arXiv AI July 24, 2026 By Marcos Florencio

Grounding Investor Views: Neural Predicates in the Black-Litterman Model

arXiv:2607. 20533v1 Announce Type: cross Abstract: Portfolio construction under the Black-Litterman model requires investors to specify views on asset returns alongside explicit uncertainty estimates -- a process that remains largely subjective and difficult to scale.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 7

Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading

arXiv:2607. 02864v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful approach in financial trading, enabling agents to learn optimal strategies through direct market interaction.

By Lin Li, Li Rong Wang, Hsuan Fu, Xiuyi Fan

agents reinforcement-learning

arXiv Machine Learning

Jun 25

Generating Input Distributions for Explaining Portfolio Optimization Pipelines

arXiv:2606. 25808v1 Announce Type: cross Abstract: We propose a predict-optimize-explain framework that uses gradient-based sample generation to interpret various portfolio models by identifying macroeconomic conditions that induce specified portfolio outcomes.

By Batuhan Ata\c{s}, Nur\c{s}en Ayd{\i}n, E. Mehmet K{\i}ral, \c{S}. \.Ilker Birbil

benchmarks

arXiv AI

Jun 8

PandaAI: A Practical Agent CQ2 for Neuro-symbolic Data Analysis And Integrated Decision-Making in Quantitative Finance

arXiv:2606. 06823v1 Announce Type: cross Abstract: While deep learning has excelled in various domains, its application to sequential decision-making in finance remains challenging due to the low Signal-to-Noise Ratio (SNR) and non-stationarity of financial data.

By Yuqi Li, Siyuan Liu, Bingjun Liu

llms agents fine-tuning benchmarks safety

arXiv Machine Learning

Sep 23

Financially Guided Deep Portfolio Optimization

arXiv:2605.28853v2 Announce Type: replace-cross Abstract: Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction cos...

By Rahul Fernandes, Travis Desell

rag

arXiv Machine Learning

Jul 31

FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning

arXiv:2607. 28127v1 Announce Type: cross Abstract: Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs).

By Giorgos Iacovides, Wuyang Zhou, Danilo Mandic

llms nlp reinforcement-learning benchmarks

arXiv Machine Learning

Jul 23

STN-TGAT: Top-K Portfolio Construction via Prior-Guided Graph Attention with Learnable Soft-Threshold Sparsification

arXiv:2607. 19385v1 Announce Type: new Abstract: This paper tackles the problem of stock ranking and portfolio construction under realistic investment settings by jointly modeling temporal dynamics and cross-sectional dependencies.

By Haoran Guo, Yutong Lu, Li Zhang

llms benchmarks

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Related stories

Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading

Generating Input Distributions for Explaining Portfolio Optimization Pipelines

PandaAI: A Practical Agent CQ2 for Neuro-symbolic Data Analysis And Integrated Decision-Making in Quantitative Finance

Financially Guided Deep Portfolio Optimization

FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning

STN-TGAT: Top-K Portfolio Construction via Prior-Guided Graph Attention with Learnable Soft-Threshold Sparsification