This paper proposes PALM (Point-in-time Adaptation for financial Language Models) as an alternative to annual pre-training of financial language models. It achieves adaptation by fitting low-rank adapters on permitted financial text without modifying any pre-trained weights.
Read originalBased on 4.57 million news articles covering approximately 3,000 US stocks from 2023 to 2026, the paper uses a compact classifier distilled from a large model teacher to label news with 17 event categories and 5 attributes, clustering them into 1.68 million stock-day events, of which 364,405 are neutral sentiment events serving as a placebo.
Read originalA new arXiv paper uses Gaussian surrogate models to precisely calculate the finite-sample risk of candidate scoring decision models, analyzing the boundaries of the role of three training elements: teacher soft targets, frozen pre-trained backbones, and temperature calibration. The paper points out that after a student model inherits teacher bias, more student data cannot eliminate that bias; fixed representations only reduce risk below an explicit sample size threshold; under the probit model, exact recalibration maps to temperature scaling, and can only eliminate all excess risk when the direction of the teacher's coefficients is correct.
Read originalAuDQ, an auditable data quality layer for high-risk tabular data pipelines, proposes an ontology-driven, warehouse-native design vision that treats data quality rules as first-class artifacts, supporting version control, governance text traceability, and immutable execution trajectory replay. Its agentic rule discovery core includes both fully autonomous and guided rule acquisition modes, along with human-machine collaborative review interfaces, and has completed preliminary validation experiments on real-world datasets from the finance and retail industries.
Read originalA new arXiv paper studies the predictive performance of foundation models for return prediction when historical observations are inconsistent with recent return distributions. Authored by Levent Bulut, the 10-page paper includes 5 figures and belongs to the statistical finance (q-fin.ST) field.
Read originalThis 9-page paper accepted by ICAIF '26 proposes a diagnostic protocol to examine the true source of incremental information from LLM embeddings of r/WallStreetBets posts for volatility prediction. The study covers panel data of 202,145 mentioned stock-days from 2015 to 2022, using a retrospective rolling out-of-sample test.
Read originalResearchers have distilled five principles from established practices in software engineering and security research to form the TRACE framework for governing AI-assisted research, covering five dimensions: record tracking, source verification, independent acceptance, process control, and correction evaluation. The framework is accompanied by the open-source operating system TRACE-OS, which can adapt to researchers' existing workflows, helping teams maintain control over their research work when adopting AI and promoting the establishment of trust standards for AI-assisted research.
Read originalOpenAI is improving ChatGPT’s code generation performance for low-resource programming languages, adding three evaluation dimensions: software engineering, SQLite, and COBOL migration. These capability improvements are centered on code generation scenarios for low-resource programming languages.
Read originalResearch shows that large language model pricing agents can deviate from the repeated Bertrand competition benchmark where prices converge to marginal costs in homogeneous goods markets, with their performance depending on deployment decisions. Whether LLM pricing agents can maintain prices above marginal costs in self-play is directly related to deployment choices: whether they set prices period-by-period based directly on raw market history including competitor prices, or periodically output probability distributions based on summaries of their own profit history.
Read originalThe research team built behavioral models for three types of trading agents—rational arbitrageurs, manipulators, and retail momentum followers—based on reinforcement learning, and conducted a pilot study on hourly cryptocurrency data (AAVE, ETC, SUI). The results show that the behavioral characteristics of different agents have differentiated impacts on market pressure, liquidity, volatility, and risk-adjusted returns. The research conclusions support formulating differentiated regulatory rules for different behavioral types, rather than uniformly restricting volatility or turnover rates.
Read originalAn experiment on a simulated collateralized lending market shows that shared action ordering significantly affects the scale of executed credit for LLM borrowers. The experiment set up 12 ordinary order markets and 12 balanced order markets, using an exact randomization test to obtain a two-sided p-value of 2/8100, with a credit difference of 1.12 units of initial unused reference borrowing capacity between leverage-prioritized and deleveraging-prioritized ordinary order markets. No liquidations or actual bad debts occurred in all 24 markets; under the fixed-price 30% shock benchmark, most write-offs came from collateral consumed by liquidation bonuses.
Read originalThis arXiv paper proposes a NoLBERT encoder pre-trained only on texts from 1976 to 1995, comparing it with LDA, MiniLM, and FinBERT on 1.26 million news articles from 2012 to 2024 to predict returns of the 1000 largest U.S. stocks.
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