Tagged articles

22 articles

Page 1 of 1

May 15, 2026 · Artificial Intelligence

2026 Big Data Challenge Announces Monthly Star Winners and Shares Winning Teams’ Insights

The 2026 China University Computer Competition – Big Data Challenge reveals the Monthly Star award winners, each receiving 800 RMB, and presents detailed experience reports from the top teams covering feature engineering, model selection, training validation, and ensemble strategies for stock prediction.

Big DataModel FusionStock Prediction

0 likes · 7 min read

2026 Big Data Challenge Announces Monthly Star Winners and Shares Winning Teams’ Insights

Bighead's Algorithm Notes

Apr 20, 2026 · Artificial Intelligence

Exploring CSMD: A China‑Specific Multimodal Stock Dataset and the LightQuant Quantitative Framework

The article introduces CSMD, a high‑quality multimodal dataset built from Chinese financial news for the CSI‑300 and SSE‑50 stocks, describes LLM‑enhanced factor extraction and rigorous data validation, presents the modular LightQuant framework, and shows through extensive experiments that CSMD and LightQuant outperform existing resources such as CMIN‑CN in stock trend prediction and backtesting.

CSMDLLM factor extractionLightQuant

0 likes · 12 min read

Exploring CSMD: A China‑Specific Multimodal Stock Dataset and the LightQuant Quantitative Framework

Bighead's Algorithm Notes

Mar 20, 2026 · Artificial Intelligence

Weekly Quantitative Finance Paper Summaries (Mar 14‑Mar 20, 2026)

This article compiles abstracts of four recent AI‑driven quantitative finance papers, covering an autonomous factor‑investing framework, a program‑level factor‑mining system, an adaptive regime‑aware stock‑price predictor with reinforcement learning, and a comprehensive analysis of AI agents in financial markets.

AI agentsStock Predictionfactor investing

0 likes · 10 min read

Weekly Quantitative Finance Paper Summaries (Mar 14‑Mar 20, 2026)

Bighead's Algorithm Notes

Jan 4, 2026 · Artificial Intelligence

How VTA Combines Large‑Model Reasoning for Precise and Explainable Stock Time‑Series Forecasting

The VTA framework integrates large language model reasoning with textual annotation of technical indicators, employs a Time‑GRPO reinforcement‑learning objective and multi‑stage joint conditional training, and achieves state‑of‑the‑art accuracy and expert‑rated interpretability on US, Chinese and European stock datasets.

LLMStock PredictionTime Series

0 likes · 19 min read

How VTA Combines Large‑Model Reasoning for Precise and Explainable Stock Time‑Series Forecasting

Bighead's Algorithm Notes

Dec 30, 2025 · Artificial Intelligence

MaGNet: Dual‑Hypergraph Mamba Network for Time‑Causal and Global Stock Trend Forecasting

MaGNet introduces a three‑component architecture—MAGE block with bidirectional Mamba, adaptive gating and sparse MoE, 2‑D spatio‑temporal attention, and a dual hypergraph framework (time‑causal and global probability hypergraphs)—that outperforms 17 baselines on six major stock indices in both prediction accuracy and risk‑adjusted returns.

Financial AIHypergraphMaGNet

0 likes · 14 min read

MaGNet: Dual‑Hypergraph Mamba Network for Time‑Causal and Global Stock Trend Forecasting

Bighead's Algorithm Notes

Dec 23, 2025 · Artificial Intelligence

How H3M‑SSMoEs Combines Hypergraph Multimodal Learning and LLM Reasoning to Predict Stock Direction

The paper introduces H3M‑SSMoEs, a framework that integrates a multi‑context hypergraph for fine‑grained spatio‑temporal dynamics with a frozen Llama‑3.2‑1B LLM adapter, and a style‑structured expert mixture to jointly model stock relationships, multimodal semantics, and market regimes, achieving superior accuracy and investment returns on DJIA, NASDAQ‑100, and S&P‑100 benchmarks.

Financial AIHypergraphLLM

0 likes · 14 min read

How H3M‑SSMoEs Combines Hypergraph Multimodal Learning and LLM Reasoning to Predict Stock Direction

Bighead's Algorithm Notes

Dec 13, 2025 · Artificial Intelligence

Key Quantitative Finance Papers (Dec 6‑12 2025) – AI‑Driven Insights

This article summarizes ten recent arXiv papers (Dec 6‑12 2025) that explore AI‑driven techniques—from neural‑network ranking and reinforcement learning to quantum models and LLM agents—for quantitative finance and investment decision‑making.

Quantitative FinanceStock Predictioncryptocurrency

0 likes · 18 min read

Key Quantitative Finance Papers (Dec 6‑12 2025) – AI‑Driven Insights

Bighead's Algorithm Notes

Dec 4, 2025 · Artificial Intelligence

Paper Review: RETuning Boosts Large‑Model Stock Trend Prediction Reasoning

This article analyzes the RETuning framework, which addresses LLMs' bias toward analyst opinions and lack of evidence weighting in stock movement prediction by introducing a two‑stage cold‑start fine‑tuning and reinforcement learning pipeline, evaluating it on the large Fin‑2024 dataset and demonstrating significant F1 gains, inference‑time scaling, and out‑of‑distribution robustness.

