March 4, 2026 - Financial datasets are a goldmine for machine learning: they’re complex, time-based, and high-stakes. We have picked top 20 datasets for you.

April 8, 2025 - Developing ML models for finance? Here are 13 great open financial datasets to develop and train ML models for finance.

Checking your browser before accessing www.kaggle.com · Click here if you are not automatically redirected after 5 seconds

It depends on your use case. Kaggle and Quandl are top finance datasets for machine learning projects. For macro data, use FRED or World Bank Open Data.

October 17, 2025 - EDGAR also has insider trading datasets collected through forms 4, 5, and 144 that reveal purchases and sales of company securities by insiders. Such data is useful in identifying signs of conflict of interest within a company. Corporate governance research: Researchers use EDGAR filings to study corporate governance practices, including board composition, executive compensation, and shareholder rights. Financial health prediction: Machine learning models can be trained on EDGAR’s financial statement data to predict bankruptcy, financial distress, or credit defaults.

April 18, 2025 - Quandl: One of the premier sources for financial datasets, Quandl has been used by over 250,000 analysts, asset managers, and investment banks for years. The data has consistently proven to be reliable, accurate, and useful in prediction modeling. EU Open Data Portal: This repository of information originally published by EU institutions and agencies in 2012 contains information ranging from the environment & employment to science & education. World Bank Open Data: Among financial datasets, World Bank Open Data is unique.

March 7, 2025 - Use Cases: NLP-based financial sentiment analysis, forensic accounting ... Once you’ve selected the right dataset, the next step is leveraging it effectively for AI and machine learning applications.

March 15, 2025 - There’s been a lot happening in alternative financial datasets recently, especially in emerging markets. One niche set that’s become really interesting is Indian index microstructure: Tick-by-tick for NIFTY 50 / BANKNIFTY / GIFT NIFTY (offshore futures)/250 equity stocks and their futures.

Remember that a stock prediction model would only be helpful if it has a high accuracy value. You can use the Huge Stock Market Dataset or the NY Stock Exchange Dataset to implement this machine learning for finance project.

1 month ago - Weights & Biases, developer tools for machine learning

February 4, 2019 - 50 free Machine Learning datasets: finance and economics Welcome to the second part of our five-part series! In our first post we outlined useful portals you can use to locate a wide range of quirky …

Checking your browser before accessing www.kaggle.com · Click here if you are not automatically redirected after 5 seconds

September 29, 2022 - One of the premier sources for financial datasets, Quandl has been used by over 250,000 analysts, asset managers, and investment banks for years. The data has consistently proven to be reliable, accurate, and useful in prediction modeling. ... This dataset covers population demographics throughout the world, along with a wide variety of economic and development indicators that are useful for predictive modeling.

March 11, 2026 - CWU affiliates can refer to the following data guide for sample Python code scripts or reach out to support@alphavantage.co for 24/7 technical support. Bureau of Economic Analysis / U.S. Department of Commerce This link opens in a new window · Bureau of Economic Analysis (BEA) promotes a better understanding of the U.S. economy by providing the most timely, relevant, and accurate economic accounts data in an objective and cost-effective manner. ... The stock API industry, and financial data in general, have nuances that explain quality, evolution, and how knowing this can benefit your algorithms.

Skip to main content · Loading · About data.world · Terms & Privacy · © 2026 data.world, inc

April 25, 2023 - Building high-quality market environments for training financial reinforcement learning (FinRL) agents is difficult due to major factors such as the low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting. In this paper, we present FinRL-Meta, a data-centric and openly accessible library that processes dynamic datasets from real-world markets into gym-style market environments and has been actively maintained by the AI4Finance community.

August 13, 2024 - This paper presents ENTRANT, a financial dataset that comprises millions of tables, which are transformed to reflect cell attributes, as well as positional and hierarchical information. Hence, they facilitate, among other things, pre-training tasks for table understanding with deep learning methods. The dataset provides table and cell information along with the corresponding metadata in a machine-readable format.

March 27, 2026 - Weights & Biases, developer tools for machine learning

September 11, 2023 - Finally, we save the modified DataFrame to a new CSV file specified by DESTINATION_FILE. The resulting dataset contains the original price data and the associated buy (1) and sell (-1) signals, making it suitable for training machine learning models for financial prediction.

March 3, 2026 - The dataset contains 2234 financial banking comments from Romanian financial banking social media collected via web scraping technique. ... Content may be subject to copyright. ... Content may be subject to copyright. Informatica Economică vol. 23, no. 1/2019 37 ... Machine Learning context.