Rigorous event studies, ready for publication.

1. t₀
   Define the event
   Pick a ticker, a date, and an event window. ARC defaults to [-10, +10] but accepts anything up to [-250, +250].
2. E(Rᵢₜ)
   Fit the expected-return model
   Choose market model, CAPM, Fama-French 3 or 5-factor, Carhart 4-factor, or constant mean. Estimation window defaults to [-250, -11].
3. ARᵢₜ
   Compute abnormal returns
   ARᵢₜ = Rᵢₜ − E(Rᵢₜ). Daily abnormal returns, aggregated cross-sectionally into AAR and cumulatively into CAR and CAAR.
4. H₀: AAR=0
   Test significance
   Apply parametric (t-test, BMP, Patell) and non-parametric (Corrado rank, generalized sign) tests in parallel.
5. → .tex
   Publish with confidence
   Export to LaTeX, CSV, or Stata-ready formats. Every output ships with the model spec and test choice for reproducibility.

An event study is a statistical method that measures how a specific event, such as an earnings announcement, merger, or regulatory decision, moves a company's stock. The steps above trace it: fit a normal-return model over the estimation window, measure the abnormal return (actual minus expected) during the event window around the event date, then cumulate and average those abnormal returns into the CAR and CAAR and test them for statistical significance. New here? Read the methodology guide or look up a term in the glossary.