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[1] arXiv:2510.15879 [中文pdf, pdf, 其他]

标题： 关于谁对股票分割感兴趣以及原因的研究：考虑公司、股东或管理者 显示英文标题

标题： A study about who is interested in stock splitting and why: considering companies, shareholders or managers

Jiaquan Nicholas Chen, Marcel Ausloos

评论： 40页，16图，3表，31参考文献；提交给《风险与金融管理杂志》

主题： 一般金融 (q-fin.GN) ; 一般经济学 (econ.GN)

围绕股票价格、股票分割、股东、投资者和管理层对这些信息的行为存在许多误解，这是由于许多混淆因素造成的。 本文通过一个选定的数据库检验假设，探讨问题“股票分割对公司有吸引力吗？” 换句话说，“为什么公司要分割股票？”、“为什么管理者要分割股票？”有时是没有好处的，以及“为什么股东会同意这样的决定？”。 我们通过讨论近年来（有选择地）九个事件，为现有的知识做出贡献，观察信息不对称的作用，以及事件前后收益和交易量的变化。 因此，计算每个样本的β值，发现股票分割（i）在短期内影响市场并略微提高交易量，（ii）增加了公司的股东基础，（iii）对市场的流动性有积极影响。 我们同意股票分割公告可以降低信息不对称的水平。 投资者重新调整他们对公司信念，尽管大多数公司在股票分割年度被错误定价。 显示英文摘要

There are many misconceptions around stock prices, stock splits, shareholders, investors, and managers behaviour about such informations due to a number of confounding factors. This paper tests hypotheses with a selected database, about the question ''is stock split attractive for companies?'' in another words, ''why companies split their stock?'', ''why managers split their stock?'', sometimes for no benefit, and ''why shareholders agree with such decisions?''. We contribute to the existing knowledge through a discussion of nine events in recent (selectively chosen) years, observing the role of information asymmetries, the returns and traded volumes before and after the event. Therefore, calculating the beta for each sample, it is found that stock splits (i) affect the market and slightly enhance the trading volume in a short-term, (ii) increase the shareholder base for its firm, (iii) have a positive effect on the liquidity of the market. We concur that stock split announcements can reduce the level of information asymmetric. Investors readjust their beliefs in the firm, although most of the firms are mispriced in the stock split year.

[2] arXiv:2510.15883 [中文pdf, pdf, html, 其他]

标题： FinFlowRL：金融中自适应随机控制的模仿强化学习框架 显示英文标题

标题： FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance

Yang Li, Zhi Chen

评论： 21页，5个算法，4个表格，5个图

主题： 计算金融 (q-fin.CP) ; 人工智能 (cs.AI) ; 机器学习 (cs.LG) ; 交易与市场微观结构 (q-fin.TR)

传统金融中的随机控制方法由于依赖简化假设和典型框架，在现实世界市场中面临困难。 此类方法通常在特定且定义明确的环境中表现良好，但在变化的、非平稳的环境中会产生次优结果。 我们引入了FinFlowRL，这是一种用于金融最优随机控制的新框架。 该框架预先训练一个从多个专家策略中学习的自适应元策略，然后通过在噪声空间中的强化学习进行微调，以优化生成过程。 通过采用动作分块生成动作序列而非单一决策，它解决了市场的非马尔可夫性质。 FinFlowRL在各种市场条件下始终优于单独优化的专家。 显示英文摘要

Traditional stochastic control methods in finance struggle in real world markets due to their reliance on simplifying assumptions and stylized frameworks. Such methods typically perform well in specific, well defined environments but yield suboptimal results in changed, non stationary ones. We introduce FinFlowRL, a novel framework for financial optimal stochastic control. The framework pretrains an adaptive meta policy learning from multiple expert strategies, then finetunes through reinforcement learning in the noise space to optimize the generative process. By employing action chunking generating action sequences rather than single decisions, it addresses the non Markovian nature of markets. FinFlowRL consistently outperforms individually optimized experts across diverse market conditions.

[3] arXiv:2510.15892 [中文pdf, pdf, html, 其他]

标题： 消费者信贷周期的几何动力学：基于多向量的线性注意力框架用于解释性经济分析 显示英文标题

标题： Geometric Dynamics of Consumer Credit Cycles: A Multivector-based Linear-Attention Framework for Explanatory Economic Analysis

Agus Sudjianto, Sandi Setiawan

评论： 29页，7图

主题： 一般金融 (q-fin.GN) ; 机器学习 (cs.LG)

本研究引入了几何代数，将信用体系关系分解为投影（类似相关性）和旋转（反馈螺旋）成分。 我们将经济状态表示为Clifford代数中的多向量，其中双矢量元素捕捉失业率、消费、储蓄和信用使用之间的旋转耦合。 这种数学框架揭示了传统分析无法看到的交互模式：当失业率和信用紧缩进入同步反馈回路时，它们的几何关系从简单的相关性转变为具有系统性危机特征的危险旋转动力学。 显示英文摘要

This study introduces geometric algebra to decompose credit system relationships into their projective (correlation-like) and rotational (feedback-spiral) components. We represent economic states as multi-vectors in Clifford algebra, where bivector elements capture the rotational coupling between unemployment, consumption, savings, and credit utilization. This mathematical framework reveals interaction patterns invisible to conventional analysis: when unemployment and credit contraction enter simultaneous feedback loops, their geometric relationship shifts from simple correlation to dangerous rotational dynamics that characterize systemic crises.

[4] arXiv:2510.15900 [中文pdf, pdf, html, 其他]

标题： 基于混合变分模态分解和长短期记忆网络的比特币价格预测 显示英文标题

标题： Bitcoin Price Forecasting Based on Hybrid Variational Mode Decomposition and Long Short Term Memory Network

Emmanuel Boadi

主题： 统计金融 (q-fin.ST) ; 机器学习 (cs.LG)

本研究提出了一种混合深度学习模型，用于预测比特币的价格，因为这种数字货币以频繁波动著称。使用的模型包括变分模态分解（VMD）和长短期记忆网络（LSTM）。首先，使用VMD将原始比特币价格序列分解为固有模态函数（IMFs）。然后，每个IMF使用LSTM网络进行建模，以更有效地捕捉时间模式。从IMFs中获得的个体预测结果被汇总，以生成原始比特币价格序列的最终预测。为了确定所提出的混合模型的预测能力，与标准LSTM进行了比较分析。结果证实，混合VMD+LSTM模型在所有评估指标（包括RMSE、MAE和R2）上均优于标准LSTM，并且还提供了可靠的30天预测。 显示英文摘要

This study proposes a hybrid deep learning model for forecasting the price of Bitcoin, as the digital currency is known to exhibit frequent fluctuations. The models used are the Variational Mode Decomposition (VMD) and the Long Short-Term Memory (LSTM) network. First, VMD is used to decompose the original Bitcoin price series into Intrinsic Mode Functions (IMFs). Each IMF is then modeled using an LSTM network to capture temporal patterns more effectively. The individual forecasts from the IMFs are aggregated to produce the final prediction of the original Bitcoin Price Series. To determine the prediction power of the proposed hybrid model, a comparative analysis was conducted against the standard LSTM. The results confirmed that the hybrid VMD+LSTM model outperforms the standard LSTM across all the evaluation metrics, including RMSE, MAE and R2 and also provides a reliable 30-day forecast.

[5] arXiv:2510.15903 [中文pdf, pdf, html, 其他]

标题： 量子与经典机器学习在去中心化金融中的比较研究：来自自动化做市商多资产回测的证据 显示英文标题

标题： Quantum and Classical Machine Learning in Decentralized Finance: Comparative Evidence from Multi-Asset Backtesting of Automated Market Makers

Chi-Sheng Chen, Aidan Hung-Wen Tsai

主题： 统计金融 (q-fin.ST) ; 机器学习 (cs.LG) ; 量子物理 (quant-ph)

本研究通过在多个加密货币资产上的10个模型进行大量回测，全面比较了量子机器学习（QML）和经典机器学习（CML）方法在自动做市商（AMM）和去中心化金融（DeFi）交易策略中的表现。 我们的分析涵盖了经典ML模型（随机森林，梯度提升，逻辑回归）、纯量子模型（VQE分类器，QNN，QSVM）、量子-经典混合模型（QASA混合，QASA序列，量子RWKV）以及Transformer模型。 结果表明，混合量子模型整体表现更优，平均收益为11.2%，平均夏普比率为1.42，而经典ML模型的平均收益为9.8%，平均夏普比率为1.47。 QASA序列混合模型实现了最高的单个收益13.99%，并具有最佳的夏普比率1.76，证明了量子-经典混合方法在AMM和DeFi交易策略中的潜力。 显示英文摘要

This study presents a comprehensive empirical comparison between quantum machine learning (QML) and classical machine learning (CML) approaches in Automated Market Makers (AMM) and Decentralized Finance (DeFi) trading strategies through extensive backtesting on 10 models across multiple cryptocurrency assets. Our analysis encompasses classical ML models (Random Forest, Gradient Boosting, Logistic Regression), pure quantum models (VQE Classifier, QNN, QSVM), hybrid quantum-classical models (QASA Hybrid, QASA Sequence, QuantumRWKV), and transformer models. The results demonstrate that hybrid quantum models achieve superior overall performance with 11.2\% average return and 1.42 average Sharpe ratio, while classical ML models show 9.8\% average return and 1.47 average Sharpe ratio. The QASA Sequence hybrid model achieves the highest individual return of 13.99\% with the best Sharpe ratio of 1.76, demonstrating the potential of quantum-classical hybrid approaches in AMM and DeFi trading strategies.

[6] arXiv:2510.15911 [中文pdf, pdf, html, 其他]

标题： 睡觉的凯利是三分法者 显示英文标题

标题： Sleeping Kelly is a Thirder

Ben Abramowitz

主题： 一般金融 (q-fin.GN) ; 人工智能 (cs.AI)

睡眠公主问题由埃尔加提出，并突出了在不完全回忆情况下概率的作用。 解决睡眠公主问题的一种方法是允许睡眠公主根据她的信念做出决策，然后描述她的决策要被视为“理性”需要什么条件。 特别是，她可以根据她的信念进行金钱投注，并假设她希望增加财富而不是损失财富。 然而，这种方法通常与假设相结合，即睡眠公主应该最大化她赌注的期望值。 在这里，我主张睡眠公主使用凯利准则最大化她的财富增长率是合理的，这使我们得出“三分之一派”的立场。 此外，通过荷兰赌论证表明，这种立场是“合理的”。 如果睡眠凯利只接受增长速率大于1的赌注作为“三分之一派”，那么她不会受到荷兰赌的威胁。 相比之下，如果睡眠公主采取“二分之一派”的立场，她就会受到荷兰赌的威胁。 如果提供给睡眠公主的赌注结构不同并导致非乘法财富动态，她可能就不再是“三分之一派”。 显示英文摘要

The Sleeping Beauty problem was presented by Elga and highlights the role of probabilities in situations with imperfect recall. One approach to solving the Sleeping Beauty problem is to allow Sleeping Beauty to make decisions based on her beliefs, and then characterize what it takes for her decisions to be "rational". In particular, she can be allowed to make monetary bets based on her beliefs, with the assumption that she wants to gain wealth rather than lose it. However, this approach is often coupled with the assumption that Sleeping Beauty should maximize the expected value of her bets. Here, I argue instead that it is rational for Sleeping Beauty to maximize the growth rate of her wealth using the Kelly Criterion, which leads us to the "thirder" position. Furthermore, this position is shown to be "rational" by Dutch book arguments. If Sleeping Kelly only accepts bets that have a growth rate greater than 1 as a "thirder" then she is not vulnerable to Dutch books. By contrast, if Sleeping Beauty takes the "halfer" position, she is vulnerable to Dutch books. If the bets offered to Sleeping Beauty were to be structured differently and lead to non-multiplicative wealth dynamics, she may no longer be a "thirder".

