High-Quality Financial News Dataset

Description

This repository contains a meticulously scraped dataset from various financial websites. The data extraction process ensures high-quality and accurate text, including content from both the websites and their embedded PDFs.

Dataset Features

• Date: The date of the announcement.
• Subject: The subject of the financial news.
• Content: The full content of the announcement, including text from the website and PDFs.

Additional Processed Fields

We applied the advanced Mixtral 7X8 model to generate the following additional fields:

• ParaphrasedSubject: A paraphrased version of the original subject.
• CompactedSummary: A concise summary limited to 1.5 lines.
• DetailedSummary: A detailed summary of the content.
• Impact: The impact of the announcement, summarized in 2 lines.

Methodology

The prompt used to generate the additional fields was highly effective, thanks to extensive discussions and collaboration with the Mistral AI team. This ensures that the dataset provides valuable insights and is ready for further analysis and model training.

Usage

This dataset can be used for various applications, including but not limited to:

• Financial news analysis
• Abstractive/Exctractive Summarization tasks
• Machine learning model training
• Natural language processing tasks

Dataset Description 📊🔍

The Sujet-Finance-QA-Vision-100k is a comprehensive dataset containing over 100,000 question-answer pairs derived from more than 9,800 financial document images. This dataset is designed to support research and development in the field of financial document analysis and visual question answering.

  Key Features:

🖼️ 9,801 unique financial document images ❓ 107,050 question-answer pairs 🇬🇧 English language 📄 Diverse financial document types… See the full description on the dataset page: https://huggingface.co/datasets/sujet-ai/Sujet-Finance-QA-Vision-100k.

These three datasets provide closing price information for the following assets: Google, Apple, Microsoft, Netflix, Amazon, Pfizer, Astra Zeneca, Johnson & Johnson, ETH, BTC and LTC.The time period spans from 2012 to the end of 2020.

This dataset, titled "Financial-QA-10k", contains 10,000 question-answer pairs derived from company financial reports, specifically the 10-K filings. The questions are designed to cover a wide range of topics relevant to financial analysis, company operations, and strategic insights, making it a valuable resource for researchers, data scientists, and finance professionals. Each entry includes the question, the corresponding answer, the context from which the answer is derived, the company's stock ticker, and the specific filing year. The dataset aims to facilitate the development and evaluation of natural language processing models in the financial domain.

About the Dataset Dataset Structure:

• Rows: 7000
• Columns: 5
• question: The financial or operational question asked.
• answer: The specific answer to the question.
• context: The textual context extracted from the 10-K filing, providing additional information.
• ticker: The stock ticker symbol of the company.
• filing: The year of the 10-K filing from which the question and answer are derived.

Sample Data:

Question: What area did NVIDIA initially focus on before expanding into other markets? Answer: NVIDIA initially focused on PC graphics. Context: Since our original focus on PC graphics, we have expanded into various markets. Ticker: NVDA Filing: 2023_10K

Potential Uses:

Natural Language Processing (NLP): Develop and test NLP models for question answering, context understanding, and information retrieval. Financial Analysis: Extract and analyze specific financial and operational insights from large volumes of textual data. Educational Purposes: Serve as a training and testing resource for students and researchers in finance and data science.

Francis Financial is a reputable financial services company that provides a range of products and services to its clients. The company's data holdings are vast and varied, encompassing financial market data, economic trends, and industry insights. With a strong focus on serving its clients' needs, Francis Financial's data repository is a treasure trove of valuable information for anyone looking to gain a deeper understanding of the financial world.

From company reports and financial statements to market analysis and industry news, Francis Financial's data collection is a comprehensive archive of important financial information. By leveraging this data, users can gain valuable insights into market trends, spot emerging patterns, and make informed decisions. With its extensive data holdings and commitment to providing high-quality information, Francis Financial is an important player in the financial data landscape.

The data sets provide the text and detailed numeric information in all financial statements and their notes extracted from exhibits to corporate financial reports filed with the Commission using eXtensible Business Reporting Language (XBRL).

Dataset

This dataset was created by DanishJavedCodes

Contents

Ciclo Italian Investment Co., a trusted financial services provider, offers unique market insights and research to its clients. With a focus on Italy, the company provides in-depth analysis of the country's economic trends, making it an valuable resource for investors and business professionals.

Through their platform, Ciclo Italian Investment Co. provides access to a wide range of financial data, including market reports, economic indicators, and company profiles. By understanding the Italian market, businesses can make informed decisions and capitalize on new opportunities.

