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\title{\textbf{Agent vs RAG for Financial Analysis: A Hybrid Approach}}
\author{Christopher Ongko\\
Global Master Science, Yuan Ze University, Taiwan}
\date{November 2025}
\begin{document}
\maketitle
\begin{abstract}
Large Language Models (LLMs) have enabled two dominant architectural paradigms for AI-driven financial analysis: multi-agent systems with iterative reasoning and Retrieval-Augmented Generation (RAG) with single-pass inference. This study presents the first comprehensive empirical comparison of these approaches, introducing a novel Hybrid architecture and rigorous validation methodology including ground truth accuracy tracking, statistical significance testing, and multi-model evaluation. We conducted 72 controlled experiments across 8 stocks, 6 market sectors, and 3 analysis tasks, validated with 136 production API experiments across four Groq-accessible models (Meta LLaMA-70B, Meta LLaMA-8B, Alibaba Qwen3-32B, Moonshot Kimi-K2) covering 15 stocks $\times$ 2 tasks plus baselines. Results reveal fundamental trade-offs: agents achieve 1.28$\times$ higher quality scores (78.1 vs 61.1) through 4.3 tool calls and 11.1 reasoning steps, while RAG systems deliver 7.2$\times$ faster responses (6.0s vs 43.4s). Our Hybrid architecture achieves optimal balance: 72.3 quality score (92\% of agent performance) at 13.4s latency (3.2$\times$ faster than agents) and 33\% of agent cost. Production validation with Groq API confirms architectural patterns with up to 15.8$\times$ latency improvement. Statistical analysis framework with p-values, effect sizes, and confidence intervals establishes methodological rigor.
\end{abstract}
\textbf{Keywords:} Large Language Models, Multi-Agent Systems, Retrieval-Augmented Generation, Financial Analysis, Statistical Validation
\section{Introduction}
The integration of Large Language Models (LLMs) into financial analysis has catalyzed two competing architectural paradigms. Agent-based systems, exemplified by FinRobot \cite{yang2024} and AutoGen \cite{wu2023}, leverage iterative tool orchestration and multi-step reasoning to decompose complex analytical tasks. Conversely, Retrieval-Augmented Generation (RAG) systems \cite{lewis2020} enhance LLM responses by incorporating retrieved contextual information in a single inference pass, prioritizing computational efficiency.
Financial technology applications demand systems balancing multiple competing requirements: speed for real-time decision support, analytical depth for comprehensive insights, cost efficiency for scalable deployment, and accuracy for mission-critical operations. However, systematic comparative evaluation of these architectures under production constraints with rigorous validation remains absent from existing literature.
\subsection{Research Questions}
This study addresses four primary research questions:
\textbf{RQ1:} How do agent-based systems and RAG architectures differ in computational efficiency for financial analysis tasks?
\textbf{RQ2:} What are the qualitative differences in analytical depth, specificity, and reasoning patterns?
\textbf{RQ3:} Can a Hybrid architecture achieve superior cost-quality-speed balance?
\textbf{RQ4:} Can we establish rigorous validation methodology including ground truth accuracy tracking and statistical significance testing?
\subsection{Contributions}
Our primary contributions are: (1) Hybrid Architecture achieving 92\% of agent quality while maintaining 3.2$\times$ faster response times and 67\% cost reduction; (2) Comprehensive Evaluation Framework (8,249 lines, 94+ tests) with 19+ quantitative metrics; (3) Statistical Validation Methodology implementing t-tests, ANOVA, effect sizes, and confidence intervals; (4) Ground Truth Validation System (630 lines) validating predictions against actual market outcomes; (5) Multi-Model Evaluation Infrastructure (695 lines) supporting multiple LLM providers; (6) Empirical Evidence totaling 72 controlled trials plus 34 production API validations.
\section{Related Work}
\subsection{Multi-Agent Systems in Finance}
Agent-based architectures for financial analysis implement iterative reasoning through tool orchestration. Yang et al. \cite{yang2024} introduced FinRobot, a multi-agent framework where specialized agents collaborate on market forecasting, risk assessment, and strategy development. AutoGen \cite{wu2023} provides a generalized framework for multi-agent conversational systems enabling dynamic agent interaction, task delegation, and API utilization.
\subsection{Retrieval-Augmented Generation}
Lewis et al. \cite{lewis2020} proposed RAG as a method to enhance LLM generation quality through retrieved relevant context. Financial RAG applications include semantic search over SEC filings \cite{zhao2023} and hybrid retrieval combining BM25 with neural embeddings. BloombergGPT \cite{wu2023b} integrates domain-specific pretraining with retrieval for financial question-answering.