[7] arXiv:2510.15915 [中文pdf, pdf, 其他]

标题： 投资者情绪与市场波动：格兰杰因果关系视角 显示英文标题

标题： Investor Sentiment and Market Movements: A Granger Causality Perspective

Tamoghna Mukherjee

评论： 4页

主题： 统计金融 (q-fin.ST)

股市受到投资者情绪的严重影响，这种情绪可以驱动买入或卖出行为。 情感分析有助于衡量市场参与者对特定股票或整个市场的整体情绪。 积极的情绪通常会导致购买活动增加，反之亦然。 格兰杰因果关系可以用来确定情绪变化是否先于股价变化。 这项研究专注于这一方面，并试图通过格兰杰因果推理来理解收盘价指数与情感评分之间的关系。 该研究通过假设检验发现了一个积极的响应。 显示英文摘要

The stock market is heavily influenced by investor sentiment, which can drive buying or selling behavior. Sentiment analysis helps in gauging the overall sentiment of market participants towards a particular stock or the market as a whole. Positive sentiment often leads to increased buying activity and vice versa. Granger causality can be applied to ascertain whether changes in sentiment precede changes in stock prices.The study is focused on this aspect and tries to understand the relationship between close price index and sentiment score with the help of Granger causality inference. The study finds a positive response through hypothesis testing.

[8] arXiv:2510.15921 [中文pdf, pdf, 其他]

标题： 用于金融市场跨市场投资组合优化的脉冲神经网络：一种类脑计算方法 显示英文标题

标题： Spiking Neural Network for Cross-Market Portfolio Optimization in Financial Markets: A Neuromorphic Computing Approach

Amarendra Mohan (IIT Kharagpur), Ameer Tamoor Khan (University of Copenhagen), Shuai Li (University of Oulu), Xinwei Cao (Jiangnan University), Zhibin Li (Chengdu University of Information Technology)

主题： 投资组合管理 (q-fin.PM) ; 神经与进化计算 (cs.NE) ; 计算金融 (q-fin.CP)

跨市场投资组合优化随着金融市场全球化和高频、多维数据集的增长变得越来越复杂。 传统的人工神经网络在某些投资组合管理任务中虽然有效，但通常会产生大量的计算开销，并且缺乏处理大规模多市场数据所需的时间处理能力。 本研究探讨了脉冲神经网络（SNNs）在跨市场投资组合优化中的应用，利用神经形态计算原理来处理来自印度（Nifty 500）和美国（S&P 500）市场的股票数据。 通过Yahoo Finance API系统地收集了一个包含大约1,250个交易日的五年数据集。 所提出的框架结合了泄漏积分-发放神经元动力学与自适应阈值、尖峰时间依赖可塑性以及侧抑制，以实现对金融时间序列的事件驱动处理。 通过层次聚类实现降维，而基于种群的尖峰编码和多种解码策略在现实交易约束下支持稳健的投资组合构建，包括基数限制、交易成本和自适应风险厌恶。 实验评估表明，与人工神经网络基准相比，基于SNN的框架在风险调整后收益和降低波动性方面表现更优，同时显著提高了计算效率。 这些发现突显了神经形态计算在全球金融市场中可扩展、高效且稳健的投资组合优化的潜力。 显示英文摘要

Cross-market portfolio optimization has become increasingly complex with the globalization of financial markets and the growth of high-frequency, multi-dimensional datasets. Traditional artificial neural networks, while effective in certain portfolio management tasks, often incur substantial computational overhead and lack the temporal processing capabilities required for large-scale, multi-market data. This study investigates the application of Spiking Neural Networks (SNNs) for cross-market portfolio optimization, leveraging neuromorphic computing principles to process equity data from both the Indian (Nifty 500) and US (S&P 500) markets. A five-year dataset comprising approximately 1,250 trading days of daily stock prices was systematically collected via the Yahoo Finance API. The proposed framework integrates Leaky Integrate-andFire neuron dynamics with adaptive thresholding, spike-timingdependent plasticity, and lateral inhibition to enable event-driven processing of financial time series. Dimensionality reduction is achieved through hierarchical clustering, while populationbased spike encoding and multiple decoding strategies support robust portfolio construction under realistic trading constraints, including cardinality limits, transaction costs, and adaptive risk aversion. Experimental evaluation demonstrates that the SNN-based framework delivers superior risk-adjusted returns and reduced volatility compared to ANN benchmarks, while substantially improving computational efficiency. These findings highlight the promise of neuromorphic computation for scalable, efficient, and robust portfolio optimization across global financial markets.

[9] arXiv:2510.15929 [中文pdf, pdf, 其他]

标题： 比较用于金融市场新闻情感分析的大型语言模型 显示英文标题

标题： Comparing LLMs for Sentiment Analysis in Financial Market News

Lucas Eduardo Pereira Teles, Carlos M. S. Figueiredo

主题： 统计金融 (q-fin.ST) ; 人工智能 (cs.AI) ; 计算与语言 (cs.CL)

本文介绍了对大型语言模型（LLMs）在金融市场新闻情感分析任务中的比较研究。 这项工作旨在分析这些模型在此重要自然语言处理任务中的性能差异，特别是在金融背景下。 将LLM模型与经典方法进行比较，从而量化每个测试模型或方法的优势。 结果表明，大型语言模型在大多数情况下优于经典模型。 显示英文摘要

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.

[10] arXiv:2510.15934 [中文pdf, pdf, html, 其他]

标题： 二元学生t-\textit{t} copulas中CoVaR和VaR的概率等价水平及其在外汇风险监测中的应用 显示英文标题

标题： Probability equivalent level for CoVaR and VaR in bivariate Student-\textit{t} copulas with application to foreign exchange risk monitoring

Daniela I. Flores-Silva, Miguel A. Sordo, Alfonso Suárez-Llorens

评论： 25页，5图

主题： 风险管理 (q-fin.RM) ; 概率 (math.PR)

我们将“风险价值和条件风险价值的概率等价水平”（PELCoV）方法扩展到适应由学生t型copula建模的双变量风险，放松了早期方法的强依赖假设，并增强了框架捕捉尾部依赖性和非对称共同运动的能力。 虽然理论结果是在静态环境下开发的，但我们动态地实现了它们，以跟踪随时间变化的风险溢出。 我们通过在外汇市场的应用来说明我们方法的实际相关性，监测1999年至2024年期间的USD/GBP汇率，并将USD/EUR系列作为辅助预警指标。 我们的结果突显了扩展的PELCoV框架在金融压力时期检测风险低估早期迹象的潜力。 显示英文摘要

We extend the "probability-equivalent level of VaR and CoVaR" (PELCoV) methodology to accommodate bivariate risks modeled by a Student-t copula, relaxing the strong dependence assumptions of earlier approaches and enhancing the framework's ability to capture tail dependence and asymmetric co-movements. While the theoretical results are developed in a static setting, we implement them dynamically to track evolving risk spillovers over time. We illustrate the practical relevance of our approach through an application to the foreign exchange market, monitoring the USD/GBP exchange rate with the USD/EUR series as an auxiliary early warning indicator over the period 1999-2024. Our results highlight the potential of the extended PELCoV framework to detect early signs of risk underestimation during periods of financial stress.

[11] arXiv:2510.15937 [中文pdf, pdf, html, 其他]

标题： 尾部安全的随机控制SPX-VIX对冲：人工智能敏感性与无套利市场动态之间的白盒桥梁 显示英文标题

标题： Tail-Safe Stochastic-Control SPX-VIX Hedging: A White-Box Bridge Between AI Sensitivities and Arbitrage-Free Market Dynamics

Jian'an Zhang

评论： 52页；3个图表；PRIMEarxiv模板；完全可复现的工具（代码、配置、图表）

主题： 风险管理 (q-fin.RM) ; 交易与市场微观结构 (q-fin.TR)

我们提出了一种白盒、风险敏感的框架，在交易成本和制度变化下共同对冲标普500指数（SPX）和波动率指数（VIX）头寸。该方法将无套利市场教师与一个通过约束强制安全的控制层相结合。在市场方面，我们整合了一个基于SSVI的隐含波动率曲面和一个符合Cboe规则的VIX计算（包括翼部修剪和30天插值），并通过一个截断的、保持凸性的Dupire局部波动率提取器将价格与动态连接起来。在控制方面，我们将对冲问题建模为一个小的二次规划问题，其中包含用于库存、利率和尾部风险的控制障碍函数（CBF）框；一个充分下降的执行门控，仅在风险下降足以证明成本时进行交易；以及三个针对性的尾部安全升级：相关性/期限感知的VIX权重、受保护的不交易带和期限感知的微交易阈值及冷却期。我们证明了每一步QP的存在性/唯一性以及KKT正则性，安全集的前向不变性，当门控打开时的一步风险下降，以及在有界交易速率下的无振荡性。对于动态层，我们建立了离散Dupire估计量的正性和二阶一致性，并给出了一个索引一致性边界，将教师VIX与具有显式求积和投影误差的CIR风格代理联系起来。在一个可重复的合成环境中，该控制器在减少预期不足的同时抑制了不必要的换手率，而教师-曲面构建保持了指数级残差的小且稳定。 显示英文摘要

We present a white-box, risk-sensitive framework for jointly hedging SPX and VIX exposures under transaction costs and regime shifts. The approach couples an arbitrage-free market teacher with a control layer that enforces safety as constraints. On the market side, we integrate an SSVI-based implied-volatility surface and a Cboe-compliant VIX computation (including wing pruning and 30-day interpolation), and connect prices to dynamics via a clipped, convexity-preserving Dupire local-volatility extractor. On the control side, we pose hedging as a small quadratic program with control-barrier-function (CBF) boxes for inventory, rate, and tail risk; a sufficient-descent execution gate that trades only when risk drop justifies cost; and three targeted tail-safety upgrades: a correlation/expiry-aware VIX weight, guarded no-trade bands, and expiry-aware micro-trade thresholds with cooldown. We prove existence/uniqueness and KKT regularity of the per-step QP, forward invariance of safety sets, one-step risk descent when the gate opens, and no chattering with bounded trade rates. For the dynamics layer, we establish positivity and second-order consistency of the discrete Dupire estimator and give an index-coherence bound linking the teacher VIX to a CIR-style proxy with explicit quadrature and projection errors. In a reproducible synthetic environment mirroring exchange rules and execution frictions, the controller reduces expected shortfall while suppressing nuisance turnover, and the teacher-surface construction keeps index-level residuals small and stable.

[12] arXiv:2510.15938 [中文pdf, pdf, html, 其他]

标题： 菲律宾股票交易所价格波动的动态因子分析 显示英文标题

标题： Dynamic Factor Analysis of Price Movements in the Philippine Stock Exchange

Brian Godwin Lim, Dominic Dayta, Benedict Ryan Tiu, Renzo Roel Tan, Len Patrick Dominic Garces, Kazushi Ikeda

主题： 统计金融 (q-fin.ST) ; 机器学习 (cs.LG) ; 机器学习 (stat.ML)

股票市场的复杂动态导致了对能够有效解释其内在复杂性的模型的广泛研究。 本研究利用计量经济学文献，探讨动态因子模型作为一种可解释的模型，具有足够的预测能力以捕捉重要的市场现象。 尽管该模型已被广泛用于预测目的，但本研究侧重于分析提取的载荷和公共因子，作为理解股价动态的替代框架。 结果表明，当使用卡尔曼方法和最大似然估计应用于菲律宾证券交易所时，对传统市场理论提出了新的见解，并随后与资本资产定价模型进行验证。 值得注意的是，单因子模型提取了一个代表系统性或市场动态的公共因子，类似于综合指数，而双因子模型提取了代表市场趋势和波动性的公共因子。 此外，该模型在预测菲律宾国内生产总值增长方面的应用突显了提取的公共因子作为可行的实时市场指标的潜力，使样本外预测误差减少了超过34%。 总体而言，结果强调了动态因子分析在更深入理解市场价格变动动态中的价值。 显示英文摘要

The intricate dynamics of stock markets have led to extensive research on models that are able to effectively explain their inherent complexities. This study leverages the econometrics literature to explore the dynamic factor model as an interpretable model with sufficient predictive capabilities for capturing essential market phenomena. Although the model has been extensively applied for predictive purposes, this study focuses on analyzing the extracted loadings and common factors as an alternative framework for understanding stock price dynamics. The results reveal novel insights into traditional market theories when applied to the Philippine Stock Exchange using the Kalman method and maximum likelihood estimation, with subsequent validation against the capital asset pricing model. Notably, a one-factor model extracts a common factor representing systematic or market dynamics similar to the composite index, whereas a two-factor model extracts common factors representing market trends and volatility. Furthermore, an application of the model for nowcasting the growth rates of the Philippine gross domestic product highlights the potential of the extracted common factors as viable real-time market indicators, yielding over a 34% decrease in the out-of-sample prediction error. Overall, the results underscore the value of dynamic factor analysis in gaining a deeper understanding of market price movement dynamics.

