(Registration Closed) HKML S7E2 - Meetup @ Maven Securities

***ATTENTION***: You need to register in the below link to attend the meetup

Register here: https://forms.gle/sPjqqU1J6Eik5gM39

We are delighted to announce that our next meetup will be in cooperation with Maven Securities.
Special thanks to our sponsor for F&B.
Special Episode: This event comes with 2-hours of CPT certificate.

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Speaker 1: Alex Hunsberger
Topic: MAVRAG: Multi-Agent Vectorless RAG
Bio: Alex Hunsberger is a platform engineer at Maven Securities, and has also been a developer for 10+ years with interests ranging from Python to Linux to DevOps and recently Machine Learning. He's excited to learn more and become an active member of the wider Machine Learning community
Summary: Learn from trial and error about answering questions in natural language by querying multiple data sources without stale data using a multi-agent hierarchy within the RAG framework.

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Speaker 2: Manishi Raychaudhuri
Title: Investing in a complex world
Bio: Former APAC Head of Equity Research at BNP Paribas, with 27 years in sell-side research. Led teams, built market-beating strategies, and boosted research quality. Ranked top in Asiamoney/II polls at UBS India. Expert in Asia-Pacific equities, investment themes, and corporate access. Frequent media commentator and speaker. Began career at ICICI Securities (JP Morgan JV).
Summary: To be updated

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Speakr 3: Juncheng (Johnson) Li
Title: Risk Monitoring for Financial Firms: An Application with Machine Learning Methods
Bio: Johnson (Juncheng) Li is a Ph.D. in Business Statistics at The Hong Kong University of Science and Technology, specializing in financial econometrics and high-dimensional data analysis. Johnson has experience processing over high-frequency financial data and developing statistical models for risk assessment. Before his postgraduate studies, he led a team to 4th place among 150 teams in the Asian Supercomputer Contest. With his combination of strong academic credentials and practical industry experience, Johnson brings valuable expertise to data analysis and financial risk modeling.

Summary: In this talk, I will explore how financial firms’ risks can be monitored using accessible machine learning methods. Emphasizing practicality over cutting-edge complexity, I will demonstrate how we developed a robust empirical framework for studying the interconnectedness of financial institutions and its evolution over time. Using only daily volatility data, which is readily available to practitioners, we estimate total connectivity through a vector autoregressive (VAR) model, enhanced with factor-adjusted regularization techniques. I will highlight key insights, including how global financial connectivity has responded to systemic risk events. Finally, we will discuss how this approach might extend beyond systemic risk measurement.