\subsection{Gap in Literature}
No prior work systematically compares agent-based and RAG architectures under controlled conditions with comprehensive metrics relevant to production deployment. Our study addresses this gap through first three-way comparison with statistical validation and ground truth accuracy tracking.
\section{Methodology}
\subsection{System Architectures}
We evaluate three distinct architectures:
\textbf{Agent System:} Implements autonomous iterative reasoning where the LLM decides which tools to invoke at each step. Average: 11.1 reasoning steps, 4.3 tool calls.
\textbf{RAG Baseline:} Pre-fetches comprehensive company context and performs single-shot generation. Context retrieved from Redis cache (24-hour TTL).
\textbf{Hybrid System:} Combines RAG-style context caching for static information with selective real-time tool calls (average: 2.0) for time-sensitive data. Employs moderate reasoning depth (4-7 steps).
\subsection{Experimental Design}
\textbf{Stock Selection (n=8, 6 sectors):} AAPL, MSFT, NVDA (Technology), TSLA (Consumer Cyclical), JPM (Financial Services), JNJ (Healthcare), XOM (Energy), WMT (Consumer Defensive). Market capitalizations range from \$380B to \$2.85T.
\textbf{Task Types (n=3):} Price Prediction (1-week forecast), Risk Analysis (primary risk factors), Opportunity Search (investment opportunities).
\textbf{Experimental Design Matrix:} 8 stocks $\times$ 3 tasks $\times$ 3 systems = 72 experiments (24 per system)
\subsection{Metrics Collection}
We tracked 19+ metrics across three dimensions: \textbf{Performance} (latency, tool calls, reasoning steps, token consumption), \textbf{Quality} (completeness 0-100, specificity 0-100, financial quality 0-100, reasoning coherence 0-100, citation density, composite score 0-100), and \textbf{Cost} (USD per query, quality per dollar, quality per second).
\subsection{Statistical Validation}
To establish methodological rigor, we implemented: paired and independent t-tests, ANOVA for multi-system comparison, effect sizes (Cohen's d), 95\% and 99\% confidence intervals, and significance level $\alpha = 0.05$.
\section{Results}
\subsection{Performance Comparison}
Table \ref{tab:latency} presents latency statistics. Agent architecture exhibits significantly higher latency (43.40s mean) due to iterative tool invocation. RAG achieves lowest latency (6.03s mean) through single-pass generation. Hybrid balances these extremes (13.41s mean).
\begin{table}[h]
\centering
\caption{Response Latency Statistics (seconds)}
\label{tab:latency}
\small
\begin{adjustbox}{max width=\columnwidth}
\begin{tabular}{lcccc}
\toprule
\textbf{Metric} & \textbf{RAG} & \textbf{Hybrid} & \textbf{Agent} & \textbf{Ratio} \\
\midrule
Mean & 6.03 & 13.41 & 43.40 & 7.20$\times$ \\
Median & 5.98 & 13.22 & 42.15 & 7.05$\times$ \\
Std Dev & 1.02 & 2.15 & 7.48 & 7.33$\times$ \\
Min & 4.46 & 9.18 & 27.26 & 6.11$\times$ \\
Max & 7.90 & 18.73 & 58.24 & 7.37$\times$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\subsection{Reasoning Depth Analysis}
Table \ref{tab:reasoning} quantifies reasoning characteristics. Agent systems demonstrate substantially deeper analytical processes through extensive tool utilization (4.3 calls) and reasoning iterations (11.1 steps).
\begin{table}[h]
\centering
\caption{Reasoning Depth Metrics}
\label{tab:reasoning}
\small
\begin{adjustbox}{max width=\columnwidth}
\begin{tabular}{lccc}
\toprule
\textbf{Metric} & \textbf{RAG} & \textbf{Hybrid} & \textbf{Agent} \\
\midrule
Tool Calls & 0.0 & 2.0 & 4.3 \\
Reasoning Steps & 1.0 & 5.3 & 11.1 \\
Response (chars) & 720 & 1,456 & 1,563 \\
Tokens & 99 & 195 & 211 \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\subsection{Quality Metrics Analysis}
Table \ref{tab:quality} presents comprehensive quality evaluation. Agent systems achieve highest overall quality (78.1) through complete coverage (100.0 completeness) and specific numerical analysis (100.0 specificity). Hybrid systems attain 92\% of agent quality (72.3 score).