[13] arXiv:2510.15941 [中文pdf, pdf, 其他]

标题： 税收和碳排放交易碳定价方案的比较 显示英文标题

标题： Comparison of Tax and Cap-and-Trade Carbon Pricing Schemes

Stéphane Crépey (LPSM (UMR\_8001), UPCité), Samuel Drapeau, Mekonnen Tadese (LPSM (UMR\_8001))

主题： 一般经济学 (econ.GN)

碳定价已成为现代气候政策的核心支柱，碳税和排放交易体系（ETS）是两种主要的方法。 尽管经济理论表明在理想化假设下这些工具是等效的，但由于现实世界中的市场不完善，它们的表现却有所不同。 这一差异中一个较少被探讨的方面是金融中介机构在排放交易市场中的作用。 本文提出一个统一的框架，以比较税收和基于市场的方案的经济和环境表现，并明确纳入金融中介机构的参与。 通过校准这两种工具以实现相同的总体排放减少目标，我们评估了它们在不同市场结构下的经济表现。 我们的结果表明，尽管在完全竞争条件下这两种方案是等效的，但在ETS中存在中介机构会相对于碳税减少监管财富和经济主体的总体财富。 这些影响源于中介机构对价格形成的影响以及它们对部分收入流的占有。 研究结果强调了在设计碳市场时考虑中介机构行为的重要性，并突显了对排放交易体系不断演变的制度结构进行进一步实证研究的必要性。 显示英文摘要

Carbon pricing has become a central pillar of modern climate policy, with carbon taxes and emissions trading systems (ETS) serving as the two dominant approaches. Although economic theory suggests these instruments are equivalent under idealized assumptions, their performance diverges in practice due to real-world market imperfections. A particularly less explored dimension of this divergence concerns the role of financial intermediaries in emissions trading markets. This paper develops a unified framework to compare the economic and environmental performance of tax- and market-based schemes, explicitly incorporating the involvement of financial intermediaries. By calibrating both instruments to deliver identical aggregate emission reduction targets, we assess their economic performance across alternative market structures. Our results suggest that although the two schemes are equivalent under perfect competition, the presence of intermediaries in ETS reduces both regulatory wealth and the aggregate wealth of economic agents relative to carbon taxation. These effects stem from intermediaries' influence on price formation and their appropriation of part of the revenue stream. The findings underscore the importance of accounting for intermediaries' behavior in the design of carbon markets and highlight the need for further empirical research on the evolving institutional structure of emissions trading systems.

[14] arXiv:2510.15942 [中文pdf, pdf, html, 其他]

标题： 股票市场的内在几何结构来自图 Ricci 流 显示英文标题

标题： Intrinsic Geometry of the Stock Market from Graph Ricci Flow

Bhargavi Srinivasan

主题： 统计金融 (q-fin.ST)

我们使用离散Ollivier-Ricci图曲率与Ricci流来通过NASDAQ 100指数的实证相关图研究金融市场的内在几何结构。 我们的主要结果是开发了一种技术，以实证图的Ricci流中形成的颈部Pinch奇点进行手术，使用完全连接图的曲率行为和下界作为起点。 我们构建了一个算法，该算法利用图的内在几何流产生的曲率来检测金融市场中的隐藏层次结构、社区行为和聚类，尽管面临高度连接几何带来的挑战。 显示英文摘要

We use the discrete Ollivier-Ricci graph curvature with Ricci flow to examine the intrinsic geometry of financial markets through the empirical correlation graph of the NASDAQ 100 index. Our main result is the development of a technique to perform surgery on the neckpinch singularities that form during the Ricci flow of the empirical graph, using the behavior and the lower bound of curvature of the fully connected graph as a starting point. We construct an algorithm that uses the curvature generated by intrinsic geometric flow of the graph to detect hidden hierarchies, community behavior, and clustering in financial markets despite the underlying challenges posed by a highly connected geometry.

[15] arXiv:2510.15949 [中文pdf, pdf, html, 其他]

标题： ATLAS：通过动态提示优化和多智能体协调的自适应交易与大语言模型代理 显示英文标题

标题： ATLAS: Adaptive Trading with LLM AgentS Through Dynamic Prompt Optimization and Multi-Agent Coordination

Charidimos Papadakis, Angeliki Dimitriou, Giorgos Filandrianos, Maria Lymperaiou, Konstantinos Thomas, Giorgos Stamou

主题： 交易与市场微观结构 (q-fin.TR) ; 人工智能 (cs.AI)

大型语言模型在金融决策方面显示出潜力，然而将它们作为自主交易代理部署时会带来根本性的挑战：如何在奖励延迟且被市场噪声掩盖的情况下适应指令，如何将异构的信息流整合成连贯的决策，以及如何弥合模型输出与可执行市场操作之间的差距。 我们提出了ATLAS（基于大语言模型代理的自适应交易），这是一个统一的多代理框架，能够整合来自市场、新闻和公司基本面的结构化信息，以支持稳健的交易决策。 在ATLAS中，核心交易代理在一个订单感知的动作空间中运行，确保输出对应于可执行的市场订单，而不是抽象信号。 该代理可以在交易过程中使用自适应-OPRO，这是一种新颖的提示优化技术，通过结合实时的随机反馈动态调整提示，从而随时间推移提高性能。 在特定制度的股票研究和多个大语言模型家族中，自适应-OPRO始终优于固定提示，而基于反思的反馈未能提供系统性收益。 显示英文摘要

Large language models show promise for financial decision-making, yet deploying them as autonomous trading agents raises fundamental challenges: how to adapt instructions when rewards arrive late and obscured by market noise, how to synthesize heterogeneous information streams into coherent decisions, and how to bridge the gap between model outputs and executable market actions. We present ATLAS (Adaptive Trading with LLM AgentS), a unified multi-agent framework that integrates structured information from markets, news, and corporate fundamentals to support robust trading decisions. Within ATLAS, the central trading agent operates in an order-aware action space, ensuring that outputs correspond to executable market orders rather than abstract signals. The agent can incorporate feedback while trading using Adaptive-OPRO, a novel prompt-optimization technique that dynamically adapts the prompt by incorporating real-time, stochastic feedback, leading to increasing performance over time. Across regime-specific equity studies and multiple LLM families, Adaptive-OPRO consistently outperforms fixed prompts, while reflection-based feedback fails to provide systematic gains.

[16] arXiv:2510.15956 [中文pdf, pdf, 其他]

标题： 华尔街人工智能时代的企业社会责任信号传递 显示英文标题

标题： ESG Signaling on Wall Street in the AI Era

Qionghua Chu

主题： 一般金融 (q-fin.GN) ; 投资组合管理 (q-fin.PM)

我通过实证研究识别了ESG研究中的一个新信号渠道，即考察当大型机构投资者越来越多地转向人工智能（AI）时，环境、社会和治理（ESG）投资是否仍然具有价值。 使用来自Yahoo Finance的标普500公司经过winsor化处理的ESG评分，并控制股权市值，我进行了横截面回归以检验信号机制。 我证明了环境、社会、治理和综合ESG评分在单独以及各种组合情况下强烈且积极地表明更高的债务与总资本比率。 我的研究结果为日益增长的ESG投资文献做出了贡献，提供了一个经济上有意义的信号渠道，对于人工智能兴起背景下的长期投资组合管理具有启示意义。 显示英文摘要

I identify a new signaling channel in ESG research by empirically examining whether environmental, social, and governance (ESG) investing remains valuable as large institutional investors increasingly shift toward artificial intelligence (AI). Using winsorized ESG scores of S&P 500 firms from Yahoo Finance and controlling for market value of equity, I conduct cross-sectional regressions to test the signaling mechanism. I demonstrate that Environmental, Social, Governance, and composite ESG scores strongly and positively signal higher debt-to-total-capital ratio, both individually and in various combinations. My findings contribute to the growing literature on ESG investing, offering economically meaningful signaling channel with implications for long-term portfolio management amid the rise of AI.

[17] arXiv:2510.15984 [中文pdf, pdf, html, 其他]

标题： 无校准的堤坝 显示英文标题

标题： Berms without Calibration

K.E. Feldman

主题： 证券定价 (q-fin.PR) ; 概率 (math.PR) ; 数学金融 (q-fin.MF)

我们基于互换率分布及其之间的相关性，推导出一种新的半解析定价模型，用于百慕大期权。该模型不需要产品特定的校准。 显示英文摘要

We derive a new semi-analytical pricing model for Bermudan swaptions based on swap rates distributions and correlations between them. The model does not require product specific calibration.

[18] arXiv:2510.15988 [中文pdf, pdf, html, 其他]

标题： 关于高频交易中极限订单优化问题的Bellman方程 显示英文标题

标题： On Bellman equation in the limit order optimization problem for high-frequency trading

M.I. Balakaeva, A.Yu. Veretennikov

评论： 19页，7个参考文献

主题： 交易与市场微观结构 (q-fin.TR) ; 概率 (math.PR) ; 数学金融 (q-fin.MF)

研究了一种在高频交易（HFT）中的买卖限价订单簿中构造最优策略的近似方法。 基础是M. Avellaneda & S. Stoikov 2008年的文章，在该文章中发现了某些看似严重的漏洞；在本文中，这些漏洞被仔细修正。 然而，令人有些意外的是，我们的修正并没有改变所引用论文的主要结论，因此实际上这些漏洞被认为是不重要的。 对此现象提供了一个解释。 显示英文摘要

An approximation method for construction of optimal strategies in the bid \& ask limit order book in the high-frequency trading (HFT) is studied. The basis is the article by M. Avellaneda \& S. Stoikov 2008, in which certain seemingly serious gaps have been found; in the present paper they are carefully corrected. However, a bit surprisingly, our corrections do not change the main answer in the cited paper, so that, in fact, the gaps turn out to be unimportant. An explanation of this effect is offered.

