financial time series — 15 Technical Articles

Apr 11, 2026 · Artificial Intelligence

How Kronos Redefines Quantitative Analysis with a Financial‑Market Language Model

Kronos, an open‑source large model trained on OHLCV data from over 45 exchanges, treats financial time‑series as a specialized language, using a custom tokenizer and a two‑stage Transformer to enable price prediction, market state detection, signal generation, and risk simulation, with easy Hugging Face integration and a live demo for BTC/USDT.

KronosLarge Language ModelTransformer

0 likes · 6 min read

How Kronos Redefines Quantitative Analysis with a Financial‑Market Language Model

Bighead's Algorithm Notes

Feb 18, 2026 · Artificial Intelligence

Which Loss Function Ranks Stocks Best? An Empirical Study with Transformer Models

This paper evaluates point‑wise, pair‑wise, and list‑wise loss functions for Transformer‑based stock‑return prediction on 110 S&P 500 stocks, showing that Margin loss achieves the highest annual return (16.23%) and Sharpe ratio (0.75), ListNet delivers strong returns with low volatility, and BPR minimizes maximum drawdown, highlighting how loss design critically shapes ranking‑driven portfolio performance.

Loss FunctionsStock RankingTransformer

0 likes · 15 min read

Which Loss Function Ranks Stocks Best? An Empirical Study with Transformer Models

Bighead's Algorithm Notes

Dec 25, 2025 · Artificial Intelligence

Paper Review: DeltaLag – An End‑to‑End Deep Learning Framework for Dynamically Learning Lead‑Lag Patterns in Financial Markets

DeltaLag introduces a sparse cross‑attention mechanism that dynamically discovers pair‑specific, time‑varying lead‑lag relationships in US equity markets and uses them to construct interpretable trading signals, achieving significantly higher annualized returns, Sharpe ratios, and information coefficients than fixed‑lag, statistical, and other spatio‑temporal deep learning baselines.

DeltaLagdeep learningfinancial time series

0 likes · 13 min read

Paper Review: DeltaLag – An End‑to‑End Deep Learning Framework for Dynamically Learning Lead‑Lag Patterns in Financial Markets

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.

BERTGRUMultimodal Learning

0 likes · 11 min read

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

Bighead's Algorithm Notes

Nov 30, 2025 · Artificial Intelligence

Paper Review: Hermes – Multi‑Scale Hypergraph for Stock Forecasting with Lead‑Lag Modeling

The Hermes framework introduces a moving‑aggregation module and a multi‑scale fusion module within a hypergraph network to capture industry lead‑lag interactions and multi‑scale stock relationships, achieving superior accuracy and efficiency over existing SOTA methods on three real US stock datasets, as demonstrated by extensive experiments and ablations.

financial time serieshypergraph neural networklead‑lag interaction

0 likes · 11 min read

Paper Review: Hermes – Multi‑Scale Hypergraph for Stock Forecasting with Lead‑Lag Modeling

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 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

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 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 lossTransformerfinancial time series

0 likes · 12 min read

FinCast: A Foundation Model for Financial Time‑Series Forecasting

Bighead's Algorithm Notes

Oct 10, 2025 · Artificial Intelligence

Quantitative Finance Paper Digest (Sep 27 – Oct 10 2025)

This digest summarizes recent arXiv papers that introduce new AI‑driven methods for portfolio similarity, Bayesian portfolio optimization, end‑to‑end deep‑learning portfolio construction, large‑language‑model‑based financial prediction, and multi‑agent crypto‑trading systems, highlighting their datasets, architectures, and empirical gains.

asset allocationbayesian optimizationcrypto trading

0 likes · 18 min read

Quantitative Finance Paper Digest (Sep 27 – Oct 10 2025)

Bighead's Algorithm Notes

Oct 2, 2025 · Artificial Intelligence

FinZero: Multimodal Large‑Model Reasoning for Financial Time‑Series Forecasting

FinZero is a multimodal large‑model that leverages a 30‑billion‑parameter Qwen2.5‑VL backbone fine‑tuned with the UARPO strategy on the FVLDB dataset, enabling accurate financial time‑series prediction, uncertainty quantification, and outperforming larger models such as GPT‑4o by about 13.5% in high‑confidence groups.

FinZeroGPT-4o comparisonQwen2.5-VL-3B

0 likes · 10 min read

FinZero: Multimodal Large‑Model Reasoning for Financial Time‑Series Forecasting

Data Party THU

Sep 11, 2025 · Big Data

How We Conquered the 2025 Chinese University Big Data Challenge: Financial Time‑Series Lessons

Our team "Stay Overnight" from Chongqing University of Posts and Telecommunications placed second nationally in the 2025 China University Computer Competition Big Data Challenge, navigating volatile financial data, shifting from time‑series to supervised learning, and emphasizing feature engineering to boost model performance.

big datacompetition reportfeature engineering

0 likes · 4 min read

How We Conquered the 2025 Chinese University Big Data Challenge: Financial Time‑Series Lessons

Bighead's Algorithm Notes

Sep 7, 2025 · Artificial Intelligence

Paper Review: Kronos – A Temporal Foundation Model for Financial Market Language

This article reviews Kronos, a unified and scalable pre‑training framework designed for financial K‑line data, detailing its tokenization approach, autoregressive architecture, large‑scale pre‑training on 12 billion records, and experimental results that show substantial gains in price prediction, volatility forecasting, synthetic data generation, and investment simulation.

Kronosautoregressive pretrainingfinancial time series

0 likes · 9 min read

Paper Review: Kronos – A Temporal Foundation Model for Financial Market Language

Bighead's Algorithm Notes

Aug 29, 2025 · Artificial Intelligence

Weekly Quantitative Finance Paper Digest (Aug 23‑29, 2025)

This digest summarizes nine recent arXiv papers covering quantum portfolio optimization, thematic investing with semantic stock representations, multi‑indicator reinforcement learning for trading, attention‑based asset pricing, ESG variable selection, deep neural networks for return distribution forecasting, a foundation model for financial time‑series, a multi‑agent trading system with self‑reflection, and dynamic weighting machine‑learning stock selection strategies.

ESGQuantum Computingdeep learning

0 likes · 17 min read

Weekly Quantitative Finance Paper Digest (Aug 23‑29, 2025)

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.

financial time seriesmachine learningreal-time architecture

0 likes · 9 min read

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