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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

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 PredictionInterpretability

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 DetectionKnowledge DistillationTime-series

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.

Foundation ModelPQ lossSparse MoE

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

Bighead's Algorithm Notes

Oct 19, 2025 · Artificial Intelligence

QuantAgent Unveiled: A Multi‑Agent LLM Framework for High‑Frequency Trading (Code Open)

QuantAgent introduces a multi‑agent LLM framework that replaces text‑based inputs with raw OHLC price signals, decomposes trading decisions into Indicator, Pattern, Trend, Risk, and Decision agents, and achieves substantially higher direction accuracy and returns across ten financial assets in zero‑shot HFT experiments.

Financial AILLMhigh-frequency trading

0 likes · 10 min read

QuantAgent Unveiled: A Multi‑Agent LLM Framework for High‑Frequency Trading (Code Open)

Bighead's Algorithm Notes

Oct 18, 2025 · Artificial Intelligence

Time Series Paper Digest (Oct 11‑17 2025): FIRE, CauchyNet, EvoRate, CoRA

From Oct 11‑17 2025, this digest presents four recent AI papers on time‑series forecasting: FIRE introduces a frequency‑domain decomposition with independent amplitude‑phase modeling and adaptive weighting; CauchyNet leverages holomorphic activations for compact, data‑efficient learning; the EvoRate framework quantifies learnability via mutual information; and CoRA adds covariate‑aware adaptation to foundation models, all reporting significant accuracy gains and enhanced interpretability.

AI researchcovariate-aware adaptationdeep learning

0 likes · 10 min read

Time Series Paper Digest (Oct 11‑17 2025): FIRE, CauchyNet, EvoRate, CoRA