What is Factor Investing?
Factor investing is an investment approach that targets specific drivers of returns across asset classes. Rather than picking individual stocks, factor investors systematically allocate capital based on measurable characteristics.
Key Factors
The most widely studied factors include:
- Value — stocks trading below intrinsic value
- Momentum — assets that have performed well recently
- Quality — companies with strong fundamentals
- Size — smaller companies tend to outperform
- Low Volatility — less volatile stocks, higher risk-adjusted returns
Implementing a Factor Model
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
def calculate_factor_exposures(returns_df, factor_df):
"""Calculate factor exposures using regression."""
model = LinearRegression()
model.fit(factor_df, returns_df)
exposures = pd.Series(
model.coef_,
index=factor_df.columns,
name='Factor Exposures'
)
return exposures
Risk Management
Factor investing also helps in understanding portfolio risk decomposition:
def risk_decomposition(weights, factor_cov, specific_risk):
"""Decompose total portfolio risk into factor and specific risk."""
factor_risk = weights.T @ factor_cov @ weights
total_risk = factor_risk + specific_risk
return {
'factor_risk': np.sqrt(factor_risk),
'specific_risk': np.sqrt(specific_risk),
'total_risk': np.sqrt(total_risk),
}
Conclusion
Factor investing provides a systematic framework for understanding and capturing return premiums. By combining quantitative analysis with disciplined execution, investors can build more robust portfolios.