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Bighead's Algorithm Notes

Focused on AI applications in the fintech sector

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Latest from Bighead's Algorithm Notes

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Bighead's Algorithm Notes

Dec 5, 2025 · Artificial Intelligence

Quantitative Finance Paper Summaries (Nov 29–Dec 5 2025)

This article presents concise summaries of five recent AI‑driven finance papers, covering a stress‑testing framework for LLM trading agents, an orchestration framework for financial agents, an event‑reflection memory model for stock forecasting, a hybrid LLM‑Bayesian network architecture for options wheel strategies, and their experimental results.

Financial AILLMRisk analysis

0 likes · 12 min read

Quantitative Finance Paper Summaries (Nov 29–Dec 5 2025)

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

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

Bighead's Algorithm Notes

Dec 2, 2025 · Artificial Intelligence

Dual-Relation Fusion Network (DRFN) for Accurate Stock Prediction

The paper introduces DRFN, a dual‑relation fusion network that jointly models static and dynamic stock relationships using multimodal BERT and GRU encodings, achieving significantly lower RMSE and MAE than baseline models on both Chinese and US market datasets.

BERTGRUGraph Neural Network

0 likes · 11 min read

Dual-Relation Fusion Network (DRFN) for Accurate Stock Prediction

Bighead's Algorithm Notes

Nov 30, 2025 · Artificial Intelligence

Paper Review: Hermes – Multi‑Scale Hypergraph for Stock Forecasting with Lead‑Lag Modeling

The Hermes framework introduces a moving‑aggregation module and a multi‑scale fusion module within a hypergraph network to capture industry lead‑lag interactions and multi‑scale stock relationships, achieving superior accuracy and efficiency over existing SOTA methods on three real US stock datasets, as demonstrated by extensive experiments and ablations.

financial time serieshypergraph neural networklead‑lag interaction

0 likes · 11 min read

Paper Review: Hermes – Multi‑Scale Hypergraph for Stock Forecasting with Lead‑Lag Modeling

Bighead's Algorithm Notes

Nov 30, 2025 · Artificial Intelligence

How TSci Uses LLMs to Automate End‑to‑End Time‑Series Forecasting

The article reviews the TSci framework, an LLM‑driven multi‑agent system that automates data diagnosis, model selection, ensemble forecasting, and report generation for time‑series prediction, achieving up to 38 % lower MAE than LLM baselines and improving report quality across five evaluation dimensions.

Agent FrameworkLLMTSci

0 likes · 10 min read

How TSci Uses LLMs to Automate End‑to‑End Time‑Series Forecasting

Bighead's Algorithm Notes

Nov 28, 2025 · Artificial Intelligence

Weekly Quantitative Finance Paper Digest (Nov 22‑28, 2025)

This digest summarizes five recent arXiv papers on AI-driven portfolio optimization and financial time‑series forecasting, covering G‑Learning with GIRL, transfer‑learning strategies, hybrid LSTM‑PPO frameworks, time‑series foundation models, and a KAN versus LSTM performance comparison, highlighting their methods, datasets, and reported Sharpe improvements.

Financial AIportfolio optimizationreinforcement learning

0 likes · 9 min read

Weekly Quantitative Finance Paper Digest (Nov 22‑28, 2025)

Bighead's Algorithm Notes

Nov 27, 2025 · Artificial Intelligence

IKNet: Explainable Stock Price Forecasting with News Keywords and Technical Indicators

IKNet combines FinBERT‑derived news keywords with technical‑indicator time series, uses SHAP to quantify each feature's impact, and achieves a 32.9% RMSE reduction and 18.5% higher cumulative returns on the S&P 500 (2015‑2024) compared with RNN and Transformer baselines, while providing fine‑grained, context‑aware explanations of price movements.

FinBERTSHAPdeep learning

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IKNet: Explainable Stock Price Forecasting with News Keywords and Technical Indicators

Bighead's Algorithm Notes

Nov 25, 2025 · Artificial Intelligence

FinSentLLM: A Multi‑LLM Framework for Financial Sentiment Prediction

FinSentLLM integrates multiple LLM experts with structured financial semantic signals, achieving 3‑6% higher accuracy and F1 on the Financial PhraseBank compared to baselines, while DCC‑GARCH and Johansen cointegration analyses confirm a statistically significant long‑term co‑movement between the predicted sentiment signals and stock market dynamics.

DCC-GARCHFinSentLLMFinancial Sentiment Analysis

0 likes · 12 min read

FinSentLLM: A Multi‑LLM Framework for Financial Sentiment Prediction

Bighead's Algorithm Notes

Nov 24, 2025 · Industry Insights

STRAPSim: A Component‑Level Portfolio Similarity Metric for ETF Alignment and Trade Execution

The paper introduces STRAPSim, a semantic, two‑stage, residual‑aware similarity measure that captures component‑level semantics and weight distribution for ETFs, and demonstrates through extensive toy and corporate‑bond ETF experiments that it consistently outperforms Jaccard, weighted Jaccard and BERTScore variants in classification, regression, recommendation and Spearman correlation tasks.

ETF similarityFinancial AISTRAPSim

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STRAPSim: A Component‑Level Portfolio Similarity Metric for ETF Alignment and Trade Execution

Bighead's Algorithm Notes

Nov 22, 2025 · Artificial Intelligence

Quantitative Finance Paper Roundup (Nov 15‑21, 2025)

This roundup presents six recent arXiv papers covering crypto portfolio optimization, Sharpe‑driven stock selection with liquidity constraints, ensemble deep reinforcement learning for stock trading, dynamic machine‑learning‑based stock recommendation, a risk‑sensitive trading framework, and a generative AI model for limit order book messages, each with reported empirical results.

Machine LearningQuantitative Financecryptocurrency

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Quantitative Finance Paper Roundup (Nov 15‑21, 2025)