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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 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 AIQuantitative Financedeep learning

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.

Financial AILLMMultimodal Learning

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

Bighead's Algorithm Notes

Oct 26, 2025 · Artificial Intelligence

How Shapelet-Based Patterns Predict Financial Market Direction

The article presents a two‑stage framework—SIMPC for invariant multivariate pattern clustering and JISC‑Net for shape‑subclass detection—that achieves accurate and interpretable financial market direction forecasts, outperforming strong baselines on Bitcoin and S&P 500 datasets across most metric‑dataset combinations.

DTWDirection PredictionJISC-Net

0 likes · 11 min read

How Shapelet-Based Patterns Predict Financial Market Direction

Bighead's Algorithm Notes

Oct 25, 2025 · Artificial Intelligence

Time Series Paper Digest: Extreme Event Prediction, Multimodal Fusion & Anomaly Detection

This article summarizes four recent arXiv papers on time‑series forecasting, covering a hierarchical knowledge‑distillation framework for extreme events, a graph‑enhanced multimodal fusion network, an interpretable unsupervised anomaly detector, and an adaptive masking loss that improves prediction accuracy.

Anomaly Detectionadaptive maskingexpert mixture

0 likes · 10 min read

Time Series Paper Digest: Extreme Event Prediction, Multimodal Fusion & Anomaly Detection

Bighead's Algorithm Notes

Oct 24, 2025 · Artificial Intelligence

Weekly AI‑Finance Paper Digest (Oct 18‑24 2025)

This digest presents seven recent arXiv papers that explore large‑language‑model‑driven portfolio scoring, hybrid ResNet‑RMT covariance denoising for crypto, LLM‑enhanced financial causal analysis, multilingual news alignment for stock returns, three‑step bubble prediction with news and macro data, multimodal volatility forecasting, and news‑aware reinforcement trading, each with reported performance gains.

Financial AILLMMultimodal Learning

0 likes · 15 min read

Weekly AI‑Finance Paper Digest (Oct 18‑24 2025)

Bighead's Algorithm Notes

Oct 23, 2025 · Artificial Intelligence

FinCast: A Foundation Model for Financial Time‑Series Forecasting

FinCast introduces a decoder‑only Transformer foundation model for financial time‑series forecasting that tackles non‑stationarity, multi‑domain diversity, and multi‑resolution challenges through input chunking with frequency embeddings, a sparse MoE decoder, and a PQ‑loss, achieving zero‑shot and supervised gains over state‑of‑the‑art baselines while running five times faster on consumer GPUs.

PQ lossSparse MoETransformer

0 likes · 12 min read

FinCast: A Foundation Model for Financial Time‑Series Forecasting

Bighead's Algorithm Notes

Oct 21, 2025 · Artificial Intelligence

KANMixer: A New KAN‑Centric Paradigm for Long‑Term Time Series Forecasting

This article reviews the KANMixer model, which places Kolmogorov‑Arnold Networks at the core of a lightweight architecture for long‑term time series forecasting, detailing its design, extensive benchmark experiments on seven real‑world datasets, ablation analyses, and its computational trade‑offs versus MLP and Transformer baselines.

Ablation StudyKANLong-term Time Series Forecasting

0 likes · 8 min read

KANMixer: A New KAN‑Centric Paradigm for Long‑Term Time Series Forecasting