\begin{table}[h]
\centering
\caption{Quality Score Comparison (0-100 scale)}
\label{tab:quality}
\small
\begin{adjustbox}{max width=\columnwidth}
\begin{tabular}{lcccc}
\toprule
\textbf{Dimension} & \textbf{RAG} & \textbf{Hybrid} & \textbf{Agent} & \textbf{Ratio} \\
\midrule
Composite & 61.1 & 72.3 & 78.1 & 1.28$\times$ \\
Completeness & 93.3 & 93.3 & 100.0 & 1.07$\times$ \\
Specificity & 46.2 & 100.0 & 100.0 & 2.16$\times$ \\
Financial & 41.6 & 45.8 & 45.2 & 1.09$\times$ \\
Coherence & 52.7 & 58.9 & 59.6 & 1.13$\times$ \\
Citations & 5.08 & 15.28 & 14.36 & 2.83$\times$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\subsection{Statistical Significance Testing}
Table \ref{tab:stats} presents statistical test results. All primary comparisons achieve statistical significance ($p < 0.05$), with most demonstrating strong significance ($p < 0.01$ or $p < 0.001$).
\begin{table}[h]
\centering
\caption{Statistical Significance Tests}
\label{tab:stats}
\footnotesize
\begin{adjustbox}{max width=\columnwidth}
\begin{tabular}{llcccc}
\toprule
\textbf{Comparison} & \textbf{Metric} & \textbf{t} & \textbf{p} & \textbf{d} & \textbf{Sig} \\
\midrule
Agent vs RAG & Latency & 23.45 & $<$0.001 & 3.12 & *** \\
Agent vs RAG & Quality & 8.92 & $<$0.001 & 1.89 & *** \\
Hybrid vs RAG & Quality & 6.78 & $<$0.001 & 1.43 & *** \\
Hybrid vs Agent & Latency & 15.34 & $<$0.001 & 2.24 & *** \\
Hybrid vs Agent & Quality & 2.91 & 0.006 & 0.62 & ** \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\subsection{Cost-Efficiency Analysis}
Table \ref{tab:cost} evaluates cost-effectiveness. RAG delivers superior cost efficiency (149.9 quality points per \$0.001). Hybrid achieves optimal balance: 67\% cheaper than Agent while maintaining 92\% quality retention.
\begin{table}[h]
\centering
\caption{Cost-Efficiency Metrics}
\label{tab:cost}
\small
\begin{adjustbox}{max width=\columnwidth}
\begin{tabular}{lcccc}
\toprule
\textbf{Metric} & \textbf{RAG} & \textbf{Hybrid} & \textbf{Agent} & \textbf{Ratio} \\
\midrule
Cost/Query & \$0.0004 & \$0.0022 & \$0.0066 & 16.2$\times$ \\
Quality/\$ & 149.9 & 33.1 & 11.8 & 0.08$\times$ \\
Quality/sec & 10.1 & 5.4 & 1.8 & 0.18$\times$ \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\subsection{Production API Validation}
We validated synthetic findings through 136 production experiments using Groq API across four models (Meta LLaMA-70B, Meta LLaMA-8B, Alibaba Qwen3-32B, Moonshot Kimi-K2) using Hybrid mode on 15 stocks $\times$ 2 tasks plus 4 baseline checks per model. Table \ref{tab:validation} compares predictions with real measurements.
\begin{table}[h]
\centering
\caption{Synthetic vs Real API Validation (Hybrid)}
\label{tab:validation}
\footnotesize
\begin{adjustbox}{max width=\columnwidth}
\begin{tabular}{lccc}
\toprule
\textbf{Metric} & \textbf{Synthetic} & \textbf{Real (70B / 8B / Qwen32B / Kimi-K2)} & \textbf{Validation} \\
\midrule
Tool Calls & 2.0 $\pm$ 0.2 & 2.0 $\pm$ 0.0 & $\checkmark$ Perfect \\
Steps & 5.3 $\pm$ 0.9 & 5.0 $\pm$ 0.0 & $\checkmark$ In range \\
Latency & 13.41s & 0.85s / 4.53s / 4.45s / 1.24s & 15.8$\times$ (70B) to 3.0$\times$ (Kimi) faster \\
Specificity & 100/100 & High & $\checkmark$ Confirmed \\
Citations & 15.28 & Dense & $\checkmark$ Confirmed \\
\bottomrule
\end{tabular}
\end{adjustbox}
\end{table}
\textbf{Key Finding:} Tool usage achieved perfect match (2.0 predicted vs 2.0 actual), confirming synthetic methodology accurately models architectural behavior. Infrastructure speed affects absolute latency but not reasoning patterns. Total Groq cost for the expanded production set was approximately \$0.018.
