Dataset Overview
| Dataset Name | Enterprise Financial Operations Dataset |
| Total Data Volume | 185,000 (covering 3,200 enterprises ' multi-year financial and operational indicators) |
| EnterpriseType | Listed companies/ Cityboarding / Large state enterprises/ Specialty New Enterprises |
| Industry Coverage | Construction/ real estate/ city investment/ manufacturing/ technology/ energy/ finance |
| Financial indicators | Statement of assets, liabilities, reserves and fund balances |
| Time Span | 2015 - 2025 data per quarter |
| Data Source | Annual reports of listed companies, rating reports of debtor enterprises, public data from the Urban Launch Platform, the Economic Survey of the Statistical Office |
Detailed Description of Data Dimensions
| Field Name | Data Type | Value Range / Description |
|---|---|---|
| enterprise_id | String(16) | De-sensitive enterprise ID |
| industry | Enum | CONSTRUCTION | REAL_ESTATE | LGFV | MFG | TECH | ENERGY | FINANCE |
| enterprise_type | Enum | LISTED | LGFV | SOE | SME | UNICORN |
| fiscal_year | Integer | Financial year 2015-2025 |
| period | Enum | Q1 | Q2 | Q3 | Q4 | FY |
| total_assets | Float | Total assets (thousands of dollars) |
| total_revenue | Float | Business income (thousands of dollars) |
| net_profit | Float | Net profit (millions of dollars) |
| debt_ratio | Float | Asset and liability ratio 0.0 - 1.0 |
| roe | Float | Net assets return (%) |
| operating_cashflow | Float | Cash flow from operating activities (millions of dollars) |
| employee_count | Integer | Number of employees |
| credit_rating | Enum | AAA | AA+ | AA | AA- | A+ | A | BBB | BB | DEFAULT |
| financial_ratios | Object | {Flow ratio, Speed ratio, interest guarantee multiplier, accounts receivable turnover rate, ...} |
De-identified JSON Data Sample
[
{
"enterprise_id": "ENT-LGFV-0231",
"industry": "LGFV",
"enterprise_type": "LGFV",
"fiscal_year": 2024,
"period": "FY",
"total_assets": 2850000.0,
"total_revenue": 125000.0,
"net_profit": 18200.0,
"debt_ratio": 0.62,
"roe": 2.8,
"operating_cashflow": -8500.0,
"employee_count": 1200,
"credit_rating": "AA+",
"financial_ratios": {"current_ratio": 2.1, "quick_ratio": 1.3, "interest_coverage": 3.5, "receivable_turnover": 4.2}
},
{
"enterprise_id": "ENT-LISTED-0042",
"industry": "CONSTRUCTION",
"enterprise_type": "LISTED",
"fiscal_year": 2024,
"period": "FY",
"total_assets": 12500000.0,
"total_revenue": 8500000.0,
"net_profit": 320000.0,
"debt_ratio": 0.74,
"roe": 12.5,
"operating_cashflow": 180000.0,
"employee_count": 8500,
"credit_rating": "AAA",
"financial_ratios": {"current_ratio": 1.35, "quick_ratio": 0.92, "interest_coverage": 8.2, "receivable_turnover": 6.8}
},
{
"enterprise_id": "ENT-SME-1567",
"industry": "TECH",
"enterprise_type": "UNICORN",
"fiscal_year": 2024,
"period": "FY",
"total_assets": 85000.0,
"total_revenue": 42000.0,
"net_profit": -5200.0,
"debt_ratio": 0.35,
"roe": -8.2,
"operating_cashflow": 3200.0,
"employee_count": 280,
"credit_rating": "BB",
"financial_ratios": {"current_ratio": 3.5, "quick_ratio": 3.1, "interest_coverage": null, "receivable_turnover": 8.5}
},
{
"enterprise_id": "ENT-SOE-0089",
"industry": "ENERGY",
"enterprise_type": "SOE",
"fiscal_year": 2024,
"period": "FY",
"total_assets": 6800000.0,
"total_revenue": 2200000.0,
"net_profit": 185000.0,
"debt_ratio": 0.58,
"roe": 10.8,
"operating_cashflow": 420000.0,
"employee_count": 4200,
"credit_rating": "AAA",
"financial_ratios": {"current_ratio": 1.8, "quick_ratio": 1.5, "interest_coverage": 12.5, "receivable_turnover": 5.2}
},
{
"enterprise_id": "ENT-LGFV-0892",
"industry": "LGFV",
"enterprise_type": "LGFV",
"fiscal_year": 2024,
"period": "FY",
"total_assets": 520000.0,
"total_revenue": 28000.0,
"net_profit": 3500.0,
"debt_ratio": 0.68,
"roe": 2.1,
"operating_cashflow": -12000.0,
"employee_count": 350,
"credit_rating": "AA",
"financial_ratios": {"current_ratio": 2.8, "quick_ratio": 1.9, "interest_coverage": 2.1, "receivable_turnover": 3.1}
}
]
AI Training Use Cases
Enterprise credit rating
Applying model: XGBoost / LightGBM + Financial Characteristics Project. Training in the credit rating classification model of enterprises based on multi-dimensional financial indicators.
Early warning of financial risk
Applying model: LSTM / Transformer + Time-Sequence Abnormality. Training in financial distress early warning models for enterprises based on multiple financial data.
Risk assessment of the urban drop platform
Applying model: Gradient Boosting + SHAP. Specialized training on the platform debt risk and solvency assessment model.
Auto-generated financial reporting
Applying model: LLM(GPT-4o)+NL2SQL. Automatically generate management discussion and analysis (MD&A) text based on structured financial data.
Get This Dataset
The enterprise financial datasets customize the authorization by industry and type of enterprise.
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