Machine Learning Applications in Finance & Economics

Machine learning is proving to be a golden opportunity for the financial sector. Financial quantitative records are kept for decades, so the industry is perfectly suited for machine learning.

Machine learning is already transforming finance and investment banking for algorithmic trading, stock market predictions, and fraud detection. In economics, machine learning can be used to test economic models and predict citizen behavior to help inform policy makers.

Financial & Economic Datasets for Machine Learning

  • Quandl: The premier source for financial and economic datasets for investment professionals. Over 250,000 people including analysts from the world’s top hedge funds, asset managers, and investment banks use Quandl.
  • EU Open Data Portal: The EU Open Data Portal gives access to open data published by EU institutions and agencies about the economy, as well as employment, science, environment, and education.
  • World Bank Open Data: Datasets covering population demographics and a huge number of economic and development indicators from across the world.
  • IMF Data: The International Monetary Fund publishes data on international finances, debt rates, foreign exchange reserves, commodity prices, and investments.
  • Financial Times Market Data: Up-to-date information on financial markets from around the world, including stock price indexes, commodities, and foreign exchange.
  • Google Trends: Examine and analyze data on internet search activity and trending news stories around the world.
  • American Economic Association (AEA): Good source for finding US macroeconomic data.
  • School System Finances: A survey of the finances of school systems in the US.
  • US Stock Data: Historical data of US stocks since 2009, updated daily.
  • CBOE Volatility Index (VIX): The CBOE Volatility Index (VIX) is a key measure of market expectations of near-term volatility conveyed by S&P. This is a time-series dataset including daily open, close, high and low.
  • Dow Jones Weekly Returns: Dataset includes percentage of return that stock has each week, for the purpose of training your algorithm to determine which stock will produce the greatest rate of return in the following week.
  • EconData: Thousands of economic time series, produced by US government agencies and distributed in various formats and media. Data has been organized in a standard, highly efficient, easy-to-use form for personal computers and made publicly available through the site.
  • Simfin: Data from financial statements uploaded on the SEC website, cleaned and organized in a single document that you can download and work with in a matter of seconds.
  • Saudi Arabia Public Debt: Data on Saudi Arabia Public Debt for 2005-2017 provided from Saudi Arabian Monetary Agency.
  • AssetMarco: Macroeconomic database that includes 25,000+ indicators for 120+ countries.
  • Eurostat Comext: Datasets on trade flows since 1988, organized by commodity.
  • CIA World Factbook: Economic stats of countries, as well as other stats on demographics, geography, communications, and military.


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