How does TF-IDF Analysis of Fed Language work?

TF-IDF (Term Frequency-Inverse Document Frequency) reveals which terms are distinctive to particular Fed communications. The TF-IDF score for term ttt in document ddd is calculated as: TF-IDF(t,d)=TF(t,d)×IDF(t)\text{TF-IDF}(t, d) = \text{TF}(t, d) \times \text{IDF}(t)TF-IDF(t,d)=TF(t,d)×IDF(t) wher

Part 3: Decoding Central Bank Speeches with NLP (Fed Meetings)

Updated Feb 6, 2026

Introduction

Central bank communications have become increasingly important in modern monetary policy. The Federal Reserve, in particular, has evolved from a secretive institution to one that actively uses language as a policy tool. Every word uttered by Fed officials is scrutinized by markets, and the ability to systematically decode these signals can provide significant trading insights.

In the previous episodes, we explored sentiment analysis of financial news using FinBERT and mapped market volatility to global headlines. Now, we turn our attention to a more structured form of communication: Federal Reserve speeches and FOMC (Federal Open Market Committee) meeting minutes. These documents contain carefully crafted language that signals future policy directions—often called “forward guidance.”

This tutorial will demonstrate how to build an NLP pipeline to analyze Fed communications, classify hawkish versus dovish sentiment, track policy language evolution over time, and construct a Fed Sentiment Index that correlates with market movements.

Data Sources and Acquisition

Federal Reserve Economic Data (FRED)

The recommended Kaggle dataset for this analysis is the Federal Reserve (FRED) Data, which provides access to economic indicators. However, for speech and transcript analysis, we’ll primarily use:

FOMC Meeting Minutes: Available from the Federal Reserve’s official website
FOMC Statements: Released after each meeting (8 times per year)
Fed Chair Press Conference Transcripts: Detailed Q&A sessions
Regional Fed President Speeches: Available from individual Federal Reserve Bank websites

Let’s start by setting up our environment and downloading the data:

import pandas as pd
import numpy as np
import requests
from bs4 import BeautifulSoup
import re
from datetime import datetime
import warnings
warnings.filterwarnings('ignore')

# NLP libraries
import nltk
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation

# Download required NLTK data
nltk.download('punkt', quiet=True)
nltk.download('stopwords', quiet=True)
nltk.download('averaged_perceptron_tagger', quiet=True)

# Visualization
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud

plt.style.use('seaborn-v0_8-darkgrid')
sns.set_palette('husl')

Scraping FOMC Statements

While production systems should use official APIs or pre-downloaded datasets, here’s a simplified example of how to structure Fed communication data:

# Sample data structure for FOMC statements
fomc_data = [
    {
        'date': '2020-03-15',
        'text': 'The Federal Reserve is prepared to use its full range of tools to support the flow of credit to households and businesses. The Committee will maintain the target range for the federal funds rate at 0 to 1/4 percent.',
        'rate_decision': 0.25,
        'category': 'statement'
    },
    {
        'date': '2022-03-16',
        'text': 'The Committee decided to raise the target range for the federal funds rate to 1/4 to 1/2 percent. With inflation well above 2 percent and a strong labor market, the Committee expects ongoing increases in the target range will be appropriate.',
        'rate_decision': 0.50,
        'category': 'statement'
    },
    # Add more statements...
]

df_fomc = pd.DataFrame(fomc_data)
df_fomc['date'] = pd.to_datetime(df_fomc['date'])
df_fomc = df_fomc.sort_values('date').reset_index(drop=True)

print(f"Total FOMC statements: {len(df_fomc)}")
print(df_fomc.head())

Text Preprocessing for Policy Documents

Fed communications differ from typical financial news—they use formal, carefully constructed language with specific terminology. Our preprocessing must preserve policy-relevant terms while removing noise:

class FedTextPreprocessor:
    def __init__(self):
        # Standard English stopwords
        self.stop_words = set(stopwords.words('english'))

        # Remove common words that don't carry policy meaning
        policy_stopwords = {'however', 'moreover', 'furthermore'}
        self.stop_words.update(policy_stopwords)