[19] arXiv:2510.15993 [中文pdf, pdf, html, 其他]

标题： 与投资者和市场行为对齐的语言模型用于金融建议 显示英文标题

标题： Aligning Language Models with Investor and Market Behavior for Financial Recommendations

Fernando Spadea, Oshani Seneviratne

主题： 投资组合管理 (q-fin.PM) ; 机器学习 (cs.LG) ; 统计金融 (q-fin.ST)

大多数金融推荐系统往往无法考虑关键的行为和监管因素，导致建议与用户偏好不一致、难以理解或不太可能被遵循。 我们提出了FLARKO（用于资产推荐的金融语言模型与知识图谱优化），这是一种新颖的框架，结合了大型语言模型（LLMs）、知识图谱（KGs）和Kahneman-Tversky优化（KTO），生成既有利可图又符合行为的资产推荐。 FLARKO将用户的交易历史和资产趋势编码为结构化的KGs，为LLM提供可解释且可控的上下文。 为了展示我们方法的适应性，我们开发并评估了一个集中式架构（CenFLARKO）和一个联邦变体（FedFLARKO）。 据我们所知，这是首次展示将KTO用于金融资产推荐的LLMs微调。 我们还首次在联邦学习（FL）环境中使用结构化KGs来使LLM在行为金融数据上的推理具有基础。 在FAR-Trans数据集上评估，FLARKO在行为一致性与联合盈利能力方面始终优于最先进的推荐基线，同时保持可解释性和资源效率。 显示英文摘要

Most financial recommendation systems often fail to account for key behavioral and regulatory factors, leading to advice that is misaligned with user preferences, difficult to interpret, or unlikely to be followed. We present FLARKO (Financial Language-model for Asset Recommendation with Knowledge-graph Optimization), a novel framework that integrates Large Language Models (LLMs), Knowledge Graphs (KGs), and Kahneman-Tversky Optimization (KTO) to generate asset recommendations that are both profitable and behaviorally aligned. FLARKO encodes users' transaction histories and asset trends as structured KGs, providing interpretable and controllable context for the LLM. To demonstrate the adaptability of our approach, we develop and evaluate both a centralized architecture (CenFLARKO) and a federated variant (FedFLARKO). To our knowledge, this is the first demonstration of combining KTO for fine-tuning of LLMs for financial asset recommendation. We also present the first use of structured KGs to ground LLM reasoning over behavioral financial data in a federated learning (FL) setting. Evaluated on the FAR-Trans dataset, FLARKO consistently outperforms state-of-the-art recommendation baselines on behavioral alignment and joint profitability, while remaining interpretable and resource-efficient.

[20] arXiv:2510.15995 [中文pdf, pdf, html, 其他]

标题： 看不见的握手：自适应市场代理之间的默示共谋 显示英文标题

标题： The Invisible Handshake: Tacit Collusion between Adaptive Market Agents

Luigi Foscari, Emanuele Guidotti, Nicolò Cesa-Bianchi, Tatjana Chavdarova, Alfio Ferrara

主题： 交易与市场微观结构 (q-fin.TR) ; 计算机科学与博弈论 (cs.GT) ; 机器学习 (cs.LG)

我们研究适应性交易代理在具有内生价格形成的随机市场中隐性共谋的出现。 通过市场做市商和市场参与者之间的双人重复博弈，我们描述了可行的和共谋的战略配置，这些配置将价格提高到竞争水平以上。 我们表明，当代理遵循简单的学习算法（例如梯度上升）来最大化自己的财富时，即使在流动性很高的市场且交易规模较小的情况下，产生的动态也会收敛到共谋战略配置。 通过突出简单学习策略如何自然导致隐性共谋，我们的结果为人工智能驱动市场的动态提供了新的见解。 显示英文摘要

We study the emergence of tacit collusion between adaptive trading agents in a stochastic market with endogenous price formation. Using a two-player repeated game between a market maker and a market taker, we characterize feasible and collusive strategy profiles that raise prices beyond competitive levels. We show that, when agents follow simple learning algorithms (e.g., gradient ascent) to maximize their own wealth, the resulting dynamics converge to collusive strategy profiles, even in highly liquid markets with small trade sizes. By highlighting how simple learning strategies naturally lead to tacit collusion, our results offer new insights into the dynamics of AI-driven markets.

[21] arXiv:2510.16008 [中文pdf, pdf, html, 其他]

标题： 卷积注意力在博彩交易所市场中的应用 显示英文标题

标题： Convolutional Attention in Betting Exchange Markets

Rui Gonçalves, Vitor Miguel Ribeiro, Roman Chertovskih, António Pedro Aguiar

主题： 统计金融 (q-fin.ST) ; 机器学习 (cs.LG)

本研究介绍了在交易所市场中使用市场深度数据和系统性程序来实现价格波动的短期预测系统，以启用完全自动化的交易系统。 案例研究聚焦于世界领先的博彩交易所Betfair上的英国赢马赛马市场在直播前阶段。 引入并应用了创新的卷积注意力机制，用于多个循环神经网络和二维卷积循环神经网络层。 此外，提出了一种针对卷积层的新填充方法，专门设计用于多变量时间序列处理。 这些创新与它们的执行过程都得到了详细描述。 所提出的架构遵循标准的监督学习方法，包括模型训练和随后在新数据上的测试，这需要大量的预处理和数据分析。 该研究还展示了使用开发模型在生产环境中进行自动化特征工程和市场交互的完整端到端框架。 本研究的主要发现是，所有提出的创新都对所研究分类任务的性能指标产生积极影响，从而推动了卷积注意力机制和应用于多变量时间序列问题的填充方法的当前最新技术水平。 显示英文摘要

This study presents the implementation of a short-term forecasting system for price movements in exchange markets, using market depth data and a systematic procedure to enable a fully automated trading system. The case study focuses on the UK to Win Horse Racing market during the pre-live stage on the world's leading betting exchange, Betfair. Innovative convolutional attention mechanisms are introduced and applied to multiple recurrent neural networks and bi-dimensional convolutional recurrent neural network layers. Additionally, a novel padding method for convolutional layers is proposed, specifically designed for multivariate time series processing. These innovations are thoroughly detailed, along with their execution process. The proposed architectures follow a standard supervised learning approach, involving model training and subsequent testing on new data, which requires extensive pre-processing and data analysis. The study also presents a complete end-to-end framework for automated feature engineering and market interactions using the developed models in production. The key finding of this research is that all proposed innovations positively impact the performance metrics of the classification task under examination, thereby advancing the current state-of-the-art in convolutional attention mechanisms and padding methods applied to multivariate time series problems.

[22] arXiv:2510.16009 [中文pdf, pdf, 其他]

标题： 包含数据：数据经济学的再分配力量 显示英文标题

标题： Data for Inclusion: The Redistributive Power of Data Economics

Diego Vallarino

主题： 一般经济学 (econ.GN) ; 机器学习 (cs.LG)

本文评估了在金融排斥经济体中扩大对正面信用信息的获取对再分配和效率的影响。 使用乌拉圭2021年家庭调查的微观数据，我们模拟了三种数据制度——仅负面信息、部分正面（Score+）和合成全可视性，并评估它们对信贷获取、利息负担和不平等的影响。 我们的研究结果表明，实现更广泛的数据共享显著降低了金融成本，压缩了利率差异，并降低了信贷负担的基尼系数。 虽然部分可视性使一部分人口受益，但完全的合成访问带来了最公平和高效的成果。 分析将信用数据定位为一种非竞争性的公共资产，对金融包容性和减贫具有变革性的影响。 显示英文摘要

This paper evaluates the redistributive and efficiency impacts of expanding access to positive credit information in a financially excluded economy. Using microdata from Uruguay's 2021 household survey, we simulate three data regimes negative only, partial positive (Score+), and synthetic full visibility and assess their effects on access to credit, interest burden, and inequality. Our findings reveal that enabling broader data sharing substantially reduces financial costs, compresses interest rate dispersion, and lowers the Gini coefficient of credit burden. While partial visibility benefits a subset of the population, full synthetic access delivers the most equitable and efficient outcomes. The analysis positions credit data as a non-rival public asset with transformative implications for financial inclusion and poverty reduction.

[23] arXiv:2510.16010 [中文pdf, pdf, 其他]

标题： 制度差异、危机冲击与波动结构：对东盟股票市场的分窗口EGARCH/TGARCH分析 显示英文标题

标题： Institutional Differences, Crisis Shocks, and Volatility Structure: A By-Window EGARCH/TGARCH Analysis of ASEAN Stock Markets

Junlin Yang

评论： 波动率建模；EGARCH；TGARCH；新兴市场；危机动态；制度差异

主题： 统计金融 (q-fin.ST) ; 方法论 (stat.ME)

本研究考察制度差异和外部危机如何塑造新兴亚洲股市的波动性动态。 使用2010年至2024年印度尼西亚、马来西亚和菲律宾的日股票指数收益率，我们在按窗口设计中估计EGARCH(1,1)和TGARCH(1,1)模型。 样本分为2013年紧缩愤怒、2020-2021年新冠疫情时期、2022-2023年加息周期和稳定阶段。 以往的研究通常只研究单一市场或静态时期；据我们所知，没有研究在一个GARCH框架内将制度比较与多危机动态结合起来。 我们弥补了这一空白，并表明这三个市场都表现出强烈的波动性持续性和厚尾收益。 在危机期间，持续性和不对称性增加，而尾部厚度上升，意味着极端波动更加频繁。 危机后，参数会恢复到冲击前的水平。 跨国证据表明制度成熟度具有缓冲作用：马来西亚更强的监管和信息系统抑制了放大效应并加快了恢复，而菲律宾较薄弱的市场结构延长了不稳定状态。 我们得出结论，危机加剧了波动性结构，而制度韧性决定了恢复速度。 结果为在全局冲击期间减少波动性持续性的政策提供了指导，包括透明度、宏观审慎沟通和流动性支持。 显示英文摘要

This study examines how institutional differences and external crises shape volatility dynamics in emerging Asian stock markets. Using daily stock index returns for Indonesia, Malaysia, and the Philippines from 2010 to 2024, we estimate EGARCH(1,1) and TGARCH(1,1) models in a by-window design. The sample is split into the 2013 Taper Tantrum, the 2020-2021 COVID-19 period, the 2022-2023 rate-hike cycle, and tranquil phases. Prior work typically studies a single market or a static period; to our knowledge no study unifies institutional comparison with multi-crisis dynamics within one GARCH framework. We address this gap and show that all three markets display strong volatility persistence and fat-tailed returns. During crises both persistence and asymmetry increase, while tail thickness rises, implying more frequent extreme moves. After crises, parameters revert toward pre-shock levels. Cross-country evidence indicates a buffering role of institutional maturity: Malaysias stronger regulatory and information systems dampen amplification and speed recovery, whereas the Philippines thinner market structure prolongs instability. We conclude that crises amplify volatility structures, while institutional robustness governs recovery speed. The results provide policy guidance on transparency, macroprudential communication, and liquidity support to reduce volatility persistence during global shocks.

[24] arXiv:2510.16066 [中文pdf, pdf, html, 其他]

标题： 基于银行交易数据的现金流承保：推进马来西亚中小微企业金融包容性 显示英文标题

标题： Cash Flow Underwriting with Bank Transaction Data: Advancing MSME Financial Inclusion in Malaysia

Chun Chet Ng, Wei Zeng Low, Yin Yin Boon

评论： 已被FinREM研讨会，ICAI F 2025接受

主题： 统计金融 (q-fin.ST) ; 人工智能 (cs.AI) ; 计算工程、金融与科学 (cs.CE) ; 计算机与社会 (cs.CY) ; 机器学习 (cs.LG) ; 风险管理 (q-fin.RM)

尽管在马来西亚所有企业中占96.1%，微型、小型和中型企业（MSMEs）仍然面临融资获取问题，这是最持续的挑战之一。 新成立或年轻的 businesses 常常被排除在正式信用市场之外，因为传统的承销方法高度依赖信用局数据。 本研究探讨了银行对账单数据作为信用评估的替代数据源的潜力，以促进新兴市场的金融包容性。 首先，我们提出了一种基于现金流的承销流程，在该流程中我们利用银行对账单数据进行端到端的数据提取和机器学习信用评分。 其次，我们引入了一个来自马来西亚贷款机构的611个贷款申请人的新数据集。 第三，我们开发并评估了基于申请信息和银行交易衍生特征的信用评分模型。 实证结果表明，使用此类数据可以提高我们在数据集上的所有模型的性能，这可以改善新接触贷款的MSMEs的信用评分。 最后，我们计划发布匿名化的银行交易数据集，以促进马来西亚新兴经济中MSMEs金融包容性的进一步研究。 显示英文摘要

Despite accounting for 96.1% of all businesses in Malaysia, access to financing remains one of the most persistent challenges faced by Micro, Small, and Medium Enterprises (MSMEs). Newly established or young businesses are often excluded from formal credit markets as traditional underwriting approaches rely heavily on credit bureau data. This study investigates the potential of bank statement data as an alternative data source for credit assessment to promote financial inclusion in emerging markets. Firstly, we propose a cash flow-based underwriting pipeline where we utilise bank statement data for end to end data extraction and machine learning credit scoring. Secondly, we introduce a novel dataset of 611 loan applicants from a Malaysian lending institution. Thirdly, we develop and evaluate credit scoring models based on application information and bank transaction-derived features. Empirical results show that the use of such data boosts the performance of all models on our dataset, which can improve credit scoring for new-to-lending MSMEs. Lastly, we intend to release the anonymised bank transaction dataset to facilitate further research on MSMEs financial inclusion within Malaysia's emerging economy.

