When Sentiment Analysis Falls Flat: The Challenge of Financial News

A new study reveals that common text embedding techniques struggle to accurately gauge market sentiment from limited financial news data.

A new study reveals that common text embedding techniques struggle to accurately gauge market sentiment from limited financial news data.
New research exposes how sophisticated price manipulation and network routing are exploiting global aluminium trade to obscure illicit financial flows, bypassing traditional smuggling methods.

A new review examines the growing, yet still nascent, field of using artificial intelligence to automate and improve cybersecurity’s crucial red-teaming exercises.

A new analysis reveals how decisions made by AI developers and platforms are predictably linked to the growing problem of misused video deepfakes, particularly non-consensual intimate imagery.

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.