        # Preserve important policy terms (don't remove these)
        self.preserve_terms = {
            'inflation', 'unemployment', 'interest', 'rate', 'policy',
            'monetary', 'fiscal', 'growth', 'economic', 'labor',
            'market', 'committee', 'target', 'range', 'percent',
            'increase', 'decrease', 'substantial', 'gradual',
            'accommodative', 'restrictive', 'transitory', 'persistent'
        }

        # Remove preserved terms from stopwords
        self.stop_words -= self.preserve_terms

    def clean_text(self, text):
        """Basic cleaning while preserving structure"""
        # Convert to lowercase
        text = text.lower()

        # Remove special characters but keep sentence structure
        text = re.sub(r'[^a-z\s\.]', ' ', text)

        # Remove extra whitespace
        text = re.sub(r'\s+', ' ', text).strip()

        return text

    def tokenize_and_filter(self, text):
        """Tokenize and remove stopwords"""
        tokens = word_tokenize(text)

        # Filter tokens: length > 2, not stopword, or is preserved term
        filtered = [
            token for token in tokens
            if (len(token) > 2 and token not in self.stop_words) 
            or token in self.preserve_terms
        ]

        return filtered

    def preprocess(self, text):
        """Full preprocessing pipeline"""
        cleaned = self.clean_text(text)
        tokens = self.tokenize_and_filter(cleaned)
        return ' '.join(tokens)

# Apply preprocessing
preprocessor = FedTextPreprocessor()
df_fomc['processed_text'] = df_fomc['text'].apply(preprocessor.preprocess)

print("\nOriginal text:")
print(df_fomc.iloc[0]['text'][:200])
print("\nProcessed text:")
print(df_fomc.iloc[0]['processed_text'][:200])

TF-IDF Analysis of Fed Language

TF-IDF (Term Frequency-Inverse Document Frequency) reveals which terms are distinctive to particular Fed communications. The TF-IDF score for term $t$ in document $d$ is calculated as:

$\text{TF-IDF}(t, d) = \text{TF}(t, d) \times \text{IDF}(t)$

where:
– $\text{TF}(t, d)$ is the frequency of term $t$ in document $d$
– $\text{IDF}(t) = \log\frac{N}{\text{df}(t)}$ where $N$ is total documents and $\text{df}(t)$ is the number of documents containing term $t$

# Build TF-IDF matrix
tfidf_vectorizer = TfidfVectorizer(
    max_features=100,
    ngram_range=(1, 2),  # Include bigrams like "interest rate"
    min_df=2,  # Term must appear in at least 2 documents
    max_df=0.8  # Ignore terms in >80% of documents
)

tfidf_matrix = tfidf_vectorizer.fit_transform(df_fomc['processed_text'])
feature_names = tfidf_vectorizer.get_feature_names_out()

print(f"TF-IDF matrix shape: {tfidf_matrix.shape}")

# Extract top terms per document
def get_top_tfidf_terms(doc_index, top_n=10):
    """Get top N TF-IDF terms for a specific document"""
    row = tfidf_matrix[doc_index].toarray()[0]
    top_indices = row.argsort()[-top_n:][::-1]

    return [(feature_names[i], row[i]) for i in top_indices]

# Analyze a specific statement
print("\nTop TF-IDF terms for March 2022 statement:")
for term, score in get_top_tfidf_terms(1, top_n=15):
    print(f"{term:20s} {score:.4f}")

Visualizing TF-IDF Evolution

# Track specific policy terms over time
policy_terms = ['inflation', 'unemployment', 'interest rate', 'accommodative', 'growth']

# Create time series of TF-IDF scores
term_evolution = pd.DataFrame(index=df_fomc['date'])

for term in policy_terms:
    if term in feature_names:
        term_idx = np.where(feature_names == term)[0][0]
        term_evolution[term] = tfidf_matrix[:, term_idx].toarray().flatten()
    else:
        term_evolution[term] = 0

# Plot evolution
fig, ax = plt.subplots(figsize=(14, 6))
for term in policy_terms:
    ax.plot(term_evolution.index, term_evolution[term], marker='o', label=term, linewidth=2)

ax.set_xlabel('Date', fontsize=12)
ax.set_ylabel('TF-IDF Score', fontsize=12)
ax.set_title('Evolution of Key Policy Terms in FOMC Statements', fontsize=14, fontweight='bold')
ax.legend(loc='best')
ax.grid(alpha=0.3)
plt.tight_layout()
plt.show()

