In briefBalancing Risk Pricing, Coverage and Anti-Discrimination in Insurance Tariffs
SummaryThe paper proposes a framework for insurance tariff design built on risk classification, aggregation of benchmark risk prices within classes, and balanced transfers. It evaluates tariffs on risk adequacy, portfolio coverage, and anti-discrimination, and frames the trade-offs as a multi-objective problem with a Pareto frontier. The authors prove several mathematical characterizations of these trade-offs and illustrate the results with a numerical motor-insurance study.
In briefWhether digital investment and energy use are reshaping the Kitchin business cycle, tested with U.S. and Irish data
SummaryThis paper uses quarterly U.S. and Irish data from 1995 to 2025 to explore whether IT investment and data-centre electricity use are reshaping the Kitchin cycle around energy and compute assets. It reports a structural break in the U.S. IT-GDP correlation in 2014Q3, and finds that the strong Irish data-centre electricity and GDP correlation largely disappears when GNP replaces GDP. The authors state that all coefficients are exploratory given small samples.
Bartosz Ziollko, Adam Niedzialkowski, Bartosz Szyma
In briefContext-rich trading strategy using actor-critic deep reinforcement learning with sentiment for stock trading
SummaryThis paper proposes a deep reinforcement learning approach to stock trading that partially and deeply incorporates sentiment analysis to capture external factors such as social sentiment and political instability. It compares actor-critic agents using only policy optimization (A2C, PPO) with those combining policy optimization and Q-learning (DDPG, TD3, SAC). The authors report that agents combining both principles consistently improve in profit maximization and risk-averse trading when sentiment is deeply infused.
In briefSurvey on quantifying uncertainty in financial foundation models
SummaryThis survey examines uncertainty in financial foundation models used for question answering, disclosure analysis, forecasting, and decision-making. It proposes a source-aware framework that separates six uncertainty sources across evidence and context, model and inference, and outcome and action, and a mechanism-based taxonomy for assessing methods across financial tasks. The authors map finance-specific empirical studies onto this taxonomy. They find that most studies address single parts of a workflow, with little work linking evidence and reasoning reliability to downstream prediction and constrained action.
In briefOptimal order size for market makers under a state-dependent Hawkes process model
SummaryThe paper models a market maker's executed volume as the product of average order size, an exponential demand function, and a Prony-series multiplier that depends on quote depth, with market-order counts built from a state-dependent Hawkes model. An empirical study on limit-order-book data examines calibrated models and sensitivity to inventory restrictions. In retrospective evaluations conditioned on realised order-flow paths, the selected calibration achieves higher terminal utility than a baseline while respecting the inventory bound, and the authors separate the effects of trading revenue from inventory control.
In briefProspect Theory in LLMs: Meta-Analysis of Loss Aversion and Related Biases
SummaryThis paper meta-analyzes 102 fitted estimates from 11 studies covering 32 language models to test whether LLMs show the same prospect-theory properties as people. Loss aversion is modest, with a pooled λ of 1.26, far below the strongest human estimate. Probability weighting shows a human-like inverse S shape, but diminishing sensitivity depends on the measurement method used.
