Science

New research reveals that large language models aren’t immune to the same cognitive errors that plague human judgment, raising concerns about their use in critical operational roles.

A new approach combines the power of artificial intelligence with personalized financial modeling to optimize investment strategies.

Researchers are combining advanced signal processing with graph neural networks to unlock deeper insights into the dynamics of global financial markets.

A new reinforcement learning framework offers a practical path to dynamically managing option exposures and improving portfolio performance in live markets.

A new framework leverages evolving news topics to improve the accuracy of stock price predictions by dynamically segmenting data based on shifts in public discourse.

A new study challenges the hype around artificial intelligence in financial markets, demonstrating that current large language models struggle with fundamental quantitative trading tasks.

A new framework rigorously evaluates trading strategies based on market microstructure signals, focusing on out-of-sample performance and avoiding the pitfalls of overfitting.

New research reveals that artificial intelligence agents, when interacting in networked environments, exhibit a tendency to overestimate popular participation, mirroring human susceptibility to social influence.

As artificial intelligence increasingly influences economic forecasting, understanding why models make certain predictions is becoming as crucial as the predictions themselves.

As artificial intelligence systems gain increasing autonomy in financial markets, traditional risk management approaches are proving inadequate, demanding a new regulatory paradigm.