Search arXivSearch

arXiv · 2609.07122

Centering Drives Normalization Gains: Price-Offset Nuisances in Cross-Sectional Return Prediction

Abstract

Cross-sectional return prediction from raw intraday bars is sensitive to each instrument price level, an additive nuisance under a return-ranking hypothesis. We test whether removing this offset, rather than rescaling amplitudes or changing the encoder, explains gains on a point-in-time CSI 300 five-minute panel. Eight parameter-matched encoders are evaluated with and without RevIN normalization; a parameter-free ladder then separates identity, scale-only, centering, last-value referencing, differencing, and standardization across all fields and restricted channels. Centering drives the reliable effect, while scale-only normalization does not help. All eight paired effects are positive and survive Holm correction on raw rank IC, after style residualization, and after additionally residualizing on short-term reversal. Among six stronger encoders, normalized IC is 0.0830-0.0939 and gains are 0.0376-0.0567. Price-only standardization retains 93-101% of the all-field gain. These results place the main effect in transformed price-channel offset removal rather than amplitude scaling or encoder choice.

Explore related subjects

Keep this discovery

BibTeXRIS

Mingju Chen, Qianhui Liu, Yui Lo, Yuanhang Liu. 2026-09-07. Centering Drives Normalization Gains: Price-Offset Nuisances in Cross-Sectional Return Prediction. https://arxiv.org/abs/2609.07122

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

Certified-Everlasting Quantum NIZK Proofs

We study non-interactive zero-knowledge proofs (NIZKs) for NP satisfying: 1) statistical soundness, 2) computational zero-knowledge (ZK) and 3) certified-everlasting zero-knowledge (CE-ZK). The CE-ZK property allows a verifier of a quantum proof to revoke the proof in a way that can be checked (certified) by the prover. Conditioned on successful certification, the verifier's state can be efficiently simulated with only the statement, in a statistically indistinguishable way. Our contributions regarding these certified-everlasting NIZKs (CE-NIZKs) are as follows: - We identify a barrier to obtaining CE-NIZKs in the CRS model via generalizations of known interactive ZK proofs that satisfy CE-ZK. - We circumvent this by constructing CE-NIZK using non-black-box use of NIZK for NP satisfying certain properties, along with OWFs. As a result, we obtain CE-NIZKs for NP in the CRS model, based on polynomial hardness of the learning with errors (LWE) assumption. - In addition, we observe that the aforementioned barrier does not apply to the shared EPR model. We leverage this to construct CE-NIZK for NP in this model based on any statistical binding hidden bits generator, which is known from LWE. The only quantum computation here involves single-qubit measurements of the EPR pairs.

quant-ph

Tensor-Train Weak SINDy: Identifying High-Dimensional Nonlinear Dynamics

In recent years, weak-form methods have made significant advances in data-driven discovery of dynamical systems. However, in high-dimensional settings, current techniques can prove expensive in both computation and memory. In this work, we introduce TT-WSINDy, which combines techniques of the Multidimensional Approximation of Nonlinear Dynamics (MANDy) and Weak Sparse Identification of Nonlinear Dynamics (WSINDy) methods, implementing requisite computations in the tensor-train (TT) format. We demonstrate that this method is able to search an exponentially-growing space of candidate functions -- performing weak-form transformation, regression, and sparsification -- without suffering from the curse of dimensionality.

cs.LG