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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

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

0 likes · 11 min read

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 similaritySTRAPSimfinancial AI

0 likes · 13 min read

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.

Deep Reinforcement Learningcryptocurrencylimit order book

0 likes · 12 min read

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

Bighead's Algorithm Notes

Nov 18, 2025 · Artificial Intelligence

MSTNN: Temporal Network with Time‑Hyperedge for Stock Trend Prediction

Existing stock trend prediction models overlook periodic patterns and high‑order inter‑stock relations, so the authors propose MSTNN—a framework combining a 3D multi‑scale CNN to capture yearly, monthly, and daily cycles with a time‑hyperedge attention module, achieving state‑of‑the‑art accuracy and profitability on NASDAQ and NYSE benchmarks.

3D CNNMSTNNfinancial time series

0 likes · 13 min read

MSTNN: Temporal Network with Time‑Hyperedge for Stock Trend Prediction

Bighead's Algorithm Notes

Nov 16, 2025 · Artificial Intelligence

COGRASP: Multi‑Scale Stock Price Prediction Using Co‑Occurrence Graphs

This article reviews the COGRASP method, which builds dynamic co‑occurrence graphs from online sources, embeds them with graph neural networks, extracts short, medium, and long‑term patterns via attention‑based LSTMs, and aggregates these signals to achieve state‑of‑the‑art stock price prediction performance on real‑world CSI‑300 data.

ALSTMMulti-Scaleco-occurrence graph

0 likes · 14 min read

COGRASP: Multi‑Scale Stock Price Prediction Using Co‑Occurrence Graphs

Bighead's Algorithm Notes

Nov 15, 2025 · Artificial Intelligence

Quantitative Finance Paper Digest: Nov 8‑14 2025 Highlights

This article summarizes five recent arXiv papers that apply advanced AI techniques such as diffusion models, hierarchical attention, and stochastic differential equations to multivariate financial time‑series forecasting, portfolio selection, volatility surface generation, and gold‑futures alpha strategies, presenting their core methods and experimental results.

Diffusion Modelsequilibrium portfoliofinancial time series

0 likes · 10 min read

Quantitative Finance Paper Digest: Nov 8‑14 2025 Highlights

Bighead's Algorithm Notes

Nov 13, 2025 · Artificial Intelligence

Paper Review: AlphaGAT’s Two‑Stage Learning for Adaptive Portfolio Selection

AlphaGAT introduces a two‑stage learning framework that first extracts robust alpha factors with a CATimeMixer model and a novel loss, then dynamically weights these factors via reinforcement learning (PPO) and a graph attention network, achieving superior portfolio performance across DJIA, HSI, CSI‑100 and crypto markets despite noisy data and distribution shifts.

AlphaGATfinancial AIgraph attention network

0 likes · 14 min read

Paper Review: AlphaGAT’s Two‑Stage Learning for Adaptive Portfolio Selection

Bighead's Algorithm Notes

Nov 11, 2025 · Artificial Intelligence

A New Method: Dynamic Higher-Order Relations and Event-Driven Modeling for Stock Price Prediction

The article reviews a novel stock price prediction model that integrates a Hawkes‑process layer to capture sudden co‑movements and a dynamic hypergraph to represent high‑order relationships, detailing its formulation, training objective, extensive experiments on S&P 500 data, and superior performance over transformer, graph, and hypergraph baselines.

Hawkes processdynamic hypergraphfinancial AI

0 likes · 12 min read

A New Method: Dynamic Higher-Order Relations and Event-Driven Modeling for Stock Price Prediction