The paper investigates the trade‑off between sparsity and complexity in high‑dimensional asset pricing models. By separating capacity sparsity (restrictions on effective model capacity) from factor sparsity (parsimonious structure of priced risks), the authors use nonlinear feature expansions, basis pursuit, column generation, and GPU acceleration to estimate models with up to 432 million candidate factors. Their empirical results show that while sparse portfolios underperform dense ridgeless benchmarks at lower complexity, they achieve higher Sharpe ratios and lower pricing errors when the candidate set is large, indicating that capacity expansion and factor sparsity can complement each other.
By Nima Afsharhajari, Jonathan Yu-Meng Li
arXiv:2407. 21740v3 Announce Type: replace-cross Abstract: Factor analysis, often regarded as a Bayesian variant of matrix factorization, offers superior capabilities in capturing uncertainty, modeling complex dependencies, and ensuring robustness.
By Zhibin Duan, Tiansheng Wen, Yifei Wang, Chen Zhu, Bo Chen, Mingyuan Zhou
The paper introduces Neural Low-Degree Filtering (Neural LoFi), a stylized limit of gradient-based training that turns hierarchical feature learning into an explicit iterative spectral procedure. In this framework, each layer independently selects directions with maximal low-degree correlation to the label, providing a tractable surrogate for deep learning and a kernel-space interpretation. Experiments on fully connected and convolutional networks show that Neural LoFi outperforms lazy random-feature baselines, recovers meaningful structured filters, and aligns with early gradient-descent feature discovery on real datasets.
By Yatin Dandi, Matteo Vilucchio, Luca Arnaboldi, Hugo Tabanelli, Florent Krzakala
arXiv:2506. 13139v3 Announce Type: replace-cross Abstract: Modern Machine Learning (ML) and Deep Neural Networks (DNNs) often operate on high-dimensional data and rely on overparameterized models, where classical low-dimensional intuitions break down.
By Zhenyu Liao, Michael W. Mahoney
arXiv:2512.04696v3 Announce Type: replace
Abstract: We develop a flexible feature selection framework based on deep neural networks that approximately controls the false discovery rate (FDR), a measu...
By Kazuma Sawaya
arXiv:2410.14839v5 Announce Type: replace-cross
Abstract: We study the dynamic pricing problem faced by a broker seeking to learn prices for a large number of credit market securities, such as corpor...
By Adel Javanmard, Jingwei Ji, Renyuan Xu