AI optimizes options strike listing efficiency
The speakers argued that Nasdaq is deploying machine learning and AI systems to dynamically align the supply and demand of options strike listings.
The argument
The guest explained that listing every possible strike from zero to millions would overload servers with messages. By using AI, the exchange can predict which strikes and expiries are likely to see active interest, reducing server load and infrastructure costs.
The thesis, stress-tested
✓ What validates it
- ✓Reduction in exchange infrastructure/server overhead costs
- ✓Stable or improved market maker execution metrics on listed strikes
▸ Risks discussed
- ▸Potential for missing unexpected demand if the AI model fails to list a highly specific outlier strike requested by market participants
Hear it yourself
"So we have we've implemented an AI system by which we can sort of align the supply and demand and the likely supply and demand of those strikes, so that we're being efficient as a provider of this infrastructure, and keeping our servers cool essentially."
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