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14/02/2024
US Politics and Market Predictions
8 min read
US Politics and Market Predictions
Introduction: The Inextricable Link Between Politics and Markets
In today’s interconnected financial landscape, political events aren’t just front-page news — they’re market-moving catalysts that can create both risk and opportunity. From election volatility spikes to legislative impacts on specific sectors, understanding the relationship between US politics and financial markets has become essential for modern investors.
Welcome to a comprehensive analysis where we’ll explore how elections, legislation, and political uncertainty influence market sentiment and price movements. More importantly, we’ll demonstrate how the Axion platform and its Python SDK can transform political analysis from qualitative speculation into quantitative, actionable insights.
Why Political Analysis Belongs in Your Investment Toolkit
Political events create predictable patterns in financial markets:
- Election cycles drive sector rotation based on anticipated policy changes
- Legislative milestones (like infrastructure bills or tax reforms) create winners and losers
- Geopolitical tensions impact commodity prices and currency valuations
- Regulatory changes alter industry profitability landscapes
Traditionally, analyzing these relationships required cross-referencing multiple data sources. With Axion, you can quantify political impacts directly within your Python environment.
Getting Started with Axion’s Political-Market Analysis
First, let’s set up our environment and connect to Axion’s comprehensive data universe:
from axion import Axion, visualize
import pandas as pd
# Initialize the Axion client with your API key
client = Axion(api_key="your_api_key_here")
# For political analysis, we'll focus on several key endpoints:
# - News API for political events
# - Sentiment API for market mood
# - Econ API for policy impacts
# - Stocks API for price reactionsCase Study 1: Election Season Volatility Analysis
Tracking Pre-Election Sentiment Shifts
Elections create uncertainty, and uncertainty breeds volatility. Let’s analyze how sentiment shifts during election cycles:
def analyze_election_impact(candidate_tickers, days_before=30, days_after=30):
"""
Analyze stock performance for companies linked to political candidates
"""
results = {}
for ticker, candidate in candidate_tickers.items():
# Get sentiment data
sentiment = client.sentiment.all(ticker)
# Get price data around election
prices = client.stocks.prices(
ticker,
from_date=f"2024-10-01", # One month before election
to_date=f"2024-12-01" # One month after
)
# Convert to DataFrame for analysis
df = pd.DataFrame(prices)
df['date'] = pd.to_datetime(df['time'])
df.set_index('date', inplace=True)
# Calculate election day impact
election_day = pd.Timestamp("2024-11-05")
pre_election = df[df.index < election_day]
post_election = df[df.index > election_day]
# Store results
results[candidate] = {
'pre_volatility': pre_election['close'].std(),
'post_volatility': post_election['close'].std(),
'percentage_change': ((post_election['close'].mean() -
pre_election['close'].mean()) /
pre_election['close'].mean()) * 100
}
return pd.DataFrame(results).T
# Example: Analyze renewable energy vs fossil fuel stocks
candidate_exposure = {
"FSLR": "Renewable_Favored",
"XOM": "Traditional_Energy",
"IWM": "Small_Caps", # Often sensitive to regulatory changes
"XLF": "Financials" # Banking regulations
}
election_analysis = analyze_election_impact(candidate_exposure)
print(election_analysis)Visualizing Sector Rotation During Elections
# Track sector performance shifts
sectors = ["XLE", "XLK", "XLV", "XLF", "XLI"]
sector_performance = {}
for sector in sectors:
prices = client.stocks.prices(sector, from_date="2024-10-01", to_date="2024-12-01")
df = pd.DataFrame(prices)
# Calculate cumulative returns
df['returns'] = df['close'].pct_change()
df['cumulative'] = (1 + df['returns']).cumprod()
sector_performance[sector] = df[['time', 'cumulative']]
# Create comparison visualization
combined_df = pd.concat([ pd.DataFrame({'time': df['time'], sector: df['cumulative']})
for sector, df in sector_performance.items()
], axis=1)
visualize.line(combined_df, x='time', y=sectors,
title="Sector Performance Around 2024 Election")Case Study 2: Legislative Impact Analysis
Quantifying Infrastructure Bill Effects
When major legislation passes, it creates immediate and long-term market impacts. Let’s analyze the 2021 Infrastructure Bill:
def analyze_legislative_impact(bill_date, affected_tickers, window_days=90):
"""
Analyze stock performance before and after legislative events
"""
bill_date = pd.Timestamp(bill_date)
start_date = bill_date - pd.Timedelta(days=window_days)
end_date = bill_date + pd.Timedelta(days=window_days)
results = []
for ticker in affected_tickers:
try:
# Get price data
prices = client.stocks.prices(
ticker,
from_date=start_date.strftime('%Y-%m-%d'),
to_date=end_date.strftime('%Y-%m-%d')
)
df = pd.DataFrame(prices)
df['date'] = pd.to_datetime(df['time'])
df.set_index('date', inplace=True)
# Calculate event study metrics
pre_period = df[df.index < bill_date]
post_period = df[df.index > bill_date]
