firobeid/SP_DOW_NASDAQ_stocks__News_Headlines_labeled

Quantitative Textual Analysis: Classifier Selection & Routing Logic

Subject: Algorithmic Selection of NLP Models for Financial Signal Generation Methodology: Lopez de Prado’s Framework for False Discovery Control Metric Focus: Precision (Minimization of Type I Errors)

1. Executive Summary

This report evaluates the predictive utility of various NLP architectures for generating "Buy/No-Buy" signals. In accordance with quantitative finance principles, we prioritize Precision in the Inference Partition (Out-of-Sample) to minimize the cost of False Positives (entering losing trades).

Key Findings:

1. The Generalist Champion: The FinBERT_SIF_Retrival_System achieves the highest global precision (62.41%) and demonstrates superior stability across the widest range of topics.
2. The "Lookup" Benchmark: The FinBERT_SIF_SVC_KNN functions as a valid, high-performance benchmark. While its training score reflects the expected behavior of a Nearest Neighbor "lookup table" (memorization), its Inference performance (60.92%) is robust. However, it is consistently outperformed by specialized architectures in every specific sector.
3. The Heterogeneity of Alpha: Financial news is not linguistically uniform. The inclusion of the Entertainment sector confirms that some topics are Lexical (keyword-driven), while others are Semantic (context-driven) or Structured (data-driven).
4. Strategic Implication: No single model is optimal. A Meta-Labeling Routing System is required to direct headlines to the "Specialist" model best suited for that specific topic.

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2. Global Performance Hierarchy (Inference Partition)

The following table ranks the models based on their ability to discriminate signal from noise in the Inference partition.

Rank	Model Architecture	Precision	Analysis
1	FinBERT SIF Retrieval System	0.6241	Optimal. Best discrimination capability. Uses SIF embeddings to weigh "rare" financial terms heavily.
2	L1 Sentiment Analysis	0.6188	Strong Baseline. Statistically tied with the leader, proving traditional sentiment is still highly predictive.
3	NonContext TFIDF	0.6186	High Efficiency. Outperforms complex GenAI models, suggesting high lexical dependence in the dataset.
4	TabICL Classifier	0.6138	Structured Specialist. Excellent performance, specifically in data-heavy sectors.
5	FinBERT SIF SVC KNN	0.6092	Valid Benchmark. Acts as a semantic lookup table. Robust, but lacks the edge of the top 3.
DQ	Gemma-2b Decoder	0.6160	Disqualified (Lopez de Prado Criterion). FPR of 0.987 indicates a "Permabull" bias (predicts Buy on everything).

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3. Sector-Specific "Tournaments" (Cluster Analysis)

Using a "Tournament" approach, we identified the dominant model for each sector. We observe three distinct linguistic clusters.

Cluster A: The "Lexical" Sectors (Entertainment & Earnings)

Characteristics: Binary, keyword-driven news (e.g., "Beat/Miss", "Blockbuster/Flop"). Deep context models often introduce noise by over-analyzing simple syntax.

• Topic: Entertainment
  • Winner: NonContextDependent_TFiDF
  • Precision: 60.11%
  • Vs Benchmark (KNN): +2.68% (KNN: 57.43%)
  • Analysis: Entertainment news relies on "buzzwords." TFIDF captures this efficiently.
• Topic: Earnings_Ratings
  • Winner: NonContextDependent_TFiDF
  • Precision: 65.54%
  • Vs Benchmark (KNN): +3.87% (KNN: 61.67%)
  • Analysis: Earnings are the most keyword-dense sector. TFIDF is the undisputed leader here.

Cluster B: The "Structured" Sectors (Banking & Manufacturing)

Characteristics: Quasi-tabular data (deal values, contract specs, unit orders). Models with tabular reasoning capabilities excel.

• Topic: Investment_Banking
  • Winner: TabICL_Classifier (Tabular In-Context Learning)
  • Precision: 65.14%
  • Vs Benchmark (KNN): +5.62% (KNN: 59.52%)
  • Analysis: TabICL parses structured deal data (M&A values) significantly better than semantic embeddings.
• Topic: Mechanical_Transportation
  • Winner: TabICL_Classifier
  • Precision: 67.54%
  • Vs Benchmark (KNN): +4.48% (KNN: 63.06%)
  • Analysis: A massive victory for TabICL. This sector is heavy on technical specifications, which TabICL handles superiorly.

