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StockData: An open investment transaction dataset.

Mingrui Li1, Can Wu2, Wentao Mao3

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This summary is machine-generated.

This study introduces StockVis, an open and anonymized dataset of individual investment transactions. This accessible data aims to improve portfolio analysis and strategic decision-making for researchers and analysts.

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Data visualizationFinancial dataStock market

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Area of Science:

  • Quantitative Finance
  • Data Science

Background:

  • Growing investment transactions necessitate advanced visual analysis of financial data.
  • Retail investment data is scarce due to privacy concerns, hindering research in portfolio management.
  • Existing tools lack comprehensive, accessible datasets for individual investor behavior analysis.

Purpose of the Study:

  • Introduce StockVis, the first open and anonymized dataset of individual investment transactions.
  • Facilitate research in portfolio analysis, management, and visualization.
  • Enhance strategic decision-making through accessible financial data.

Main Methods:

  • Creation of an open and anonymized dataset (StockVis) from an individual investor's transaction records.
  • Inclusion of derived metadata on U.S. stocks over a 3-4 year period.
  • Detailed overview of dataset features and anonymization process.

Main Results:

  • StockVis provides a unique, comprehensive dataset for studying investment behavior.
  • The dataset covers U.S. stock market transactions with associated metadata.
  • Case studies and exemplar images are presented to guide future research.

Conclusions:

  • The accessibility of StockVis will significantly contribute to the research community.
  • Fosters enhanced exploration and innovation in investment and portfolio analysis.
  • Aims to improve strategic decision-making in investment portfolio management.