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 (National University of Singapore)
- 2. Wei Wang (National University of Singapore)
- 3. Meihui Zhang (Beijing Institute of Technology)
- 4. Gang Chen (Zhejiang University)
- 5. Zhaojing Luo (National University of Singapore)
- 6. Beng Chin Ooi (National University of Singapore)
BibTeX Citation
@inproceedings{cui_sigmod21,
title = {{AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment}},
author = {Cui, Can and Wang, Wei and Zhang, Meihui and Chen, Gang and Luo, Zhaojing and Ooi, Beng Chin},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3457324},
url = {https://dl.acm.org/doi/10.1145/3448016.3457324},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,232 | Saga: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications | 2023 | SIGMOD | 5.6659017e-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 |
|---|---|---|---|---|
| 41 | Fast Subsequence Matching in Time-Series Databases | 1994 | SIGMOD | 0.00046675394 |
| 65 | Freebase: A Collaboratively Created Graph Database For Structuring Human Knowledge | 2008 | SIGMOD | 0.00038697603 |
| 11,789 | TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications | 2020 | SIGMOD | 5.093636e-05 |
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