QuantEvolver applies reinforcement fine-tuning to evolve an LLM policy for generating executable alpha factor expressions, yielding higher-quality and more complementary factors than prompt-based baselines on market benchmarks.
Finpt: Financial risk prediction with profile tuning on pretrained foundation models
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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PrivacyCredit is a machine learning method that combines traditional and alternative data for credit risk prediction while satisfying privacy-preserving, model-confidential, and lossless properties.
citing papers explorer
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From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery
QuantEvolver applies reinforcement fine-tuning to evolve an LLM policy for generating executable alpha factor expressions, yielding higher-quality and more complementary factors than prompt-based baselines on market benchmarks.
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Privacy-Preserving Credit Risk Prediction with Alternative Data
PrivacyCredit is a machine learning method that combines traditional and alternative data for credit risk prediction while satisfying privacy-preserving, model-confidential, and lossless properties.