Working Papers

High-Throughput Asset Pricing or: How I Learned to Stop Worrying and Love Data Mining
(with Chukwuma Dim) 

• • The solution to data mining bias is to mine data rigorously.

  • Paper (March 2024)

    • First version (November 2023)

  • Data for returns of 80,000 long-short strategies based on past returns and ticker symbols

  • Code

  • For high-throughput accounting strategies, see "Does peer-reviewed theory help predict the cross-section of stock returns?" (below)

Does Peer-Reviewed Research Help Predict Stock Returns?
(with Alejandro Lopez-Lira and Tom Zimmermann) 

• • Out-of-sample, data mining works just as well.

  • Paper (April 2024), Code (Github), First Draft (Dec 2022) 

  • The origins of 200 predictors according to peer review (csv)

  • Returns of 30,000 Data Mined Accounting Strategies (Gdrive Folder)

  • Excess Returns Podcast episode on this paper 

Most claimed statistical findings in cross-sectional return predictability are likely true

• • At least 91% are true based on simple and intuitive formulas.

  • Paper (June 2024), Code, Slides

  • Presentation video (FutFin.Info Webinar Dec 2021)

  • My slides on Post-Truth Finance are also relevant.

An Irrelevance Theorem for Risk Aversion and Time-Varying Risk (with Francisco Palomino)

• • A general theorem explains why modern theories of risk tell us little about business cycles.

  • Paper, SSRN 

  • Matlab code for demonstrating irrelevance in models with investment frictions and gamma-distributed shocks

Permanent Working Papers

Semi-Parametric Restrictions on Production-Based Asset Pricing Models

• • Matching the data on asset prices requires either extremely volatile IST shocks or huge capital adjustment costs.  

  • Paper (ssrn) 

  • The best parts of this paper are extended and can be found in "An Irrelevance Theorem for Risk Aversion and Time-Varying Risk" with Francisco Palomino.