Distilling Analysis from Generative Models for Investment Decisions

ArXiv ID: 2410.07225 “View on arXiv”

Authors: Unknown

Abstract

Professionals’ decisions are the focus of every field. For example, politicians’ decisions will influence the future of the country, and stock analysts’ decisions will impact the market. Recognizing the influential role of professionals’ perspectives, inclinations, and actions in shaping decision-making processes and future trends across multiple fields, we propose three tasks for modeling these decisions in the financial market. To facilitate this, we introduce a novel dataset, A3, designed to simulate professionals’ decision-making processes. While we find current models present challenges in forecasting professionals’ behaviors, particularly in making trading decisions, the proposed Chain-of-Decision approach demonstrates promising improvements. It integrates an opinion-generator-in-the-loop to provide subjective analysis based on each news item, further enhancing the proposed tasks’ performance.

Keywords: decision modeling, Chain-of-Decision, opinion-generator, financial forecasting, Market Analysis

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper introduces a novel dataset with specific train/test splits and task metrics, demonstrating strong empirical implementation, but relies on standard NLP/ML models (LLM prompting, classification) without heavy mathematical derivations or advanced stochastic calculus.

  flowchart TD
    A["Research Goal:<br>Modeling Professionals'<br>Decisions in Finance"] --> B{"Key Methodology"}
    B --> C["Dataset: A3<br>Simulates Decision Processes"]
    B --> D["Method: Chain-of-Decision<br>with Opinion-Generator"]
    C --> E["Computational Process:<br>News & Opinion Integration"]
    D --> E
    E --> F["Key Findings:<br>1. Current models struggle<br>2. Chain-of-Decision improves<br>performance"]
    F --> G["Outcome:<br>Enhanced Financial<br>Forecasting & Analysis"]