Modeling News Interactions and Influence for Financial Market Prediction

ArXiv ID: 2410.10614 “View on arXiv”

Authors: Unknown

Abstract

The diffusion of financial news into market prices is a complex process, making it challenging to evaluate the connections between news events and market movements. This paper introduces FININ (Financial Interconnected News Influence Network), a novel market prediction model that captures not only the links between news and prices but also the interactions among news items themselves. FININ effectively integrates multi-modal information from both market data and news articles. We conduct extensive experiments on two datasets, encompassing the S&P 500 and NASDAQ 100 indices over a 15-year period and over 2.7 million news articles. The results demonstrate FININ’s effectiveness, outperforming advanced market prediction models with an improvement of 0.429 and 0.341 in the daily Sharpe ratio for the two markets respectively. Moreover, our results reveal insights into the financial news, including the delayed market pricing of news, the long memory effect of news, and the limitations of financial sentiment analysis in fully extracting predictive power from news data.

Keywords: News Influence Network, Multi-modal Information, Market Prediction, Financial News Diffusion

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper presents a novel neural network architecture (FININ) with multi-modal fusion and attention mechanisms, involving moderate-to-advanced mathematical modeling, but grounds its claims in extensive empirical backtesting on two major indices over 15 years with 2.7 million news articles and reports significant Sharpe ratio improvements.

  flowchart TD
    A["Research Goal: Modeling News-Price Diffusion for Market Prediction"] --> B["Data Acquisition & Processing"]
    B --> C["FININ Architecture: Multi-modal Integration"]
    C --> D["Computational Process: News Influence Network"]
    D --> E["Model Training & Validation"]
    E --> F["Key Outcomes: Enhanced Sharpe Ratio & Market Insights"]
    
    subgraph Data Inputs
        B --> B1["2.7M News Articles"]
        B --> B2["S&P 500 & NASDAQ Data"]
    end
    
    subgraph Computational Layers
        D --> D1["News-News Interactions"]
        D --> D2["News-Price Links"]
        D --> D3["Temporal Diffusion Modeling"]
    end
    
    subgraph Findings
        F --> F1["+0.429 Sharpe Ratio (S&P 500)"]
        F --> F2["+0.341 Sharpe Ratio (NASDAQ 100)"]
        F --> F3["Delayed Market Pricing"]
        F --> F4["Long Memory Effect of News"]
    end