Key Topics Covered:
• Why the central challenge in AI/ML-driven investing is not prediction alone, but turning noisy forecasts into scalable portfolios under real-world costs and constraints
• How AI/ML is improving short- and medium-term return forecasts, volatility and covariance modelling, and regime detection
• Extracting structured signals from unstructured data (such as news, earnings calls, and filings) to enhance portfolio insights—using LLMs as feature generators
• Incorporating predictive signals into portfolio construction and execution while managing slippage, price impact, and turnover
Check out the incredible speaker line-up to see who will be joining Petter.
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