Event-driven Data

Provide comprehensive information on corporate capital change events, including capital increases, capital reductions, dividend distributions, mergers and acquisitions, spin-offs, Global Depositary Receipt (GDR) issuance, convertible bond (CB) conversions, and employee stock subscriptions. This data facilitates a deeper understanding of corporate capital change policies and their execution.

Provide comprehensive information on corporate capital change events, including capital increases, capital reductions, dividend distributions, mergers and acquisitions, spin-offs, Global Depositary Receipt (GDR) issuance, convertible bond (CB) conversions, and employee stock subscriptions. This data facilitates a deeper understanding of corporate capital change policies and their execution.

Provide comprehensive information on corporate capital change events, including capital increases, capital reductions, dividend distributions, mergers and acquisitions, spin-offs, Global Depositary Receipt (GDR) issuance, convertible bond (CB) conversions, and employee stock subscriptions. This data facilitates a deeper understanding of corporate capital change policies and their execution.

Provide comprehensive information on corporate capital change events, including capital increases, capital reductions, dividend distributions, mergers and acquisitions, spin-offs, Global Depositary Receipt (GDR) issuance, convertible bond (CB) conversions, and employee stock subscriptions. This data facilitates a deeper understanding of corporate capital change policies and their execution.

In this article, we use Event Study to explore the impact of announcement of different corporate events on the stock retuens. Let's find out the results and learn more about event-driven data!

TEJ Quantitative investment database covers three different categories of databases, namely market data, financial accounting data, and corporate action event data. The overall database not only has high coverage of Taiwan stock market information but also is point-in-time, which is necessary for quantitative analysis.