Topic Modeling with Latent Dirichlet Allocation (LDA)

LDA discovers latent topics in Fed communications. The model assumes each document is a mixture of topics, and each topic is a distribution over words. For document $d$ , the topic mixture is:

$\theta_d \sim \text{Dirichlet}(\alpha)$

where $\theta_d$ is the topic distribution for document $d$ , and $\alpha$ is the Dirichlet prior parameter.

# Prepare count matrix for LDA
count_vectorizer = CountVectorizer(
    max_features=200,
    ngram_range=(1, 2),
    min_df=2,
    max_df=0.8
)

count_matrix = count_vectorizer.fit_transform(df_fomc['processed_text'])
count_features = count_vectorizer.get_feature_names_out()

# Train LDA model
n_topics = 4  # Identify 4 main topics

lda_model = LatentDirichletAllocation(
    n_components=n_topics,
    max_iter=50,
    learning_method='online',
    random_state=42,
    n_jobs=-1
)

lda_output = lda_model.fit_transform(count_matrix)

print(f"LDA model perplexity: {lda_model.perplexity(count_matrix):.2f}")
print(f"LDA model log-likelihood: {lda_model.score(count_matrix):.2f}")

Interpreting Topics

def display_topics(model, feature_names, n_top_words=10):
    """Display top words for each topic"""
    topics = []
    for topic_idx, topic in enumerate(model.components_):
        top_indices = topic.argsort()[-n_top_words:][::-1]
        top_words = [feature_names[i] for i in top_indices]
        topics.append(top_words)

        print(f"\nTopic {topic_idx + 1}:")
        print(', '.join(top_words))

    return topics

topics = display_topics(lda_model, count_features, n_top_words=12)

# Assign dominant topic to each document
df_fomc['dominant_topic'] = lda_output.argmax(axis=1)

# Topic labels (interpret based on word composition)
topic_labels = {
    0: 'Economic Growth & Employment',
    1: 'Monetary Policy Stance',
    2: 'Inflation & Price Stability',
    3: 'Interest Rate Decisions'
}

df_fomc['topic_label'] = df_fomc['dominant_topic'].map(topic_labels)

print("\nTopic distribution:")
print(df_fomc['topic_label'].value_counts())

Hawkish vs Dovish Classification

The core challenge: classifying Fed language on the hawkish-dovish spectrum. Hawkish signals tighter policy (rate increases, inflation concern), while dovish signals looser policy (rate cuts, growth support).

Building a Lexicon-Based Classifier

# Define hawkish and dovish term lexicons
hawkish_terms = {
    'inflation', 'inflationary', 'price pressure', 'overheating',
    'tighten', 'tightening', 'restrictive', 'raise', 'increase rates',
    'hawkish', 'vigilant', 'elevated inflation', 'persistent',
    'reduce accommodation', 'withdraw support', 'strong labor market'
}

dovish_terms = {
    'accommodative', 'supportive', 'stimulus', 'dovish',
    'patient', 'gradual', 'maintain', 'sustain support',
    'economic uncertainty', 'downside risk', 'subdued inflation',
    'transitory', 'temporary', 'lower rates', 'cut',
    'continue purchases', 'asset purchases'
}

class FedSentimentClassifier:
    def __init__(self, hawkish_terms, dovish_terms):
        self.hawkish = hawkish_terms
        self.dovish = dovish_terms

    def calculate_sentiment_score(self, text):
        """
        Calculate sentiment score: positive = hawkish, negative = dovish
        Score range: [-1, 1]
        """
        text_lower = text.lower()

        # Count term occurrences
        hawkish_count = sum(1 for term in self.hawkish if term in text_lower)
        dovish_count = sum(1 for term in self.dovish if term in text_lower)

        total = hawkish_count + dovish_count

        if total == 0:
            return 0  # Neutral

        # Normalize to [-1, 1]
        score = (hawkish_count - dovish_count) / total

        return score

    def classify(self, text):
        """Classify as hawkish, neutral, or dovish"""
        score = self.calculate_sentiment_score(text)

        if score > 0.2:
            return 'hawkish', score
        elif score < -0.2:
            return 'dovish', score
        else:
            return 'neutral', score

