Source-linked AI summary
Financial Analysis: Intelligent Financial Data Analysis System Based on LLM-RAG
Jingru Wang, Wen Ding, Xiaotong Zhu
TL;DR
Financial analysis must handle rapidly growing, complex data, while conventional methods and standalone LLMs have limitations in processing and domain-specific financial knowledge. The paper develops an LLM-RAG system with preprocessing, vector retrieval, and query processing components, and evaluates it on NASDAQ fundamentals from 2010 to 2023. The optimized gpt-3.5-turbo-1106+RAG configuration achieved 78.6% accuracy and 89.2% recall, supporting the effectiveness of RAG integration while leaving broader data fusion and adaptability as future challenges.
Problem
Traditional financial analysis methods struggle with complex, diverse data, while direct financial-domain LLM use is limited by insufficient domain knowledge and specialized terminology.
Method
The paper develops an LLM-RAG financial analysis system combining data preprocessing, vector-based storage and retrieval, and RAG-enhanced query processing.
Results
78.6% accuracy and 89.2% recall were achieved by the gpt-3.5-turbo-1106+RAG configuration, with RAG improving accuracy and recall across model configurations.
Takeaways & Limitations
Integrating RAG with LLMs provides valuable insights for financial analysts and decision-makers and supports development of intelligent financial services.
Takeaways & Limitations
Future work should address multi-source data fusion, complex-environment adaptability, broader financial-market coverage, and domain adaptation.
Abstract
from arXiv · showhide
In the modern financial sector, the exponential growth of data has made efficient and accurate financial data analysis increasingly crucial. Traditional methods, such as statistical analysis and rule-based systems, often struggle to process and derive meaningful insights from complex financial information effectively. These conventional approaches face inherent limitations in handling unstructured data, capturing intricate market patterns, and adapting to rapidly evolving financial contexts, resulting in reduced accuracy and delayed decision-making processes. To address these challenges, this paper presents an intelligent financial data analysis system that integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) technology. Our system incorporates three key components: a specialized preprocessing module for financial data standardization, an efficient vector-based storage and retrieval system, and a RAG-enhanced query processing module. Using the NASDAQ financial fundamentals dataset from 2010 to 2023, we conducted comprehensive experiments to evaluate system performance. Results demonstrate significant improvements across multiple metrics: the fully optimized configuration (gpt-3.5-turbo-1106+RAG) achieved 78.6% accuracy and 89.2% recall, surpassing the baseline model by 23 percentage points in accuracy while reducing response time by 34.8%. The system also showed enhanced efficiency in handling complex financial queries, though with a moderate increase in memory utilization. Our findings validate the effectiveness of integrating RAG technology with LLMs for financial analysis tasks and provide valuable insights for future developments in intelligent financial data processing systems.
1. Introduction
The paper motivates an intelligent financial analysis system because conventional methods struggle with growing, complex financial data and LLMs lack sufficient domain-specific knowledge. It proposes combining LLMs with RAG to improve financial information access and analysis.
- Traditional financial analysis methods struggle to process large volumes of complex and diverse information within required timeframes.
- LLMs applied directly to finance face insufficient domain-specific knowledge and difficulties handling specialized financial terminology.
- RAG supplements LLMs with external financial knowledge retrieval, improving access to relevant information and analytical understanding.
- The paper develops an LLM-RAG financial analysis system using NASDAQ financial fundamentals and a specialized model architecture.
2. Literature Review
Prior research applies RAG-LLM methods to financial insight generation, retrieval, sentiment analysis, evaluation, optimization, domain adaptation, and report chunking. Despite this progress, multi-source data fusion and adaptability in complex environments remain open challenges.
- Research applications: Prior work demonstrated RAG-LLM's potential for extracting business insights from complex data and supporting decision-making through practical cases.
- Research applications: Financial-document retrieval research optimized index construction and semantic matching to improve information location and extraction accuracy and efficiency.
- Research applications: RAG-LLM research enhanced financial sentiment analysis by supplying external knowledge about financial context and sentiment terminology.
