Abstract
Rajesh clarifies the difference in the role played by new data vis-a-vis the role played by new analysis techniques. He also classifies data science techniques based on their academic provenance. He argues for certain areas/regimes possessing the most and the least potential for application of big data analysis techniques.
What is Covered in this Presentation
- Data vs. Techniques
- What can Machine Learning do for a Quant?
- FOMO driving $$$ towards Big Data
- Big Data ≠ Silver Bullet for Macro
- "Data Science" as Art more than Science
Takeaways
- Quant more open than ever: still risk-premia and search for orthogonality beyond EM, intraday and derivatives
- Machine Learning defined as the art of locating non-linear risk-premia dynamically
- Low SNR and smaller sample size can push to threshold between absurdly simple and naively simplistic models
- Locate inherently non-linear tasks: sector rotation, portoflio construction, short vol timing
- No neural networks yet at daily or weekly frequency
- To come: Tactical , CNN for technical patterns and Strategic deep reinforcement learning
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