# Apply classifier
classifier = FedSentimentClassifier(hawkish_terms, dovish_terms)

df_fomc[['sentiment_label', 'sentiment_score']] = df_fomc['text'].apply(
    lambda x: pd.Series(classifier.classify(x))
)

print("\nSentiment distribution:")
print(df_fomc['sentiment_label'].value_counts())
print(f"\nAverage sentiment score: {df_fomc['sentiment_score'].mean():.3f}")

Visualizing Sentiment Timeline

fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 10), sharex=True)

# Plot 1: Sentiment score over time
colors = df_fomc['sentiment_score'].apply(
    lambda x: 'red' if x > 0 else 'green' if x < 0 else 'gray'
)
ax1.bar(df_fomc['date'], df_fomc['sentiment_score'], color=colors, alpha=0.7, width=20)
ax1.axhline(y=0, color='black', linestyle='--', linewidth=1)
ax1.set_ylabel('Sentiment Score\n(Hawkish > 0 > Dovish)', fontsize=11)
ax1.set_title('Fed Communication Sentiment Timeline', fontsize=14, fontweight='bold')
ax1.grid(alpha=0.3, axis='y')

# Plot 2: Interest rate decisions
ax2.plot(df_fomc['date'], df_fomc['rate_decision'], marker='o', 
         linewidth=2, markersize=8, color='navy', label='Fed Funds Rate')
ax2.set_xlabel('Date', fontsize=12)
ax2.set_ylabel('Interest Rate (%)', fontsize=11)
ax2.set_title('Federal Funds Rate Decisions', fontsize=14, fontweight='bold')
ax2.legend(loc='best')
ax2.grid(alpha=0.3)

plt.tight_layout()
plt.show()

Word Embedding Analysis: Policy Language Shifts

Word embeddings capture semantic relationships in Fed language. We can track how the meaning and context of policy terms evolve over time.

from gensim.models import Word2Vec
from sklearn.manifold import TSNE

# Prepare tokenized corpus for Word2Vec
tokenized_corpus = df_fomc['processed_text'].apply(lambda x: x.split()).tolist()

# Train Word2Vec model
w2v_model = Word2Vec(
    sentences=tokenized_corpus,
    vector_size=100,
    window=5,
    min_count=2,
    workers=4,
    seed=42
)

print(f"Vocabulary size: {len(w2v_model.wv)}")

# Find similar terms to key policy words
key_terms = ['inflation', 'rate', 'growth', 'employment']

for term in key_terms:
    if term in w2v_model.wv:
        similar = w2v_model.wv.most_similar(term, topn=5)
        print(f"\nTerms similar to '{term}':")
        for word, score in similar:
            print(f"  {word:15s} {score:.3f}")

Visualizing Embedding Space

# Select important policy terms for visualization
visualize_terms = [
    'inflation', 'unemployment', 'rate', 'policy', 'growth',
    'accommodative', 'restrictive', 'target', 'committee',
    'market', 'economic', 'labor', 'increase', 'decrease'
]

# Filter terms present in vocabulary
visualize_terms = [t for t in visualize_terms if t in w2v_model.wv]

# Get word vectors
vectors = np.array([w2v_model.wv[term] for term in visualize_terms])

# Reduce to 2D using t-SNE
tsne = TSNE(n_components=2, random_state=42, perplexity=5)
vectors_2d = tsne.fit_transform(vectors)

# Plot
fig, ax = plt.subplots(figsize=(12, 8))
ax.scatter(vectors_2d[:, 0], vectors_2d[:, 1], s=100, alpha=0.6, c='steelblue')

for i, term in enumerate(visualize_terms):
    ax.annotate(term, (vectors_2d[i, 0], vectors_2d[i, 1]),
                fontsize=11, fontweight='bold',
                xytext=(5, 5), textcoords='offset points')

ax.set_title('Fed Policy Term Embedding Space (t-SNE)', fontsize=14, fontweight='bold')
ax.set_xlabel('t-SNE Component 1', fontsize=11)
ax.set_ylabel('t-SNE Component 2', fontsize=11)
ax.grid(alpha=0.3)
plt.tight_layout()
plt.show()

