[2601.13435] A Learnable Wavelet Transformer for Long-Short Equity Trading and Risk-Adjusted Return Optimization

Computer Science > Machine Learning arXiv:2601.13435 (cs) [Submitted on 19 Jan 2026 (v1), last revised 2 Mar 2026 (this version, v3)] Title:A Learnable Wavelet Transformer for Long-Short Equity Trading and Risk-Adjusted Return Optimization Authors:Shuozhe Li, Du Cheng, Leqi Liu View a PDF of the paper titled A Learnable Wavelet Transformer for Long-Short Equity Trading and Risk-Adjusted Return Optimization, by Shuozhe Li and 2 other authors View PDF HTML (experimental) Abstract:Learning profitable intraday trading policies from financial time series is challenging due to heavy noise, non-stationarity, and strong cross-sectional dependence among related assets. We propose \emph{WaveLSFormer}, a learnable wavelet-based long-short Transformer that jointly performs multi-scale decomposition and return-oriented decision learning. Unlike standard time-series forecasting that optimizes prediction error and typically requires a separate position-sizing or portfolio-construction step, our model directly outputs a market-neutral long/short portfolio and is trained end-to-end on a trading objective with risk-aware regularization. Specifically, a learnable wavelet front-end generates low-/high-frequency components via an end-to-end trained filter bank, guided by spectral regularizers that encourage stable and well-separated frequency bands. To fuse multi-scale information, we introduce a low-guided high-frequency injection (LGHI) module that refines low-frequency representations with ...