Panel regression for the GDP of the Central and Eastern European countries using time-varying coefficients ArXiv ID: 2510.04211 “View on arXiv” Authors: Lesya Kolinets, Vygintas Gontis Abstract The integration of Central and Eastern European (CEE) countries into the European Economic Area serves as a valuable experiment for the regional economic development theory. The long-lasting convergence of these economies with more advanced Western Europe exhibits a few standard features and varying policies implemented. Even the Baltic countries, which started from very similar starting positions, demonstrate their unique trajectories of development. We employ a panel data regression model that allows coefficients to vary over time to compare the contributions of a few macroeconomic factors to the GDP growth of CEE countries. In particular, we regress the annual change of GDP per capita in PPP terms as a function of achieved GDP, price, trade, investment, and debt levels. Time-varying common slope coefficients in this approach describe the external economic environment in which countries implement their own policies. The panel consists of 11 Central and Eastern European countries (Bulgaria, Czechia, Estonia, Croatia, Latvia, Lithuania, Hungary, Poland, Romania, Slovenia, and Slovakia), which have been observed annually from 1995 to 2024. While the main selected factors of this investigation contribute to economic growth, in agreement with previous findings, the role of private debt appears vital in determining the pace of economic growth. ...

Comparing LLMs for Sentiment Analysis in Financial Market News ArXiv ID: 2510.15929 “View on arXiv” Authors: Lucas Eduardo Pereira Teles, Carlos M. S. Figueiredo Abstract This article presents a comparative study of large language models (LLMs) in the task of sentiment analysis of financial market news. This work aims to analyze the performance difference of these models in this important natural language processing task within the context of finance. LLM models are compared with classical approaches, allowing for the quantification of the benefits of each tested model or approach. Results show that large language models outperform classical models in the vast majority of cases. ...

Do Mutual Funds Make Active and Skilled Liquidity Choices in Portfolio Management? Evidence from India ArXiv ID: 2510.02741 “View on arXiv” Authors: Pankaj K Agarwal, H K Pradhan, Konark Saxena Abstract This study examines active liquidity management by Indian open-ended equity mutual funds. We find that fund managers respond to inflows by increasing cash holdings, which are later used to purchase less-liquid stocks at favourable valuations. Funds with less liquid portfolios tend to maintain larger cash reserves to manage flows. Funds that make active liquidity choices yield statistically and economically significant gross and net returns. The performance differences between funds with varying activeness in altering liquidity highlight the importance of active liquidity management in markets with substantial cross-sectional liquidity differences such as India. ...

FinReflectKG – MultiHop: Financial QA Benchmark for Reasoning with Knowledge Graph Evidence ArXiv ID: 2510.02906 “View on arXiv” Authors: Abhinav Arun, Reetu Raj Harsh, Bhaskarjit Sarmah, Stefano Pasquali Abstract Multi-hop reasoning over financial disclosures is often a retrieval problem before it becomes a reasoning or generation problem: relevant facts are dispersed across sections, filings, companies, and years, and LLMs often expend excessive tokens navigating noisy context. Without precise Knowledge Graph (KG)-guided selection of relevant context, even strong reasoning models either fail to answer or consume excessive tokens, whereas KG-linked evidence enables models to focus their reasoning on composing already retrieved facts. We present FinReflectKG - MultiHop, a benchmark built on FinReflectKG, a temporally indexed financial KG that links audited triples to source chunks from S&P 100 filings (2022-2024). Mining frequent 2-3 hop subgraph patterns across sectors (via GICS taxonomy), we generate financial analyst style questions with exact supporting evidence from the KG. A two-phase pipeline first creates QA pairs via pattern-specific prompts, followed by a multi-criteria quality control evaluation to ensure QA validity. We then evaluate three controlled retrieval scenarios: (S1) precise KG-linked paths; (S2) text-only page windows centered on relevant text spans; and (S3) relevant page windows with randomizations and distractors. Across both reasoning and non-reasoning models, KG-guided precise retrieval yields substantial gains on the FinReflectKG - MultiHop QA benchmark dataset, boosting correctness scores by approximately 24 percent while reducing token utilization by approximately 84.5 percent compared to the page window setting, which reflects the traditional vector retrieval paradigm. Spanning intra-document, inter-year, and cross-company scopes, our work underscores the pivotal role of knowledge graphs in efficiently connecting evidence for multi-hop financial QA. We also release a curated subset of the benchmark (555 QA Pairs) to catalyze further research. ...

