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: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.
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. 15414v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as powerful tools for processing the heterogeneous information environments of modern financial markets.
arXiv:2606. 31522v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous financial agents initialized with explicit behavioral mandates such as "preserve capital" or "avoid speculative bets" that are meant to govern every decision throughout deployment.
arXiv:2608. 11785v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can vary across assets, decision fields, and market conditions.
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:2607. 11141v1 Announce Type: new Abstract: Large language models (LLMs) based agents are beginning to participate in portfolio construction and market analysis, where decisions must be justified under evolving information and risk constraints.
arXiv:2608.12283v2 Announce Type: replace-cross Abstract: Large language models can extract richer signals from financial news than fixed sentiment lexicons, and recent work has explored feeding such...
The paper introduces LiveMacroEval, a live benchmark that tests large language model (LLM) agents’ ability to produce hourly nowcasts for sixteen major U.S. macroeconomic indicators before their official release. It evaluates LLM performance against institutional nowcasts, Bloomberg ECOS consensus, and an auto-ARIMA baseline using a LiveMacro Score linked to announcement-window equity returns and a LiveBetting Score from simulated Polymarket-style trading. Over six months, state-of-the-art LLMs with web search achieved overall accuracy comparable to professional benchmarks, though performance varied across indicators.
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 reports a six‑month, population‑scale measurement of autonomous language‑model trading agents operating in two production fleets: DX Terminal Pro, with 3,505 user‑funded vaults trading real ETH in Base memecoin markets, and the DXAP live alpha fleet, with 500–599 user‑created agents trading Hyperliquid perpetuals. Across roughly 7.5 million single‑model invocations and 231,638 multi‑tool turns, the study finds that operating layer design, risk sliders, and leaderboard boundaries drive behavior more than strategy text; agents are volatility‑blind in sizing, capture little upside, and show no directional edge compared to a retail benchmark. The analysis includes regression discontinuity, permutation nulls, and a 17‑rule methodology canon to validate the findings.
arXiv:2609.34004v2 Announce Type: replace Abstract: Equity-relevant news evolves through temporally dependent corporate events, making historical information useful only when event continuity, inform...