theaayushbajaj/10-X-raw-v1

SEC 10-X Filings Dataset

This dataset contains processed SEC 10-X (10-K, 10-Q) filings, focusing on Risk Factors and Management Discussion & Analysis (MD&A) sections from corporate financial reports from 1993-2023.

🔗 Original Dataset: [[SEC-EDGAR-10X]](https://sraf.nd.edu/sec-edgar-data/cleaned-10x-files/) contains stripped down versions of the original filings, details about which can be found here.

This dataset is a further cleaned tabulated version of the original stripped down version making it more suitable for training tasks.

Note: Documents with no match to risk factors or MD&A have been filtered out.

Dataset Description

Overview

This dataset is derived from SEC 10-X filings and provides structured access to two critical sections of corporate financial reports:

• —Risk Factors (Item 1A)
• —Management's Discussion and Analysis (Item 7)

The data has been processed to enable natural language processing and financial analysis tasks.

Processing Methodology

The dataset was created using a parsing pipeline that addresses several key considerations:

1. 1.Risk Factors Extraction
2. 2.Multiple instances of risk factors may appear in a single filing
3. 3.The parser identifies all instances and selects the most comprehensive section
4. 4.When multiple valid sections are found, the longest section is retained to ensure completeness

1. 1.MD&A Extraction
2. 2.Management Discussion & Analysis sections are identified and extracted
3. 3.Multiple MD&A sections within a filing are concatenated to preserve all relevant information

1. 1.Data Organization
2. 2.Filings are grouped by CIK (Central Index Key) and filing date
3. 3.Multiple sections from the same filing are combined
4. 4.Empty or invalid entries are filtered out

Data Format

The dataset is stored in Parquet format with the following schema:

{
'CSI': 'string', # Central Index Key (CIK)
'FILE_DATE': 'string', # Filing date in YYYYMMDD format
'RISK_FACTOR': 'string', # Extracted Risk Factors section
'MD&A': 'string' # Extracted Management Discussion & Analysis section
}

Usage

Loading the Dataset

import datasets
dataset = datasets.load_dataset("theaayushbajaj/10-X-raw-v1", split="train")

Example Applications

• —Risk analysis and classification
• —Temporal analysis of corporate risk factors
• —Business strategy analysis through MD&A
• —Corporate disclosure analysis
• —Financial sentiment analysis

Acknowledgments

• —Securities and Exchange Commission (SEC) for providing access to the original filings
• —University of Notre Dame for compiled versions