Selective Multiple Testing: Inference for Large Panels with Many Covariates(Paper)(Code)(Slides) Co-author: Markus Pelger
R&R at Management Science.
We propose Panel Multiple Testing that allows us to select covariates that explain a large cross-section with false discovery control. In our empirical asset pricing study, we select sparse risk factors from a factor zoo of 114, to explain 243 doubly-sorted portfolio excess returns.
NASMES 2023, AMES 2023, INFORMS 2023, 11th Western Conference on Mathematical Finance, NBER-NSF SBIES 2022, California Econometrics Conference 2022, Stanford HAI Financial Services Industry Review
Large Dimensional Change Point Detection with FWER Control as Automatic Stopping(Paper)(Poster)(Code) Co-authors: Yang Fan, Markus Pelger
With hundreds of time series and unknown number of change points to detect, our inference-based method is better suited than the classical DP-based algorithm due to its conscientious trade-off of Type I vs Type II error. We provide FWER control theory. In simulations, we showed 20% lift in F1 scores against leading benchmarks.
Inference for Large Panel Data with Machine Learning(Paper)
This is my PhD thesis, accessible from Stanford’s archival system.
Asset pricing with Supply Chain Relationships(Paper)(Code) Co-authors: Agostino Capponi, Jose Sidaoui
We develop a nonparametric method to aggregate firm characteristics across a large supply chain network to explain cross-sectional expected returns. Each firm receives a pricing signal, nonlinearly constructed from the characteristics of neighboring firms within d-hops on the network. We find that $d = 3$ – encompassing network effects up to the third order – balances bias reduction from higher-order relations against variance from added complexity. Our model leads to a portfolio sorted by ML-driven firm-level estimated returns that condition on both historical supply chain data and firm characteristics. We achieve over a 16% out-of-sample Sharpe gain vs direct-link models, and outperform the Fama–French five-factor and PCA benchmarks. We find that the ML-managed portfolio improves mean-variance efficiency, measured by Sharpe ratio. Lastly, we show that the conditional mean return estimation of more central firms is 55% more sensitive to missingness of supply chain links compared to that of peripheral firms in the supply chain graph.
INFORMS 2024, Luohan Academy Finance Sessions, Northern Finance Association Annual Meetings, European Finance Association Annual Meeting, Inaugural Finance Research Revolution Conference Vitznau, Switzerland
