Do Daily Adaptive Machine Learning Stock Rankings Survive Trading Costs? Evidence from Cross-Sectional Technical Signals

Abstract

This study examines whether daily machine learning stock rankings based on technical information contain out-of-sample ordering information and whether that information can be converted into economically implementable returns. Using a dynamically screened Nasdaq source universe from 2021 to 2026, four XGBoost objectives are evaluated in a chronological walk-forward design. Test NDCG converges to 0.495–0.504, but permutation analysis places the corresponding random-ranking mean near 0.45, indicating statistically detectable but modest cross-sectional ordering information. Economic performance is substantially weaker. Under the execution convention implied by the next-day open-to-close target, every invested portfolio is bought at the open and liquidated at the close, so round-trip turnover equals two. Pseudo-Huber Top-1, treated as an ex-post concentration diagnostic, produces a 34.9% gross annual geometric return with 89.5% volatility and an 86.5% maximum drawdown; the Newey–West mean-return test is not significant (p = 0.106). At five basis points per trading leg, its zero-cash net CAGR falls to 4.8%; crediting idle capital with the daily risk-free rate raises total-return CAGR to 9.3%, but the excess-return inference is unchanged (p = 0.297). A matched-horizon regression on SPY open-to-close returns yields a statistically insignificant net annualized alpha (p = 0.436). Hansen’s SPA test across the synchronized 12-strategy family gives p = 0.207, and the Deflated Sharpe Ratio probability for Pseudo-Huber Top-1 is 0.462. The result is also highly time- and tail-dependent. The evidence therefore supports a distinction between statistically detectable ranking information and robust implementable abnormal performance rather than a persistent trading anomaly.