arXiv:2606. 02528v1 Announce Type: cross Abstract: Large language models now power robo-advisors and trading agents, yet whether they carry built-in biases toward specific assets is largely untested.
arXiv:2607. 12233v1 Announce Type: cross Abstract: Large language model (LLM) trading agents show promising performance in equity markets, yet remain narrowly focused on US equities with little evidence from live deployment.
arXiv:2608. 11344v1 Announce Type: cross Abstract: Financial institutions are delegating consequential decisions to agentic AI systems that decompose goals, coordinate models and tools, and act with little oversight.
arXiv:2608. 16386v1 Announce Type: cross Abstract: Financial agents must do more than recall domain knowledge: they must be both reliable, executing precise operations over grounded evidence, and executive, sustaining long-horizon research whose conclusions remain auditable.
The paper investigates how adversarial signals can infiltrate large‑language‑model (LLM) based multi‑agent trading systems through the agents’ communication channels. By restricting the attacker to realistic inputs—source data and prompts—it studies role‑specific attacks on four functional roles (Analyst, Researcher, Trader, Risk Manager) and evaluates four communication topologies under data‑ and agent‑level attacks. Experiments across multiple assets, backbones, and target directions show that no architecture is inherently robust, highlighting the need for safer designs in agentic trading systems.
arXiv:2605. 28850v2 Announce Type: replace Abstract: We study behavioral alignment and representation dynamics of large language model (LLM) agents in financial decision environments.
arXiv:2604. 24668v3 Announce Type: replace Abstract: Given the increased use of LLMs in financial systems today, it becomes important to evaluate the safety and robustness of such systems.
The paper "SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemes" introduces FARSIGHT, a framework that evaluates financial LLM agents on robustness to market turbulence and security against three attack types. Applying FARSIGHT to 15 academic schemes reveals that 80% fail at least one robustness metric and all exhibit security vulnerabilities, highlighting the risk that a single compromised agent can trigger market-wide crashes.
arXiv:2607. 10286v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used in trading systems, where model reasoning, tool use, and continual decisions incur costs that are expected to produce trading value.
The paper introduces FARSIGHT, a framework for evaluating the robustness and security of financial trading agents powered by large language models. It assesses agents on their resilience to market turbulence, such as flash crashes, and their vulnerability to three types of attacks: on information sources, on the agents themselves, and on agents acting as attackers. Applying FARSIGHT to 15 academic trading schemes reveals that 80% fail at least one robustness test and all exhibit security weaknesses, highlighting the risk of market-wide crashes from both accidental misjudgments and deliberate attacks.
Large language models (LLMs) are increasingly used in high‑stakes real‑world systems such as financial markets. This study demonstrates that enhancing individual LLM capability can actually worsen system‑level outcomes by making models behave more similarly, leading to correlated actions that increase risk. Using an agent‑based simulation of LLM traders, the authors show that while higher capability can reduce market risk when reasoning is accurate, it can amplify risk when agents share misinformation, revealing a capability paradox.
arXiv:2606. 08285v1 Announce Type: new Abstract: Large language models (LLMs) and agentic systems are increasingly proposed for financial trading, yet their reported performance remains difficult to compare because studies vary in data provenance, temporal split discipline, execution timing, turnover treatment, and transaction-cost modeling.
arXiv:2606. 31461v1 Announce Type: new Abstract: Niche asset markets, such as Counter-Strike 2 (CS2) weapon skins, are small, volatile, and heavily driven by community discussions and platform rules.