5 papers
Agentic Empirical Asset Pricing: Methodological Foundations
Yingjian Pan, Xiaowei Ding, Kay Giesecke
Recent advances in LLM agents enable a new paradigm for asset pricing, which we call Agentic Empirical Asset Pricing (AEAP): systems that autonomously conduct the scientific discov…
The Stanford EDGAR Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data
Nick Bettencourt, Xiaowei Ding, Kay Giesecke
As high-quality public web corpora become increasingly exhausted, clean long-context documents have become a scarce and expensive source of training data for large language models…
Online Conformal Prediction for Non-Exchangeable Panel Data
Daohong Tu, Kay Giesecke
Panel data, in which multiple units are repeatedly observed over time, arise throughout science and engineering. Quantifying predictive uncertainty in such settings is challenging…
AICO: Feature Significance Tests for Supervised Learning
Kay Giesecke, Enguerrand Horel, Chartsiri Jirachotkulthorn
Machine learning is central to modern science, industry, and policy, yet its predictive power often comes at the cost of transparency: we rarely know which input features truly dri…
A Set-Sequence Model for Time Series
Elliot L. Epstein, Apaar Sadhwani, Kay Giesecke
Many prediction problems across science and engineering, especially in finance and economics, involve large cross-sections of individual time series, where each unit (e.g., a loan,…