Course summary

This course introduces students to empirical research in asset pricing. Rather than aiming for encyclopedic coverage, we follow a single stream of literature – factor models for the cross-section of stocks – from foundational concepts to current debates. We will begin with a brief discussion/review of the main theoretical concepts, go through the evolution of factor models leading to the ‘factor zoo’, and discuss current topics.

This is a hands-on course intended to introduce PhD students into the research process. Students will engage directly with data and we emphasize research transparency, replication, and clarity in empirical design. While our focus is on factor models, these research practices are essential across all areas of empirical finance.

By the end of the course, students will be able to:
• Understand the evolution and critique of empirical factor models
• Handle standard datasets used in empirical finance
• Replicate and evaluate published asset pricing results
• Design and document empirical analyses with transparency and rigor

Course contents

1. Conceptual background on cross-sectional asset pricing of stocks
Mostly based on Cochrane (2009).

2. Factor models for the cross-section of stocks
The evolution of the factor model literature, from Fama and French (1992) to Hou,
Xue, and Zhang (2015).

3. The CAPM, Betting-against-Beta, Low Risk Anomalies
Starting from early evidence on the empirical failure of the CAPM (e.g., Black, Jensen, and Scholes, 1972), we discuss the apparently anomalous relation of return to (covariance) risk and potential explanations, such as frictions (Frazzini and Pedersen, 2014) and higher-moment risk (Schneider, Wagner, and Zechner, 2020).

4. The factor zoo and the replication of anomalies
Recently, the literature has become skeptical about the exorbitant number of anomalies and factors ‘discovered’ in the cross-section of stocks. We will start from the presidential address of Cochrane (2011) and work our way to the ongoing discussion on replicability in finance (e.g., Jensen, Kelly, and Pedersen, 2023).

5. Other topics
Time permitting, we may discuss alternative approaches to the cross-section of stocks, e.g., based on data on derivatives or subjective expectations, or recently developed empirical methodologies, e.g. the use of machine learning or AI.

Structure and Evaluation
• Weekly sessions
• Short empirical and reading assignments due weekly
• Class discussions of the assignments
• Final assignment: mini replication or extension project (TBD)

Structure and Evaluation
• Weekly sessions
• Short empirical and reading assignments due weekly
• Class discussions of the assignments
• Final assignment: mini replication or extension project (TBD)

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

Further readings
Standard textbooks for further reading are, for example, Cochrane (2009), Back (2010),
Campbell, Lo, and MacKinlay (2012), Bali, Engle, and Murray (2016), and Campbell (2017).
For the empirical implementation, ‘Tidy Finance with R’ (Scheuch, Voigt, and Weiss, 2023)
and its updates, including Python, is a useful reference: https://www.tidy-finance.org/.