7 papers
Conditional Tensor Diffusion: Distributional Counterfactual Learning and Inference
Xinbing Kong, Zeyu Li, Junfan Mao +1
Causal inference guides operational and managerial decisions but remains challenging in high-dimensional panel or tensor settings, where decisions may depend on the joint condition…
Expected Shortfall Factor Models: Common Tail Losses and Expected Returns
Yujie Hou, Xinbing Kong, Yalin Wang +1
We develop an expected shortfall factor model (ESFM) to estimate and price common variation in the severity of lower-tail losses in large panels of asset returns. Mean factor model…
Expected Shortfall Panel Regression
Yujie Hou, Xinbing Kong, Yalin Wang +1
Expected Shortfall (ES) is a coherent measure of tail risk that captures the average loss beyond a quantile threshold. Despite the growing literature on ES regression conditional o…
Tucker Diffusion Model for High-dimensional Tensor Generation
Jianhua Guo, Xinbing Kong, Zeyu Li +1
Statistical inference on large-dimensional tensor data has been extensively studied in the literature and widely used in economics, biology, machine learning, and other fields, but…
QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining
Jun Han, Shuo Zhang, Wei Li +14
Financial markets are noisy and non-stationary, making alpha mining highly sensitive to backtest noise and regime shifts. While recent agentic frameworks improve automation, they o…
High-Dimensional Binary Variates: Maximum Likelihood Estimation with Nonstationary Covariates and Factors
Xinbing Kong, Bin Wu, Wuyi Ye
This paper introduces a high-dimensional binary variate model that accommodates nonstationary covariates and factors, and studies their asymptotic theory. This framework encompasse…
Generalized Matrix Factor Model
Xinbing Kong, Tong Zhang
This article introduces a nonlinear generalized matrix factor model (GMFM) that allows for mixed-type variables, extending the scope of linear matrix factor models (LMFM) that are…