# Abnormal returns calculation
market_returns = get_market_returns() # SPY returns for benchmark
# ... detailed event study implementation ...
results.append({
'ticker': ticker,
'pre_return': pre_period['close'].pct_change().mean() * 252,
'post_return': post_period['close'].pct_change().mean() * 252,
'abnormal_return': calculate_abnormal_returns(df, market_returns, bill_date)
})
except Exception as e:
print(f"Error processing {ticker}: {e}")
return pd.DataFrame(results)
# Infrastructure bill beneficiaries
infrastructure_stocks = ["CAT", "DE", "VMC", "MLM", "NUE"]
bill_analysis = analyze_legislative_impact("2021-11-15", infrastructure_stocks)
# Visualize results
visualize.bar(bill_analysis, x='ticker', y='abnormal_return',
title="Abnormal Returns Following Infrastructure Bill Passage")Real-Time Legislative Tracking Dashboard
def create_political_dashboard():
"""
Create comprehensive dashboard for political-market analysis
"""
# 1. Get upcoming political events
calendar = client.econ.calendar(
from_date=pd.Timestamp.now().strftime('%Y-%m-%d'),
to_date=(pd.Timestamp.now() + pd.Timedelta(days=30)).strftime('%Y-%m-%d'),
country="US",
min_importance=2
)
# 2. Monitor related news sentiment
political_news = client.news.category("politics")
# 3. Track affected sectors
sectors = {
"Technology": ["XLK", "QQQ"],
"Healthcare": ["XLV", "IBB"],
"Energy": ["XLE", "ICLN"],
"Financials": ["XLF", "KRE"]
}
# 4. Build correlation matrix between political events and sector returns
correlations = analyze_political_correlations(calendar, sectors)
return {
'calendar': calendar,
'news_sentiment': analyze_news_sentiment(political_news),
'sector_correlations': correlations
}
# Generate the dashboard
dashboard = create_political_dashboard()
# Visualize key insights
visualize.heatmap(
dashboard['sector_correlations'],
x='event_type',
y='sector',
title="Political Event to Sector Correlation Matrix"
)Case Study 3: Geopolitical Risk Modeling
Building a Geopolitical Risk Indicator
class GeopoliticalRiskModel:
def __init__(self, client):
self.client = client
self.risk_factors = []
def add_risk_factor(self, name, data_source, weight):
"""Add a risk factor to the model"""
self.risk_factors.append({
'name': name,
'source': data_source,
'weight': weight
})
def calculate_risk_score(self, date_range):
"""Calculate composite geopolitical risk score"""
scores = []
for factor in self.risk_factors:
if factor['source'] == 'news':
# Analyze news sentiment
news = self.client.news.general()
sentiment = analyze_sentiment_volume(news)
scores.append(sentiment * factor['weight'])
elif factor['source'] == 'market':
# Use volatility indices
vix_data = self.client.stocks.prices("^VIX", **date_range)
scores.append(calculate_volatility_score(vix_data) * factor['weight'])
elif factor['source'] == 'forex':
# Safe haven currency flows
usd_data = self.client.forex.prices("DXY", **date_range)
scores.append(analyze_safe_haven_flows(usd_data) * factor['weight'])
return sum(scores)
def predict_market_impact(self, risk_score):
"""Predict market impact based on risk score"""
# Use historical regression to predict impact
historical_data = self.gather_historical_impacts()
# Build predictive model
model = self.client.linearRegression(
historical_data,
x='risk_score',
y='market_return',
n_preds=5
)
return model
# Initialize and use the model
risk_model = GeopoliticalRiskModel(client)
risk_model.add_risk_factor("US_China_Tensions", "news", 0.3)
risk_model.add_risk_factor("Middle_East_Unrest", "news", 0.25)
risk_model.add_risk_factor("Market_Volatility", "market", 0.25)
risk_model.add_risk_factor("Currency_Flows", "forex", 0.2)
# Calculate current risk
current_risk = risk_model.calculate_risk_score({
'from_date': '2024-01-01',
'to_date': '2024-03-01'
})
print(f"Current Geopolitical Risk Score: {current_risk:.2f}")Advanced Techniques: Predictive Modeling with Political Data