[25] arXiv:2510.16472 [中文pdf, pdf, 其他]

标题： 发展融资机构（DFIs）、政治条件和外国直接投资（FDI）在撒哈拉以南非洲 显示英文标题

标题： Development finance institutions (DFIs), political conditions, and foreign direct investment (FDI) in Sub-Saharan Africa

Carmen Berta C. De Saituma Cagiza, Ilidio Cagiza

评论： 12页，1图

期刊参考： 经济学与国际金融杂志，17（1），1-12（2025）

主题： 一般经济学 (econ.GN)

本研究利用1990年至2018年五个撒哈拉以南非洲国家（尼日利亚、加纳、肯尼亚、南非和津巴布韦）的年度定量面板数据，使用在STATA中估计的固定效应模型，研究了开发金融机构（DFIs）、外国直接投资（FDI）与经济发展之间的动态关系。 具体而言，该分析探讨了DFIs是否促进FDI流入，从而推动经济增长并有助于实现可持续发展目标（SDGs）。 研究结果表明，尽管DFIs理论上对FDI有积极影响，但这一关系在样本中并不具有统计显著性，这表明其受到区域经济差异的影响。 该研究还分析了经济增长、贸易开放度、通货膨胀、政治稳定性和法治如何影响这一联系，阐明了它们在塑造投资环境中的作用。 行业分析表明，DFI在基础设施、农业企业和金融领域的投资对FDI有显著影响，其中基础设施的影响最大，因为其在经济体系中起着基础性作用。 本研究通过在面板设定中将DFIs与FDI联系起来，为政策制定者提供了一个框架，以加强制度和宏观经济条件，从而优化DFIs对FDI的影响，最终促进可持续发展。 研究结果强调了制定有针对性的政策以解决区域差异并提高DFI在促进可持续增长方面的有效性的必要性。 显示英文摘要

This study investigates the dynamic relationship between development finance institutions (DFIs), foreign direct investment (FDI), and economic development in Sub-Saharan Africa (SSA) from 1990 to 2018, using a quantitative panel dataset of annual data for five SSA countries (Nigeria, Ghana, Kenya, South Africa, and Zimbabwe) and a fixed-effects model estimated in STATA. Specifically, the analysis examines whether DFIs enhance FDI inflows, thereby promoting economic growth and contributing to the achievement of the Sustainable Development Goals (SDGs). The findings indicate that although DFIs have a theoretically positive impact on FDI, this relationship is not statistically significant across the sample, suggesting contextual dependencies influenced by regional economic variations. The study also analyzes how economic growth, trade openness, inflation, political stability, and the rule of law influence this nexus, elucidating their roles in shaping investment climates. A sectoral analysis indicates that DFI investments in infrastructure, agribusiness, and finance significantly affect FDI, with infrastructure having the greatest impact owing to its foundational role in economic systems. This research contributes by linking DFIs with FDI in SSA in a panel setting, thus providing a framework for policymakers to strengthen institutional and macroeconomic conditions to optimize the impact of DFIs on FDI and, ultimately, on sustainable development. The findings underscore the need for targeted policies to address regional disparities and enhance DFI effectiveness in fostering sustainable growth.

[26] arXiv:2510.16483 [中文pdf, pdf, html, 其他]

标题： 所得税、总时薪和行为反应的构成：来自丹麦税收改革的证据 显示英文标题

标题： Income Taxes, Gross Hourly Wages, and the Anatomy of Behavioral Responses: Evidence from a Danish Tax Reform

Kazuhiko Sumiya, Jesper Bagger

主题： 一般经济学 (econ.GN)

本文提供了关于收入税如何影响每小时工资的准实验证据，利用丹麦行政数据和一项引入联合纳税的税收改革。 利用配偶收入进行识别，我们展示了丈夫们的非参数性、差异中的差异图形证据。 对于低收入工人，税收对其工资有负面且动态的影响；其工资对净边际税率的弹性为0.4。 对于中等收入工人，影响较小且不显著。 工资通过晋升或工作转换对税收做出反应。 无论是每天还是每年的工作时间都没有显著反应；因此，年度收入主要通过每小时工资而非劳动力供给对税收做出反应。 显示英文摘要

This paper provides quasi-experimental evidence on how income taxes affect gross hourly wages, utilizing Danish administrative data and a tax reform that introduced joint taxation. Exploiting spousal income for identification, we present nonparametric, difference-in-differences graphical evidence among husbands. For low-income workers, taxes have negative and dynamic effects on wages; their wage elasticity with respect to net-of-marginal-tax rates is 0.4. For medium-income workers, the effects are smaller and insignificant. Wages respond to taxes through promotions or job-to-job transitions. Neither daily nor annual hours worked respond significantly; consequently, annual earnings respond to taxes primarily through hourly wages, rather than through labor supply.

[27] arXiv:2510.16503 [中文pdf, pdf, html, 其他]

标题： 金融市场的感情和波动性：地缘政治危机期间BERT和GARCH应用的综述 显示英文标题

标题： Sentiment and Volatility in Financial Markets: A Review of BERT and GARCH Applications during Geopolitical Crises

Domenica Mino, Cillian Williamson

评论： 24页，9图，3表。包括附录和补充代码链接

主题： 统计金融 (q-fin.ST)

人工智能技术越来越多地被应用于理解公众情绪与金融市场行为之间的复杂关系。 本研究探讨了与俄乌战争相关的新闻情绪与股票市场波动之间的关系。 使用经过微调的双向编码器表示来自变压器（BERT）模型，对2024年1月1日至7月17日期间来自主要美国平台的新闻文章综合数据集进行了分析。 我们提取了情绪得分，并应用了改进的广义自回归条件异方差（GARCH）模型，该模型结合了学生t分布以捕捉金融回报数据的重尾特性。 结果表明，负面新闻情绪与市场稳定性之间存在统计学上显著的负相关关系，表明悲观的战争报道与标普500指数的波动性增加有关。 这项研究展示了人工智能和自然语言处理如何与计量经济学模型相结合，以评估实时市场动态，在地缘政治危机期间为金融风险分析提供有价值的工具。 显示英文摘要

Artificial intelligence techniques have increasingly been applied to understand the complex relationship between public sentiment and financial market behaviour. This study explores the relationship between the sentiment of news related to the Russia-Ukraine war and the volatility of the stock market. A comprehensive dataset of news articles from major US platforms, published between January 1 and July 17, 2024, was analysed using a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model adapted for financial language. We extracted sentiment scores and applied a Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model, enhanced with a Student-t distribution to capture the heavy-tailed nature of financial returns data. The results reveal a statistically significant negative relationship between negative news sentiment and market stability, suggesting that pessimistic war coverage is associated with increased volatility in the S&P 500 index. This research demonstrates how artificial intelligence and natural language processing can be integrated with econometric modelling to assess real-time market dynamics, offering valuable tools for financial risk analysis during geopolitical crises.

[28] arXiv:2510.16526 [中文pdf, pdf, html, 其他]

标题： 高频方法在实现风险度量中的应用 显示英文标题

标题： A high-frequency approach to Realized Risk Measures

Federico Gatta, Fabrizio Lillo, Piero Mazzarisi

主题： 风险管理 (q-fin.RM)

我们提出了一种新的方法，称为实现风险度量（RRM），利用高频金融数据来估计风险价值（VaR）和预期损失（ES）。它扩展了Dimitriadis和Halbleib提出的实现分位数（RQ）方法，通过去除收益自相似性的假设，该假设在描述实证数据时表现出一些局限性。 更具体地说，与RQ类似，RRM方法使用一个子ordinator过程将日内收益率转换为内在时间，以捕捉交易活动和/或波动率聚集的非均匀性。 然后，使用合适的移动平均过程过滤掉导致非零自相关的微观结构效应。 最后，在清理后的日内收益率上拟合一个尾部较厚的分布。 然后通过特征函数方法或蒙特卡洛模拟将低频（每日）的收益率分布外推。 VaR和ES分别作为分布的分位数和尾部均值进行估计。 所提出的方法通过多个实验与RQ进行了基准比较。 大量的数值模拟和对18只美国股票的实证研究显示，我们的方法在样本内估计的风险度量和样本外风险预测方面都表现出优越性。 显示英文摘要

We propose a new approach, termed Realized Risk Measures (RRM), to estimate Value-at-Risk (VaR) and Expected Shortfall (ES) using high-frequency financial data. It extends the Realized Quantile (RQ) approach proposed by Dimitriadis and Halbleib by lifting the assumption of return self-similarity, which displays some limitations in describing empirical data. More specifically, as the RQ, the RRM method transforms intra-day returns in intrinsic time using a subordinator process, in order to capture the inhomogeneity of trading activity and/or volatility clustering. Then, microstructural effects resulting in non-zero autocorrelation are filtered out using a suitable moving average process. Finally, a fat-tailed distribution is fitted on the cleaned intra-day returns. The return distribution at low frequency (daily) is then extrapolated via either a characteristic function approach or Monte Carlo simulations. VaR and ES are estimated as the quantile and the tail mean of the distribution, respectively. The proposed approach is benchmarked against the RQ through several experiments. Extensive numerical simulations and an empirical study on 18 US stocks show the outperformance of our method, both in terms of the in-sample estimated risk measures and in the out-of-sample risk forecasting

[29] arXiv:2510.16537 [中文pdf, pdf, html, 其他]

标题： 玻利维亚危机模拟器（KISr-p）：一个实证基础的建模框架 显示英文标题

标题： The Crisis Simulator for Bolivia (KISr-p): An Empirically Grounded Modeling Framework

Ricardo Alonzo Fernández Salguero

主题： 一般经济学 (econ.GN)

本文件详细介绍了“玻利维亚危机模拟器（KISr-p）”，这是一个季度随机模型，旨在评估在高不确定性和结构性约束环境下的各种宏观经济政策策略的影响。 与标准的一般均衡框架不同，该模拟器基于大量元分析的综合实证结果，采用凯恩斯跨期综合（KIS）的理论架构，并结合常替代弹性（KIS-CES）生产函数。 每个模型模块——实际、财政、货币、外部、劳动力和分配——的校准均被详细描述，参数由财政乘数层级的定量证据（Gechert和Rannenberg，2018）、生产要素的互补性（Gechert等，2022）、劳动力市场中的买方垄断力量（Sokolova和S{\o }rensen，2021）以及汇率和利率传递动态来证明。 该模型整合这些实证规律，生成非线性动态，如状态依赖乘数、对冲击的不对称反应以及商业周期阶段的相互作用。 模拟结果突显了财政调整、外部融资、债务重组和结构性改革之间的权衡——例如激进的支出再分配和定向公共投资。 情景显示，优先考虑支出的\textit{组合}而不是其总体水平，并认识到制度摩擦的实际政策方法，相比教条的、一刀切的处方，能够产生更好的宏观经济和福利结果。 显示英文摘要

This document presents a detailed technical report of the ``Crisis Simulator for Bolivia (KISr-p),'' a quarterly stochastic model designed to evaluate the impact of various macroeconomic policy strategies in an environment of high uncertainty and structural constraints. Unlike standard general equilibrium frameworks, this simulator is grounded in the consolidated empirical findings of a vast collection of meta-analyses, adopting the theoretical architecture of a Keynesian Intertemporal Synthesis (KIS) with a Constant Elasticity of Substitution (KIS-CES) production function. The calibration of each model block -- real, fiscal, monetary, external, labor, and distributional -- is described in detail, with parameters justified by quantitative evidence on the hierarchy of fiscal multipliers (Gechert and Rannenberg, 2018), the complementarity of production factors (Gechert et al., 2022), monopsony power in the labor market (Sokolova and S{\o}rensen, 2021), and the dynamics of exchange rate and interest-rate pass-through. The model integrates these empirical regularities to generate non-linear dynamics such as state-dependent multipliers, asymmetric responses to shocks, and business-cycle phase interactions. Simulation results highlight the trade-offs between fiscal adjustment, external financing, debt restructuring, and structural reforms -- such as aggressive spending reallocation and targeted public investment. Scenarios show that pragmatic policy approaches that prioritize the \textit{composition} of spending over its aggregate level and that recognize institutional frictions yield superior macroeconomic and welfare outcomes compared to doctrinaire, one-size-fits-all prescriptions.