Cluster C: The "Semantic" Sectors (Funds, Tech, Pharma)

Characteristics: Narrative-driven. "Sentiment" is hidden in the nuance of the language ("outlook," "headwinds"), requiring deep embedding vectors.

• Topic: Financial_Funds
  • Winner: FinBERT_SIF_Retrival_System
  • Precision: 68.06%
  • Vs Benchmark (KNN): +2.39% (KNN: 65.67%)
  • Analysis: The highest alpha in the dataset. Fund news requires understanding "manager sentiment," which SIF embeddings capture perfectly.
• Topic: Technology
  • Winner: FinBERT_SIF_Retrival_System
  • Precision: 64.22%
  • Vs Benchmark (KNN): +3.11% (KNN: 61.11%)
  • Analysis: Tech news is volatile. The Retrieval system stabilizes this volatility better than the KNN lookup.
• Topic: Pharmaceutical
  • Winner: FinBERT_SIF_Retrival_System
  • Precision: 57.26%
  • Vs Benchmark (KNN): +0.85% (KNN: 56.41%)
  • Analysis: A tight race, but Retrieval edges out the benchmark.

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4. Model Stability & Selection (Lopez de Prado Framework)

We apply strict criteria to reject models that show "False Discovery" characteristics.

1. FinBERT_SIF_SVC_KNN (The Valid Benchmark):
   • Status: Accepted as Benchmark.
   • Analysis: The Training Precision of 99.98% is not "overfitting" in the traditional sense; it is the mathematical property of a $k=1$ Neighbor algorithm (perfect memory). Its Inference score (60.92%) is valid and stable. However, it is rejected as the Primary Model simply because it loses every specific sector tournament to a specialist.
2. Gemma-2b_Decoder (The Beta Trap):
   • Status: Rejected.
   • Analysis: While Precision is acceptable (61.6%), the False Positive Rate is 0.987. It predicts "Buy" on 98.7% of negative news. It has zero discriminative power and maximizes risk.
3. L6_LM_LSTM (The Liquidity Trap):
   • Status: Rejected.
   • Analysis: High Precision in Banking (90%) is negated by a Recall < 6%. It fails to generate enough signals to be economically viable.

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5. Implementation: The Routing Logic Flow

To maximize the Deflated Sharpe Ratio, we implement a Meta-Model Router. This logic directs the incoming JSON payload to the specific classifier that has statistically proven dominance in that sector.

Logic Flow Diagram:

graph TD
    A[Incoming News Headline] --> B{Check 'Topic' Tag}
    
    %% Cluster A: Lexical Models
    B -- Entertainment --> C[Route to: TFIDF Classifier]
    B -- Earnings_Ratings --> C
    
    %% Cluster B: Tabular Models
    B -- Investment_Banking --> D[Route to: TabICL Classifier]
    B -- Mechanical_Transportation --> D
    
    %% Cluster C: Semantic Models (Default)
    B -- Financial_Funds --> E[Route to: FinBERT SIF Retrieval]
    B -- Technology --> E
    B -- Pharmaceutical --> E
    B -- OVERALL/Unknown --> E
    
    %% Execution
    C --> F{Prediction > Threshold?}
    D --> F
    E --> F
    
    F -- Yes --> G[Generate BUY Signal]
    F -- No --> H[Generate NO_BUY Signal]

Python Implementation Logic

def get_trading_signal(headline, topic):
    """
    Routes the headline to the specialist model based on Lopez de Prado's 
    Cluster Analysis of the Inference Partition.
    """
    
    # Cluster A: Lexical/Keyword Heavy (Use TFIDF)
    # Precision: ~60-65%
    if topic in ['Entertainment', 'Earnings_Ratings']:
        model = load_model('L7_8_NonContextDependent_TFiDF_Classifier')
        return model.predict(headline)

    # Cluster B: Structured/Tabular Data (Use TabICL)
    # Precision: ~65-67%
    elif topic in ['Investment_Banking', 'Mechanical_Transportation']:
        model = load_model('L7_8_TabICL_Classifier')
        return model.predict(headline)

    # Cluster C: Semantic/Narrative (Use FinBERT SIF Retrieval)
    # Precision: ~57-68%
    # Also acts as the 'Generalist' fallback for unknown topics
    else: 
        # Covers: Financial_Funds, Technology, Pharmaceutical, OVERALL
        model = load_model('L7_8_FinBERT_SIF_Retrival_System_Classifier')
        return model.predict(headline)