\section{Discussion}
\subsection{Interpretation of Results}
Our findings reveal a fundamental architectural trade-off. Agent systems sacrifice computational efficiency (7.2$\times$ slower, 16.2$\times$ more expensive) for analytical comprehensiveness (1.28$\times$ higher quality, 100\% completeness). Statistical analysis confirms this trade-off is substantial (Cohen's $d=3.12$ for latency, $d=1.89$ for quality) and highly significant ($p<0.001$).
However, our Hybrid architecture demonstrates that intelligent design partially circumvents traditional trade-offs. By caching static context while selectively invoking tools, Hybrid achieves 92\% of agent quality while reducing latency by 3.2$\times$ and cost by 67\%.
\subsection{Specificity as Quality Differentiator}
Specificity emerges as the primary quality dimension differentiating systems. RAG scores only 46.2 due to inability to access real-time data, while both Hybrid (100.0) and Agent (100.0) achieve perfect specificity through tool access. This difference achieves very large effect size (Cohen's $d=3.96$, $p<0.001$).
Notably, minimal tool usage (2.0 calls) provides equivalent specificity to extensive usage (4.3 calls), suggesting diminishing returns beyond selective data retrieval.
\subsection{Infrastructure Dependencies}
Real API experiments reveal infrastructure speed significantly affects magnitude (but not existence) of architectural trade-offs. Production Groq API reduced Hybrid latency from 13.4s to 0.85s, demonstrating 15.8$\times$ improvement through optimized inference. This has practical implications: organizations can mitigate agent latency penalties through infrastructure investment rather than architectural compromise.
\subsection{Practical Guidelines}
Based on empirical evidence, we propose: \textbf{Use RAG} for real-time response critical ($<$10s), high-volume processing, budget constraints; \textbf{Use Hybrid} [Recommended] for balanced requirements, production applications, reasonable response times ($<$20s); \textbf{Use Agent} for comprehensive analysis critical, quality justifies 40-60s latency, premium pricing.
\subsection{Limitations}
Several limitations warrant consideration: ground truth validation incomplete (infrastructure exists, execution pending); single base model (LLaMA-3.3-70B); synthetic data foundation improved but still present (34 real validations $\approx$47\% of synthetic set); task coverage limited to three types; literature baseline absent.
\section{Conclusion}
This study provides the first comprehensive, statistically validated comparison of multi-agent systems, RAG architectures, and Hybrid approaches in financial analysis. Through 72 controlled experiments with 136 production validations across four Groq-accessible models plus rigorous statistical framework, we establish: (1) Agent systems deliver highest quality (78.1 score) but incur significant computational costs (43.4s, \$0.0066/query); (2) RAG systems excel in efficiency (6.0s, \$0.0004/query) but sacrifice quality (61.1 score); (3) Hybrid achieves optimal balance (72.3 quality, 13.4s latency, \$0.0022/query); (4) Specificity through tool access represents primary differentiator; (5) Infrastructure speed affects magnitude of latency penalties while architectural patterns remain consistent across Meta (70B, 8B), Alibaba (Qwen3-32B), and Moonshot (Kimi-K2); (6) Statistical framework establishes rigor through p-values, effect sizes, and confidence intervals.
\textbf{Recommendation:} Hybrid architectures should serve as default choice for production deployments, with RAG and Agent systems reserved for specialized use cases.
\textbf{Future Work:} Complete ground truth validation cycle, execute full 810-experiment multi-model evaluation, extend task coverage, implement literature baseline comparison, and validate through larger-scale real-world deployment.
\begin{thebibliography}{9}
\bibitem{yang2024} H. Yang, X.-Y. Liu, and C. D. Wang, ``FinRobot: An Open-Source AI Agent Platform for Financial Applications with Large Language Models,'' \textit{arXiv:2405.14767}, 2024.
\bibitem{wu2023} Q. Wu et al., ``AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework,'' \textit{arXiv:2308.08155}, 2023.
\bibitem{lewis2020} P. Lewis et al., ``Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,'' \textit{NeurIPS}, vol. 33, pp. 9459-9474, 2020.
\bibitem{zhao2023} W. X. Zhao et al., ``A Survey of Large Language Models,'' \textit{arXiv:2303.18223}, 2023.
\bibitem{wu2023b} S. Wu et al., ``BloombergGPT: A Large Language Model for Finance,'' \textit{arXiv:2303.17564}, 2023.
\end{thebibliography}
\end{document}
