RRST: Recurrent Relational Stock Transformer for Cross-Sectional Stock Ranking

Cross-sectional stock ranking orders hundreds of stocks by their next-day return, and the public benchmarks judge the order two ways: by the correlation of scores with returns over the whole universe, and by the return of the few stocks at the top. We present the Recurrent Relational Stock Transformer (\rrst), one recurrent cell that steps over the last days of a window and carries two states per stock: a stock state that carries each stock's representation from one day to the next through a per-channel retention spread over timescales, so that recent days dominate while longer memory is kept, and a relation state holding each stock's loadings on latent factors, trained by an auxiliary loss to explain the next day's cross-section and consumed by the scoring layer as risk exposures. A scoring layer assembles one score for both kinds of metric. On the two public benchmark families, \rrst is above all 24 methods reported on NASDAQ and NYSE in cumulative return and Sharpe ratio (SR $4.48$ and $7.02$ against the best published $2.32$ and $2.35$) and above StockMixer on all four of its metrics on the same markets, with every hyperparameter chosen by a validation-only rule; the ablation locates the consistent top-of-ranking gain in the relation state used as exposures. We also prove that the benchmarks' price normalisation, by each stock's maximum over the whole series, encodes the future, measure what that column is worth, and report every result with and without the level inputs: NASDAQ and NYSE stay above every published top-of-ranking result in both settings, and S&P500 gains in IC and ICIR with the level inputs.