It is concluded that while LLMs hold genuine promise within AI trading systems, robust deployment requires careful task decomposition, rigorous backtesting protocols, and domain-aware fine-tuning strategies.
Analyzing financial documents such as 10-K filings, tabular disclosures, and macroeconomic reports demands expert reasoning and extensive time. However, existing Retrieval-Augmented Generation systems often struggle to process hybrid text-table structures or the massive scale of financial documents. To address these challenges, we propose Hierarchical Reranker, a RAG framework designed to improve retrieval performance and generative reliability across large-scale financial datasets. The system integrates three key innovations: Pre-Retrieval Optimization, enhancing query clarity and search efficiency through normalization, keyword expansion, and table transformation; Hierarchical Reranker Architecture, improving retrieval precision through a two-stage ranking mechanism; and Long-Context Management, preserving reasoning accuracy through adaptive input partitioning and fusion under extensive contexts. Across multiple benchmarks, including FinQA, FinanceBench, and ConvFinQA, the proposed system achieved an NDCG@20 score of 0.7918 and demonstrated superior factual consistency. Its robustness was further validated by achieving second place in the ACM-ICAIF'24 FinanceRAG Challenge. This work presents a deployable, domain-optimized RAG pipeline that enhances both the accuracy and scalability of financial reasoning, paving the way for automated audit reporting and quantitative investment analysis. The source code will be made publicly available on GitHub upon acceptance.
This study analyzes annual reports of firms listed on Borsa Istanbul (BIST) for the period from 1990 to 2026, and demonstrates that data-driven initia’sation significantly improves topi’sc interpretability, reduces optimization iterations, decreases model uncertainty, and en’hances reproducibility compared with traditional initializa’on strategies.
Financial information no longer arrives in a single format. Research reports come as PDFs, financial statements live in spreadsheets, market trends are captured in images, and policy documents reach analysts as scans, each carrying part of the picture the others cannot supply. Accounting information systems built around single-modality extraction pipelines and rule-based tools therefore struggle to assemble the full picture, slowing financial statement analysis, complicating audit evidence corroboration, and limiting investment decision support. This study presents FinVision, a multimodal large language model that unites vision-language models with domain-specific financial reasoning. Instead of processing documents in isolation, FinVision reads text, tables, and images together, converts them into consistent structured data, and verifies cross-modal agreement, in the same spirit as auditors corroborating evidence from independent sources. The model is trained in two stages, pre-trained on large-scale public financial corpora and fine-tuned on institution-specific investment data, so it can apply established valuation methodologies and audit risk assessment frameworks while outperforming zero-shot and single-stage baselines. A natural-language decision pipeline lets users describe what they need and turns those descriptions into executable workflows, supporting portfolio optimization, real-time risk monitoring, and refinement through multi-turn dialogue. Across 200 listed companies, FinVision reduced valuation error by 19 percent relative to the strongest baseline; a user study with 48 accounting and investment professionals reported a 51 percent reduction in task completion time. These results carry implications for audit automation, financial reporting quality, and more inclusive access to expert-level financial analysis.
We present the IGT system for PolyFiQA Task 2 of the FinMMEval Lab at CLEF 2026, a multilingual financial question answering task over English SEC filings and multilingual news articles (English, Chinese, Japanese, Spanish, Greek) for four companies. Our central observation is that the 344 development questions divide into two families requiring fundamentally different approaches: structured numeric types (R&D ratio, cash flow, capital expenditure) are best answered by direct keyword extraction on filing text, while synthesis types (investment strategy, capital allocation, top-three revenue focuses) require rule-based multilingual news passage selection. A dataset analysis reveals that 17-18 of 19 ground-truth reference answers per synthesis type share an exact evidence label prefix, whose unigram tokens contribute directly to ROUGE-1 overlap. The final system achieves development ROUGE-1 approximately 0.395, a 60% relative improvement over a generic RAG baseline (approximately 0.247), and ranks 3rd of 12 teams on the official test set with ROUGE-1 = 0.3071, Precision = 0.2821, and Recall = 0.4044.
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