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% UQO Working Paper — Economics / Finance
% VQuant: An Agentic LLM Architecture for Quantitative Financial Analysis
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% METADATA
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\newcommand{\WPnumber}{13}
\newcommand{\WPtitle}{VQuant: An Agentic Large Language Model Architecture for Automated Quantitative Financial Analysis and Document Generation}
\newcommand{\WPsubtitle}{Reasoning-Augmented Tool Orchestration, In-Loop Numerical Computation, and Multi-Format Reporting}
\newcommand{\WPdate}{October 2026}
\newcommand{\WPversion}{1.1}
\newcommand{\WPabstract}{%
  Large language models (LLMs) write fluently about finance, but fluency does not make a number
  correct, current, or reproducible. This paper is a design-science description of \textbf{VQuant},
  a deployed web platform that couples a reasoning-capable LLM with a deterministic execution
  substrate so that a natural-language request (``analyse NVDA's fundamentals and run a Monte Carlo
  simulation'') is turned into a report in which every figure is produced by live data retrieval or
  auditable code rather than by the model. We document four mechanisms as they are implemented and
  audit the description line by line against the platform's source code (vquant.git, commit
  e2a148a): (i)~an agentic orchestration loop that exposes 245 schema-defined tools---wrapping two
  market-data providers (249 typed functions over 233 endpoint paths), three web-research services,
  and a Python sandbox pre-loading 29 scientific packages---through a recursive, batched,
  streaming tool-calling protocol; (ii)~a three-stage context-management procedure (per-result
  truncation at execution, a re-send cap, and adaptive summarisation under a token budget) that
  keeps long sessions within the model's window; (iii)~a single-call computation convention that
  consolidates data acquisition, estimation, and figure rendering into one sandboxed execution; and
  (iv)~a document pipeline that renders one Markdown artifact into PDF, DOCX, \LaTeX{} Beamer, and
  spreadsheets with live formulas. The paper reports no empirical evaluation: it contains no
  measurements of numerical accuracy, hallucination rates, latency, or cost, and makes no comparative
  claims. Its contribution is a reproducible specification of the architecture, an honest account of
  where the implementation departs from its own documentation, and a pre-registered evaluation
  protocol---benchmark, metrics, ablations, and baselines---that has not yet been executed.
}
\newcommand{\WPkeywords}{large language models, LLM agents, tool use, design science, quantitative finance, automated report generation, reproducibility}
\newcommand{\WPjel}{C45, C63, C88, G17, O33}

% --- Author ---
\newcommand{\WPauthor}{Simon-Pierre Boucher}
\newcommand{\WPaffiliation}{%
  Département des sciences administratives\\
  Université du Québec en Outaouais%
}
\newcommand{\WPemail}{simon-pierre.boucher@uqo.ca}
\newcommand{\WPaddress}{%
  Gatineau -- Pavillon Alexandre-Taché\\
  283, boulevard Alexandre-Taché\\
  Gatineau, Québec, Canada J9A 1L8%
}

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% DOCUMENT
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\begin{document}

% --- Title Page ---
\input{sections/titlepage}

% --- Main Body ---
\input{sections/introduction}
\input{sections/literature}
\input{sections/data}
\input{sections/methodology}
\input{sections/results}
\input{sections/robustness}
\input{sections/conclusion}

% --- References ---
\newpage
\bibliography{references}

% --- Appendix ---
\input{appendix/appendix}

\end{document}