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

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.

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.

The Financial Risk Assessment Dataset provides detailed information on individual financial profiles. It includes demographic, financial, and behavioral data to assess financial risk. The dataset features various columns such as income, credit score, and risk rating, with intentional imbalances and missing values to simulate real-world scenarios.

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

This dataset provides comprehensive access to financial market data from Google Finance in real-time. Get detailed information on stocks, market quotes, trends, ETFs, international exchanges, forex, crypto, and related news. Perfect for financial applications, trading platforms, and market analysis tools. The dataset is delivered in a JSON format via REST API.

dataset contains detailed financial and demographic data for 20,000 individuals, focusing on income, expenses, and potential savings across various categories. The data aims to provide insights into personal financial management and spending patterns.

• Income & Demographics:
  • Income: Monthly income in currency units.
  • Age: Age of the individual.
  • Dependents: Number of dependents supported by the individual.
  • Occupation: Type of employment or job role.
  • City_Tier: A categorical variable representing the living area tier (e.g., Tier 1, Tier 2).
• Monthly Expenses:
  • Categories like Rent, Loan_Repayment, Insurance, Groceries, Transport, Eating_Out, Entertainment, Utilities, Healthcare, Education, and Miscellaneous record various monthly expenses.
• Financial Goals & Savings:
  • Desired_Savings_Percentage and Desired_Savings: Targets for monthly savings.
  • Disposable_Income: Income remaining after all expenses are accounted for.
• Potential Savings:
  • Includes estimates of potential savings across different spending areas such as Groceries, Transport, Eating_Out, Entertainment, Utilities, Healthcare, Education, and Miscellaneous.

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

A comprehensive list of the columns in your dataset, along with descriptions for each:

This chart provides a comprehensive view of the total app spend in the Finance sector, segmented by region. It illustrates how much stores in different regions are investing in apps and software to enhance their business operations and customer services. In United States, the total app spend is significant, with stores investing $2.35M, which accounts for 64.43% of the overall app expenditure in the Finance. United Kingdom follows, with a total spend of $235.58K, making up 6.46% of the category's total. Canada also shows considerable investment in technology, with a spend of $138.72K, representing 3.81% of the total. These figures not only reveal the financial commitment of stores in each region towards technology but also indicate regional trends and priorities in the Finance market.

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.

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 data set contains a summary of information about candidate campaigns and political committees by election year. For candidate campaigns and single-year/election committees, a single record is provided that covers all activity of the campaign for the given election year. Information for continuing political committees is summarized by calendar/reporting year. The data set covers that prior 16 years plus the current election year. The data are compiled from the campaign reports deposit (C3), campaign summary reports (C4), campaign registrations (C1/C1pc) and candidate declarations and elections data provided to the PDC by the Washington Secretary of State. Records are updated in near real-time, typically less than 2 minutes from the time the campaign submits new data. This dataset is a best-effort by the PDC to provide a complete set of records as described herewith. The PDC provides access to the original reports for the purpose of record verification. Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information regarding political finance disclosure requirements. CONDITION OF RELEASE: This publication and or referenced documents constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

Dataset Description

The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment.

The dataset holds 11,932 documents annotated with 3 labels:

sentiments = { "LABEL_0": "Bearish", "LABEL_1": "Bullish", "LABEL_2": "Neutral" }

The data was collected using the Twitter API. The current dataset supports the multi-class classification… See the full description on the dataset page: https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment.

Graph and download economic data for Finance Companies; FHLB Advances; Liability, Level (BOGZ1FL613169333Q) from Q4 1945 to Q3 2025 about FHLB, advances, finance companies, companies, finance, liabilities, financial, and USA.

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 contains a list of all campaign finance reports (C3, C4, C5, C6, and LMC) for the last 10 years including attached schedules. It includes reports that have been superseded by an amendment. The primary purpose of this dataset is for data consumers to track report amendments and to examine the reporting history for a filer. Refer to other datasets to get actual values for any of the reports referenced herewith. For candidates, the number of years is determined by the year of the election, not necessarily the year the report was filed. For political committees, the number of years is determined by the calendar year of the reporting period. This dataset is a best-effort by the PDC to provide a complete set of records as described herewith and may contain incomplete or incorrect information. The PDC provides access to the original reports for the purpose of record verification. Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information regarding political finance disclosure requirements. CONDITION OF RELEASE: This publication and or referenced documents constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