FR-LUX: Friction-Aware, Regime-Conditioned Policy Optimization for Implementable Portfolio Management ArXiv ID: 2510.02986 “View on arXiv” Authors: Jian’an Zhang Abstract Transaction costs and regime shifts are major reasons why paper portfolios fail in live trading. We introduce FR-LUX (Friction-aware, Regime-conditioned Learning under eXecution costs), a reinforcement learning framework that learns after-cost trading policies and remains robust across volatility-liquidity regimes. FR-LUX integrates three ingredients: (i) a microstructure-consistent execution model combining proportional and impact costs, directly embedded in the reward; (ii) a trade-space trust region that constrains changes in inventory flow rather than logits, yielding stable low-turnover updates; and (iii) explicit regime conditioning so the policy specializes to LL/LH/HL/HH states without fragmenting the data. On a 4 x 5 grid of regimes and cost levels with multiple random seeds, FR-LUX achieves the top average Sharpe ratio with narrow bootstrap confidence intervals, maintains a flatter cost-performance slope than strong baselines, and attains superior risk-return efficiency for a given turnover budget. Pairwise scenario-level improvements are strictly positive and remain statistically significant after multiple-testing corrections. We provide formal guarantees on optimality under convex frictions, monotonic improvement under a KL trust region, long-run turnover bounds and induced inaction bands due to proportional costs, positive value advantage for regime-conditioned policies, and robustness to cost misspecification. The methodology is implementable: costs are calibrated from standard liquidity proxies, scenario-level inference avoids pseudo-replication, and all figures and tables are reproducible from released artifacts. ...

Joint Bidding on Intraday and Frequency Containment Reserve Markets ArXiv ID: 2510.03209 “View on arXiv” Authors: Yiming Zhang, Wolfgang Ridinger, David Wozabal Abstract As renewable energy integration increases supply variability, battery energy storage systems (BESS) present a viable solution for balancing supply and demand. This paper proposes a novel approach for optimizing battery BESS participation in multiple electricity markets. We develop a joint bidding strategy that combines participation in the primary frequency reserve market with continuous trading in the intraday market, addressing a gap in the extant literature which typically considers these markets in isolation or simplifies the continuous nature of intraday trading. Our approach utilizes a mixed integer linear programming implementation of the rolling intrinsic algorithm for intraday decisions and state of charge recovery, alongside a learned classifier strategy (LCS) that determines optimal capacity allocation between markets. A comprehensive out-of-sample backtest over more than one year of historical German market data validates our approach: The LCS increases overall profits by over 4% compared to the best-performing static strategy and by more than 3% over a naive dynamic benchmark. Crucially, our method closes the gap to a theoretical perfect foresight strategy to just 4%, demonstrating the effectiveness of dynamic, learning-based allocation in a complex, multi-market environment. ...

Joint Stochastic Optimal Control and Stopping in Aquaculture: Finite-Difference and PINN-Based Approaches ArXiv ID: 2510.02910 “View on arXiv” Authors: Kevin Kamm Abstract This paper studies a joint stochastic optimal control and stopping (JCtrlOS) problem motivated by aquaculture operations, where the objective is to maximize farm profit through an optimal feeding strategy and harvesting time under stochastic price dynamics. We introduce a simplified aquaculture model capturing essential biological and economic features, distinguishing between biologically optimal and economically optimal feeding strategies. The problem is formulated as a Hamilton-Jacobi-Bellman variational inequality and corresponding free boundary problem. We develop two numerical solution approaches: First, a finite difference scheme that serves as a benchmark, and second, a Physics-Informed Neural Network (PINN)-based method, combined with a deep optimal stopping (DeepOS) algorithm to improve stopping time accuracy. Numerical experiments demonstrate that while finite differences perform well in medium-dimensional settings, the PINN approach achieves comparable accuracy and is more scalable to higher dimensions where grid-based methods become infeasible. The results confirm that jointly optimizing feeding and harvesting decisions outperforms strategies that neglect either control or stopping. ...

