Audrey Costabile

Buy Side Innovation Day | June 9th

What are the current and future potential applications of AI and ML and what are the practical user case studies currently existing in fixed income?
How can you best integrate generative AI into trading operations, to ensure governance, boost transparency and drive performance?
How can you leverage LLM and analytics models to improve decision-making, from security selection to portfolio optimization?
How can you create an effective AI analytics framework, including descriptive, predictive, and causal models?
How can you best address the challenges of AI, such as model hallucination and regulatory concerns, and what strategies should you use to overcome them?

How can you diversify across various fixed income instruments to optimize yield while managing risk exposure?
How can you use duration management to control interest rate risk?
How can you utilize credit ratings to assess issuer risk and minimize exposure to downgrades and defaults?
How can you apply scenario analysis to evaluate potential market shifts and adjust your strategy accordingly?

How can you implement clean, real-time data to empower traders and technologists with improved trading decisions and post-trade analytics, fostering more informed strategies throughout the trading lifecycle?
How can you create a seamless working environment between quant specialists, traders, and tech teams, emphasizing cultural integration and shared objectives?
How can you automate complex workflows, from RFQ to all-to-all trading, while ensuring minimal manual intervention and improving scalability?
How can you best navigate a fragmented market with multiple trading venues and protocols, and what are the tools required to maintain seamless connectivity?
How can you implement dashboards and notification systems that reduce interface complexity and make essential data easily accessible across various platforms?

How are factors like pre-trade analysis and platform comparison impacting liquidity and trading decisions?
Leveraging emerging protocols: How can you explore new protocols such as all-to-all trading, market-on-close, and portfolio trading, even as data remains limited?
How can you harness data access tools to automate protocol selection, particularly in more liquid fixed income products?
How can you adapt to platform expansion and respond to growth in market-on-close trades and the rise of all-to-all platforms, which are reshaping liquidity for traditionally hard-to-trade bonds?
The rise of ETFs: As fixed income ETFs grow in market share, how are they fueling innovation in trading protocols, automation, and algo-driven strategies?