82.62 Mn |
| Mar 31, 2025 | 79.26 Mn |
| Dec 31, 2024 | 82.08 Mn |
| Sep 30, 2024 | 74.78 Mn |
| Jun 30, 2024 | 70.56 Mn |
| Mar 31, 2024 | 70.29 Mn |
| Dec 31, 2023 | 49.95 Mn |
| Sep 30, 2023 | 62.95 Mn |
| Jun 30, 2023 | 82.63 Mn |
| Mar 31, 2023 | 92.05 Mn |
| Dec 31, 2022 | 105.60 Mn |
| Sep 30, 2022 | 81.16 Mn |
| Jun 30, 2022 | 75.51 Mn |
| Mar 31, 2022 | 76.64 Mn |
| Dec 31, 2021 | 81.99 Mn |
| Sep 30, 2021 | 58.50 Mn |
API Access
Codexis 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=CDXS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "CDXS", "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=CDXS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();