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Bighead's Algorithm Notes
Bighead's Algorithm Notes
Feb 28, 2026 · Artificial Intelligence

Quantitative Finance Paper Digest: Key AI‑Driven Research Highlights (Feb 21‑27 2026)

This article curates six recent quantitative‑finance papers, covering Bayesian portfolio policies, signed‑network dimensionality reduction, fine‑grained multi‑agent LLM trading, sentiment‑driven momentum prediction for AAPL, event‑driven hierarchical‑gated reward trading, and a lightweight multi‑model anchoring framework for financial forecasting, summarizing each study’s methodology and empirical results.

Bayesian methodsMachine LearningQuantitative Finance
0 likes · 14 min read
Quantitative Finance Paper Digest: Key AI‑Driven Research Highlights (Feb 21‑27 2026)
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Jan 4, 2026 · Artificial Intelligence

How VTA Combines Large‑Model Reasoning for Precise and Explainable Stock Time‑Series Forecasting

The VTA framework integrates large language model reasoning with textual annotation of technical indicators, employs a Time‑GRPO reinforcement‑learning objective and multi‑stage joint conditional training, and achieves state‑of‑the‑art accuracy and expert‑rated interpretability on US, Chinese and European stock datasets.

LLMTime-seriesVTA
0 likes · 19 min read
How VTA Combines Large‑Model Reasoning for Precise and Explainable Stock Time‑Series Forecasting
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Dec 4, 2025 · Artificial Intelligence

Paper Review: RETuning Boosts Large‑Model Stock Trend Prediction Reasoning

This article analyzes the RETuning framework, which addresses LLMs' bias toward analyst opinions and lack of evidence weighting in stock movement prediction by introducing a two‑stage cold‑start fine‑tuning and reinforcement learning pipeline, evaluating it on the large Fin‑2024 dataset and demonstrating significant F1 gains, inference‑time scaling, and out‑of‑distribution robustness.

Fin-2024GRPOInference Scaling
0 likes · 12 min read
Paper Review: RETuning Boosts Large‑Model Stock Trend Prediction Reasoning
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Sep 13, 2025 · Artificial Intelligence

Paper Summary: Recent AI Advances in Financial Time-Series (Sep 6‑12, 2025)

This article summarizes four recent AI research papers that explore zero‑shot PDE extrapolation with text‑trained LLMs, causal hidden‑state interventions for rare financial events, tabular reformulation of graph node classification, and a multimodal model for financial time‑series forecasting, detailing their methods, experiments, and key findings.

LLMTime-seriescausal intervention
0 likes · 10 min read
Paper Summary: Recent AI Advances in Financial Time-Series (Sep 6‑12, 2025)
Data Party THU
Data Party THU
Sep 6, 2025 · Big Data

From Data Chaos to Predictive Insight: My Solo Journey in the 2025 Big Data Competition

An individual participant recounts their journey in the 2025 China University Computer Competition Big Data Challenge, detailing data cleaning, feature engineering, model building on 300‑stock historical prices, and insights gained from solo competition experience, highlighting challenges, lessons, and future directions in financial AI.

Big Datacompetitiondata engineering
0 likes · 4 min read
From Data Chaos to Predictive Insight: My Solo Journey in the 2025 Big Data Competition