Hope Bancorp (HOPE) Provision for Losses (2009 - 2026)
Hope Bancorp's Provision for Losses was $3.6 million in Q1 2024, up 111.8% from $1.7 million a year earlier and up 111.8% from the prior quarter.
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Analysis
Hope Bancorp (HOPE) Provision for Losses (2009 - 2026) Analysis & Trends
On a trailing twelve-month basis, Hope Bancorp's Provision for Losses was $31 million through Mar 31, 2024, up 39.0% year-over-year; for FY2023, it came in at $29.1 million, up 203.1% from FY2022.
- Provision for Losses shows a five-year compound annual growth rate of 14.3% (FY2018 to FY2023).
- In earlier years, Provision for Losses was $9.6 million in FY2022, -$12.2 million in FY2021, $95 million in FY2020 and $7.3 million in FY2019 (-51.0%).
- Quarterly Provision for Losses has moved between -$11 million (Q1 2022) and $28 million (Q1 2020) over five years.
- Compared with a year earlier, Provision for Losses was higher in four of the last five quarters, with growth averaging 148.0%.
- The best year-over-year quarter for Provision for Losses over five years was Q3 2020 (growth of 947.6%); the worst was Q4 2021 (a decline of 94.5%).
- Per Business Quant data, HOPE's Provision for Losses in the three quarters before Q1 2024 was $1.7 million (Q4 2023), $16.8 million (Q3 2023) and $8.9 million (Q2 2023).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Provision for Losses (Qtr) |
|---|---|---|---|---|---|
| 1 | Jpmorgan Chase | 909.02 Bn | 886.77 Bn | - | 2.74 Bn |
| 2 | Banco Santander Chile | 406.67 Bn | -7,110.43 Bn | - | - |
| 3 | Bank Of America | 350.91 Bn | -2,025.20 Bn | - | 1.30 Bn |
| 4 | Hsbc Holdings | 308.71 Bn | -2,966.46 Bn | - | - |
| 5 | Morgan Stanley | 293.66 Bn | -249.15 Bn | - | - |
| 6 | Royal Bank Of Canada | 283.56 Bn | -546.44 Bn | - | 717.20 Mn |
| 7 | Goldman Sachs | 262.71 Bn | -3,480.74 Bn | - | 368.00 Mn |
| 8 | Wells Fargo & Company | 257.44 Bn | 92.87 Bn | - | 1.03 Bn |
| 9 | Mitsubishi Ufj Financial | 256.82 Bn | -75.42 Bn | 10.89 Bn | - |
| 10 | Hope Bancorp | 1.69 Bn | -772.17 Mn | - | 8.56 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | 8.56 Mn |
| Jun 30, 2026 | 6.77 Mn |
| Mar 31, 2026 | 8.65 Mn |
| Dec 31, 2025 | 7.20 Mn |
| Sep 30, 2025 | 8.71 Mn |
| Jun 30, 2025 | 11.09 Mn |
| Mar 31, 2025 | 4.80 Mn |
| Dec 31, 2024 | 10.00 Mn |
| Sep 30, 2024 | 3.28 Mn |
| Jun 30, 2024 | 400,000.00 |
| Mar 31, 2024 | 3.60 Mn |
| Dec 31, 2023 | 1.70 Mn |
| Sep 30, 2023 | 16.80 Mn |
| Jun 30, 2023 | 8.90 Mn |
| Mar 31, 2023 | 1.70 Mn |
| Dec 31, 2022 | 8.20 Mn |
| Sep 30, 2022 | 9.20 Mn |
| Jun 30, 2022 | 3.20 Mn |
| Mar 31, 2022 | -11.00 Mn |
| Dec 31, 2021 | 1.50 Mn |
API Access
Hope Bancorp Provision for Losses 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=provision-for-losses&ticker=HOPE&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "provision-for-losses", "ticker": "HOPE", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=provision-for-losses&ticker=HOPE&period=max&api_key=YOUR_API_KEY");
const data = await res.json();