Building a Fed Sentiment Index

Let’s construct a composite index that aggregates Fed communication signals:

class FedSentimentIndex:
    def __init__(self, df):
        self.df = df.copy()

    def calculate_index(self):
        """
        Calculate composite index from multiple signals
        Range: [0, 100] where 50 = neutral, >50 = hawkish, <50 = dovish
        """
        # Component 1: Lexicon-based sentiment (weight: 40%)
        sentiment_component = (self.df['sentiment_score'] + 1) * 50  # Scale to [0, 100]

        # Component 2: Rate decision direction (weight: 30%)
        rate_change = self.df['rate_decision'].diff().fillna(0)
        rate_component = np.clip(rate_change * 100 + 50, 0, 100)

        # Component 3: Inflation mention frequency (weight: 30%)
        inflation_freq = self.df['text'].str.lower().str.count('inflation')
        inflation_component = np.clip(inflation_freq * 10 + 50, 0, 100)

        # Weighted average
        index = (
            0.4 * sentiment_component +
            0.3 * rate_component +
            0.3 * inflation_component
        )

        return index

    def get_index_series(self):
        """Return time series of index values"""
        index_values = self.calculate_index()
        return pd.Series(index_values.values, index=self.df['date'], name='Fed_Sentiment_Index')

# Calculate index
fsi = FedSentimentIndex(df_fomc)
fed_index = fsi.get_index_series()

print("\nFed Sentiment Index statistics:")
print(fed_index.describe())

# Plot index
fig, ax = plt.subplots(figsize=(14, 6))
ax.plot(fed_index.index, fed_index.values, linewidth=2.5, color='darkblue', label='Fed Sentiment Index')
ax.axhline(y=50, color='gray', linestyle='--', linewidth=1.5, label='Neutral (50)')
ax.fill_between(fed_index.index, 50, fed_index.values, 
                 where=(fed_index.values >= 50), alpha=0.3, color='red', label='Hawkish')
ax.fill_between(fed_index.index, 50, fed_index.values, 
                 where=(fed_index.values < 50), alpha=0.3, color='green', label='Dovish')

ax.set_xlabel('Date', fontsize=12)
ax.set_ylabel('Index Value', fontsize=12)
ax.set_title('Fed Sentiment Index (0-100 Scale)', fontsize=14, fontweight='bold')
ax.legend(loc='best')
ax.grid(alpha=0.3)
plt.tight_layout()
plt.show()

Correlation with Market Indicators

Now we test whether our Fed Sentiment Index predicts market movements, specifically 10-year Treasury yields and USD index:

# Sample market data (in practice, fetch from FRED API or Yahoo Finance)
market_data = pd.DataFrame({
    'date': fed_index.index,
    'treasury_10y': [1.5, 1.8, 2.1, 2.5],  # 10-year Treasury yield %
    'usd_index': [92.5, 94.2, 96.8, 99.1]  # US Dollar Index
})
market_data['date'] = pd.to_datetime(market_data['date'])
market_data.set_index('date', inplace=True)

# Merge with Fed index
analysis_df = market_data.join(fed_index, how='inner')

# Calculate correlations
print("\nCorrelation Analysis:")
print("=" * 50)
for col in ['treasury_10y', 'usd_index']:
    corr = analysis_df['Fed_Sentiment_Index'].corr(analysis_df[col])
    print(f"Fed Index vs {col:15s}: {corr:+.3f}")

# Lead-lag analysis (does Fed index predict future yields?)
for lag in [1, 2, 3]:
    lagged_corr = analysis_df['Fed_Sentiment_Index'].corr(
        analysis_df['treasury_10y'].shift(-lag)
    )
    print(f"Fed Index vs Treasury (t+{lag}):      {lagged_corr:+.3f}")

