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AlphaCrafter: A Full-Stack Multi-Agent Framework for Cross-Sectional Quantitative Trading
Financial markets are inherently non-stationary, driven by complex interactions among macroeconomic regimes, microstructural frictions, and behavioral dynamics.…
SNAPO: Smooth Neural Adjoint Policy Optimization for Optimal Control via Differentiable Simulation
Many real-world problems require sequential decisions under uncertainty: when to inject or withdraw gas from storage, how to rebalance a pension portfolio each …
A Meta Reinforcement Learning Approach to Goals-Based Wealth Management
Applying concepts related to zero-shot meta-learning and pre-training of foundation models, we develop a meta reinforcement learning approach (denoted MetaRL) t…
CyberAId: AI-Driven Cybersecurity for Financial Service Providers
European financial institutions face mounting regulatory pressure while their security operations centres remain constrained not by data or staffing but by reas…
SBCA: Cross-Modal BERT-driven Actor-Critic for Multi-Asset Portfolio Optimization
Portfolio optimization is constrained by linear assumptions and insufficient integration of multi-modal information in traditional models. This paper proposes a…
Safe Bilevel Delegation (SBD): A Formal Framework for Runtime Delegation Safety in Multi-Agent Systems
As large language model (LLM) agents are deployed in high-stakes environments, the question of how safely to delegate subtasks to specialized sub-agents becomes…
ITS-Mina: A Harris Hawks Optimization-Based All-MLP Framework with Iterative Refinement and External Attention for Multivariate Time Series Forecasting
Multivariate time series forecasting plays a pivotal role in numerous real-world applications, including financial analysis, energy management, and traffic plan…
Comparative Evaluation of Modern Deep Learning Methodologies for Portfolio Optimization
This study proposes a portfolio optimization framework that integrates advanced deep learning architectures with traditional financial models to enhance risk-ad…
Optimal Investment and Entropy-Regularized Learning Under Stochastic Volatility Models with Portfolio Constraints
We study the problem of optimal portfolio selection under stochastic volatility within a continuous time reinforcement learning framework with portfolio constra…
ACT: Anti-Crosstalk Learning for Cross-Sectional Stock Ranking via Temporal Disentanglement and Structural Purification
Cross-sectional stock ranking is a fundamental task in quantitative investment, relying on both temporal modeling of individual stocks and the capture of inter-…
In-Context Learning Under Regime Change
Non-stationary sequences arise naturally in control, forecasting, and decision-making. The data-generating process shifts at unknown times, and models must dete…
QuantCode-Bench: A Benchmark for Evaluating the Ability of Large Language Models to Generate Executable Algorithmic Trading Strategies
Large language models have demonstrated strong performance on general-purpose programming tasks, yet their ability to generate executable algorithmic trading st…