finance

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European Funds dataset from Morningstar

European Funds dataset from Morningstar

Stefan
Open Dataset
Creators: (Leone, Stefano)
Publication Date: 2019
Creators: (Leone, Stefano)

The file contains 57,603 Mutual Funds and 9,495 ETFs with general aspects (as Total Net Assets, management company and size), portfolio indicators (as cash, stocks, bonds, and sectors), returns (as yeartodate, 2020-11) and financial ratios (as price/earning, Treynor and Sharpe ratios, alpha, and beta).
Additional data in terms of sustainability is also available. A key feature of this dataset is the inclusion of detailed Morningstar ratings, which are widely used in the financial industry to assess fund quality based on past performance, risk-adjusted returns, and analyst evaluations. Additionally, it offers categorization of funds, allowing for segmentation by investment type, sector, region, and fund style (e.g., growth vs. value investing). The dataset has a total size of approximately 103.88 MB.

Overall, the dataset is structured into the following variables:​

  • ticker: Fund ticker code.​
  • isin: Fund ISIN code.​
  • fund_name: Extended name of the fund.​
  • inception_date: Date of the fund’s inception.​
  • category: Fund category.​
  • rating: Morningstar rating.​
  • analyst_rating: Morningstar analyst rating.​
  • risk_rating: Morningstar risk rating.​
  • performance_rating: Morningstar performance rating.​
Credit Card Fraud Detection

Credit Card Fraud Detection

Stefan
Open Dataset
Creators: Worldline and the Machine Learning Group of ULB ((Universite Libre de Bruxelles)
Publication Date: 2016
Creators: Worldline and the Machine Learning Group of ULB ((Universite Libre de Bruxelles)

The Credit Card Fraud Detection dataset is a rich collection of credit card transactions made by European cardholders in September 2013. It presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. The dataset is 0.15 GB large.

The data has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection.

Each transaction record in the dataset includes several features:​

  • Time: The number of seconds elapsed between this transaction and the first transaction in the dataset.​

  • V1 to V28: These are the result of a Principal Component Analysis (PCA) transformation applied to the original features to protect sensitive information.​

  • Amount: The monetary value of the transaction.​

  • Class: A binary indicator where ‘1’ signifies a fraudulent transaction and ‘0’ denotes a legitimate one.​

US Funds dataset from Yahoo Finance

US Funds dataset from Yahoo Finance

Stefan
Open Dataset
Creators: (Leone, Stefano)
Publication Date: 2018
Creators: (Leone, Stefano)

The US Funds dataset from Yahoo Finance collects data on 24,821 mutual funds and 1,680 exchange-traded funds (ETFs). This contains detailed information on various aspects of each fund, including general characteristics, portfolio indicators, returns, and financial ratios. A notable feature of this dataset is its extensive coverage, offering insights into both mutual funds and ETFs, which can be instrumental for comparative analyses and investment research. The dataset was published in 2018 and contains data up to November 2020, providing a temporal coverage that spans several years leading up to that point. In total, it covers 1.7 GB.

The dataset includes various variables for each fund, such as:​

  • fund_symbol: Symbol of the ETF.​
  • price_date: Date of the price (in YYYY-MM-DD format).​
  • open: Open daily price.​
  • high: Highest daily price.​
  • low: Lowest daily price.​
  • close: Close daily price.​
  • adj_close: Adjusted close daily price, which considers elements that have impacted the price such as share splits, dividends, etc.​
  • volume: Daily traded volume.​
  • nav_per_share: Daily Net Asset Value (NAV) per share.​
  • region: Name of the region in which the fund has the domicile.​
  • initial_investment: Minimum amount for initial investment.​
  • subsequent_investment: Minimum amount for subsequent investments.​
  • exchange_code: Code of the exchange where the fund is traded.​
  • exchange_name: Name of the exchange where the fund is traded

 

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