Global Style Counselling: Introducing Two New Global Systematic Equity Factor Long Short Indices

Societe Generale introduces two new global systematic multi-factor indices, emphasizing the addition of Machine Learning to improve adaptability and capture non-linear alpha. These strategies aim to navigate the post-QE market environment while avoiding the concentration risks of cap-weighted indices.

Key Takeaways

• 1.Societe Generale has launched two new investable global long/short multi-factor indices: the Traditional version (SGEPPFW) and an ML-enhanced version (SGEPMFW).
• 2.The inclusion of Machine Learning (ML) helps identify non-linear relationships and provides a dynamic factor assessment that adapts better to unusual economic periods like the 'Quant Winter'.
• 3.The end of the zero-interest-rate era has recalibrated the market; stock performance is increasingly driven by company fundamentals and earnings rather than US treasury bond correlations.

Table of Contents

• Systematic stock-picking
• Our Machine-Learning factor model is a dynamic model
• ML has produced a more effective mean-reversion signal
• Why now?
• Global long/short multi-factor strategies
• Traditional versus Traditional + ML model
• Methodology
• Performance net of costs
• Non-linear factor alpha
• Performance across regimes
• Portable alpha
• Data appendix
• Factor definitions

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