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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 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.

Graph Neural NetworksHawkes processdynamic hypergraph

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.

Graph Neural Networkfinancial AIheuristic 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.

Deep Learninganomaly detectioncausal 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.

Graph Neural NetworkLarge Language ModelStock Prediction

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.

Deep LearningStock Predictionfinancial AI

0 likes · 13 min read

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)

Bighead's Algorithm Notes

Oct 31, 2025 · Artificial Intelligence

Weekly Quantitative Paper Digest (Oct 25‑31 2025)

This article summarizes six recent arXiv papers that explore how large language models, graph‑theoretic methods, generative frameworks, hypergraph multimodal architectures, GroupSHAP‑enhanced forecasting, and multi‑agent LLM workflows can improve financial signal extraction, portfolio optimization, and stock‑price prediction, providing empirical results on S&P 500 data.

LLMMultimodal LearningStock Prediction

0 likes · 13 min read

Weekly Quantitative Paper Digest (Oct 25‑31 2025)

Bighead's Algorithm Notes

Oct 30, 2025 · Artificial Intelligence

FinSearchComp: ByteDance’s Expert‑Level Financial Search and Reasoning Benchmark for Real‑World Scenarios

FinSearchComp is the first fully open‑source benchmark that evaluates large‑language‑model agents' search and reasoning abilities in realistic financial workflows, featuring 635 expert‑annotated questions across three task types, built with 70 finance experts, and revealing that web‑enabled models with financial plugins markedly outperform API‑only models.

AI evaluationFinSearchCompLLM Agents

0 likes · 12 min read

FinSearchComp: ByteDance’s Expert‑Level Financial Search and Reasoning Benchmark for Real‑World Scenarios

Bighead's Algorithm Notes

Oct 28, 2025 · Artificial Intelligence

Paper Review: THEME – Thematic Investing via Stock Semantic Embeddings and Temporal Dynamics

The article reviews the THEME framework, which tackles static and coverage limitations of traditional thematic investing by constructing a large Thematic Representation Set (TRS) and applying a two‑stage hierarchical contrastive learning process that first aligns stock text embeddings with theme semantics and then refines them with short‑term return dynamics, achieving superior retrieval and portfolio performance across extensive experiments.

financial AIhierarchical contrastive learningportfolio optimization

0 likes · 12 min read

Paper Review: THEME – Thematic Investing via Stock Semantic Embeddings and Temporal Dynamics