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Bighead's Algorithm Notes
Bighead's Algorithm Notes
May 1, 2026 · Artificial Intelligence

Quantum‑Enhanced A3C² Leverages Time‑Series Dynamic Clustering for Adaptive ETF Stock Picking

Traditional ETF selection and plain A3C reinforcement learning struggle with high‑dimensional features and static clustering, so the authors propose Q‑A3C², which embeds variational quantum circuits and time‑series dynamic clustering into the A3C framework, achieving a 17.09% cumulative return versus a 7.09% benchmark on S&P 500 components.

A3CETF stock selectiondynamic clustering
0 likes · 16 min read
Quantum‑Enhanced A3C² Leverages Time‑Series Dynamic Clustering for Adaptive ETF Stock Picking
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Apr 27, 2026 · Artificial Intelligence

STEAM: Wavelet‑Enhanced Attention Model for Stock Price Prediction

The STEAM model combines discrete wavelet transform, a wavelet‑enhanced attention mechanism, and a market‑index prefix within a Mamba‑2 encoder to capture multi‑frequency spatial and temporal dependencies in stock data, achieving state‑of‑the‑art performance across multiple international markets as measured by IC, PnL and Sharpe ratios.

Mamba-2attention mechanismdeep learning
0 likes · 17 min read
STEAM: Wavelet‑Enhanced Attention Model for Stock Price Prediction
AI Explorer
AI Explorer
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 ModelOpen Source
0 likes · 6 min read
How Kronos Redefines Quantitative Analysis with a Financial‑Market Language Model
Bighead's Algorithm Notes
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 FunctionsMachine LearningQuantitative Trading
0 likes · 15 min read
Which Loss Function Ranks Stocks Best? An Empirical Study with Transformer Models
Bighead's Algorithm Notes
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
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
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
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
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
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
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.

Bayesian OptimizationMulti-Agent Systemsasset allocation
0 likes · 18 min read
Quantitative Finance Paper Digest (Sep 27 – Oct 10 2025)
Bighead's Algorithm Notes
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
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 DataModel selectioncompetition report
0 likes · 4 min read
How We Conquered the 2025 Chinese University Big Data Challenge: Financial Time‑Series Lessons
Bighead's Algorithm Notes
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.

Foundation ModelKronosautoregressive pretraining
0 likes · 9 min read
Paper Review: Kronos – A Temporal Foundation Model for Financial Market Language
Bighead's Algorithm Notes
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.

ESGMachine LearningQuantitative Finance
0 likes · 17 min read
Weekly Quantitative Finance Paper Digest (Aug 23‑29, 2025)
Huawei Cloud Developer Alliance
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.

Machine Learningfinancial time seriesreal-time architecture
0 likes · 9 min read
Can Open‑Source AI Predict the Stock Market? Inside a Real‑Time Forecasting Architecture