Papers for

financial technology teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Large language models show limits in simulating individual financial trades

Are LLMs Good Financial User Simulators? A Preliminary Study

Abstract: Large language models (LLMs) are increasingly used as user simulators, but their ability to reproduce evolving individual financial decisions remains unclear. We present a preliminary study in a controlled paper-trading environment with 120 volunteers. Participants used non-redeemable virtual funds under real-time market conditions; no real brokerage accounts, real-money positions, or real transaction records were accessed. Given only information available before a prediction cutoff, a simulator predicts the participant's next-trading-day action, traded security, and transaction quantity. We evaluate temporally aligned rolling predictions and compare settings with and without point-in-time market information. Market context improves action and ticker prediction in the controlled ablation, while transaction sizing remains difficult. We also observe systematic behavioral compression: models overproduce hold actions, underpredict sell decisions, and simplify multi-security transactions. These results provide an initial empirical characterization and motivate larger-scale evaluation of individual, temporal, and portfolio-level behavioral fidelity.

Mon 14 SeptArtificial IntelligenceComputers and SocietyHuman-Computer Interaction
The gist
The paper studies how well large language models (LLMs) can mimic people's day-to-day financial trading decisions using virtual money in a fake stock market. The researchers found that these models can better predict what stocks a person will buy or sell when they have current market information. However, the models struggle to guess how many shares will be traded and tend to simplify behaviors, such as waiting too long to sell. These findings help us understand where LLMs succeed and where they fall short in simulating personal finance choices.
Open → 2609.15727v1

Financial vision language models struggle with reliable chart action advice

E2A-Bench: Benchmarking Evidence-to-Action Reliability in Financial Chart Reasoning

Abstract: Can financial vision-language models (VLMs) turn chart evidence into reliable action recommendations? Existing hallucination evaluations are mostly claim-centric; they assess whether generated statements are supported, but not whether evidence remains traceable through rationale, confidence, and final action. We introduce E2A-Bench, a 969-query benchmark for financial chart reasoning, constructed from 323 HS300 constituents under three input modalities with deterministic OHLCV-derived evidence anchors. E2A-Bench evaluates grounding, reasoning-action consistency, evidence-confidence calibration, and directional coverage through UCR, RCI, ECI, and NDR, where NDR measures coverage-aware evidence-to-action reliability rather than realized trading performance. Evaluating 20 VLMs reveals three failures hidden by scalar hallucination scores: the lowest-UCR model ranks near the bottom by NDR due to only 6.4% directional coverage; oracle-aided verification reduces unsupported claims but can collapse coverage; and financial fine-tuning amplifies the BUY:SELL ratio by factors of 4.21 to 4.68 across strict base-fine-tuned pairs. These results show that financial VLM evaluation should trace the full evidence-to-action chain rather than rely on a single hallucination score. Code and data: https://github.com/wanng-ide/E2A-Bench

Sun 13 SeptComputation and Language
The gist
Financial models that look at charts and give advice often say things that don't fully match the data they see. The authors created a new test called E2A-Bench to check if these models truly base their advice on chart evidence and if their reasoning is consistent. When testing 20 popular models, they found many give unreliable or biased action suggestions, especially without tracing the whole reasoning from evidence to action. This work shows it’s important to check the entire chain of evidence supporting financial advice from AI models, not just whether their claims sound correct.
Open → 2609.14302v1