Finance

SOFTS: An efficient MLP-based model for multivariate time series forecasting using a novel STAR module for efficient channel interaction.

GLAFF: A novel framework that significantly improves time series forecasting robustness by fusing global timestamp information with local observations, achieving 12.5% average performance enhancement.

Performative control, where control policies change system dynamics, is analyzed; offering sufficient conditions for unique solutions, and proposing a convergent algorithm for achieving them.

This paper develops efficient algorithms for pricing homogeneous data in online settings, achieving low regret using novel discretization schemes that scale well with data size and number of buyer typ…

High-Rank PCF-GAN uses a novel metric (HRPCFD) based on high-rank path development to learn filtration of stochastic processes, outperforming state-of-the-art methods in hypothesis testing and time-se…

Boost time series forecasting accuracy by integrating news data and LLM-based agents!

FINCON: an LLM-based multi-agent system uses conceptual verbal reinforcement for superior financial decision-making, generalizing well across various tasks.

Hybrid LLM & rule-based system accurately detects costly smart contract bugs!

ClavaDDPM synthesizes multi-relational data using cluster-guided diffusion models, efficiently capturing long-range dependencies and outperforming existing methods.

CausalStock: A novel framework for accurate news-driven multi-stock movement prediction, using lag-dependent causal discovery and LLMs for enhanced noise reduction and explainability.

CaPaint: a novel causal spatio-temporal prediction framework that uses causal reasoning and diffusion inpainting to boost model accuracy and generalizability, especially in data-scarce settings.

BPQP: A new differentiable convex optimization framework accelerates end-to-end learning by an order of magnitude, achieving significant efficiency gains over existing methods.

Popular large language model (LLM)-based time series forecasting methods perform no better than simpler alternatives, often worse, and require vastly more compute.

This paper introduces mSSRM-PGA, achieving globally optimal m-sparse Sharpe ratios, addressing the nonconvexity issue in portfolio optimization through a novel proximal gradient algorithm.