Market-Derived Financial Sentiment Analysis: Context-Aware Language Models for Crypto Forecasting

ArXiv ID: 2502.14897 “View on arXiv”

Authors: Unknown

Abstract

Financial Sentiment Analysis (FSA) traditionally relies on human-annotated sentiment labels to infer investor sentiment and forecast market movements. However, inferring the potential market impact of words based on their human-perceived intentions is inherently challenging. We hypothesize that the historical market reactions to words, offer a more reliable indicator of their potential impact on markets than subjective sentiment interpretations by human annotators. To test this hypothesis, a market-derived labeling approach is proposed to assign tweet labels based on ensuing short-term price trends, enabling the language model to capture the relationship between textual signals and market dynamics directly. A domain-specific language model was fine-tuned on these labels, achieving up to an 11% improvement in short-term trend prediction accuracy over traditional sentiment-based benchmarks. Moreover, by incorporating market and temporal context through prompt-tuning, the proposed context-aware language model demonstrated an accuracy of 89.6% on a curated dataset of 227 impactful Bitcoin-related news events with significant market impacts. Aggregating daily tweet predictions into trading signals, our method outperformed traditional fusion models (which combine sentiment-based and price-based predictions). It challenged the assumption that sentiment-based signals are inferior to price-based predictions in forecasting market movements. Backtesting these signals across three distinct market regimes yielded robust Sharpe ratios of up to 5.07 in trending markets and 3.73 in neutral markets. Our findings demonstrate that language models can serve as effective short-term market predictors. This paradigm shift underscores the untapped capabilities of language models in financial decision-making and opens new avenues for market prediction applications.

Keywords: Financial Sentiment Analysis, Language Models, Market-derived Labeling, Trading Signals, Backtesting, Cryptocurrency (Bitcoin)

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper focuses on practical implementation with a detailed backtesting methodology (Sharpe ratios, market regimes) and real data application, but relies on established NLP/ML techniques without novel mathematical derivations or heavy statistical theory.

  flowchart TD
    A["Research Goal: <br>Can market-derived sentiment <br>outperform human-annotated sentiment?"] --> B["Data Preparation"]
    
    subgraph B ["Data Preparation"]
        B1["Input: Historical Crypto Tweets"]
        B2["Input: Bitcoin Price Data"]
        C1["Market-Derived Labeling: <br>Assign labels based on <br>subsequent price trends"]
    end
    
    C1 --> D["Model Development"]
    
    subgraph D ["Model Development"]
        D1["Fine-tune Language Model <br>on market-derived labels"]
        D2["Prompt-Tuning: <br>Inject market & temporal context"]
    end
    
    D --> E["Validation & Backtesting"]
    
    subgraph E ["Validation & Backtesting"]
        E1["Short-term trend prediction"]
        E2["Test on news events dataset"]
        E3["Backtest trading signals <br>across market regimes"]
    end
    
    E --> F["Key Outcomes"]
    
    subgraph F ["Key Outcomes"]
        F1["+11% accuracy <br>vs sentiment benchmarks"]
        F2["89.6% accuracy <br>on news event prediction"]
        F3["Sharpe ratios up to 5.07 <br>(trending markets)"]
        F4["Outperforms traditional <br>fusion models"]
    end