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Bighead's Algorithm Notes

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Latest from Bighead's Algorithm Notes

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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.

Few-Shot LearningKnowledge DistillationT-LLM

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 FunctionsTransformerfinancial time series

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.

Deep Learningattention estimatordistribution shift

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.

Graph Neural Networksdeep reinforcement learningmachine learning

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.

Data Augmentationadaptive data synthesisconcept drift

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

Bighead's Algorithm Notes

Jan 25, 2026 · Artificial Intelligence

FinAgent Orchestration Framework: Shifting from Algorithmic to Agent‑Based Trading

The article presents FinAgent, a multi‑agent orchestration framework that maps traditional algorithmic trading components to autonomous agents, validates it on hourly stock and minute‑level Bitcoin back‑tests, and reports superior risk control, auditability, and scalability compared with standard benchmarks.

Algorithmic TradingFinAgentagent-based trading

0 likes · 15 min read

FinAgent Orchestration Framework: Shifting from Algorithmic to Agent‑Based Trading

Bighead's Algorithm Notes

Jan 21, 2026 · Artificial Intelligence

Lead–LagNet: Modeling Cross‑Series Lead‑Lag Dependencies for Time‑Series Forecasting

Lead–LagNet addresses three key limitations of existing graph neural networks for multivariate time‑series forecasting—loss of fine‑grained temporal detail, shared weight assumptions, and reduced interpretability—by introducing a sequence preprocessor with a global influence separator and subsequence detector, a subsequence dependency encoder, and a decoupled message‑passing mechanism, achieving superior performance on synthetic benchmarks and S&P 500 market data.

Financial Market PredictionLead‑Lag DependencyLead–LagNet

0 likes · 13 min read

Lead–LagNet: Modeling Cross‑Series Lead‑Lag Dependencies for Time‑Series Forecasting

Bighead's Algorithm Notes

Jan 13, 2026 · Artificial Intelligence

Do Complex Multi‑Agent Mechanisms Really Boost Investment Returns? A CMU Validation

A five‑agent GPT‑4o‑mini trading system was evaluated over 21 months across technology, general, and financial markets, revealing that while communication among agents can boost returns, the optimal dialogue style depends on market volatility, and higher dialogue quality does not guarantee better performance.

LLM tradingMarket Analysisalpha generation

0 likes · 12 min read

Do Complex Multi‑Agent Mechanisms Really Boost Investment Returns? A CMU Validation