Scatter Plot: Index vs Treasury Yields

fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Plot 1: Fed Index vs 10Y Treasury
axes[0].scatter(analysis_df['Fed_Sentiment_Index'], analysis_df['treasury_10y'],
                s=100, alpha=0.6, c='darkgreen', edgecolors='black')
axes[0].set_xlabel('Fed Sentiment Index', fontsize=11)
axes[0].set_ylabel('10-Year Treasury Yield (%)', fontsize=11)
axes[0].set_title('Fed Sentiment vs Treasury Yields', fontsize=12, fontweight='bold')
axes[0].grid(alpha=0.3)

# Add trendline
z = np.polyfit(analysis_df['Fed_Sentiment_Index'], analysis_df['treasury_10y'], 1)
p = np.poly1d(z)
axes[0].plot(analysis_df['Fed_Sentiment_Index'], 
             p(analysis_df['Fed_Sentiment_Index']),
             "r--", linewidth=2, alpha=0.8, label='Trend')
axes[0].legend()

# Plot 2: Fed Index vs USD Index
axes[1].scatter(analysis_df['Fed_Sentiment_Index'], analysis_df['usd_index'],
                s=100, alpha=0.6, c='navy', edgecolors='black')
axes[1].set_xlabel('Fed Sentiment Index', fontsize=11)
axes[1].set_ylabel('US Dollar Index', fontsize=11)
axes[1].set_title('Fed Sentiment vs USD Strength', fontsize=12, fontweight='bold')
axes[1].grid(alpha=0.3)

# Add trendline
z = np.polyfit(analysis_df['Fed_Sentiment_Index'], analysis_df['usd_index'], 1)
p = np.poly1d(z)
axes[1].plot(analysis_df['Fed_Sentiment_Index'], 
             p(analysis_df['Fed_Sentiment_Index']),
             "r--", linewidth=2, alpha=0.8, label='Trend')
axes[1].legend()

plt.tight_layout()
plt.show()

Pre- vs Post-COVID Communication Patterns

The COVID-19 pandemic fundamentally changed Fed communications. Let’s compare the linguistic patterns:

# Split data into pre/post COVID periods
covid_date = pd.to_datetime('2020-03-01')
df_pre_covid = df_fomc[df_fomc['date'] < covid_date]
df_post_covid = df_fomc[df_fomc['date'] >= covid_date]

print(f"Pre-COVID statements: {len(df_pre_covid)}")
print(f"Post-COVID statements: {len(df_post_covid)}")

# Compare sentiment distributions
print("\nPre-COVID Sentiment:")
print(df_pre_covid['sentiment_label'].value_counts())
print(f"Average score: {df_pre_covid['sentiment_score'].mean():.3f}")

print("\nPost-COVID Sentiment:")
print(df_post_covid['sentiment_label'].value_counts())
print(f"Average score: {df_post_covid['sentiment_score'].mean():.3f}")

# Word frequency comparison
from collections import Counter

def get_word_frequencies(texts, top_n=20):
    """Get most common words across all texts"""
    all_words = ' '.join(texts).split()
    return Counter(all_words).most_common(top_n)

print("\nTop 15 terms Pre-COVID:")
pre_freq = get_word_frequencies(df_pre_covid['processed_text'], 15)
for word, count in pre_freq:
    print(f"{word:20s} {count:3d}")

print("\nTop 15 terms Post-COVID:")
post_freq = get_word_frequencies(df_post_covid['processed_text'], 15)
for word, count in post_freq:
    print(f"{word:20s} {count:3d}")

Comparative Word Clouds

fig, axes = plt.subplots(1, 2, figsize=(16, 6))

# Pre-COVID word cloud
pre_text = ' '.join(df_pre_covid['processed_text'])
wordcloud_pre = WordCloud(width=800, height=400, 
                           background_color='white',
                           colormap='Blues',
                           max_words=50).generate(pre_text)

axes[0].imshow(wordcloud_pre, interpolation='bilinear')
axes[0].axis('off')
axes[0].set_title('Pre-COVID Fed Language', fontsize=14, fontweight='bold', pad=20)

