Factor modeling and style premia are historically well documented and extensively researched in generating abnormal returns. Despite the large amount of research around factors, there is less clarity around effectively capturing and extracting this alpha from a given universe. In this presentation, Cheng will demonstrate different techniques for combining multiple factors, and the rationale behind maximizing alpha while maintaining scalability.
2. Problem:
- Given a basket of Factors, how do we extract the most alpha?
- Concerns: Scalability and consistency
Solution:
- A systematic approach for analyzing and testing factor alpha
- Key Points:
- Universe Factor Tilting
- Alpha Combination Techniques
- Portfolio Diversification
What is this about?
3. Backtesting Conditions
Quantopian Platform
- Universe: 1500 most tradeable US Equities
- Timeframe:
- In Sample: 01/04/2003 - 01/01/2015 (12 Years)
- Out Of Sample: 01/01/2015 - 08/01/2017 (2.5 Years)
- Trading Costs:
- $0.0035 per share (IB Tiered)
- Volume limitations of 2.5% of a minute’s trade volume, with a price impact of 0.1
- Starting balance of $ 1,000,000
- Full rebalance at start of every Month
- Equal weighting with 10% constraint on each stock
4. Picking Factors
Momentum
- 1 Month / 12 Month Price Momentum
Quality
- Return On Equity = Net Income / Shareholder’s Equity
Volatility
- Standard Deviation of Daily Price in last 21 days
40. Takeaways:
- Carefully investigate each factor before drawing conclusions
- Combine factors by ranking factors, mixing them and finding cross sections
- Utilize factor tilting universes to help extract hidden alphas
- Avoid overfitting by holding diversified portfolios
Next Steps:
- Try a different set of factors and rebalance periods
- Try different markets and universes
Conclusion