The "yahoo_finance_dataset(2018-2023)" dataset is a financial dataset containing daily stock market data for multiple assets such as equities, ETFs, and indexes. It spans from April 1, 2018 to March 31, 2023, and contains 1257 rows and 7 columns. The data was sourced from Yahoo Finance, and the purpose of the dataset is to provide researchers, analysts, and investors with a comprehensive dataset that they can use to analyze stock market trends, identify patterns, and develop investment strategies. The dataset can be used for various tasks, including stock price prediction, trend analysis, portfolio optimization, and risk management. The dataset is provided in XLSX format, which makes it easy to import into various data analysis tools, including Python, R, and Excel.

The dataset includes the following columns:

Date: The date on which the stock market data was recorded. Open: The opening price of the asset on the given date. High: The highest price of the asset on the given date. Low: The lowest price of the asset on the given date. Close*: The closing price of the asset on the given date. Note that this price does not take into account any after-hours trading that may have occurred after the market officially closed. Adj Close**: The adjusted closing price of the asset on the given date. This price takes into account any dividends, stock splits, or other corporate actions that may have occurred, which can affect the stock price. Volume: The total number of shares of the asset that were traded on the given date.

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

TallyFormer Finance Datasets

This repository contains the cleaned and curated datasets used to train TallyFormer-Finance-51M, a compact decoder-only transformer specialized in financial language understanding. All datasets are provided in Apache Parquet format, optimized for high-throughput training and deterministic sampling.

  📊 Dataset Overview

The data is organized by training stage:

  1️⃣ Pretraining Data

Used for continual pretraining and general language… See the full description on the dataset page: https://huggingface.co/datasets/haidar-ali/tallyformer-finance-dataset.

This dataset presents standardized climate finance data for IDB operations approved in 2024. It includes project-level climate finance shares tagged for mitigation, adaptation, and dual objectives, with breakdowns by instrument type and sector. The database supports transparency, comparability across years and divisions, and aligns with MDB climate finance tracking methodologies. Under the IDB Group Impact Framework 2024–2030, the IDB committed to a climate finance target of 45% of total approved volume. In 2024, the IDB met this target, approving US $5.6 billion in climate finance, representing 45% of total approvals. Climate finance refers to financial resources committed to development projects and components that enable activities mitigating or adapting to climate change in developing and emerging economies. This dataset pertains specifically to the IDB. Climate finance for the entire IDB Group—which includes the IDB, IDB Invest (formerly IIC), and IDB Lab—totaled US $8.2 billion in 2024.

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.

https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSO20g5cBn_b3UvD4HrPSKMrujGXq8LfT2NQP3LC3F3k8ufSV6TP97l7Har-625Bju08bc&usqp=CAU" alt="File:Yahoo Finance Logo 2013.svg - Wikipedia">

Yahoo! Finance is a media property that is part of the Yahoo! network. It provides financial news, data and commentary including stock quotes, press releases, financial reports, and original content. It also offers some online tools for personal finance management. In addition to posting partner content from other web sites, it posts original stories by its team of staff journalists. It is ranked 20th by Similar Web on the list of largest news and media websites.

Description: This dataset contains financial information for companies listed on major stock exchanges around the world, as provided by Yahoo Finance. The data covers a range of industries and includes key financial metrics such as price, volume, market capitalization, P/E ratio, and more.

### python 1.Content: 2.Symbol: 3.Name: 4.Price: 5.Volume: 6.Market cap: 7.P/E ratio:

The data is sourced from Yahoo Finance and is updated daily, providing users with the most up-to-date financial information for each company listed.

The dataset is suitable for anyone interested in analyzing or predicting stock market trends and is particularly useful for financial analysts, investors, and traders.

Paper |Homepage |Github

  🛠️ Usage

Regarding the data, first of all, you should download the MMfin.tsv and MMfin_CN.tsv files, as well as the relevant financial images. The folder structure is shown as follows: ├─ datasets ├─ images ├─ MMfin ... ├─ MMfin_CN ... │ MMfin.tsv │ MMfin_CN.tsv

The following is the process of inference and evaluation (Qwen2-VL-2B-Instruct as an example): export LMUData="The path of the datasets" python… See the full description on the dataset page: https://huggingface.co/datasets/hithink-ai/MME-Finance.

VOSA Financial system incorporating General Ledger, Accounts Payable and Accounts Receivable, Cash Management

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).

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.

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.

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 Companies House as part of the Government’s commitment to transparency in expenditure

The Comprehensive Annual Financial Reports are presented in three main sections; the Introductory Section, the Financial Section, and the Statistical Section. The Introductory Section includes a financial overview, discussion of Iowa's economy and an organizational chart for State government. The Financial Section includes the state auditor's report, management's discussion and analysis, audited basic financial statements and notes thereto, and the underlying combining and individual fund financial statements and supporting schedules. The Statistical Section sets forth selected unaudited economic, financial trend and demographic information for the state on a multi-year basis. Reports for multiple fiscal years are available.

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.

The Personal Finance Tools Market Report is Segmented by Type (Web-Based, and Mobile-Based Software), Deployment Model (Cloud-Based, and On-Premise), End User (Small Business Users, and Individual Consumers), Application (Budgeting and Expense Tracking, Tax Filing and Compliance, and More), Revenue Model (Subscription, Freemium, Transaction Fee), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

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.

Delving into the Finance sector, our data presents a revealing look at store distribution by region, highlighting regional preferences and market penetration in this niche. United States leads with 12.46K stores, which is 38.92% of the total. United Kingdom follows, contributing 2.85K stores, which is 8.90% of the total. Unknown comes third, with 2.68K stores, making up 8.36% of the total.