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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

Feb 27, 2026 · Artificial Intelligence

Paper Review: NeurIF – Feature‑Controlled Learning of Dynamic Asset‑Pricing Factors and Loadings

NeurIF introduces a neural instrumented factorization framework that leverages company features as instruments, combines spatial and temporal attention to learn time‑varying latent factors and their loadings, achieves 1‑18% RMSE improvement over transformer baselines, and produces statistically significant long‑short portfolios that explain cross‑sectional pricing anomalies.

NeurIFasset pricingattention

0 likes · 15 min read

Paper Review: NeurIF – Feature‑Controlled Learning of Dynamic Asset‑Pricing Factors and Loadings

Bighead's Algorithm Notes

Feb 23, 2026 · Artificial Intelligence

How AlphaPROBE Leverages DAGs for Efficient Alpha‑Factor Mining

AlphaPROBE reformulates alpha‑factor discovery as a strategy‑navigation problem on a directed acyclic graph, combining a Bayesian factor retriever with a DAG‑aware generator to achieve superior prediction accuracy, stable returns, and faster training across three major Chinese stock markets.

Alpha FactorAlphaPROBEBayesian Retrieval

0 likes · 22 min read

How AlphaPROBE Leverages DAGs for Efficient Alpha‑Factor Mining

Bighead's Algorithm Notes

Feb 20, 2026 · Industry Insights

Weekly Quantitative Paper Digest (Feb 14‑Feb 20, 2026)

This article presents concise summaries of three recent arXiv papers covering a high‑performance Python library for systematic financial factor computation, a self‑evolving agent for discovering explainable alpha factors, and an empirical study of the Shanghai‑Hong Kong Stock Connect's impact on A‑H share price premiums under varying market efficiency conditions.

Quantitative Financealpha discoveryarXiv

0 likes · 9 min read

Weekly Quantitative Paper Digest (Feb 14‑Feb 20, 2026)

Bighead's Algorithm Notes

Feb 20, 2026 · Artificial Intelligence

How Time Distillation Empowers Large Language Models for Time‑Series Forecasting (T‑LLM)

The paper introduces T‑LLM, a time‑distillation framework that transfers predictive behavior from a lightweight teacher model to a general‑purpose LLM, enabling accurate multivariate time‑series forecasting across full‑sample, few‑shot, and zero‑shot settings while eliminating the need for large‑scale pre‑training.

Knowledge DistillationT-LLMfew-shot learning

0 likes · 18 min read

How Time Distillation Empowers Large Language Models for Time‑Series Forecasting (T‑LLM)

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

Feb 13, 2026 · Artificial Intelligence

How ReVol’s Return‑Volatility Normalization Reduces Distribution Shift in Stock Price Prediction

The paper introduces ReVol, a three‑stage framework that normalizes price features, uses an attention‑based estimator to recover return and volatility, and denormalizes predictions, demonstrating consistent improvements of over 0.03 in IC and 0.7 in Sharpe ratio across multiple time‑series models.

Financial AIattention estimatordeep learning

0 likes · 15 min read

How ReVol’s Return‑Volatility Normalization Reduces Distribution Shift in Stock Price Prediction

Bighead's Algorithm Notes

Feb 6, 2026 · Artificial Intelligence

Weekly Quantitative Finance Paper Summary (Jan 31–Feb 6 2026)

This article summarizes recent quantitative‑finance research, presenting abstracts and key findings of three papers—BPASGM for machine‑learning‑driven portfolio construction, PIKAN‑enhanced deep reinforcement learning with physics‑informed regularization, and GAPNet’s dynamic graph‑based stock relation learning—along with links to numerous related studies.

Machine Learningdeep reinforcement learninggraph neural networks

0 likes · 11 min read

Weekly Quantitative Finance Paper Summary (Jan 31–Feb 6 2026)

Bighead's Algorithm Notes

Feb 1, 2026 · Artificial Intelligence

Beyond Historical Data: Adaptive Synthesis for Financial Time Series

This article reviews a recent paper that proposes a drift‑aware data‑stream system integrating machine‑learning‑based adaptive control into financial data management, introducing a parametric data‑operation module, a gradient‑based bi‑level optimizer, and a curriculum planner to improve model robustness and risk‑adjusted returns in non‑stationary markets.

Curriculum LearningQuantitative Financeadaptive data synthesis

0 likes · 18 min read

Beyond Historical Data: Adaptive Synthesis for Financial Time Series

Bighead's Algorithm Notes

Jan 30, 2026 · Artificial Intelligence

Weekly Quantitative Finance Paper Digest (Jan 24‑Jan 30, 2026)

This article presents concise summaries of three recent quantitative finance papers—MarketGAN for high‑dimensional asset return generation, AlphaCFG for grammar‑guided Alpha factor discovery, and a hybrid AI‑driven trading system integrating technical analysis, machine learning, and sentiment—highlighting their methodologies, experimental results, and economic value, and provides links to additional related research.

Alpha Factor DiscoveryGenerative Adversarial NetworksHybrid AI Trading

0 likes · 9 min read

Weekly Quantitative Finance Paper Digest (Jan 24‑Jan 30, 2026)

Bighead's Algorithm Notes

Jan 28, 2026 · Artificial Intelligence

How HiveMind Optimizes LLM Multi‑Agent Trading Systems via Contribution‑Guided Online Prompts

The HiveMind framework introduces a contribution‑guided online prompt optimization (CG‑OPO) that quantifies each LLM‑driven agent’s impact with Shapley values and uses a DAG‑Shapley algorithm to efficiently attribute credit, enabling real‑time adaptive optimization of multi‑agent stock‑trading systems and achieving superior returns with far fewer LLM calls.

DAG-ShapleyFinancial TradingLLM

0 likes · 15 min read

How HiveMind Optimizes LLM Multi‑Agent Trading Systems via Contribution‑Guided Online Prompts