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stock ranking

4 articles · Page 1 of 1
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Aug 21, 2026 · Artificial Intelligence

RankGLU: Residual Gated Component Boosts Cross‑Sectional Stock Ranking Performance by 11%

The article analyzes cross‑sectional stock prediction as a ranking task, identifies the prediction head as a bottleneck, introduces the RankGLU residual bottleneck GLU module that preserves a linear scoring path while adding bounded multiplicative interaction, and demonstrates an 11% IC improvement on CSI300 with extensive experiments and ablations.

CSI300GLURankGLU
0 likes · 14 min read
RankGLU: Residual Gated Component Boosts Cross‑Sectional Stock Ranking Performance by 11%
Data Party THU
Data Party THU
Jul 10, 2026 · Artificial Intelligence

2026 Big Data Challenge: Award Winners Revealed with In‑Depth Competition Experience Shares (Phase 2)

The article announces the winning teams of the 2026 China University Computer Competition Big Data Challenge and provides detailed, step‑by‑step experience reports covering data processing, feature engineering, model design, training strategies, and post‑processing for a cross‑sectional stock ranking task.

LightGBMTime SeriesXGBoost
0 likes · 10 min read
2026 Big Data Challenge: Award Winners Revealed with In‑Depth Competition Experience Shares (Phase 2)
Data Party THU
Data Party THU
Jun 22, 2026 · Artificial Intelligence

Who Won the 2026 Big Data Challenge Monthly Star Awards? Winners Share Their Competition Insights

The 2026 China University Big Data Challenge announced its Monthly Star winners, each receiving a prize, and the top three teams detailed their data processing, feature engineering, model design, training strategies, and post‑processing techniques for cross‑sectional stock ranking.

Big Data CompetitionTime Seriesfeature engineering
0 likes · 10 min read
Who Won the 2026 Big Data Challenge Monthly Star Awards? Winners Share Their Competition Insights
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Feb 18, 2026 · Artificial Intelligence

Which Loss Function Ranks Stocks Best? An Empirical Study with Transformer Models

This paper evaluates point‑wise, pair‑wise, and list‑wise loss functions for Transformer‑based stock‑return prediction on 110 S&P 500 stocks, showing that Margin loss achieves the highest annual return (16.23%) and Sharpe ratio (0.75), ListNet delivers strong returns with low volatility, and BPR minimizes maximum drawdown, highlighting how loss design critically shapes ranking‑driven portfolio performance.

Loss FunctionsTransformerfinancial time series
0 likes · 15 min read
Which Loss Function Ranks Stocks Best? An Empirical Study with Transformer Models