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Bighead's Algorithm Notes

Focused on AI applications in the fintech sector

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Bighead's Algorithm Notes

Mar 13, 2026 · Artificial Intelligence

Paper Reading: STABLE – A Robust Portfolio Allocation Method Using Conditional Diffusion Estimates

The STABLE framework integrates a conditional diffusion generator with a Black‑Litterman mean‑variance optimizer to produce style‑aware return forecasts and risk‑aware portfolio weights, achieving up to a 122.9% Sharpe‑ratio boost, lower drawdowns, and a 15.7% MSE reduction across major equity markets.

Black-LittermanDiffusion Modelsconditional diffusion

0 likes · 17 min read

Paper Reading: STABLE – A Robust Portfolio Allocation Method Using Conditional Diffusion Estimates

Bighead's Algorithm Notes

Mar 11, 2026 · Artificial Intelligence

Paper Review: AlphaBench – Benchmarking LLMs for Formalized Alpha‑Factor Mining

The article reviews AlphaBench, the first benchmark suite for assessing large language models in formalized alpha‑factor mining (FAFM), detailing its three core tasks—factor generation, evaluation, and search—along with experiments on various commercial and open‑source LLMs that reveal strong potential but challenges in robustness, efficiency, and practical usability.

AlphaBenchFAFMLLM

0 likes · 14 min read

Paper Review: AlphaBench – Benchmarking LLMs for Formalized Alpha‑Factor Mining

Bighead's Algorithm Notes

Mar 5, 2026 · Artificial Intelligence

AB‑SSM: Adaptive Bidirectional State‑Space Model for High‑Frequency Portfolio Management

The paper introduces AB‑SSM, an adaptive bidirectional state‑space model that incorporates a time‑varying linear structure and a bidirectional layer to capture market non‑stationarity and asset correlations, and demonstrates through extensive US, China, and crypto experiments that it outperforms traditional, deep‑learning, and DRL baselines in profit‑risk trade‑offs, efficiency, and scalability.

Deep Reinforcement Learningadaptive linear time-varyingbidirectional SSM

0 likes · 12 min read

AB‑SSM: Adaptive Bidirectional State‑Space Model for High‑Frequency Portfolio Management

Bighead's Algorithm Notes

Mar 3, 2026 · Artificial Intelligence

How HORAI Uses Large‑Scale Multimodal Pretraining to Boost Time‑Series Forecasting and Anomaly Detection

The article reviews the HORAI model, which introduces a frequency‑enhanced multimodal pretraining paradigm and the massive MM‑TS dataset, showing that integrating derived images, endogenous text, and real‑world news dramatically improves zero‑shot forecasting and anomaly detection across six domains.

HORAIMultimodal Learninganomaly detection

0 likes · 23 min read

How HORAI Uses Large‑Scale Multimodal Pretraining to Boost Time‑Series Forecasting and Anomaly Detection

Bighead's Algorithm Notes

Feb 28, 2026 · Artificial Intelligence

Quantitative Finance Paper Digest: Key AI‑Driven Research Highlights (Feb 21‑27 2026)

This article curates six recent quantitative‑finance papers, covering Bayesian portfolio policies, signed‑network dimensionality reduction, fine‑grained multi‑agent LLM trading, sentiment‑driven momentum prediction for AAPL, event‑driven hierarchical‑gated reward trading, and a lightweight multi‑model anchoring framework for financial forecasting, summarizing each study’s methodology and empirical results.

Bayesian methodsfinancial forecastinglarge language models

0 likes · 14 min read

Quantitative Finance Paper Digest: Key AI‑Driven Research Highlights (Feb 21‑27 2026)

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.

AttentionNeurIFasset pricing

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.

alpha discoveryarXivfactor analysis

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.

Few‑Shot LearningT-LLMknowledge distillation

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 FunctionsStock RankingTransformer

0 likes · 15 min read

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