How can beginners quickly understand listed companies’ financial reports?

The traditional process is typically: Search for the company → Download the PDF → Read manually → Copy key metrics → Organize in Excel → Begin analysis. With DataSinking, I aim to streamline the workflow as follows: Enter stock code and reporting period → Retrieve structured data → Process in bulk using Python → Identify outliers → Begin analysis. The key differences between the two approaches can be summarized simply in the table below: Comparison Item Traditional PDF Approach Structured Data Approach Result Acquisition PDF file Machine-readable data Batch Processing Challenging Feasible via API loops Data Organization Reliant on manual copying Directly integrated into Python workflows Cross-Company Comparison Labor-intensive Well-suited for unified processing Data Reusability Requires re-copying each time Can be saved as Excel, CSV, or database files Anomaly Detection Primarily manual Automatable via rule-based batch checks If you’re only reading a single company’s annual report, PDFs are perfectly sufficient. However, if your goal is to analyze multiple companies, multiple years, and multiple metrics, structured data significantly shortens the path “from document to analysis.” Three: My Real-World Case — 6 Companies, 3 Years, 18 Annual Reports In this project, I first used DataSinking to verify the completeness of annual reports for six companies covering 2023–2025. Rather than jumping straight into extracting financial metrics, I first compiled a report checklist to confirm: ・Correct company name; ・Correct stock code; ・Availability of annual reports for all three years (2023–2025); ・Correct report type (i.e., annual report); ・Absence of duplicate versions; ・No missing years. After confirming the completeness of all 18 reports, I organized them uniformly as follows: New Energy Vehicle Companies’ Annual Reports_2023–2025 ├── BYD │ ├── 2023 │ ├── 2024 │ └── 2025 ├── Seres ├── Changan Automobile ├── SAIC Motor ├── GAC Group └── BAIC BluePark Next, I built a financial metric data dictionary. Recognizing that I am not a finance professional, I avoided overwhelming myself with dozens of complex metrics in Phase One. Instead, I selected eight intuitive yet analytically cohesive metrics: Metric Question It Answers Revenue Is the company’s scale growing? Net Profit Attributable to Parent Company Is the company ultimately profitable? Non-Recurring Net Profit Attributable to Parent Company Is the core business truly profitable? Net Operating Cash Flow Are profits being converted into cash? Total Assets Is the company expanding its asset base? Net Assets Attributable to Parent Company Are shareholders’ equity increasing? R&D Expenditure How much has the company invested in R&D? Number of R&D Personnel How is the R&D team size evolving? Based on these raw metrics, I further calculated: ・Year-on-year revenue growth rate; ・Compound annual growth rate (CAGR) of revenue for 2023–2025; ・Net profit margin attributable to parent company; ・Non-recurring net profit margin; ・Cash-to-profit ratio; ・R&D intensity; ・ROE (Return on Equity) based on end-of-period equity. This ultimately yielded a wide analytical table spanning “6 companies × 3 years.”

How do I learn to read financial reports?

A well-designed template serves as the foundation for knowledge accumulation. A good template ensures your note repository—after one year of consistent use—holds genuine reference value. 5.1 Financial Report Analysis Template Create a new file in Obsidian: Templates/Financial Report Analysis Template.md: --- Company: "{{Company Name}}" Code: "{{Stock Code}}" Fiscal Year: "{{Fiscal Year}}" Quarter: "{{Quarter}}" Report Type: "{{Report Type}}" Analysis Date: "{{Analysis Date}}" Revenue: {{Revenue}} Net Profit: {{Net Profit}} ROE: {{ROE}} tags: - Financial Report - "{{Company Name}}" - "{{Industry}}" --- # {{Company Name}} {{Fiscal Year}}{{Quarter}} Analysis ## Original Files - Location: `Raw Data/{{Company Name}}/{{Fiscal Year}}-{{Quarter}}/` - 10-Q: `{{10Q Filename}}` - Earnings Call Transcript: `{{Transcript Filename}}` - KPI Table: `{{KPI Filename}}` ## Key Metrics at a Glance | Metric | Current Period | Prior Period | YoY Change | |--------|----------------|--------------|------------| | Revenue | {{Revenue}} | {{Prior Period Revenue}} | {{Revenue YoY}} | | Net Profit Attributable to Parent Company | {{Net Profit}} | {{Prior Period Net Profit}} | {{Net Profit YoY}} | | Gross Margin | {{Gross Margin}} | {{Prior Period Gross Margin}} | — | | Net Margin | {{Net Margin}} | {{Prior Period Net Margin}} | — | | Operating Cash Flow | {{Operating Cash Flow}} | {{Prior Period Operating Cash Flow}} | — | | Capital Expenditures (CapEx) | {{CapEx}} | {{Prior Period CapEx}} | — | ## Business Segment Breakdown {{Business Segment Revenue Breakdown}} ## Earnings Quality Analysis > The ratio of net operating cash flow to net profit is the core indicator for assessing earnings quality. A ratio close to 1 indicates high-quality earnings; significant deviations warrant deeper investigation. Current Period Operating Cash Flow / Net Profit = {{Cash Flow to Net Profit Ratio}} {{Earnings Quality Analysis}} ## Management’s Key Statements > Source: Earnings Call Transcript 1. {{Management Point 1}} 2. {{Management Point 2}} 3. {{Management Point 3}} ## Next Quarter Guidance {{Guidance Content}} ## Key Risk Factors 1. {{Risk 1}} 2. {{Risk 2}} 3. {{Risk 3}} ## Key Changes This Period

Ashland: Fiscal Q3 Earnings Snapshot

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Ashland Global Holdings reported a fiscal Q3 net income of $16 million, beating prior losses, while revenue hit $497 million against forecasts. Full-year guidance stands at $1.84–1.87 billion, contributing to a 33% share price increase over the last year.

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Ashland: Fiscal Q3 Earnings Snapshot

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