Main Conference Day TwoWednesday, 19 November 2025 - GMT (Greenwich Mean Time, GMTZ)

Quantitative research often gets held back by overly complex infrastructure and rigid systems that unintentionally slow innovation. This talk explores how lightweight, serverless databases like ArcticDB can help researchers overcome these challenges by removing the need for extensive setup and operational overhead. Instead of being stuck managing infrastructure, researchers can focus on accessing data, testing ideas, and iterating on results.

By decentralizing data ownership and reducing reliance on centralized IT teams, organizations can empower researchers to move quickly without sacrificing control. Features like versioning, audit trails, and access controls can help ensure that lightweight systems still meet the high standards of financial organizations, striking a balance between agility and risk management.

This presentation discusses technology solutions and does not constitute investment advice.

As algo wheels reshape how buy-side firms select brokers, top quality algo performance is no longer a competitive edge—it’s a baseline requirement. Generic factors like bid-ask spread, volatility, top of the book liquidity, order size, etc., are no longer sufficient to navigate today’s intricate market dynamics. This session introduces “Lenses,” a powerful, new framework designed for the dynamic evaluation and enhancement of algorithmic trading performance.

We extend stationary portfolio optimisation to maximise the probability of achieving target returns - a key objective for sovereign wealth funds (SWF) and pension funds.

Our framework optimizes portfolio weights as functions of market drivers rather than time. We derive analytical solutions for the Gaussian case, revealing a "phase transition" where optimal solutions switch character as targets cross certain thresholds - specifically, the solution switches from maximum to minimum probability regimes when the target barrier crosses the portfolio's natural drift level. To obtain allocation weights for the general stationary case, we use the obtained Gaussian solution to initialize a highly efficient numerical iterative procedure based on quadratic utility approximations. This approach shows significant improvements over initial Gaussian approximations, with material differences in both optimal weights and achievable probabilities.

Numerical experiments demonstrate advantages over static allocation strategies, offering practical tools for long-horizon institutional investing.

The talk covers theoretical foundations, implementation, and applications to institutional asset management

This talk starts with a deep dive into 2025’s corporate margin compression — how the market has absorbed a $1.2 trillion cost shock via tariffs, inflation, and supply-chain frictions. From there, we pivot to a 20-year backtest of supply-chain contagion using Business Relationship Analytics (BRA), demonstrating a lead-lag signal that historically delivered over 10% annual alpha. We’ll connect today’s macro shock to enduring network dynamics, explore how relationships transmit risk, and show how firms embedded in resilient networks can generate systematic, tradable insights.

Join us for an exclusive wellness session in Peninsula East. Rest, relax, recharge, and reset to absorb more in the upcoming sessions.

In today’s fast-paced world, stress and burnout are all too common, but they don’t have to be the norm. Join Paul, Conscious Leadership Coach and Mindfulness Teacher, for an engaging and interactive talk that puts well-being at the heart of how we work and connect.

Through practical strategies you can apply straight away, Paul will explore:

• Understanding stress and burnout
• The power of meditation
• Cultivating mindfulness
• Creating a culture of well-being

This session is designed to leave you with simple, actionable tools to manage stress, build resilience, and strengthen connections with those around you. Expect to leave feeling refreshed, empowered, and ready to bring mindful momentum into both your work and everyday life.

We investigate portfolio optimization in financial markets from a trading and risk management perspective. We term this task Risk-Aware Trading Portfolio Optimization (RATPO), formulate the corresponding optimization problem, and propose an efficient Risk-Aware Trading Swarm (RATS) algorithm to solve it. The key elements of RATPO are a generic initial portfolio P, a specific set of Unique Eligible Instruments (UEIs), their combination into an Eligible Optimization Strategy (EOS), an objective function, and a set of constraints. RATS searches for an optimal EOS that, added to P, improves the objective function repecting the constraints.
RATS is a specialized Particle Swarm Optimization method that leverages the parameterization of P in terms of UEIs, enables parallel computation with a large number of particles, and is fully general with respect to specific choices of the key elements, which can be customized to encode financial knowledge and needs of traders and risk managers.
We showcase two RATPO applications involving a real trading portfolio made of hundreds of different financial instruments, an objective function combining both market risk (VaR) and profit&loss measures, constrains on market sensitivities and UEIs trading costs. In the case of small-sized EOS, RATS successfully identifies the optimal solution and demonstrates robustness with respect to hyper-parameters tuning. In the case of large-sized EOS, RATS markedly improves the portfolio objective value, optimizing risk and capital charge while respecting risk limits and preserving expected profits.
Our work bridges the gap between the implementation of effective trading strategies and compliance with stringent regulatory and economic capital requirements, allowing a better alignment of business and risk management objectives.