- Evaluation and optimization: OmniEval addressed the lack of unified financial RAG evaluation standards with multidimensional measures including retrieval accuracy, generated-content relevance, and financial logic.
- Evaluation and optimization: AutoRAG automatically adjusted retrieval sources and model hyperparameters, improving financial document-analysis performance while reducing manual tuning costs.
- Evaluation and optimization: RAG-Studio used self-alignment for financial domain adaptation, while specialized report chunking helped models focus retrieval on relevant financial-report sections.
- Research gaps: Remaining challenges include multi-source data fusion and model adaptability in complex environments.
3. Data Introduction
The study uses quarterly NASDAQ financial fundamentals from 2010 to 2023, covering major technology companies and core financial indicators. Preprocessing standardizes formats and cleans anomalies to support longitudinal corporate-performance analysis.
- Dataset scope: The dataset contains quarterly financial fundamentals for NASDAQ-listed companies, including Apple, Microsoft, and Google.
- Dataset scope: The data spans 2010–2023 and records reporting periods, company identifiers, stock tickers, financial indicators, and monetary values.
- Financial variables: Core indicators include total assets, revenue, net income, and operating income for assessing performance, profitability, and operational efficiency.
- Data preprocessing: Preprocessing removes currency symbols and separators, normalizes dates, validates consistency, and resolves missing values and anomalies.
- Analytical value: Quarterly frequency and longitudinal coverage support analysis of financial trends, seasonal patterns, and performance evolution over time.
- Analytical value: NASDAQ reporting standards and regulations support the dataset's credibility for research on technology-sector corporate performance and market dynamics.
4. Model design and results analysis
The system combines financial-data preprocessing, vector storage, and query retrieval with LLM-RAG components, then evaluates model configurations under controlled experiments. Results show that RAG-enhanced configurations improve accuracy and recall, with gpt-3.5-turbo-1106+RAG achieving the strongest reported performance.
- Model design: Financial preprocessing standardizes monetary values and dates while cleaning missing values and outliers before analysis.The procedure aims to preserve financial information while reducing noise in subsequent analysis.
- Model design: Vector representations preserve semantic information in low-dimensional storage and support similarity-based retrieval of relevant financial data.The vector storage architecture is designed to respond quickly to later queries and retrieval requests.
- Model design: Query processing combines vector retrieval, natural-language query understanding, and financial-domain semantic matching to produce relevant data subsets.The retrieval component depends on indexes, search algorithms, and query-understanding optimization for specialized financial terminology.
- Experimental design: The experiments compare baseline, RAG-enhanced, and updated-model configurations using documented hardware, software, model, and data-processing settings.The experimental design includes a baseline gpt-3.5-turbo group and evaluates the impact of RAG integration and model-version updates.
- Results: Table 6 compares accuracy and recall across baseline and RAG-enhanced models, highlighting performance gains from RAG integration.The comparison covers gpt-3.5-turbo and gpt-4.0-mini baselines alongside their RAG-enhanced variants.
- Results: 78.6% accuracy and 89.2% recall were achieved by gpt-3.5-turbo-1106+RAG, while gpt-3.5-turbo+RAG reached 63.7% accuracy and 88.7% recall.The baseline gpt-3.5-turbo configuration achieved 55.6% accuracy and 78.3% recall, providing the comparison context for the enhanced configurations.
5. Conclusion
The LLM-RAG financial analysis system combines standardized preprocessing, vector storage and retrieval, and RAG-enhanced processing to support efficient financial information access. Experiments show improved model performance, while future work targets broader optimization and financial coverage.
- The system integrates preprocessing, vector storage, query retrieval, and RAG-enhanced processing to improve access to financial information.These components support data quality, consistency, efficient retrieval, and responsive user queries.
- The model comparison results show that integrating RAG with LLMs improves accuracy and recall in financial data processing.The authors attribute these gains to RAG augmenting the models’ knowledge base.
- Future research should optimize RAG-LLM integration and expand datasets across financial markets and instruments to improve generalization.Suggested directions include advanced retrieval algorithms and domain adaptation techniques.