Problem
Research questions and friction points this paper is trying to address.
Cross-sectional Return Prediction
Price Level Offset
Normalization
Centering Drives Normalization Gains: Price-Offset Nuisances in Cross-Sectional Return Prediction
🤖 AI Summary
研究通过去除价格水平偏移而非调整幅度或改变编码器，解决了横截面收益预测中的敏感性问题，使用了RevIN归一化方法。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
centering
price-offset removal
RevIN normalization
🔎 Similar Papers
No similar papers found.