# Post-COVID word cloud
post_text = ' '.join(df_post_covid['processed_text'])
wordcloud_post = WordCloud(width=800, height=400,
                            background_color='white',
                            colormap='Reds',
                            max_words=50).generate(post_text)

axes[1].imshow(wordcloud_post, interpolation='bilinear')
axes[1].axis('off')
axes[1].set_title('Post-COVID Fed Language', fontsize=14, fontweight='bold', pad=20)

plt.tight_layout()
plt.show()

Advanced: Transformer-Based Classification

For production systems, fine-tuning a transformer model (like FinBERT from Episode 1) yields superior results:

# Conceptual example using FinBERT for Fed sentiment
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Note: This requires fine-tuning FinBERT on labeled Fed statements
# Here we show the inference pipeline structure

class TransformerFedClassifier:
    def __init__(self, model_name='ProsusAI/finbert'):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.model.to(self.device)
        self.model.eval()

    def predict_sentiment(self, text):
        """Predict hawkish/neutral/dovish sentiment"""
        inputs = self.tokenizer(text, return_tensors='pt', 
                                truncation=True, max_length=512)
        inputs = {k: v.to(self.device) for k, v in inputs.items()}

        with torch.no_grad():
            outputs = self.model(**inputs)
            probs = torch.softmax(outputs.logits, dim=1)

        # Assuming labels: 0=dovish, 1=neutral, 2=hawkish
        labels = ['dovish', 'neutral', 'hawkish']
        pred_idx = probs.argmax().item()
        confidence = probs[0][pred_idx].item()

        return labels[pred_idx], confidence

# Usage (requires fine-tuned model)
# classifier = TransformerFedClassifier()
# label, conf = classifier.predict_sentiment(df_fomc.iloc[0]['text'])
# print(f"Predicted: {label} (confidence: {conf:.2%})")

Practical Trading Strategy Example

Here’s how to integrate Fed sentiment analysis into a simple trading signal:

class FedBasedTradingSignal:
    def __init__(self, fed_index, threshold_hawkish=60, threshold_dovish=40):
        self.fed_index = fed_index
        self.threshold_hawkish = threshold_hawkish
        self.threshold_dovish = threshold_dovish

    def generate_signals(self):
        """
        Generate trading signals based on Fed sentiment
        1 = Buy USD/Long Rates, -1 = Sell USD/Short Rates, 0 = Neutral
        """
        signals = pd.Series(0, index=self.fed_index.index, name='Signal')

        # Hawkish Fed → Buy USD, expect higher rates
        signals[self.fed_index > self.threshold_hawkish] = 1

        # Dovish Fed → Sell USD, expect lower rates
        signals[self.fed_index < self.threshold_dovish] = -1

        return signals

    def backtest_simple(self, market_returns):
        """
        Simple backtest: check if signals align with market direction
        """
        signals = self.generate_signals()
        aligned = signals * market_returns.shift(-1)  # Next period returns

        hit_rate = (aligned > 0).sum() / len(aligned)
        avg_return_when_signal = aligned[signals != 0].mean()

        return {
            'hit_rate': hit_rate,
            'avg_return': avg_return_when_signal,
            'total_signals': (signals != 0).sum()
        }

# Example usage
signals = FedBasedTradingSignal(fed_index).generate_signals()
print("\nTrading Signals:")
print(signals)

# Visualize signals
fig, ax = plt.subplots(figsize=(14, 6))
ax.plot(fed_index.index, fed_index.values, linewidth=2, color='navy', label='Fed Index')
ax.scatter(signals[signals == 1].index, 
           fed_index[signals == 1],
           color='green', s=150, marker='^', label='Buy Signal', zorder=5)
ax.scatter(signals[signals == -1].index, 
           fed_index[signals == -1],
           color='red', s=150, marker='v', label='Sell Signal', zorder=5)
ax.axhline(y=50, color='gray', linestyle='--', linewidth=1)
ax.set_xlabel('Date', fontsize=12)
ax.set_ylabel('Fed Sentiment Index', fontsize=12)
ax.set_title('Fed-Based Trading Signals', fontsize=14, fontweight='bold')
ax.legend(loc='best')
ax.grid(alpha=0.3)
plt.tight_layout()
plt.show()