Fin-2024GRPOInference Scaling

0 likes · 12 min read

Paper Review: RETuning Boosts Large‑Model Stock Trend Prediction Reasoning

Bighead's Algorithm Notes

Dec 2, 2025 · Artificial Intelligence

Dual-Relation Fusion Network (DRFN) for Accurate Stock Prediction

The paper introduces DRFN, a dual‑relation fusion network that jointly models static and dynamic stock relationships using multimodal BERT and GRU encodings, achieving significantly lower RMSE and MAE than baseline models on both Chinese and US market datasets.

BERTGRUGraph Neural Network

0 likes · 11 min read

Dual-Relation Fusion Network (DRFN) for Accurate Stock Prediction

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.

Deep LearningFinBERTSHAP

0 likes · 11 min read

IKNet: Explainable Stock Price Forecasting with News Keywords and Technical Indicators

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 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 NetworkStock 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 LearningFinancial AIQuantitative Finance

0 likes · 13 min read

Key Quantitative Finance Papers from WWW2025 – Summaries & Insights

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)

Code Wrench

Oct 16, 2025 · Artificial Intelligence

Build a Go‑Powered Stock Trend Predictor with ONNX Runtime in Minutes

This guide walks you through setting up an Ubuntu environment, training a LightGBM stock‑movement model in Python, exporting it to ONNX, and deploying fast, cross‑platform inference in Go using ONNX Runtime, complete with code snippets and project structure.

AIGoInference

0 likes · 11 min read

Build a Go‑Powered Stock Trend Predictor with ONNX Runtime in Minutes

Bighead's Algorithm Notes

Sep 21, 2025 · Artificial Intelligence

FinKario: Event‑Enhanced Financial Knowledge Graphs Boost A‑Share Sharpe Ratio to 4.9

This article reviews the FinKario paper, which introduces an event‑augmented financial knowledge graph and a two‑stage RAG retrieval strategy that together enable real‑time knowledge updates and efficient integration of long‑form research reports, yielding a Sharpe ratio of 4.9 and outperforming baseline LLMs and institutional strategies in back‑testing.

FinKarioLLMRAG

0 likes · 10 min read

FinKario: Event‑Enhanced Financial Knowledge Graphs Boost A‑Share Sharpe Ratio to 4.9

Data Party THU

Sep 12, 2025 · Big Data

Key Lessons from Winning the 2025 China University Big Data Competition

The author shares a detailed account of their experience in the 2025 China University Big Data Competition, describing the team’s top national ranking, the shift from absolute stock price prediction to robust ranking learning, extensive feature engineering, and reflections on balancing technical ambition with real‑world constraints.

Big DataStock Predictiondata competition

0 likes · 5 min read

Key Lessons from Winning the 2025 China University Big Data Competition

Bighead's Algorithm Notes

Aug 28, 2025 · Artificial Intelligence

Key AI-Driven Quantitative Finance Papers from KDD2025

This article summarizes recent AI research on quantitative finance, covering AlphaAgent's LLM-driven alpha mining, UMI's multi‑level irrationality factors, PDU's progressive dependency learning for stock ranking, SSPT's stock‑specific pretraining transformer, and Enhancer's distribution‑aware meta‑learning framework, all of which demonstrate improved stock prediction and resistance to alpha decay.

Alpha MiningFinancial AILLM

0 likes · 9 min read

Key AI-Driven Quantitative Finance Papers from KDD2025

Test Development Learning Exchange

Nov 29, 2024 · Artificial Intelligence

Using LSTM Networks for Stock Price Time Series Prediction with Keras

This tutorial demonstrates how to apply an LSTM deep‑learning model in Python to forecast stock closing prices, covering data acquisition, preprocessing, model construction, training, evaluation, and visualization of results for time‑series prediction.

AIDeep LearningKeras

0 likes · 8 min read

Using LSTM Networks for Stock Price Time Series Prediction with Keras

Big Data and Microservices

Apr 19, 2016 · Industry Insights

Designing a Scalable Real‑Time Stock Prediction Architecture with Open‑Source Tools

This article outlines a reference architecture for a low‑latency, horizontally scalable real‑time stock prediction system built with open‑source components such as Spring Cloud Data Flow, Apache Geode, Spark MLlib, and Hadoop, and discusses data flow steps, simplified deployment, and algorithm choices for market forecasting.

Big DataReal-TimeStock Prediction

0 likes · 7 min read

Designing a Scalable Real‑Time Stock Prediction Architecture with Open‑Source Tools

Huawei Cloud Developer Alliance

Jan 8, 2016 · Artificial Intelligence

Can Open‑Source AI Predict the Stock Market? Inside a Real‑Time Forecasting Architecture

The article examines the suspension of China's stock‑market circuit‑breaker, then explores whether open‑source frameworks and machine‑learning algorithms can realistically forecast stock prices by leveraging massive historical data, real‑time streams, and sentiment analysis from social media and news sources.

Stock Predictionfinancial time seriesmachine learning

0 likes · 9 min read

Architect

Dec 26, 2015 · Artificial Intelligence

Open-Source Reference Architecture for Real-Time Stock Prediction

The article presents an open‑source, highly scalable reference architecture that combines real‑time data ingestion, machine‑learning model training, and low‑latency prediction using components such as Spring Cloud Data Flow, Apache Geode, Spark MLlib, and Hadoop to enable continuous stock price forecasting.

Apache GeodeReal-TimeSpring Cloud Data Flow

0 likes · 7 min read

Open-Source Reference Architecture for Real-Time Stock Prediction