arXiv:2602. 03395v4 Announce Type: replace Abstract: While deep learning has revolutionized financial forecasting through sophisticated architectures, the design of the supervision signal itself is rarely scrutinized.
By Chen-Hui Song, Shuoling Liu, Liyuan Chen
arXiv:2606. 09104v1 Announce Type: cross Abstract: Deep reinforcement learning (DRL) frameworks for portfolio optimization have shown promise for their ability to learn allocation rules dynamically from market data.
By Daniil Mikriukov (University of Liverpool, Xi'an Jiaotong-Liverpool University), Ruoyu Sun (Xi'an Jiaotong-Liverpool University), Angelos Stefanidis (Xi'an Jiaotong-Liverpool University), Jionglong Su (Xi'an Jiaotong-Liverpool University), Zhengyong Jiang (Xi'an Jiaotong-Liverpool University)
arXiv:2606. 07382v1 Announce Type: new Abstract: We recast classical shrinkage of high-dimensional covariance estimators as empirical risk minimization over a parametric stochastic interpolant between a source and a target distribution.
By Mathieu Chalvidal, Florentin Coeurdoux, Eric Vanden-Eijnden
arXiv:2411. 17136v2 Announce Type: replace-cross Abstract: Realised volatility has become increasingly prominent in volatility forecasting due to its ability to capture intraday price fluctuations.
By Qianli Zhao, Chao Wang, Richard Gerlach, Giuseppe Storti, Lingxiang Zhang
arXiv:2606. 04576v1 Announce Type: cross Abstract: Learning Value-at-Risk (VaR) and Expected Shortfall (ES) is important for managing financial risks effectively.
By Yichi Zhang, Ke Zhu, Zhoufan Zhu
arXiv:2608. 07281v1 Announce Type: cross Abstract: This paper investigates the asymptotic behavior of the out-of-sample prediction risk of the high-dimensional ridgeless least-squares estimator when the feature dimension $p$ and the sample size $n$ grow proportionally.
By Zhijun Liu, Dandan Jiang