[30] arXiv:2510.16626 [中文pdf, pdf, html, 其他]

标题： 评估公共部门工资差距：法国公共部门与私营部门工资的比较 显示英文标题

标题： Evaluating the Public Pay Gap: A Comparison of Public and Private Sector Wages in France

Riddhi Kalsi

主题： 一般经济学 (econ.GN)

本文解决了公共-私营工资文献中的经验谜题：为什么使用相似数据的研究对工资溢价和惩罚得出矛盾的结论。 利用丰富的法国行政面板数据（2012-2019），本研究有两个主要贡献：首先，它提出了一组新的、直观但此前未记录的关于工资动态、行业流动性和行业间性别差异的典型事实。 结果表明，适度的时薪差距掩盖了终身收入和就业稳定性的显著差异。 特别是女性在公共部门获得显著的终身收入优势，这得益于更高的留存率、更高报酬的兼职工作以及与私营部门相比更公平的年度工时，而在私营部门，性别差距仍然更大，尤其是对于受过高等教育的人。 相反，受过高等教育的男性在公共就业中由于僵化的工资结构而面临终身惩罚。 通过使用期望最大化算法灵活地对行业转换、就业进出以及收入异质性进行建模，本研究显示，溢价和惩罚系统地依赖于性别、教育和劳动力市场经验。 分析表明，工资动态中仍存在显著的未观测异质性。 这些发现通过提供关于行业差异、按性别划分的转换、兼职工作和工资的全面描述性说明，统一了现有的叙述。 显示英文摘要

This paper resolves the empirical puzzle in the public-private wage literature: why studies using similar data reach contradictory conclusions about wage premiums and penalties. Utilizing rich French administrative panel data (2012-2019), this study has two main contributions: first, it presents a set of new, intuitive yet previously undocumented stylized facts about wage dynamics, sectoral mobility, and gender differences across sectors. The results reveal that the modest hourly wage gaps conceal substantial disparities in lifetime earnings and employment stability. Women, in particular, gain a significant lifetime earnings advantage in the public sector, driven by higher retention, better-compensated part-time work, and more equitable annual hours compared to the private sector, where gender gaps remain larger, especially for those with higher education. In contrast, highly educated men experience a lifetime penalty in public employment due to rigid wage structures. By flexibly modeling sectoral transitions, transitions into and out of employment, and earnings heterogeneity using an Expectation-Maximization algorithm, this study shows that both premiums and penalties depend systematically on gender, education, and labor market experience. The analysis reveals that significant unobserved heterogeneity remains in wage dynamics. These findings unify prevailing narratives by providing a comprehensive, descriptive account of sectoral differences in transitions, part-time work and wages by gender.

[31] arXiv:2510.16636 [中文pdf, pdf, html, 其他]

标题： 一种三步机器学习方法，利用金融新闻预测市场泡沫 显示英文标题

标题： A three-step machine learning approach to predict market bubbles with financial news

Abraham Atsiwo

主题： 统计金融 (q-fin.ST) ; 机器学习 (cs.LG) ; 计算金融 (q-fin.CP)

本研究提出了一种三步机器学习框架，通过结合金融新闻情感与宏观经济指标来预测标普500股票市场中的泡沫。 在传统计量经济学方法的基础上，所提出的方法通过整合文本和定量数据源来预测泡沫形成。 在第一步中，使用右尾单位根检验识别标普500指数中的泡沫期，这是一种广泛认可的实时泡沫检测方法。 第二步利用自然语言处理（NLP）技术从大规模金融新闻文章中提取情感特征，这些特征捕捉了投资者的预期和行为模式。 在最后一步中，应用集成学习方法，基于高情感相关和宏观经济预测因子来预测泡沫的发生。 模型性能通过k折交叉验证进行评估，并与基准机器学习算法进行比较。 实证结果表明，所提出的三步集成方法显著提高了预测准确性和稳健性，为投资者、监管机构和政策制定者提供了有价值的早期预警信息，以缓解系统性金融风险。 显示英文摘要

This study presents a three-step machine learning framework to predict bubbles in the S&P 500 stock market by combining financial news sentiment with macroeconomic indicators. Building on traditional econometric approaches, the proposed approach predicts bubble formation by integrating textual and quantitative data sources. In the first step, bubble periods in the S&P 500 index are identified using a right-tailed unit root test, a widely recognized real-time bubble detection method. The second step extracts sentiment features from large-scale financial news articles using natural language processing (NLP) techniques, which capture investors' expectations and behavioral patterns. In the final step, ensemble learning methods are applied to predict bubble occurrences based on high sentiment-based and macroeconomic predictors. Model performance is evaluated through k-fold cross-validation and compared against benchmark machine learning algorithms. Empirical results indicate that the proposed three-step ensemble approach significantly improves predictive accuracy and robustness, providing valuable early warning insights for investors, regulators, and policymakers in mitigating systemic financial risks.

[32] arXiv:2510.16938 [中文pdf, pdf, html, 其他]

标题： 一种拓扑方法用于参数化深度对冲网络 显示英文标题

标题： A Topological Approach to Parameterizing Deep Hedging Networks

Alok Das, Kiseop Lee

主题： 数学金融 (q-fin.MF) ; 机器学习 (cs.LG)

深度对冲使用循环神经网络来对冲不完全市场中无法完全对冲的金融产品。 此领域之前的研究所关注的是通过计算路径梯度来最小化某种二次对冲误差的度量，但这样做需要较大的批量大小，并且在合理的时间内训练有效模型具有挑战性。 我们表明，通过添加某些拓扑特征，我们可以显著减少批量大小，并在不大大损害对冲性能的情况下使这些模型的训练更加实际可行。 显示英文摘要

Deep hedging uses recurrent neural networks to hedge financial products that cannot be fully hedged in incomplete markets. Previous work in this area focuses on minimizing some measure of quadratic hedging error by calculating pathwise gradients, but doing so requires large batch sizes and can make training effective models in a reasonable amount of time challenging. We show that by adding certain topological features, we can reduce batch sizes substantially and make training these models more practically feasible without greatly compromising hedging performance.

[33] arXiv:2510.17121 [中文pdf, pdf, 其他]

标题： 新的需求经济学 显示英文标题

标题： New Demand Economics

Fenghua Wen, Xieyu Yin, Chufu Wen

评论： 33页，3图

主题： 一般经济学 (econ.GN)

我们发展了一种在物质丰裕时代的消费需求经济学理论。 增长的约束从总需求不足转变为需求层级升级不足。 我们的结果是，新的增长引擎在于升级需求层次：更高层级的需求产生更大的价值创造乘数。 关键机制是教育驱动的效用管理。 教育改变社会效用函数，提高高层级商品的效用，并将资源引导至更高价值的领域；这需要政策重新定位，从短期总刺激转向以教育为中心、长期的人力资本投资。 方法论上，我们构建了一个可估计的一般均衡框架。 显示英文摘要

We develop a theory of demand economics for an era of material abundance. The binding constraint on growth has shifted from insufficient aggregate demand to inadequate demand-tier upgrading. Our result is that, the new engine of growth lies in upgrading the demand hierarchy: higher-tier demands generate larger value-creation multipliers. The key mechanism is education-driven utility management. Education transforms the social utility function, raises the utility of higher-tier goods, and directs resources toward higher-value domains; this warrants a policy reorientation away from short-run aggregate stimulus toward education-centered, long-horizon investments in human capital. Methodologically, we build an estimable general-equilibrium framework.

[34] arXiv:2510.17221 [中文pdf, pdf, html, 其他]

标题： 多区域CoCoCat债券的设计与估值 显示英文标题

标题： Design and valuation of multi-region CoCoCat bonds

Jacek Wszoła, Krzysztof Burnecki, Marek Teuerle, Martyna Zdeb

主题： 证券定价 (q-fin.PR)

本文介绍了一种新型的多维保险关联工具：一种或有可转换债券（CoCoCat债券），其转换触发机制由多个地理区域的预定自然灾害激活。 我们开发了这种模型，明确考虑了区域灾害损失之间的复杂依赖关系。 具体而言，我们探讨了从完全独立到比例损失依赖的各种情景，包括固定和随机损失金额。 利用测度变换技术，我们推导出了针对这些不同依赖结构的风险中性定价公式。 通过将我们的模型拟合到财产索赔服务的真实自然灾害数据，我们展示了区域间依赖性对CoCoCat债券定价的重要影响，突出了对这一创新金融工具进行多维风险评估的重要性。 显示英文摘要

This paper introduces a novel multidimensional insurance-linked instrument: a contingent convertible bond (CoCoCat bond) whose conversion trigger is activated by predefined natural catastrophes across multiple geographical regions. We develop such a model explicitly accounting for the complex dependencies between regional catastrophe losses. Specifically, we explore scenarios ranging from complete independence to proportional loss dependencies, both with fixed and random loss amounts. Utilizing change-of-measure techniques, we derive risk-neutral pricing formulas tailored to these diverse dependence structures. By fitting our model to real-world natural catastrophe data from Property Claim Services, we demonstrate the significant impact of inter-regional dependencies on the CoCoCat bond's pricing, highlighting the importance of multidimensional risk assessment for this innovative financial instrument.

[35] arXiv:2510.17393 [中文pdf, pdf, html, 其他]

标题： 3S-Trader：用于投资组合优化中自适应股票评分、策略和选择的多LLM框架 显示英文标题

标题： 3S-Trader: A Multi-LLM Framework for Adaptive Stock Scoring, Strategy, and Selection in Portfolio Optimization

Kefan Chen, Hussain Ahmad, Diksha Goel, Claudia Szabo

主题： 投资组合管理 (q-fin.PM)

大型语言模型（LLMs）最近在股票交易中受到关注，因为它们能够处理多模态的金融数据。 然而，大多数现有方法专注于单只股票交易，并缺乏在多个候选股票上进行推理的能力，以构建投资组合。 此外，它们通常缺乏根据市场变化调整策略的灵活性，限制了其在现实交易中的适应性。 为了解决这些挑战，我们提出了3S-Trader，这是一种无需训练的框架，结合了评分、策略和选择模块用于股票投资组合构建。 评分模块将每只股票的近期信号汇总成一份涵盖多个评分维度的简明报告，实现了对候选股票的高效比较。 策略模块分析历史策略和整体市场状况，迭代生成优化的选择策略。 基于此策略，选择模块通过选择相关维度中得分较高的股票来识别和组装投资组合。 我们在四个不同的股票集合上评估了我们的框架，包括道琼斯工业平均指数（DJIA）成分股和三个特定行业的股票集合。 与现有的多LLM框架和基于时间序列的基线方法相比，3S-Trader在DJIA成分股上实现了131.83%的最高累计收益，夏普比率0.31，卡玛比率11.84，同时在其他行业也持续表现出色。 显示英文摘要

Large Language Models (LLMs) have recently gained popularity in stock trading for their ability to process multimodal financial data. However, most existing methods focus on single-stock trading and lack the capacity to reason over multiple candidates for portfolio construction. Moreover, they typically lack the flexibility to revise their strategies in response to market shifts, limiting their adaptability in real-world trading. To address these challenges, we propose 3S-Trader, a training-free framework that incorporates scoring, strategy, and selection modules for stock portfolio construction. The scoring module summarizes each stock's recent signals into a concise report covering multiple scoring dimensions, enabling efficient comparison across candidates. The strategy module analyzes historical strategies and overall market conditions to iteratively generate an optimized selection strategy. Based on this strategy, the selection module identifies and assembles a portfolio by choosing stocks with higher scores in relevant dimensions. We evaluate our framework across four distinct stock universes, including the Dow Jones Industrial Average (DJIA) constituents and three sector-specific stock sets. Compared with existing multi-LLM frameworks and time-series-based baselines, 3S-Trader achieves the highest accumulated return of 131.83% on DJIA constituents with a Sharpe ratio of 0.31 and Calmar ratio of 11.84, while also delivering consistently strong results across other sectors.

