AI-Powered (Finance) Scholarship

ArXiv ID: ssrn-5060022 “View on arXiv”

Authors: Unknown

Abstract

Keywords: Generative AI, Large Language Models (LLMs), Academic Research, Natural Language Processing, Automation, Technology

Complexity vs Empirical Score

• Math Complexity: 1.0/10
• Empirical Rigor: 2.0/10
• Quadrant: Philosophers
• Why: The paper focuses on the conceptual process of using LLMs to generate academic papers, rather than presenting complex mathematical models or empirical backtesting results.

  flowchart TD
    A["Research Goal<br>Automate Academic Paper Generation"] --> B{"Methodology"}
    B --> C["Data/Input<br>LLM & Financial Datasets"]
    B --> D["Data/Input<br>Research Questions"]
    C --> E["Computational Process<br>LLM Content Generation"]
    D --> E
    E --> F["Key Findings<br>Successful Paper Automation"]
    E --> G["Key Findings<br>Validated Methodology"]