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arXiv Machine Learning By Nima Afsharhajari, Jonathan Yu-Meng Li

The Virtue of Sparsity in Complexity

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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