Keynotes

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Intelligence with Integrity : Towards a Trust-Worthy AI

TBC

Bio: Mr Ravi Menon has a portfolio of work in sustainability, innovation, and inclusion.

Mr Menon is Singapore’s first Ambassador for Climate Action and Senior Adviser to the National Climate Change Secretariat at the Prime Minister’s Office.  He is also Chairman of: (i) the Glasgow Financial Alliance for Net Zero Asia-Pacific Advisory Board; and (ii) Financing Asia’s Transition Partnership International Advisory Board.

Mr Menon is Chairman of the Global Finance & Technology Network, which promotes innovation for more efficient, resilient, and inclusive financial systems.  He is also a Trustee of the National University of Singapore (NUS) and Chairman of its Innovation and Enterprise Committee.

Mr Menon is Chairman of ImpactSG, a philanthropy which aims to grow a community of purposeful givers.  He is a Board Member of The Majurity Trust, a charity which deploys funds for unmet social needs; and a Trustee of the Singapore Indian Development Association, a community self-help group.  

Prior to his current roles, Mr Menon served for 36 years in the Singapore Public Service.  

As Managing Director of the Monetary Authority of Singapore (2011-23), Mr Menon oversaw monetary and macroprudential policies, reformed the financial regulatory framework, and developed Singapore as a green finance centre and a global FinTech hub.  On the international front, he served as Chairman of the Network of Central Banks and Supervisors for Greening the Financial System and Chairman of the Financial Stability Board Standing Committee on Standards Implementation.

As Permanent Secretary at the Ministry of Trade & Industry (2007-11), Mr Menon helped to steer the economy during the global financial crisis.  As Deputy Secretary at the Ministry of Finance (2003-07), he oversaw fiscal policy and government reserves.

Mr Menon is a recipient of the Meritorious Service Medal from the Singapore Government, the Distinguished Alumni Award from NUS, and the Distinguished Leadership and Service Award from the Institute of International Finance in Washington.

Mr Menon holds a Master’s in Public Administration from Harvard University and a Bachelor of Social Science (Honours) in Economics from NUS.

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Mean–Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study

Abstract: We study continuous-time mean–variance portfolio selection in markets where stock prices are diffusion processes driven by observable factors that are also diffusion processes, yet the coefficients of these processes are unknown. Based on the recently developed reinforcement learning (RL) theory for diffusion processes, we present a general data-driven RL algorithm that learns the pre-committed investment strategy directly without attempting to learn or estimate the market coefficients. For multi-stock Black–Scholes markets without factors, we further devise a baseline algorithm and prove its performance guarantee by deriving a sublinear regret bound in terms of the Sharpe ratio. For performance enhancement and practical implementation, we modify the baseline algorithm and carry out an extensive empirical study to compare its performance, in terms of a host of common metrics, with a large number of widely employed portfolio allocation strategies on S&P 500 constituents. The results demonstrate that the proposed continuous-time RL strategy is consistently among the best, especially in a volatile bear market, and decisively outperforms the model-based continuous-time counterparts by significant margins. Joint work with Yilie Huang and Yanwei Jia.

Bio: Xunyu Zhou is the Liu Family Professor of Industrial Engineering and Operations Research at Columbia University in New York. Before joining Columbia, he was the Nomura Professor of Mathematical Finance, the Director of Nomura Center for Mathematical Finance and the Director of Oxford-Nie Financial Big Data Lab at University of Oxford during 2007-2016, and Choh-Ming Li Professor of Systems Engineering and Engineering Management at The Chinese University of Hong Kong during 2013-2014.

He is well known for his work in indefinite stochastic LQ control theory and application to dynamic mean—variance portfolio selection, in quantitative behavioral asset allocation and pricing theory, and in general time inconsistent problems. His current research focuses on reinforcement learning for controlled diffusion processes and applications to generative AI and intelligent wealth management solutions. He directs the Nie Center for Intelligent Asset Management, a research center funded by a FinTech company, at Columbia. He has addressed the 2010 International Congress of Mathematicians, and has been awarded the Wolfson Research Award from The Royal Society (UK), the Outstanding Paper Prize from the Society for Industrial and Applied Mathematics, the Humboldt Distinguished Lecturer, the Alexander von Humboldt Research Fellowship, the Archimedes Lecturer at Columbia, and Distinguished Faculty Teaching Award at Columbia University. He is both an IEEE Fellow and a SIAM Fellow.

Professor Zhou received his Ph.D. in Operations Research and Control Theory from Fudan University in China in 1989.

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From stochastic control to generative AI: learning dynamics in quantitative finance

Abstract: Modern advances in machine learning are reshaping how we model decision-making and uncertainty in finance. This talk traces a journey from classical stochastic control to today’s reinforcement and generative learning frameworks, highlighting a unifying mathematical thread: learning to model, control, and generate dynamic systems under uncertainty. I will begin with how deep neural networks can solve high-dimensional stochastic control and PDE problems that arise in portfolio optimisation and risk management. I will then show how reinforcement learning and policy-gradient methods extend these ideas to interactive and mean-field settings, where agents learn optimal behaviours in evolving markets. Finally, I will discuss recent work connecting optimal transport and generative diffusion models, offering new ways to simulate and learn financial time series through probabilistic dynamics.

Bio: Huyên Pham is a Full Professor of Applied Mathematics at École Polytechnique, where he leads the Mathematical Finance team and directs the Chairs Machine Learning and Systematic Methods (MLSM) and Risques Financiers. His research interests span stochastic control, quantitative finance, and machine learning, with recent work on generative models via optimal transport and reinforcement learning applied to complex systems and large-population dynamics. He is the author of more than 120 publications, including the monograph Continuous-Time Stochastic Control and Optimization with Financial Applications. Prof. Pham was appointed to the Institut Universitaire de France in 2006 and received the Louis Bachelier Prize of the French Academy of Sciences in 2007. He currently serves as Vice-President of the Bachelier Finance Society and Editor-in-Chief of the SIAM Journal on Control and Optimization.