LSTM Model for Election Outcome Prediction
def build_election_prediction_model():
"""
Build LSTM model to predict market reactions to election polls
"""
# 1. Gather polling data and market data
# (In practice, you'd integrate with polling APIs)
polling_data = load_polling_data() # Your polling data source
market_data = client.stocks.prices("SPY", from_date="2024-01-01")
# 2. Create feature set
features = pd.DataFrame({
'poll_lead': polling_data['candidate_lead'],
'uncertainty_index': polling_data['margin_of_error'],
'days_to_election': polling_data['days_remaining'],
'vix_level': market_data['close'].rolling(5).mean(),
'sector_rotation': calculate_sector_rotation()
})
# 3. Target variable: Next day market return
target = market_data['close'].pct_change().shift(-1)
# 4. Build LSTM model using Axion's built-in function
predictions = client.lstm(
pd.concat([features, target], axis=1),
x='date',
target='market_return',
features=['poll_lead', 'uncertainty_index', 'vix_level'],
n_preds=10 # Predict 10 days ahead
)
return predictions
# Generate predictions
election_predictions = build_election_prediction_model()
# Visualize predictions vs actual
visualize.graph(
election_predictions,
x='time',
lines=['predicted_return'],
title="Election Period Market Return Predictions"
)Sentiment Analysis Pipeline for Political Speech Impact
def analyze_political_speech_impact(speaker, date, tickers):
"""
Analyze market impact of key political speeches
"""
# 1. Get news articles about the speech
news_articles = client.news.general()
speech_articles = [ article for article in news_articles
if speaker.lower() in article['title'].lower()
and date in article['date']
]
# 2. Extract sentiment
sentiments = []
for article in speech_articles:
# Use Axion's sentiment analysis or integrate with NLP library
sentiment = analyze_text_sentiment(article['content'])
sentiments.append(sentiment)
avg_sentiment = sum(sentiments) / len(sentiments) if sentiments else 0
# 3. Analyze market reaction
reactions = {}
for ticker in tickers:
# Get intraday prices around speech time
prices = client.stocks.prices(
ticker,
from_date=f"{date}T09:30:00",
to_date=f"{date}T16:00:00",
frame='5min' # 5-minute intervals
)
# Calculate immediate reaction
speech_time = f"{date}T14:00:00" # Example: 2 PM speech
pre_speech = [p for p in prices if p['time'] < speech_time]
post_speech = [p for p in prices if p['time'] > speech_time]
if pre_speech and post_speech:
reaction = (post_speech[0]['close'] - pre_speech[-1]['close']) / pre_speech[-1]['close']
reactions[ticker] = reaction * 100 # Percentage change
return {
'speaker': speaker,
'speech_sentiment': avg_sentiment,
'market_reactions': reactions,
'correlation': calculate_sentiment_market_correlation(avg_sentiment, reactions)
}
# Analyze recent presidential address impact
impact_analysis = analyze_political_speech_impact(
speaker="President",
date="2024-02-15",
tickers=["SPY", "QQQ", "DIA", "IWM"]
)
print(f"Speech Sentiment Score: {impact_analysis['speech_sentiment']:.2f}")
print(f"Market Reactions: {impact_analysis['market_reactions']}")Practical Applications: Building a Political Hedge Portfolio
def build_political_hedge_portfolio(election_scenario):
"""
Construct portfolio that hedges against political outcomes
"""
# Define scenario-specific allocations
scenarios = {
"democratic_sweep": {
"long": ["ICLN", "TAN", "XLU"], # Clean energy, utilities
"short": ["XLE", "KRE"], # Traditional energy, regional banks
"neutral": ["XLK", "XLV"] # Tech, healthcare
},
"republican_sweep": {
"long": ["XLE", "XLI", "XLF"], # Energy, industrials, financials
"short": ["ICLN", "TAN"], # Clean energy
"neutral": ["XLP", "XLV"] # Staples, healthcare
},
"divided_government": {
"long": ["SPY", "BND"], # Broad market, bonds
"short": [], # Minimal shorts
"neutral": ["XLK", "XLV", "XLI"] # Diversified
}
}
allocation = scenarios.get(election_scenario, scenarios["divided_government"])
