2025/05/18 by Junzhe Jiang, Jiang, Junzhe, Chang Yang +5 · 1 voice
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Engineering · #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Economics and business #Forecasting Techniques and Applications #Machine Learning (cs.LG) #Statistical Finance (q-fin.ST) #Stock Market Forecasting Methods #cs.LG #q-fin.ST
paper · pdf · doi:10.48550/arxiv.2506.03153
openalex publication_date 2025/05/18 · arxiv published 2025/05/18 · arxiv updated 2025/05/18 · openalex created_date 2025/09/26 · openalex updated_date 2026/07/28
Stock market indices serve as fundamental market measurement that quantify systematic market dynamics. However, accurate index price prediction remains challenging, primarily because existing approaches treat indices as isolated time series and frame the prediction as a simple regression task. These methods fail to capture indices' inherent nature as aggregations of constituent stocks with complex, time-varying interdependencies. To address these limitations, we propose Cubic, a novel end-to-end framework that explicitly models the adaptive fusion of constituent stocks for index price prediction. Our main contributions are threefold. i) Fusion in the latent space: we introduce the fusion mechanism over the latent embedding of the stocks to extract the information from the vast number of stocks. ii) Binary encoding classification: since regression tasks are challenging due to continuous value estimation, we reformulate the regression into the classification task, where the target value is converted to binary and we optimize the prediction of the value of each digit with cross-entropy loss. iii) Confidence-guided prediction and trading: we introduce the regularization loss to address market prediction uncertainty for the index prediction and design the rule-based trading policies based on the confidence. Extensive experiments across multiple stock markets and indices demonstrate that Cubic consistently outperforms state-of-the-art baselines in stock index prediction tasks, achieving superior performance on both forecasting accuracy metrics and downstream trading profitability.