Tuning into Climate Risks: Extracting Innovation from Television News for Clean Energy Firms

ArXiv ID: 2409.08701 “View on arXiv”

Authors: Unknown

Abstract

This article develops multiple novel climate risk measures (or variables) based on the television news coverage by Bloomberg, CNBC, and Fox Business, and examines how they affect the systematic and idiosyncratic risks of clean energy firms in the United States. The measures are built on climate related keywords and cover the volume of coverage, type of coverage (climate crisis, renewable energy, and government & human initiatives), and media sentiments. We show that an increase in the aggregate measure of climate risk, as indicated by coverage volume, reduces idiosyncratic risk while increasing systematic risk. When climate risk is segregated, we find that systematic risk is positively affected by the physical risk of climate crises and transition risk from government & human initiatives, but no such impact is evident for idiosyncratic risk. Additionally, we observe an asymmetry in risk behavior: negative sentiments tend to decrease idiosyncratic risk and increase systematic risk, while positive sentiments have no significant impact. These findings remain robust to including print media and climate policy uncertainty variables, though some deviations are noted during the COVID-19 period.

Keywords: climate risk measures, systematic risk, idiosyncratic risk, clean energy firms, media sentiment analysis, Equities

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper uses relatively standard econometric methods (fixed-effect regressions) with limited advanced mathematical derivations, but is heavily data-driven, constructing novel media-based climate risk metrics from TV news and conducting multiple robustness checks on a specific set of firms.

  flowchart TD
    A["Research Goal: How does TV news coverage of climate risk<br>affect risks of clean energy firms?"] --> B["Data Collection"]
    
    subgraph B ["Inputs"]
        B1["Bloomberg TV"]
        B2["CNBC"]
        B3["Fox Business"]
    end
    
    B --> C["Methodology: NLP Processing"]
    
    subgraph C ["Computational Process"]
        C1["Keyword Extraction<br>Climate Risk Keywords"]
        C2["Sentiment Analysis<br>Negative/Positive Scoring"]
        C3["Category Classification<br>Crisis, Renewables, Gov Initiatives"]
    end
    
    C --> D["Risk Measurement & Regression"]
    
    subgraph D ["Analysis"]
        D1["Systematic Risk Beta<br>Market Sensitivity"]
        D2["Idiosyncratic Risk<br>Stock-specific Variance"]
        D3["Climate Risk Measures<br>Volume, Sentiment, Categories"]
    end
    
    D --> E["Key Findings"]
    
    subgraph E ["Outcomes"]
        E1["↑ Coverage Volume → ↑ Systematic Risk / ↓ Idiosyncratic Risk"]
        E2["Physical Risk (Crisis) → ↑ Systematic Risk"]
        E3["Transition Risk (Gov) → ↑ Systematic Risk"]
        E4["Negative Sentiment → ↑ Systematic Risk / ↓ Idiosyncratic Risk"]
        E5["Positive Sentiment → No Significant Impact"]
    end
    
    E --> F["Robustness: Print media & policy uncertainty controls<br>COVID-19 period deviations noted"]