---
license: cc-by-4.0
task_categories:
- table-question-answering
- document-question-answering
- feature-extraction
- text-generation
- translation
- summarization
language:
- id
- en
tags:
- finance
- indonesia
- idx
- stock-market
- financial-statements
- xbrl
- ocr
- multimodal-rag
- accounting
- corporate-reporting
---

# IDX_Financial_Statements: The Multimodal Indonesian Financial Dataset

**IDX_Financial_Statements** is a centralized repository providing the most complete set of financial disclosures for public companies listed on the **Indonesia Stock Exchange (IDX)**. This dataset is designed for advanced financial research, spanning from raw document archival to structured data extraction.

## Dataset Overview

This is a multimodal dataset that captures the full lifecycle of financial reporting. It includes:

1.  **PDF Reports**: Original Annual Reports (Laporan Tahunan) and Financial Statements as submitted by issuers. Ideal for Document AI, layout analysis, and OCR tasks.
2.  **Excel Sheets**: Semi-structured spreadsheets containing balance sheets, income statements, and cash flow details.
3.  **XBRL Instances**: High-precision, machine-readable XML-based data using the IDX Taxonomy. This is the gold standard for quantitative data integrity.
4.  **Metadata**: Associated tickers, industry sectors, reporting periods (Q1, Q2, Q3, FY), and submission timestamps.

## Key Features

- **Format Diversity**: Supports a wide range of technical workflows, from computer vision (PDF parsing) to direct database ingestion (XBRL).
- **Historical Depth**: Comprehensive coverage of the Indonesian capital market over multiple fiscal years.
- **Bilingual Context**: Documents often contain side-by-side Indonesian and English text, making this a valuable resource for financial-domain NMT (Neural Machine Translation).
- **Audit-Ready**: Includes original source documents, allowing for the verification of extracted data against official corporate filings.

## Use Cases

- **Multimodal RAG**: Build AI agents that can "read" a 200-page PDF annual report and cross-reference it with numerical Excel data.
- **XBRL Analysis**: Utilize the standardized taxonomy for high-speed fundamental screening without the need for traditional scraping.
- **Document AI Training**: Fine-tune models for table detection, key-value pair extraction, and financial entity recognition.
- **Market Intelligence**: Analyze corporate governance statements and management discussion & analysis (MD&A) sections for sentiment and strategic shifts.

## Data Structure

The repository is organized by `Ticker` and `Period`. A typical directory structure looks like:
- `[TICKER]/[YEAR]/[PERIOD]/`
    - `FinancialStatement-202X-I-Ticker.pdf`
    - `FinancialStatement-202X-I-Ticker.xlsx`
    - `FinancialStatement-202X-I-Ticker.zip` (XBRL)

## License

This dataset is licensed under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). You are free to share and adapt the material for any purpose, including commercial use, provided you give appropriate credit.

## Disclaimer

This dataset aggregates publicly available information for research and analytical purposes. Users are encouraged to cross-reference data with the [IDX official disclosures](https://www.idx.co.id) for formal investment or legal decisions.

---