Hedging the AI Singularity
I wrote a paper about hedging the risk that AI takes my job... ...using AI.
Optimal Post-Hoc Theorizing
If "Statistical Learning" exceeds "Darwinian Learning," then it's better to look at the data before theorizing.
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.
Data for returns of 80,000 long-short strategies based on past returns and ticker symbols
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)
Most claimed statistical findings in cross-sectional return predictability are likely true
At least 91% are true based on simple and intuitive formulas.
This draft addresses these referee reports.
Code, Slides, WFA 2024 Presentation w/ Cam Harvey's Discussion
My slides on Post-Truth Finance are also relevant.
An Irrelevance Theorem for Risk Aversion and Time-Varying Risk (with Francisco Palomino)
(R&R Review of Economic Dynamics)
A theorem explains why modern theories of risk tell us little about business cycles.
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.
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.