The first table of the G.20 shows seasonally adjusted data for the flows and levels of finance company receivables outstanding. These data include simple annual percent changes of total, consumer, real estate, and business receivables. The percent change in a given period is calculated as the flow of receivables in the current period divided by the level in the previous period. Percent changes and levels are calculated from unrounded data. The second and third pages of the G.20 show data that are not seasonally adjusted. The second page contains levels of outstanding receivables by receivable type, while the third page contains flow of receivables by type.

All financial transactions made by the Intellectual Property Office as part of the Government’s commitment to transparency in expenditure

This (financial and personal) data is required to be kept as part of the auditing process of the co-ordinating country. It is required to be retained for several years after the ESSnet is completed.

Finance-Instruct-500k Dataset

  Overview

Finance-Instruct-500k is a comprehensive and meticulously curated dataset designed to train advanced language models for financial tasks, reasoning, and multi-turn conversations. Combining data from numerous high-quality financial datasets, this corpus provides over 500,000 entries, offering unparalleled depth and versatility for finance-related instruction tuning and fine-tuning. The dataset includes content tailored for financial… See the full description on the dataset page: https://huggingface.co/datasets/oieieio/Finance-Instruct-500k.

Image generated by DALL-E. See prompt for more details

  💼 📊 Synthetic Financial Domain Documents with PII Labels

gretelai/synthetic_pii_finance_multilingual is a dataset of full length synthetic financial documents containing Personally Identifiable Information (PII), generated using Gretel Navigator and released under Apache 2.0. This dataset is designed to assist with the following use cases:

🏷️ Training NER (Named Entity Recognition) models to detect and label PII in… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic_pii_finance_multilingual.

Financial Times Interactive Data LLC offers a vast repository of economic and financial data, providing valuable insights into global markets and trading. With a focus on delivering timely and accurate information, the company has established itself as a go-to source for financial institutions, investors, and researchers seeking to stay ahead of the curve.

our vast database is comprised of historic financial statements, economic indicators, and proprietary data from leading sources, including government agencies, regulatory bodies, and industry associations. By providing access to this trove of information, Financial Times Interactive Data LLC enables its clients to make informed decisions, identify trends, and uncover new opportunities in the rapidly evolving world of finance.

Personal Finance Tools Market is Segmented by Type( Web-Based, Mobile-Based Software ), by End-User Industry (Small Businesses Users, Individual Consumers), and Geography.

Our data sheds light on the distribution of Finance stores across different online platforms. WooCommerce leads with a substantial number of stores, holding 25.47K stores, which accounts for 49.97% of the total in this category. Custom Cart follows with 7.73K stores, making up 15.18% of the Finance market. Meanwhile, Shopify offers a significant presence as well, with 6.03K stores, or 11.84% of the total. This chart gives a clear picture of how stores within the Finance sector are spread across these key platforms.

Neonomics is a company that specializes in open banking solutions, providing a new generation of payments and financial data solutions that prioritize people. With a focus on innovation and security, Neonomics aims to revolutionize the way businesses and individuals interact with their financial data. The company's products and services include Checkout, a payment solution that allows customers to pay online, and Platform Services, which offers extensive payment connectivity and account data integration.

By leveraging its expertise in open banking, Neonomics enables businesses to streamline their financial operations, improve customer experiences, and reduce costs. With a strong presence in the Nordics, Neonomics is well-positioned to support the region's rapidly evolving fintech landscape. As a licensed Payment Institution (PI), Payment Initiation Service Provider (PISP), and Account Information Service Provider (AISP), Neonomics ensures that its solutions are secure, compliant, and compliant with regulatory requirements.

Graph and download economic data for Domestic Finance Companies, All Other Assets and Accounts and Notes Receivable, Flow (STFAFOXDFBANA) from Q2 1984 to Q2 2025 about notes, flow, finance companies, accounting, companies, finance, financial, domestic, assets, and USA.

The FR 3033p is the first part of a two-stage survey series, which has been conducted at regular five-year intervals since 1955. It is a census survey designed to identify the universe of finance companies eligible for potential inclusion in the FR 3033s. It gathers limited information including total assets, areas of specialization, and information on the corporate structure of such companies. The second part of these information collections, the FR 3033s, collects balance sheet data on major categories of consumer and business credit receivables and major liabilities, along with income and expenses, and is used to gather information on the scope of a company's operations and loan and lease servicing activities. In addition, additional questions were added to collect lending information related to the COVID-19 impacts.