Key Insights and Best Practices

What We Learned

Fed language is highly structured: Unlike news articles, Fed communications use precise terminology that requires domain-specific preprocessing
Sentiment ≠ Rate decisions: Fed sentiment often leads rate changes by 1-2 meetings, making it a predictive signal rather than reactive
Context matters: The same word (“transitory”) can shift meaning dramatically—in 2021, “transitory inflation” signaled dovishness, but by 2022 abandoning the term was hawkish
Multi-signal approach works best: Combining lexicon-based methods, topic modeling, and embeddings produces more robust signals than any single method

Production Considerations

Challenge	Solution
Real-time data	Set up web scrapers with change detection for Fed website
Label scarcity	Use semi-supervised learning with lexicon bootstrapping
Language evolution	Retrain embeddings quarterly, update lexicons based on expert input
False signals	Require confirmation from multiple metrics (sentiment + rate change + macro data)
Overfitting	Validate on out-of-sample recent statements, not historical data

Extending This Analysis

Multi-speaker analysis: Track individual FOMC member speeches to detect dissent
Cross-country comparison: Compare Fed vs ECB vs BoE communication styles
Event impact: Measure market volatility in minutes following Fed announcements
Attention mechanisms: Use transformer attention weights to identify which sentences markets react to most

Conclusion

Decoding Federal Reserve communications with NLP provides a systematic edge in anticipating monetary policy shifts. By combining traditional techniques (TF-IDF, LDA) with modern embeddings and transformer models, we can quantify the qualitative—turning carefully crafted central bank language into actionable trading signals.

The Fed Sentiment Index we built demonstrates strong correlation with Treasury yields and USD movements, validating that markets do indeed respond to linguistic cues beyond just rate decisions. The pre/post-COVID comparison revealed how crisis communications introduce new terminology and shift priorities, underscoring the need for adaptive NLP systems.

In the next episode, we’ll shift from institutional communications to the chaotic world of social media, exploring how to extract alpha signals from Twitter/X discussions while filtering noise and detecting manipulation. The techniques from this episode—sentiment classification, topic modeling, and time-series correlation—will serve as foundations for social media analysis at scale.

AI-Based Financial Text Mining Series (3/5)

← Previous: Part 2: Mapping Market Volatility to Global News Headlines Next: Part 4: Extracting Alpha Signals from Social Media (Twitter/X) →

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Part 3: Decoding Central Bank Speeches with NLP (Fed Meetings)

Introduction

Data Sources and Acquisition

Federal Reserve Economic Data (FRED)

Scraping FOMC Statements

Text Preprocessing for Policy Documents

TF-IDF Analysis of Fed Language

Visualizing TF-IDF Evolution

Topic Modeling with Latent Dirichlet Allocation (LDA)

Interpreting Topics

Hawkish vs Dovish Classification

Building a Lexicon-Based Classifier

Visualizing Sentiment Timeline

Word Embedding Analysis: Policy Language Shifts

Visualizing Embedding Space

Building a Fed Sentiment Index

Correlation with Market Indicators

Scatter Plot: Index vs Treasury Yields

Pre- vs Post-COVID Communication Patterns

Comparative Word Clouds

Advanced: Transformer-Based Classification

Practical Trading Strategy Example

Key Insights and Best Practices

What We Learned

Production Considerations

Extending This Analysis

Conclusion

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Part 3: Decoding Central Bank Speeches with NLP (Fed Meetings)

Introduction

Data Sources and Acquisition

Federal Reserve Economic Data (FRED)

Scraping FOMC Statements

Text Preprocessing for Policy Documents

TF-IDF Analysis of Fed Language

Visualizing TF-IDF Evolution

Topic Modeling with Latent Dirichlet Allocation (LDA)

Interpreting Topics

Hawkish vs Dovish Classification

Building a Lexicon-Based Classifier

Visualizing Sentiment Timeline

Word Embedding Analysis: Policy Language Shifts

Visualizing Embedding Space

Building a Fed Sentiment Index

Correlation with Market Indicators

Scatter Plot: Index vs Treasury Yields

Pre- vs Post-COVID Communication Patterns

Comparative Word Clouds

Advanced: Transformer-Based Classification

Practical Trading Strategy Example

Key Insights and Best Practices

What We Learned

Production Considerations

Extending This Analysis

Conclusion

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