Panel: Data Analytics In Investment Banks
summary
Panelists describe concrete wins and limits: inconsistent inputs produce valuations like $1B vs $1.5B for the same company, cloud migrations and unified pipelines fix that, automation of manual reports precedes modelling, and an algo wheel for execution reduces market impact. Production ML examples include a Q learning equity executor that raised live profitability and exploratory deep‑hedging research; nowcasting using real‑time signals such as car‑price feeds fills monthly inflation gaps. Readiness and explainability remain the main blockers.
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chapters (13)
- 0:00Panel introduction
- 1:54Panelists' backgrounds
- 4:05Data for M&A and valuation
- 7:42Asset management risks
- 9:09Trading data pain points
- 13:23Building analytics platforms
- 16:55Data readiness evolution
- 18:41Unified data systems advantage
- 20:27Machine learning in finance
- 24:07Career transition to education
- 26:31Q&A: career advice
- 32:55Synthetic data methods
- 36:14Closing remarks
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