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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 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.

ALSTMFinancial AIGraph Neural Network

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 AITime-series

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.

Financial AIHawkes processTime-series

0 likes · 12 min read

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

Bighead's Algorithm Notes

Nov 9, 2025 · Artificial Intelligence

How Heuristic‑Guided Inverse Reinforcement Learning Boosts Portfolio Optimization

The article presents a heuristic‑guided inverse reinforcement learning framework that generates expert strategies respecting industry diversification and correlation constraints, employs a multi‑objective reward to balance return and risk, and uses a heterogeneous graph attention network to model stock relationships, achieving superior risk‑adjusted returns on CSI‑300, CSI‑500, NASDAQ‑100 and S&P‑500 benchmarks.

Financial AIGraph Neural Networkheuristic expert policy

0 likes · 13 min read

How Heuristic‑Guided Inverse Reinforcement Learning Boosts Portfolio Optimization

Bighead's Algorithm Notes

Nov 8, 2025 · Artificial Intelligence

Time-Series Paper Digest: Nov 1‑7 2025 Highlights

This digest summarizes three recent AI papers—DoFlow, Forecast2Anomaly, and ForecastGAN—detailing their causal generative flow model for interventions, a retrieval‑augmented framework for zero‑shot anomaly prediction, and a decomposition‑based adversarial approach that improves multi‑horizon forecasting across diverse datasets.

Anomaly DetectionTime-seriescausal inference

0 likes · 8 min read

Time-Series Paper Digest: Nov 1‑7 2025 Highlights

Bighead's Algorithm Notes

Nov 7, 2025 · Artificial Intelligence

Weekly AI Finance Paper Digest (Nov 1‑7 2025)

This digest summarizes three recent AI‑driven finance papers—DeltaLag’s dynamic lead‑lag detection, MS‑HGFN’s multi‑scale graph network for stock movement, and LiveTradeBench’s real‑time LLM trading benchmark—highlighting their methods, datasets, and performance gains.

Financial AIGraph Neural NetworkLarge Language Model

0 likes · 8 min read

Weekly AI Finance Paper Digest (Nov 1‑7 2025)

Bighead's Algorithm Notes

Nov 4, 2025 · Artificial Intelligence

Key Quantitative Finance Papers from WWW2025 – Summaries & Insights

This article compiles concise English summaries of recent AI-driven quantitative finance papers presented at WWW2025, covering novel stock‑price forecasting frameworks such as CSPO, MERA, Ploutos, DINS, HedgeAgents, HRFT, and IDED, with links to the original PDFs, code repositories, authors, and abstracts.

Financial AIMachine LearningQuantitative Finance

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Key Quantitative Finance Papers from WWW2025 – Summaries & Insights

Bighead's Algorithm Notes

Nov 1, 2025 · Artificial Intelligence

Recent Time-Series Research Summaries (Oct 25‑31 2025)

This article presents concise summaries of five newly released arXiv papers on time‑series forecasting and causal discovery, highlighting each work’s objectives, proposed methods such as FreLE, selective learning, TempoPFN, and DOTS, and the reported experimental improvements.

causal discoveryselective learningspectral bias

0 likes · 8 min read

Recent Time-Series Research Summaries (Oct 25‑31 2025)