Quantitative Research Methods

Research methodology, hypothesis development, feature engineering, and research best practices.

Research Process

1. Question Formulation

• Start with a clear, testable question
• Example: "Do momentum strategies work in small-cap stocks?"

2. Hypothesis Development

• Formulate null and alternative hypotheses
• Define success criteria

3. Data Collection

# Collect relevant data
data = yf.download("SPY", period="10y")
factors = get_factor_data()  # Market factors

4. Feature Engineering

# Create features from raw data
data['Returns'] = data['Close'].pct_change()
data['Volatility'] = data['Returns'].rolling(20).std()
data['Momentum'] = data['Returns'].rolling(10).sum()

5. Model Development

from sklearn.linear_model import LinearRegression

# Develop predictive model
X = data[['Volatility', 'Momentum']]
y = data['Returns'].shift(-1)  # Next period return

model = LinearRegression()
model.fit(X.dropna(), y.dropna())

6. Validation

• Out-of-sample testing
• Walk-forward analysis
• Statistical significance testing

Research Best Practices

• Documentation: Document all steps
• Reproducibility: Use version control
• Robustness: Test multiple scenarios
• Peer Review: Get feedback from others

Key Takeaways

• Follow systematic research process
• Formulate clear hypotheses
• Engineer meaningful features
• Validate thoroughly
• Document everything

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