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Event Studies and Causal Measurement in Finance

Study event-study design, abnormal returns and robust causal measurement in financial data.

Why study Event Studies and Causal Measurement in Finance?

A price move after an announcement does not, on its own, measure the announcement’s effect. Learn to define an event, construct a defensible comparison and separate descriptive return evidence from stronger causal claims.

Who this is for

For empirical researchers with regression, inference and time-series foundations.

What you should be able to do

  • Define events, estimation windows and expected-return benchmarks.
  • Measure abnormal returns and account for dependent observations.
  • Evaluate causal extensions and inference limitations.

An example of the work this prepares you for

An event-study report with a documented event sample, expected-return benchmark, uncertainty analysis and explicit identification limits.

Prerequisites and suggested preparation

You should be comfortable with the background covered in these programmes. Equivalent knowledge from university study or independent learning is also suitable.

The goal is to use the foundations in context: explain the assumptions, carry out a calculation and interpret the result. If one of those steps feels unfamiliar, review the linked preparation first.

A preview of the learning journey

The lessons build towards these kinds of reasoning and practical work.

Define the comparison

Explain the event and the outcome that would have been expected otherwise.

Measure the response

Interpret abnormal returns in the context of timing and overlapping news.

Challenge a causal claim

Identify alternative explanations and the assumptions needed to rule them out.

Members work through the detailed lessons, derivations, worked examples and solutions inside the learning dashboard.

Explore lesson previews by topic

Open a topic preview to see its introduction, learning objectives and prerequisites. Previous and next links let you explore the lessons in study order. Full lessons are available in the member dashboard; selected samples are free to read in full.

Topic previewEvent-study design3 lessonsTopic previewAbnormal-return measurement3 lessonsTopic previewDependence and robust inference3 lessonsTopic previewCausal extensions3 lessons

Free resources to start learning

Use these alongside the programme or to prepare independently. Start with the introductory material, work through an example, then return to the research papers when the underlying ideas are familiar.

  1. Textbook · Free

    The Effect: An Introduction to Research Design and Causality

    Nick Huntington-Klein

    Build the research-design and causal-inference framework before choosing a method.

  2. Textbook · Free

    Introduction to Econometrics with R

    Christoph Hanck, Martin Arnold, Alexander Gerber and Martin Schmelzer

    Review regression, dependent observations and inference.

  3. Data · Free

    Kenneth R. French Data Library

    Dartmouth · Tuck School of Business

    Obtain public factors for an expected-return benchmark.

  4. Video lectures · Free

    Statistical Learning on YouTube

    Stanford · companion lectures

    Review regression and model assessment; prediction alone does not establish causality.

  5. Research paper · Free

    The Probability of Backtest Overfitting

    David H. Bailey, Jonathan M. Borwein, Marcos López de Prado and Qiji Jim Zhu

    Consider how repeated choices of windows and samples can weaken the evidence.

Links reviewed 14 October 2026. Free refers to the linked reading, notes or videos; printed books and certificates may cost extra.

Find more papers and learning materials in the SelfMadeQuant Directory.

Where this programme fits

  • Systematic Trading and Quantitative Research · elective programme
  • Portfolio, Factor and Asset-Management Quant · elective programme

Ready to study Event Studies and Causal Measurement in Finance? Explore membership for access to the complete lessons.

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