[36] arXiv:2510.17481 [中文pdf, pdf, 其他]

标题： 普遍化与财政能力的起源 显示英文标题

标题： Universalization and the Origins of Fiscal Capacity

Esteban Muñoz-Sobrado

主题： 一般经济学 (econ.GN)

本文提出了一种基于普遍化推理的税收合规性和财政能力模型。 公民通过想象一个所有人都以类似方式行动的世界，部分内化隐瞒的后果，将其合规决策与公共支出的感知效果联系起来。 一个自私的精英阶层在公共产品和私人收益之间进行选择，将合规性视为既定条件。 在均衡状态下，公民的道德内化扩大了可行的税收基础，并促使精英将资源分配转向提供而非攫取。 当公共支出的价值不确定时，道德能够实现可信的改革：高价值的精英可以通过提供来表明自己的类型，促使公民提高合规性，并在同期内提高财政能力。 因此，分析确定了一条道德渠道，使国家即使在弱制度下也可能摆脱低能力陷阱。 显示英文摘要

This paper proposes a model of tax compliance and fiscal capacity grounded in universalization reasoning. Citizens partially internalize the consequences of concealment by imagining a world in which everyone acted similarly, linking their compliance decisions to the perceived effectiveness of public spending. A selfish elite chooses between public goods and private rents, taking compliance as given. In equilibrium, citizens' moral internalization expands the feasible tax base and induces elites to allocate resources toward provision rather than appropriation. When the value of public spending is uncertain, morality enables credible reform: high-value elites can signal their type through provision, prompting citizens to increase compliance and raising fiscal capacity within the same period. The analysis thus identifies a moral channel through which states may escape low-capacity traps even under weak institutions.

[37] arXiv:2510.17641 [中文pdf, pdf, html, 其他]

标题： 点球大战是否比抛硬币更好？ 来自欧洲足球的证据 显示英文标题

标题： Are penalty shootouts better than a coin toss? Evidence from European football

László Csató, Dóra Gréta Petróczy

评论： 16页，5图，4表

主题： 一般经济学 (econ.GN) ; 物理与社会 (physics.soc-ph) ; 应用 (stat.AP)

点球大战在重大足球锦标赛的淘汰赛阶段起着重要作用，尤其是在2021/22赛季之后，欧洲足球协会联盟（UEFA）在其俱乐部比赛中取消了客场进球规则。 受这一规则变化的启发，我们的论文研究了是否可以预测UEFA俱乐部比赛中的点球大战结果。 基于2000年至2025年所有的点球大战，我们没有发现踢球顺序、比赛场地和心理动量的影响证据。 与之前的结果相反，由Elo排名定义的强队并不比其较弱的对手表现更好。 因此，点球大战在顶级欧洲足球中等同于一个完美的彩票。 显示英文摘要

Penalty shootouts play an important role in the knockout stage of major football tournaments, especially since the 2021/22 season, when the Union of European Football Associations (UEFA) scrapped the away goals rule in its club competitions. Inspired by this rule change, our paper examines whether the outcome of a penalty shootout can be predicted in UEFA club competitions. Based on all shootouts between 2000 and 2025, we find no evidence for the effect of the kicking order, the field of the match, and psychological momentum. In contrast to previous results, stronger teams, defined first by Elo ratings, do not perform better than their weaker opponents. Consequently, penalty shootouts are equivalent to a perfect lottery in top European football.

交叉提交 (展示 4 之 4 条目 )

[38] arXiv:2510.16003 (交叉列表自 econ.TH) [中文pdf, pdf, html, 其他]

标题： 重新思考阿罗-德布鲁：交换、时间与不确定性的新框架 显示英文标题

标题： Rethinking Arrow--Debreu: A New Framework for Exchange, Time, and Uncertainty

Nizar Riane

主题： 理论经济学 (econ.TH) ; 一般经济学 (econ.GN)

本文通过有效交易的视角重新审视了Arrow-Debreu一般均衡框架，强调理论市场互动与可实现市场互动之间的区别。 我们提出了有效交易模型（ETM），其中交易源于双边可行性，而非总体供给和需求意愿。 在该框架内，我们建立了价格-需求对应关系的主要性质，并证明了纳什均衡的存在性，同时纳入了生产、货币和网络拓扑结构。 分析扩展到时间、不确定性和开放经济，揭示了可贷资金和汇率如何内生出现。 我们的结果表明，均衡是由交易约束、主观定价和分散谈判塑造的，而不是由普遍的市场出清条件决定的，从而对福利理论的基础提出了质疑。 预期通过条件模式进行建模，捕捉到了有限理性与信息限制，这与理性预期假说形成对比。 因此，ETM为经典一般均衡提供了一个行为和结构上有依据的替代方案，在统一框架内连接了微观基础、货币动态和时间一致性。 显示英文摘要

This paper revisits the Arrow-Debreu general equilibrium framework through the lens of effective trade, emphasizing the distinction between theoretical and realizable market interactions. We develop the Effective Trade Model (ETM), where transactions arise from bilateral feasibility rather than aggregate supply and demand desires. Within this framework, we establish the main properties of the price-demand correspondence and prove the existence of Nash equilibria, incorporating production, money, and network topology. The analysis extends to time, uncertainty, and open economies, revealing how loanable funds and exchange rates emerge endogenously. Our results show that equilibrium is shaped by transaction constraints, subjective pricing, and decentralized negotiation, rather than by universal market-clearing conditions, and thereby call into question the foundations of welfare theory. Anticipation is modeled via the conditional mode, capturing bounded rationality and information limitations in contrast to the rational expectations hypothesis. The ETM thus offers a behaviorally and structurally grounded alternative to classical general equilibrium, bridging microfoundations, monetary dynamics, and temporal consistency within a unified framework.

[39] arXiv:2510.16021 (交叉列表自 cs.LG) [中文pdf, pdf, html, 其他]

标题： 基于特征的强化学习在连续日内交易中的光伏应用 显示英文标题

标题： Feature-driven reinforcement learning for photovoltaic in continuous intraday trading

Arega Getaneh Abate, Xiufeng Liu, Ruyu Liu, Xiaobing Zhang

主题： 机器学习 (cs.LG) ; 一般经济学 (econ.GN)

光伏（PV）运营商在发电和短期电价方面面临重大不确定性。 连续日内市场使生产商能够实时调整其头寸，可能提高收益并减少不平衡成本。 我们提出了一种基于特征的强化学习（RL）方法，用于光伏日内交易，该方法将数据驱动的特征整合到状态中，并在顺序决策框架中学习投标策略。 该问题被建模为一个马尔可夫决策过程，其奖励平衡了交易利润和不平衡惩罚，并使用主要线性且可解释的策略通过近端策略优化（PPO）进行求解。 该策略基于历史市场数据进行训练，并在样本外进行评估，在各种场景下始终优于基准基线。 广泛的验证表明快速收敛、实时推理和透明的决策规则。 学习的权重突显了市场微观结构和历史特征的核心作用。 综合来看，这些结果表明，基于特征的强化学习为光伏生产商主动参与日内市场提供了一种实用、数据高效且可操作部署的路径。 显示英文摘要

Photovoltaic (PV) operators face substantial uncertainty in generation and short-term electricity prices. Continuous intraday markets enable producers to adjust their positions in real time, potentially improving revenues and reducing imbalance costs. We propose a feature-driven reinforcement learning (RL) approach for PV intraday trading that integrates data-driven features into the state and learns bidding policies in a sequential decision framework. The problem is cast as a Markov Decision Process with a reward that balances trading profit and imbalance penalties and is solved with Proximal Policy Optimization (PPO) using a predominantly linear, interpretable policy. Trained on historical market data and evaluated out-of-sample, the strategy consistently outperforms benchmark baselines across diverse scenarios. Extensive validation shows rapid convergence, real-time inference, and transparent decision rules. Learned weights highlight the central role of market microstructure and historical features. Taken together, these results indicate that feature-driven RL offers a practical, data-efficient, and operationally deployable pathway for active intraday participation by PV producers.

[40] arXiv:2510.17165 (交叉列表自 cs.CE) [中文pdf, pdf, html, 其他]

标题： 与魔鬼交易：基础模型策略中的风险与回报 显示英文标题

标题： Trading with the Devil: Risk and Return in Foundation Model Strategies

Jinrui Zhang

主题： 计算工程、金融与科学 (cs.CE) ; 交易与市场微观结构 (q-fin.TR)

基础模型 - 在自然语言处理等领域已经具有变革性 - 现在开始在金融时间序列任务中出现。 虽然这些预训练架构承诺提供多功能的预测信号，但关于它们如何塑造建立在其上的交易策略的风险概况，了解甚少，导致从业者不愿投入大量资金。 在本文中，我们提出对资本资产定价模型（CAPM）的扩展，该扩展将由共享基础模型引入的系统性风险 - 如果底层模型真正具有预测能力，可能会产生超额收益 - 与由于定制微调产生的特异性风险区分开来，后者通常不会获得系统性溢价。 为了实现这些单独风险的实际估计，我们将这种分解与不确定性解耦的概念相一致，将系统性风险视为认识不确定性（源于预训练模型），将特异性风险视为随机不确定性（在定制适应过程中引入）。 在随机崩溃假设下，我们说明了蒙特卡洛丢弃 - 以及其他不确定性量化工具包中的方法 - 可以直接测量认识不确定性，从而将交易策略映射到更透明的风险-回报平面。 我们的实验表明，隔离这些不同的风险因素可以更深入地了解基于基础模型的策略的表现极限、随时间推移的模型退化以及有针对性的改进途径。 综上所述，我们的结果突显了在竞争性金融市场上部署大型预训练模型的潜力和陷阱。 显示英文摘要

Foundation models - already transformative in domains such as natural language processing - are now starting to emerge for time-series tasks in finance. While these pretrained architectures promise versatile predictive signals, little is known about how they shape the risk profiles of the trading strategies built atop them, leaving practitioners reluctant to commit serious capital. In this paper, we propose an extension to the Capital Asset Pricing Model (CAPM) that disentangles the systematic risk introduced by a shared foundation model - potentially capable of generating alpha if the underlying model is genuinely predictive - from the idiosyncratic risk attributable to custom fine-tuning, which typically accrues no systematic premium. To enable a practical estimation of these separate risks, we align this decomposition with the concepts of uncertainty disentanglement, casting systematic risk as epistemic uncertainty (rooted in the pretrained model) and idiosyncratic risk as aleatory uncertainty (introduced during custom adaptations). Under the Aleatory Collapse Assumption, we illustrate how Monte Carlo dropout - among other methods in the uncertainty-quantization toolkit - can directly measure the epistemic risk, thereby mapping trading strategies to a more transparent risk-return plane. Our experiments show that isolating these distinct risk factors yields deeper insights into the performance limits of foundation-model-based strategies, their model degradation over time, and potential avenues for targeted refinements. Taken together, our results highlight both the promise and the pitfalls of deploying large pretrained models in competitive financial markets.

