Gold Fields (GFI) Operating Expenses (2012 - 2017)
Gold Fields' (GFI) quarterly Operating Expenses came in at -$119.0 million in Q4 2017, down 21.68% on a YoY basis from -$151.9 million in Q4 2016, and down 21.68% quarter-over-quarter from -$97.8 million in Q4 2016.
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Analysis
Gold Fields (GFI) Operating Expenses (2012 - 2017) Analysis & Trends
Gold Fields (GFI) has reported Operating Expenses for 6 consecutive years, with -$119.0 million the latest figure, recorded in Q4 2017.
- For the quarter ending Q4 2017, Operating Expenses fell 21.68% year-over-year to -$119.0 million; the trailing twelve-month figure through Dec 2017 stood at -$364.3 million (up 8.38% YoY), and the FY2025 full-year result was -$308.1 million, down 193.43% from the prior year.
- Operating Expenses slipped to -$119.0 million in Q4 2017 per GFI's latest filing, from -$97.8 million in the prior quarter.
- Across five years, Operating Expenses topped out at -$61.1 million in Q4 2015 and bottomed at -$152.3 million in Q4 2013.
- Historically, Operating Expenses has averaged -$103.3 million across 5 years, with a median of -$97.8 million in 2016.
- The sharpest annual moves came in 2014 and 2016: Operating Expenses surged 43.27% in 2014, then plunged 60.07% in 2016.
- Over 5 years, Operating Expenses stood at -$152.3 million in 2013, then soared by 43.27% to -$86.4 million in 2014, then grew by 29.28% to -$61.1 million in 2015, then slumped by 60.07% to -$97.8 million in 2016, then fell by 21.68% to -$119.0 million in 2017.
- According to Business Quant data, Operating Expenses over the past three periods registered -$119.0 million, -$97.8 million, and -$61.1 million for Q4 2017, Q4 2016, and Q4 2015 respectively.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Asml Holding | 647.47 Bn | 631.49 Bn | 5.90 Bn | - |
| 2 | General Electric | 325.83 Bn | 316.71 Bn | 6.95 Bn | 10.86 Bn |
| 3 | Astrazeneca | 257.75 Bn | 252.54 Bn | 12.86 Bn | -9.70 Bn |
| 4 | Citigroup | 221.04 Bn | -2,078.84 Bn | 24.75 Bn | 14.22 Bn |
| 5 | Bhp | 219.16 Bn | 200.62 Bn | - | - |
| 6 | Diageo | 208.21 Bn | 208.26 Bn | - | - |
| 7 | Rio Tinto | 188.09 Bn | 183.71 Bn | - | - |
| 8 | Ferrari | 151.21 Bn | 149.46 Bn | 1.18 Bn | 471.42 Mn |
| 9 | Unilever | 134.81 Bn | 130.23 Bn | - | - |
| 10 | Gold Fields | 38.03 Bn | 36.49 Bn | - | - |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Dec 31, 2017 | -119.00 Mn |
| Dec 31, 2016 | -97.80 Mn |
| Dec 31, 2015 | -61.10 Mn |
| Dec 31, 2014 | -86.40 Mn |
| Dec 31, 2013 | -152.30 Mn |
| Dec 31, 2012 | -179.30 Mn |
API Access
Gold Fields Operating 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=operating-expenses&ticker=GFI&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "GFI", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=GFI&period=max&api_key=YOUR_API_KEY");
const data = await res.json();