Signature-Informed Transformer for Asset Allocation ArXiv ID: 2510.03129 “View on arXiv” Authors: Yoontae Hwang, Stefan Zohren Abstract Robust asset allocation is a key challenge in quantitative finance, where deep-learning forecasters often fail due to objective mismatch and error amplification. We introduce the Signature-Informed Transformer (SIT), a novel framework that learns end-to-end allocation policies by directly optimizing a risk-aware financial objective. SIT’s core innovations include path signatures for a rich geometric representation of asset dynamics and a signature-augmented attention mechanism embedding financial inductive biases, like lead-lag effects, into the model. Evaluated on daily S&P 100 equity data, SIT decisively outperforms traditional and deep-learning baselines, especially when compared to predict-then-optimize models. These results indicate that portfolio-aware objectives and geometry-aware inductive biases are essential for risk-aware capital allocation in machine-learning systems. The code is available at: https://github.com/Yoontae6719/Signature-Informed-Transformer-For-Asset-Allocation ...

FINCH: Financial Intelligence using Natural language for Contextualized SQL Handling ArXiv ID: 2510.01887 “View on arXiv” Authors: Avinash Kumar Singh, Bhaskarjit Sarmah, Stefano Pasquali Abstract Text-to-SQL, the task of translating natural language questions into SQL queries, has long been a central challenge in NLP. While progress has been significant, applying it to the financial domain remains especially difficult due to complex schema, domain-specific terminology, and high stakes of error. Despite this, there is no dedicated large-scale financial dataset to advance research, creating a critical gap. To address this, we introduce a curated financial dataset (FINCH) comprising 292 tables and 75,725 natural language-SQL pairs, enabling both fine-tuning and rigorous evaluation. Building on this resource, we benchmark reasoning models and language models of varying scales, providing a systematic analysis of their strengths and limitations in financial Text-to-SQL tasks. Finally, we propose a finance-oriented evaluation metric (FINCH Score) that captures nuances overlooked by existing measures, offering a more faithful assessment of model performance. ...

Mean-field theory of the Santa Fe model revisited: a systematic derivation from an exact BBGKY hierarchy for the zero-intelligence limit-order book model ArXiv ID: 2510.01814 “View on arXiv” Authors: Taiki Wakatsuki, Kiyoshi Kanazawa Abstract The Santa Fe model is an established econophysics model for describing stochastic dynamics of the limit order book from the viewpoint of the zero-intelligence approach. While its foundation was studied by combining a dimensional analysis and a mean-field theory by E. Smith et al. in Quantitative Finance 2003, their arguments are rather heuristic and lack solid mathematical foundation; indeed, their mean-field equations were derived with heuristic arguments and their solutions were not explicitly obtained. In this work, we revisit the mean-field theory of the Santa Fe model from the viewpoint of kinetic theory – a traditional mathematical program in statistical physics. We study the exact master equation for the Santa Fe model and systematically derive the Bogoliubov-Born-Green-Kirkwood-Yvon (BBGKY) hierarchical equation. By applying the mean-field approximation, we derive the mean-field equation for the order-book density profile, parallel to the Boltzmann equation in conventional statistical physics. Furthermore, we obtain explicit and closed expression of the mean-field solutions. Our solutions have several implications: (1)Our scaling formulas are available for both $μ\to 0$ and $μ\to \infty$ asymptotics, where $μ$ is the market-order submission intensity. Particularly, the mean-field theory works very well for small $μ$, while its validity is partially limited for large $μ$. (2)The ``method of image’’ solution, heuristically derived by Bouchaud-Mézard-Potters in Quantitative Finance 2002, is obtained for large $μ$, serving as a mathematical foundation for their heuristic arguments. (3)Finally, we point out an error in E. Smith et al. 2003 in the scaling law for the diffusion constant due to a misspecification in their dimensional analysis. ...