Introduces FinTruthQA, a 6,000-entry annotated benchmark for AI assessment of financial disclosure quality across four criteria, with model evaluations showing strong results on question tasks but weaker on answer relevance.
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Finbert: A pretrained language model for financial communications
10 Pith papers cite this work. Polarity classification is still indexing.
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FinAgent-RAG achieves 76.81-78.46% execution accuracy on financial QA benchmarks by combining contrastive retrieval, program-of-thought code generation, and adaptive strategy routing, outperforming baselines by 5.62-9.32 points.
SBCA is a reinforcement learning framework using BERT cross-modal fusion and Actor-Critic to integrate price data with sentiment text for multi-asset portfolio optimization with practical trading constraints.
CGCMA separates text-conditioned grounding from lag-aware trust gating to fuse asynchronous price and web data, yielding the highest Sharpe ratio of +0.449 on a new crypto news corpus.
FinFRE-RAG combines importance-guided feature reduction with label-aware retrieval-augmented generation to boost LLM performance on tabular fraud detection across four public datasets while providing human-readable rationales.
Media sentiment indicators from Canadian news, when added to a New Keynesian model with endogenous central-bank response, improve out-of-sample forecasts and account for part of monetary-policy propagation to output and prices.
Systematic tests show that specific PDF parsers combined with overlapping chunking strategies better preserve structure and improve RAG answer correctness on financial QA benchmarks including the new TableQuest dataset.
PRAGMA pre-trains a Transformer on heterogeneous banking events with a tailored self-supervised masked objective, yielding embeddings that support strong downstream performance on credit scoring, fraud detection, and lifetime value prediction using linear heads or light fine-tuning.
A survey synthesizing recent LLM research and assessing its applicability to financial data analysis.
A literature review that categorizes bias in LLMs, surveys evaluation and mitigation techniques, and discusses ethical implications.
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FinTruthQA: A Benchmark for AI-Driven Financial Disclosure Quality Assessment in Investor -- Firm Interactions
Introduces FinTruthQA, a 6,000-entry annotated benchmark for AI assessment of financial disclosure quality across four criteria, with model evaluations showing strong results on question tasks but weaker on answer relevance.
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Agentic Retrieval-Augmented Generation for Financial Document Question Answering
FinAgent-RAG achieves 76.81-78.46% execution accuracy on financial QA benchmarks by combining contrastive retrieval, program-of-thought code generation, and adaptive strategy routing, outperforming baselines by 5.62-9.32 points.
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SBCA: Cross-Modal BERT-driven Actor-Critic for Multi-Asset Portfolio Optimization
SBCA is a reinforcement learning framework using BERT cross-modal fusion and Actor-Critic to integrate price data with sentiment text for multi-asset portfolio optimization with practical trading constraints.
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CGCMA: Conditionally-Gated Cross-Modal Attention for Event-Conditioned Asynchronous Fusion
CGCMA separates text-conditioned grounding from lag-aware trust gating to fuse asynchronous price and web data, yielding the highest Sharpe ratio of +0.449 on a new crypto news corpus.
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Understanding Structured Financial Data with LLMs: A Case Study on Fraud Detection
FinFRE-RAG combines importance-guided feature reduction with label-aware retrieval-augmented generation to boost LLM performance on tabular fraud detection across four public datasets while providing human-readable rationales.
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Monetary Policy in the Media Spotlight: Sentiments, Signals, and Economic Impact
Media sentiment indicators from Canadian news, when added to a New Keynesian model with endogenous central-bank response, improve out-of-sample forecasts and account for part of monetary-policy propagation to output and prices.
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Empirical Evaluation of PDF Parsing and Chunking for Financial Question Answering with RAG
Systematic tests show that specific PDF parsers combined with overlapping chunking strategies better preserve structure and improve RAG answer correctness on financial QA benchmarks including the new TableQuest dataset.
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PRAGMA: Revolut Foundation Model
PRAGMA pre-trains a Transformer on heterogeneous banking events with a tailored self-supervised masked objective, yielding embeddings that support strong downstream performance on credit scoring, fraud detection, and lifetime value prediction using linear heads or light fine-tuning.
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Bridging Language Models and Financial Analysis
A survey synthesizing recent LLM research and assessing its applicability to financial data analysis.
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Bias in Large Language Models: Origin, Evaluation, and Mitigation
A literature review that categorizes bias in LLMs, surveys evaluation and mitigation techniques, and discusses ethical implications.