AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment
Summary: AutoML-based AlphaEvolve discovers a new class of alphas that fuse scalar, vector, and matrix features to boost predictive power and enable weakly correlated high returns. It introduces alpha generation operators, relational stock-domain knowledge injection, and a pruning technique to remove redundant alphas, with empirical validation on diversification-friendly performance. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Can Cui
- 2. Wei Wang
- 3. Meihui Zhang
- 4. Gang Chen
- 5. Zhaojing Luo
- 6. Beng Chin Ooi
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,092 | Saga: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications | 2023 | SIGMOD | 4.587921e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 62 | Freebase: A Collaboratively Created Graph Database For Structuring Human Knowledge | 2008 | SIGMOD | 0.0006429466 |
| 65 | Fast Subsequence Matching in Time-Series Databases | 1994 | SIGMOD | 0.00062029383 |
| 11,594 | TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications | 2020 | SIGMOD | 4.1945683e-05 |
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