Discussion Points:
- How value investors can adopt conviction in a process, not in individual names.
- Avoiding style drift: the importance in exercising consistency and patience, especially during times when value is most tested.
- How value investors can adopt data-driven rules instead of using subjective gut feelings, removing bias and opinion from execution.
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Discussion Points:
- How decades of government tech and AI experience led to a unique alpha-seeking strategy.
- How the use of curated datasets outperform brute-force modeling.
- How to avoid AI hallucinations and contaminated training data.
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Discussion Points:
- How to leverage big data and machine learning to identify market turning points early.
- Emphasis on momentum and trend analysis across different asset groups.
- How high-conviction signals enable quicker, smarter decisions for traders and portfolio managers.
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Discussion Points:
- How alternative data such as website visits, search trends, and app usage could be used to seek to predict company performance ahead of Wall Street analysts
- How machine learning turns this data into real trading signals, and
- How to construct portfolios - long, hedged, and market-neutral - based on these insights.
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