Discussion Points:
- What it takes to build crypto quant multifactor models and market-neutral strategies.
- Why automation beats emotional decision-making.
- How systematic crypto trading systems can do the work for you, how quant models can adapt dynamically, and how investors spend less time watching charts and more time living their lives.
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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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