AI Content Disclaimer: The first draft of this case study was created using my pair-coding agent’s review of my codebase + two paragraph project outline I wrote (pure human content). Further context was added with a prompt (human) that detailed expecations, content hierarchy, and preferred markdown structure. Statistical overviews are notes created with a combination of a custom stats GPT and friend/stats professional with time on their hands. Final draft is mostly human.

NLP + XGBoost Pipeline Preview

This preview highlights key methodological steps in the nlp_xgboost pipeline, illustrating core NLP techniques with minimal code snippets and academic references.

NOTE: This is just the outline for the project. I’ll turn this into a proper post soon. It’s still WIP since I’m testing the entire pipeline for incosistencies. Few changes have already been made in how XGBoost is used. Will update documentation once I’m tested my own work.

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1. News Ingestion & Text Cleaning

Methodology
Collect news articles asynchronously from Yahoo Finance and clean extracted text for downstream NLP tasks.

Statistical Overview

• De-duplication reduces repeated information that can inflate apparent sample size and induce serial correlation in text-derived signals; near-duplicate filtering is especially important in aggregated feeds.
• Normalization (regex cleaning, casefolding, punctuation standardization) reduces linguistic variance due to formatting artifacts without altering semantic content.
• Sample size affects the stability of estimated sentiment/keyword distributions and the precision of downstream feature estimates (even without formal hypothesis tests).
• Takeaway: cleaner, less redundant text ⇒ more stable features and less biased backtests.

Code Snippet

import asyncio
from data.news_ingester import NewsIngester

async def fetch_news():
    ingester = NewsIngester()
    news_df = await ingester.ingest_pipeline(custom_search_terms=['AI', 'semiconductors'])
    return news_df

news_data = asyncio.run(fetch_news())
print(f"Collected {len(news_data)} articles")

Academic Reference
Tetlock (2007) emphasizes comprehensive news ingestion and preprocessing to capture investor sentiment signals effectively.

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2. Named Entity Recognition & Ticker Attribution

Methodology
Use spaCy transformer-based NER with a custom alias dictionary to identify company mentions and assign ticker symbols with relevance scoring.

Statistical Overview

• Model scores from NER and alias matching are model-estimated probabilities/likelihoods, not guaranteed frequentist probabilities; treat them as confidence signals.
• Thresholds set the operating point on the precision–recall curve; you should report both and select thresholds with validation data.
• If you blend signals (title hit, body mentions, proximity, symbol presence) via a weighted sum, state it as a heuristic composite unless weights are learned; optionally fit weights by logistic regression on a labeled subset.
• Takeaway: precise ticker attribution is a prerequisite for company-level features and is supported by recent work on extracting structured insights from raw articles.

Code Snippet

from utils.ticker_matcher import TickerMatcher

matcher = TickerMatcher()
article_title = "Apple Inc. announces new product launch"
article_content = "The new iPhone is expected to boost AAPL's revenue."
attributions = matcher.process_article(title=article_title, content=article_content)

for attr in attributions:
    print(f"Ticker: {attr.ticker}, Relevance: {attr.relevance_score}")

Academic Reference
Loughran & McDonald (2011) highlight the need for domain-specific entity recognition in financial text to improve attribution accuracy.

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3. Targeted Sentiment Analysis

Methodology
Apply FinBERT to analyze headline sentiment and sentence-level sentiment for ticker mentions, blending scores with calibrated confidence weighting.

Statistical Overview

• FinBERT (finance-adapted) is appropriate for domain text; compute sentiment at the sentence(s) mentioning the target ticker to avoid cross-talk.
• Empirical evidence suggests body/content sentiment tends to be more informative than headlines. <- This feature will be incorporated in the next phase - currently it’s mostly focused on headlines.
• If you use “confidence” (softmax probability) as a weight, note that neural confidences are often miscalibrated; adopt temperature scaling or isotonic regression on a validation set before using them as reliability weights. <- Not fully tested within the current setup. Still working on it.
• Takeaway: target-aware sentiment + calibrated confidence yields a more defensible numerical feature than headline-only polarity.

Code Snippet

from data.sentiment_analyzer import FinBERTSentimentAnalyzer

analyzer = FinBERTSentimentAnalyzer("ProsusAI/finbert")
headline = "Apple shares soar after record iPhone sales"
sentiment = analyzer.analyze_headline_sentiment(headline)

print(f"Sentiment score: {sentiment.sentiment_score}, Confidence: {sentiment.confidence}")

Academic Reference
Ke et al. (2019) demonstrate that targeted, fine-grained sentiment analysis outperforms headline-only approaches in forecasting stock returns.

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4. XGBoost Forecasting

Methodology
Train an XGBoost regression model on engineered features including NLP signals to predict next-day stock returns.

Statistical Overview

• XGBoost fits trees to residuals under a specified loss (e.g., squared error), with shrinkage, subsampling, and depth constraints acting as regularizers that reduce overfitting.
• Sample weights re-weight the empirical risk; using higher weights for “high-confidence” news can help if and only if confidence correlates with label accuracy (hence the calibration step above).
• Takeaway: calibrated, high-quality features + regularized boosting can provide competitive baselines in financial prediction tasks.

Code Snippet

from models.xgb_forecaster import XGBoostForecaster
import numpy as np

forecaster = XGBoostForecaster()
X_sample = np.random.rand(1, 22)  # Example feature vector with 22 features
prediction = forecaster.predict(X_sample)

print(f"Predicted return: {prediction[0]:.4f}")

Academic Reference
Ke et al. (2019) validate gradient boosting methods combining textual and numeric features for superior return prediction.

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Limitations & Validity Checks

Potential Risks

• Look-ahead bias: Ensure that news ingestion timestamps precede market data cutoffs.
• Data leakage: Prevent overlap between training and validation via careful temporal splits.
• Duplicate handling thresholds: Verify that aggressive deduplication doesn’t remove distinct but related stories.
• Confidence calibration: Uncalibrated confidences can mislead weighting schemes; validate with held-out sets.