# Calculate optimal weights using modern portfolio theory
portfolio = optimize_portfolio_weights(allocation)
# Backtest against historical similar scenarios
backtest_results = backtest_political_scenario(portfolio, election_scenario)
return {
'allocation': portfolio,
'expected_return': backtest_results['expected_return'],
'expected_volatility': backtest_results['volatility'],
'sharpe_ratio': backtest_results['sharpe_ratio']
}
# Example: Prepare for potential democratic sweep
hedge_portfolio = build_political_hedge_portfolio("democratic_sweep")
# Visualize allocation
visualize.pie(
pd.DataFrame(hedge_portfolio['allocation']),
values='weight',
labels='ticker',
title="Political Hedge Portfolio Allocation"
)Best Practices for Political-Market Analysis with Axion
- Data Freshness Matters
# Always check data timestamps
def ensure_data_freshness(data, max_age_hours=24):
latest_time = pd.to_datetime(data[-1]['time']) if data else None
if latest_time:
age_hours = (pd.Timestamp.now() - latest_time).total_seconds() / 3600
if age_hours > max_age_hours:
print(f"Warning: Data is {age_hours:.1f} hours old")
return age_hours- Multi-Timeframe Analysis
# Analyze political impacts across different timeframes
timeframes = {
'immediate': '5min',
'short_term': 'hourly',
'medium_term': 'daily',
'long_term': 'weekly'
}
for name, freq in timeframes.items():
data = client.stocks.prices("SPY", frame=freq, from_date="2024-01-01")
# Perform timeframe-specific analysis- Correlation Validation
# Always validate apparent relationships
def validate_political_correlation(political_series, market_series):
correlation = political_series.corr(market_series)
# Check for spurious correlation
if abs(correlation) > 0.7:
# Test for Granger causality or other causal inference
causal_test = test_causality(political_series, market_series)
return {
'correlation': correlation,
'is_causal': causal_test,
'lag_structure': find_optimal_lag(political_series, market_series)
}
return {'correlation': correlation, 'is_causal': False}Conclusion: Transforming Political Insight into Investment Alpha
The intersection of US politics and financial markets represents one of the most significant sources of both risk and opportunity for modern investors. Through this analysis, we’ve demonstrated how Axion’s comprehensive SDK enables:
- Quantitative political analysis — transforming qualitative events into measurable metrics
- Real-time monitoring — tracking political developments as they impact markets
- Predictive modeling — anticipating market reactions to political events
- Risk management — constructing portfolios resilient to political uncertainty
Key Takeaways:
- Elections create predictable sector rotation patterns that can be captured with proper analysis
- Legislative milestones offer asymmetric opportunities for prepared investors
- Sentiment analysis provides early signals of market-moving political developments
- Multi-asset correlation analysis reveals hidden political risk exposures
Start Your Political-Market Analysis Journey:
# The simplest starting point: track political sentiment and market correlation
client = Axion(api_key="your_api_key")
def quick_analysis():
# Get political news
politics_news = client.news.category("politics")
# Get market sentiment
spy_sentiment = client.sentiment.all("SPY")
# Compare trends
visualize.graph(
combine_data(politics_news, spy_sentiment),
x='date',
lines=['news_volume', 'market_sentiment'],
title="Political News Volume vs Market Sentiment"
)
# Begin uncovering the political-alpha in your portfolio today
quick_analysis()The most successful investors in the coming years won’t just understand markets or politics — they’ll master the intersection of both. With tools like Axion, this complex analysis becomes accessible, actionable, and integratable into your existing investment workflow.