snapshot-tab-pane Structured Finance Market Size 2025-2029The structured finance market size is valued to increase by USD 1128.5 billion, at a CAGR of 11.9% from 2024 to 2029. Increasing demand for alternative investment products will drive the structured finance market.Major Market Trends & InsightsAPAC dominated the market and accounted for a 42% growth during the forecast period.By End-user - Large enterprises segment was valued at USD 771.40 billion in 2023By Type - CDO segment accounted for the largest market revenue share in 2023Market Size & ForecastMarket Opportunities: USD 163.86 billionMarket Future Opportunities: USD 1128.50 billionCAGR from 2024 to 2029 : 11.9%Market SummaryThe market is witnessing significant growth due to the increasing demand for alternative investment products. This trend is driven by investors' quest for yield and risk diversification, particularly in an era of low-interest rates. One notable development in this space is the increasing popularity of Environmental, Social, and Governance (ESG) linked structured finance products. These instruments offer investors the opportunity to align their investments with their values while also potentially achieving attractive returns. Another factor fueling market growth is the increasing complexity of structured finance products. As financial institutions seek to innovate and differentiate themselves, they are developing increasingly sophisticated structures to meet the evolving needs of their clients.For instance, a leading global manufacturing company recently optimized its supply chain financing by implementing a structured finance solution. This enabled the company to improve its working capital position and enhance operational efficiency, resulting in a significant reduction in days sales outstanding (DSO) by 15%. Despite these opportunities, the market faces challenges, including regulatory compliance and counterparty risk. As financial regulations continue to evolve, institutions must ensure that their structured products comply with the latest rules and regulations. Additionally, managing counterparty risk remains a critical concern, particularly in the wake of the 2008 financial crisis. To mitigate these risks, institutions are increasingly leveraging technology and Data Analytics to assess and monitor counterparty risk in real-time.In conclusion, the market is experiencing robust growth, driven by increasing demand for alternative investment products and the development of innovative structures. While challenges persist, institutions that can effectively navigate the complex regulatory landscape and manage counterparty risk will be well-positioned to capitalize on the opportunities in this dynamic market.What will be the Size of the Structured Finance Market during the forecast period?Get Key Insights on Market Forecast (PDF) Request Free SampleHow is the Structured Finance Market Segmented ?The structured finance industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD billion" for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.End-user Large enterprisesSMEsType CDOAsset-backed securitiesMortgage-backed securitiesProduct LoansBondsMortgagesCredit card and trade receivablesOthersApplication Type Real EstateAutomotiveConsumer CreditInfrastructureGeography North America USCanadaEurope FranceGermanyUKAPAC AustraliaChinaIndiaJapanSouth KoreaRest of World (ROW) By End-user InsightsThe large enterprises segment is estimated to witness significant growth during the forecast period.In the dynamic world of structured finance, major enterprises play a pivotal role, engaging in intricate financing agreements to manage their capital and mitigate risk. Structured finance transactions involve the combination of various financial instruments, including bonds, mortgages, and loans, which are then securitized and sold to investors. This process enables businesses to raise capital by transferring related risks, with large businesses often serving as the original creators of the underlying assets. The market is characterized by ongoing activities and evolving patterns. For instance, portfolio risk management strategies involve the use of credit derivatives, such as credit default swaps and interest rate swaps, for hedging purposes.Leveraged finance and Private Equity financing employ synthetic securitization techniques, like structured notes and synthetic collateralized debt obligations, to optimize capital structures. Credit rating agencies assess credit risk, while investment grade ratings provide benchmarks for investors. Liquidity management and due diligence processes are crucial aspects of the market. Hedge funds employ derivatives valuation models and

Finance Datasets

Historical stock and cryptocurrency price data.

  Contents





  Stocks (5 years of daily OHLCV data)

AAPL - Apple Inc. GOOGL - Alphabet Inc. MSFT - Microsoft Corp. AMZN - Amazon.com Inc. TSLA - Tesla Inc. META - Meta Platforms NVDA - NVIDIA Corp. AMD - Advanced Micro Devices INTC - Intel Corp. NFLX - Netflix Inc.

  Cryptocurrencies (full history)

BTC_USD - Bitcoin ETH_USD - Ethereum SOL_USD - Solana ADA_USD - Cardano DOT_USD - Polkadot… See the full description on the dataset page: https://huggingface.co/datasets/misterdonn/finance-datasets.