Financial Finance objectives are applied to the investment problem. The investment problem (IP) asks for the determination of the amount to be invested in risky assets. Convex cones of random outcomes define the set of arbitrarily acceptable risks for which the IP has no solution. For risky outcomes that are not arbitrarily acceptable, the acceptable risks are defined by a convex set containing the arbitrarily acceptable risks. For such risks the investment problem is solved using Disciplined Saddle Point (DSP) programming. Risks with a negative expectation under test probabilities remain acceptable at a larger scale provided the expected losses are bounded by rebate levels associated with the test probability. The economic values of random outcomes are given by the amount that can be withdrawn while yet maintaining risk acceptability. The IP solution maximizes this economic value. Solutions are developed and implemented into trading strategies in both univariate and multivariate contexts. The methods have also been applied to determine value maximizing investments in the options

We report on recent progress in dealing with the intrinsic combinatorics of the chain rule of differentiation. Building on [1,2,3], a tool for modelling and optimization of Algorithmic Differentiation (AD) workloads (also: AD mission planning) is showcased. Guidelines for differentiable programming in computational finance are formulated.

[1] U. Naumann: Matrix-Free Jacobian Chaining. Proceedings of the SIAM Conference on Algorithmic Differentiation (AD24). To appear.

[2] U. Naumann: Differentiable Programming: A Desirable Paradigm for Scientific Computing? GAMM Newsletter 1/25, 4-10, 2025.

[3] U. Naumann, E. Schneidereit, S. Maertens, and M. Towara: Elimination Techniques for Algorithmic Differentiation Revisited. Proceedings of the SIAM Conference on Applied and Computational Discrete Algorithms (ACDA23), 201-212, 2023.

We study quality–diversity reinforcement learning for optimal execution, focusing on generating a repertoire of behaviourally distinct execution schedulers. Using MAP-Elites, we construct a structured portfolio of schedulers indexed by liquidity and volatility descriptors, enabling systematic coverage of regime-specialist strategies rather than reliance on a single optimised policy.

To achieve this, we develop GEO (Gymnasium for Executing Optimally), a Gymnasium-based environment for execution under empirically calibrated transient propagator models. Minute-bar equity data are used to estimate impact kernels out-of-sample, producing realistic dynamics consistent with transient, concave impact.

Execution policies are trained with Proximal Policy Optimisation (PPO) and evaluated against established baselines—TWAP, VWAP, POV—under strict train–test splits. Within this setup, schedulers adapt trajectories while respecting completion constraints, and quality–diversity search ensures that the solution space spans different market regimes.

The framework thus combines (i) empirically grounded propagator calibration, (ii) reinforcement learning–based schedulers, (iii) baseline-aligned evaluation, and (iv) quality–diversity methods for execution under regime heterogeneity.

The inflationary environment of 2022 reminded investors that a standard 60/40 portfolio of stocks and bonds that produced double-digit losses was not immune to market turbulence. Experienced investors recognize that crisis periods are challenging to predict and their impact on performance can be devastating since most asset classes and investment strategies struggle during crises. A notable exception is the trend-following strategy used by Commodity Trading Advisors, which is often described as "crisis alpha" due to its propensity to perform well during crisis periods. While investors may allocate to CTAs for various reasons, including strong long-term performance and a low correlation to most asset classes and strategies, crisis alpha is a particularly attractive characteristic of CTA investments. Thus, when crises do occur, CTA investors expect to see strong performance and benefit from crisis alpha. What some investors may not have considered is how sensitive the crisis alpha benefits are to manager selection and to the number of managers in a portfolio. This presentation summarizes our recent research, which investigates the impact of manager selection and the number of managers in CTA portfolios on crisis alpha.

Constructing an efficient hedge portfolio for derivatives within risk limits, which are typically based on Greeks, is crucial from both trading and risk management perspectives. Therefore, we propose simple algorithms to optimize the hedge portfolio with complex Greeks, inspired by the proximal point method with the Sinkhorn algorithm. One algorithm focuses on optimizing cost, while the other optimizes both cost and CVaR (conditional value at risk) with constraints to limit the residual Greeks. Our algorithms consist of iterations involving only simple vector and matrix calculations, similar to the Sinkhorn algorithm, which is now widely used in machine learning; thus, linear programming solvers are not needed. In numerical examples, we explore two ways to improve the hedge portfolio for Bermudan swaptions, moving away from conventional anti-diagonal vega hedging with coterminal swaptions. The results of the optimized cost, CVaR, and hedge positions produced by our algorithms are consistent with insights on hedging under various constraints.

1. A new kind around the block in insurance finance.

2. Market overview and opportunities

3. Modeling solvency via equity

1. Thousands of pricing models for pricing a derivative
2. Model risk, calibration risk, parameters risk, implementation risk, data risk, …
3. Conic finance as a way out