Preconference Day: Summits & WorkshopsMonday, 17 November 2025 - GMT (Greenwich Mean Time, GMTZ)

How infrastructure, computation, and AI are redefining the edge

Large Language Models (LLMs) exhibit remarkable capabilities across a spectrum of tasks in financial services, including report generation, chatbots, sentiment analysis, regulatory compliance, investment advisory, financial knowledge retrieval, and summarization. However, their intrinsic complexity and lack of transparency pose significant challenges, especially in the highly regulated financial sector, where interpretability, fairness, and accountability are critical. Our research presents the first application in the finance domain of understanding and utilizing the inner workings of LLMs through mechanistic interpretability, addressing the pressing need for transparency and control in AI systems. Mechanistic interpretability is the most intuitive and transparent way to understand LLM behavior by reverse-engineering their internal workings. By dissecting the activations and circuits within these models, it provides insights into how specific features or components influence predictions - making it possible not only to observe but also to modify model behavior. While not yet widely adopted, mechanistic interpretability is expected to become increasingly vital as adoption of LLMs increases. Advanced interpretability tools can ensure AI systems remain ethical, transparent, and aligned with evolving financial regulations. In this research, we have put special emphasis on how these techniques can help unlock interpretability requirements for regulatory and compliance purposes - addressing both current needs and anticipating future expectations from financial regulators globally.

This session introduces two cutting-edge applications of large language models (LLMs) in quantitative finance:

1. Sentiment Trading with LLMs (Finance Research Letters, 2024):
   This study demonstrates how financial sentiment extracted via transformer-based LLMs (e.g., FinBERT) can predict short-term return anomalies. The model aggregates media tone to form daily sentiment signals, which enhance directional trading strategies across equity markets. Presented at over 20 international conferences, including ACL, QES, and CFAR. You can find this paper online.
2. Sentiment-Augmented Reinforcement Learning for Portfolio Optimization (Accepted at ACL 2025, ICLR 2025, Wolfe Research Quant Conference; under NeurIPS 2025 review):
   This paper proposes SAPPO, a novel reinforcement learning framework that integrates asset-level sentiment—extracted using LLaMA 3.3 from Refinitiv financial news—directly into the PPO policy update. SAPPO significantly improves Sharpe ratios and annual returns (2.07 vs. 1.67; 83% vs. 57%) by aligning allocation decisions with evolving investor expectations. Empirical results confirm its robustness across different market regimes and sentiment models.

Together, these contributions demonstrate how LLMs can move beyond text analytics into core decision-making, enabling adaptive, sentiment-driven asset allocation in volatile markets.

In finance, valuation must obey law; conventional AI risks economic hallucination, distorting pricing, hedging, and tail-risk. Rather than adapting generic AI and correcting arbitrage post hoc, we rebuild neural networks on asset pricing first principles. The result is the first finance-native neural network, where neurons encode market states and Markovian Activation Functions turn the First Fundamental Theorem of Finance into a binding computational constraint. Applied to real-world QIS portfolios, it enables XVA-style forward-looking strategy design, learning markets in 8 minutes and simulating 100,000 scenarios across 800 layers in 80 seconds on a MacBook Pro. Rooted in pricing axioms, this framework transforms AI from a black box into a transparent engine delivering coherent valuation, robust risk diagnostics, and strategy innovation — where every neuron obeys the immutable laws of finance.

This work studies the dynamic risk management of the risk-neutral value of the potential credit losses on a portfolio of derivatives. Sensitivities-based hedging of such liability is sub-optimal because of bid-ask costs, pricing models which cannot be completely realistic, and a discontinuity at default time. We leverage recent advances on risk-averse Reinforcement Learning developed specifically for option hedging with an ad hoc practice-aligned objective function aware of pathwise volatility, generalizing them to stochastic horizons. We formalize accurately the evolution of the hedger's portfolio stressing such aspects. We showcase the efficacy of our approach by a numerical study for a portfolio composed of a single FX forward contract.

Deploying models, not just training them

Predictions across infrastructure, people, and process

An overview of deep neural networks, activation functions, loss functions and training algorithms. Practical implementation in Pytorch.

Unsupervised hedging and pricing of derivatives, high-frequency asset return prediction.

Deep learning volatility and calibration of (rough) stochastic volatility models.

Market generators and generative AI.

• Shape of the volatility surface
• Scaling of implied volatility smiles
• Monofractal scaling of realized variance
• Estimation of H
• Realized variance forecasting

• The forward variance curve
• Change of measure
• The rough Bergomi model
• The rough Heston model
• The quadratic rough Heston model
• Financial meaning of parameters

• Affine forward intensity models
• Affine forward volatility models
• Microstructural foundation of the QRH model
• Diamonds and the exponentiation theorem
• The leverage swap
• Moment computations

• Rational approximation of rough Heston
• The HQE scheme for affine models
• The QRH scheme for quadratic rough Heston
• Parameter sensitivities
• Joint fitting of SPX and VIX smiles
• Computing the skew-stickiness ratio (SSR)

Description:

Hackathon participants will work to create prompts that prevent cognitive biases of AI from affecting the result across four categories of tasks.

Each task will be designed to trigger one or more of the cognitive biases and psychological effects in AI.

Biases and Psychological Effects:

The participants will design an approach to counter the following biases and psychological effects in AI:

• Confirmation Bias
• Truth Bias
• Framing Effect
• Priming Effect
• Informational Anchoring
• Priming-Induced Anchoring

Categories

1. Sentiment Analysis (predict the impact of a news headline on stock prices)

2. Regulatory Compliance (determine compliance or noncompliance with a regulatory clause)

3. Document Evaluation (evaluate the quality of a paragraph on the scale from 0 to 100)

4. Classification (assign one of several possible classes based on class descriptions)

Prizes:

There will be a prize for the best result in each category, as well as the Grand Prize that will be awarded to the best result across all four categories.

Notes:

The winning entries will be profiled in Alexander Sokol's AI workshop on Tuesday.

Scoring:

Before the competition, 50% of the tasks will be randomly assigned to the public dataset for use during the hackathon, and the other 50% will be used for scoring. Participants will use the public dataset to create a prompt for each of the competition categories they choose to participate in.

For scoring, each task in the scoring dataset will be presented in a way that triggers one of the cognitive biases and psychological effects in AI. The participant's prompt will be combined with the task, and the results will then be scored by running statistical analysis for the magnitude of bias.

Participants are free to use coding or any external tools (including proprietary tools) for prompt development, or develop their prompts using ChatGPT or any other software.

A tool from CompatibL for scoring the public dataset will be made available during the hackathon online and as an open source package on GitHub. The use of this tool is optional.

To prepare:

The participants are encouraged to review the book "Thinking, Fast and Slow" by Daniel Kahneman and other literature on cognitive biases and psychological effects.

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