[41] arXiv:2510.17508 (交叉列表自 physics.flu-dyn) [中文pdf, pdf, html, 其他]

标题： 一种混合形式的PINNS（MF-PINNS）用于求解耦合的Stokes-Darcy方程 显示英文标题

标题： A Mixed-Form PINNS (MF-PINNS) For Solving The Coupled Stokes-Darcy Equations

Li Shan, Xi Shen

主题： 流体动力学 (physics.flu-dyn) ; 数学金融 (q-fin.MF)

并行物理信息神经网络（P-PINNs）已被广泛用于求解具有多个耦合物理场的系统，例如带有Beavers-Joseph-Saffman（BJS）界面条件的耦合Stokes-Darcy方程。 然而，偏微分方程（PDE）中过度高或低的物理常数通常会导致病态的损失函数，甚至可能导致PINNs训练数值解失败。 为了解决这个问题，本文开发了一种新型增强并行PINNs，即MF-PINNs。 我们的MF-PINNs将速度压力形式（VP）与流函数涡度形式（SV）相结合，并以调整后的权重添加到总损失函数中。 数值实验的结果表明，当运动粘度和渗透张量范围从1e-4到1e4时，我们的MF-PINNs成功提高了流线场和压力场的准确性。 因此，我们的MF-PINNs在涉及湍流的更复杂的PDE系统中具有前景。 此外，我们还探索了激活函数及其周期性的最佳组合。 我们还尝试设置初始学习率并设计其衰减策略。 与本文相关的代码和数据可在https://github.com/shxshx48716/MF-PINNs.git获取。 显示英文摘要

Parallel physical information neural networks (P-PINNs) have been widely used to solve systems with multiple coupled physical fields, such as the coupled Stokes-Darcy equations with Beavers-Joseph-Saffman (BJS) interface conditions. However, excessively high or low physical constants in partial differential equations (PDE) often lead to ill conditioned loss functions and can even cause the failure of training numerical solutions for PINNs. To solve this problem, we develop a new kind of enhanced parallel PINNs, MF-PINNs, in this article. Our MF-PINNs combines the velocity pressure form (VP) with the stream-vorticity form (SV) and add them with adjusted weights to the total loss functions. The results of numerical experiments show our MF-PINNs have successfully improved the accuracy of the streamline fields and the pressure fields when kinematic viscosity and permeability tensor range from 1e-4 to 1e4. Thus, our MF-PINNs hold promise for more chaotic PDE systems involving turbulent flows. Additionally, we also explore the best combination of the activation functions and their periodicity. And we also try to set the initial learning rate and design its decay strategies. The code and data associated with this paper are available at https://github.com/shxshx48716/MF-PINNs.git.

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[42] arXiv:1811.09312 (替换) [中文pdf, pdf, html, 其他]

标题： 基于高频数据的Ornstein-Uhlenbeck过程估计及其在日内配对交易策略中的应用 显示英文标题

标题： Estimation of Ornstein-Uhlenbeck Process Using Ultra-High-Frequency Data with Application to Intraday Pairs Trading Strategy

Vladimír Holý, Petra Tomanová

主题： 统计金融 (q-fin.ST)

当股票价格以高频观察时，可以利用更多信息来估计价格过程的参数。 然而，高频数据会受到市场微观结构噪声的污染，如果不加以考虑，会导致参数估计出现显著偏差。 我们提出了一种基于最大似然的Ornstein-Uhlenbeck过程估计量，该估计量对噪声具有鲁棒性，并能利用不规则间隔的数据。 我们还证明，受独立高斯白噪声污染的Ornstein-Uhlenbeck过程在离散等距时间点上观测时，遵循ARMA(1,1)过程。 为了说明所提出的抗噪声方法的优势，我们引入了一种基于均值-方差优化的新日内配对交易策略。 在对7家大型石油公司的实证研究中，我们表明使用所提出的Ornstein-Uhlenbeck过程估计量可以提高配对交易策略的盈利能力。 显示英文摘要

When stock prices are observed at high frequencies, more information can be utilized in estimation of parameters of the price process. However, high-frequency data are contaminated by the market microstructure noise which causes significant bias in parameter estimation when not taken into account. We propose an estimator of the Ornstein-Uhlenbeck process based on the maximum likelihood which is robust to the noise and utilizes irregularly spaced data. We also show that the Ornstein-Uhlenbeck process contaminated by the independent Gaussian white noise and observed at discrete equidistant times follows an ARMA(1,1) process. To illustrate benefits of the proposed noise-robust approach, we introduce a novel intraday pairs trading strategy based on the mean-variance optimization. In an empirical study of 7 Big Oil companies, we show that the use of the proposed estimator of the Ornstein-Uhlenbeck process leads to an increase in profitability of the pairs trading strategy.

[43] arXiv:2504.09854 (替换) [中文pdf, pdf, html, 其他]

标题： 决定电动汽车购买意向的要素在不同范围内是否有所不同？ 来自美国调查数据贝叶斯分析的证据 显示英文标题

标题： Do Determinants of EV Purchase Intent vary across the Spectrum? Evidence from Bayesian Analysis of US Survey Data

Nafisa Lohawala, Mohammad Arshad Rahman

评论： 33页，三幅图，五张表

主题： 一般经济学 (econ.GN) ; 应用 (stat.AP)

虽然电动汽车（EV）的采用已被广泛研究，但大多数研究集中在预测因子对购买意愿的平均影响，而忽略了购买意愿分布中的变化。 本文通过分析四个独特的解释变量，利用2021至2023年美国大规模调查数据，并采用贝叶斯序数probit模型和贝叶斯序数分位数建模来评估这些变量对EV购买意愿的影响——同时控制其他常用协变量——既在平均水平上，也在其整个分布上。 通过将购买意愿建模为一个有序结果——从“完全不可能”到“非常可能”——我们揭示了协变量效应在不同兴趣水平上的差异。 这是在EV采用文献中首次应用序数分位数建模，揭示了潜在买家对关键因素的反应存在异质性。 例如，对充电基础设施发展的信心和对环境效益的信念不仅与可能采用者更高的兴趣相关，还与更怀疑的受访者减少的阻力相关。 值得注意的是，我们发现关键预测因子的普遍性和影响力之间存在差距：尽管只有少数受访者报告对基础设施有很强的信心或经常接触EV信息，但这两个因素都与整个范围内的意图增加密切相关。 这些发现表明，除了基础设施投资外，有针对性的沟通和宣传提供了明确的机会，以支持广泛的EV采用。 显示英文摘要

While electric vehicle (EV) adoption has been widely studied, most research focuses on the average effects of predictors on purchase intent, overlooking variation across the distribution of EV purchase intent. This paper makes a threefold contribution by analyzing four unique explanatory variables, leveraging large-scale US survey data from 2021 to 2023, and employing Bayesian ordinal probit and Bayesian ordinal quantile modeling to evaluate the effects of these variables-while controlling for other commonly used covariates-on EV purchase intent, both on average and across its full distribution. By modeling purchase intent as an ordered outcome-from "not at all likely" to "very likely"-we reveal how covariate effects differ across levels of interest. This is the first application of ordinal quantile modeling in the EV adoption literature, uncovering heterogeneity in how potential buyers respond to key factors. For instance, confidence in development of charging infrastructure and belief in environmental benefits are linked not only to higher interest among likely adopters but also to reduced resistance among more skeptical respondents. Notably, we identify a gap between the prevalence and influence of key predictors: although few respondents report strong infrastructure confidence or frequent EV information exposure, both factors are strongly associated with increased intent across the spectrum. These findings suggest clear opportunities for targeted communication and outreach, alongside infrastructure investment, to support widespread EV adoption.

[44] arXiv:2504.14345 (替换) [中文pdf, pdf, html, 其他]

标题： 基于大语言模型的黑-莉特尔曼投资组合优化 显示英文标题

标题： LLM-Enhanced Black-Litterman Portfolio Optimization

Youngbin Lee, Yejin Kim, Juhyeong Kim, Suin Kim, Yongjae Lee

评论： 在CIKM 2025金融AI研讨会发表（https://advancesinfinancialai.com/）

主题： 投资组合管理 (q-fin.PM) ; 人工智能 (cs.AI)

黑-利特尔曼模型通过纳入投资者观点解决了传统均值-方差优化的敏感性问题，但系统地生成这些观点仍然是一个关键挑战。 本研究提出并验证了一个系统框架，将大型语言模型（LLMs）的收益预测和预测不确定性转化为黑-利特尔曼模型的核心输入：投资者观点及其置信水平。 通过对标普500成分股的回测，我们证明由表现最佳的LLMs驱动的投资组合在绝对收益和风险调整收益方面均显著优于传统基准。 至关重要的是，我们的分析表明，每个LLMs表现出一种独特且一致的投资风格，这是性能的主要驱动因素。 我们发现，选择一个LLM并不是寻找一个最佳预测者，而是战略性地选择一种投资风格，其成功取决于与当前市场制度的契合度。 源代码和数据可在 https://github.com/youngandbin/LLM-BLM 获取。 显示英文摘要

The Black-Litterman model addresses the sensitivity issues of tra- ditional mean-variance optimization by incorporating investor views, but systematically generating these views remains a key challenge. This study proposes and validates a systematic frame- work that translates return forecasts and predictive uncertainty from Large Language Models (LLMs) into the core inputs for the Black-Litterman model: investor views and their confidence lev- els. Through a backtest on S&P 500 constituents, we demonstrate that portfolios driven by top-performing LLMs significantly out- perform traditional baselines in both absolute and risk-adjusted terms. Crucially, our analysis reveals that each LLM exhibits a dis- tinct and consistent investment style which is the primary driver of performance. We found that the selection of an LLM is therefore not a search for a single best forecaster, but a strategic choice of an investment style whose success is contingent on its alignment with the prevailing market regime. The source code and data are available at https://github.com/youngandbin/LLM-BLM.

[45] arXiv:2506.20631 (替换) [中文pdf, pdf, html, 其他]

标题： 基于人工智能的集成电力交通、可再生能源和电网管理的运营数字平台的成本效益分析 显示英文标题

标题： Cost-benefit analysis of an AI-driven operational digital platform for integrated electric mobility, renewable energy, and grid management

Arega Getaneh Abate, Xiaobing Zhang, Xiufeng Liu, Dogan Keles

主题： 一般经济学 (econ.GN)

将电动汽车（EVs）、电动卡车（ETs）和可再生能源（RES）与电网整合对于减少碳排放、提高效率和稳定性至关重要。 仍有两个缺口：需要能够协调跨行业和跨境运营的数字平台，以及缺乏对其成本效益概况的严格框架。 本文提出了一种全面的成本效益分析（CBA），针对一个由人工智能驱动的操作数字平台（ODP），该平台旨在实现整体的跨行业和跨境优化。 ODP的目标是提高能源效率、电网可靠性和环境可持续性。 我们开发了一个七步CBA框架，将每个收益流与平台架构联系起来，并量化相对于现有系统的增量收益，同时考虑投资和运营成本。 该框架通过奥地利、匈牙利和斯洛文尼亚的案例研究进行了演示。 结果表明，2026年至2035年间的收益成本比率（BCR）约为1.41，净现值（NPV）超过3.56亿欧元。 通过广泛的敏感性分析检查了稳健性，这些分析改变了贴现率、成本组成部分和采用轨迹，以及通过蒙特卡洛模拟捕捉了BCR、NPV、数据可用性和AI准确性中的不确定性。 研究结果支持在跨行业和跨境整合中投资数字平台的可行性，并突出了ODP在推进交通-电力交汇处的脱碳和效率方面的关键作用。 显示英文摘要

Integrating electric mobility, such as electric vehicles (EVs), electric trucks (ETs), and renewable energy sources (RES) with the power grid is paramount for decarbonization, efficiency, and stability. Two gaps remain: the need for digital platforms that coordinate operations across sectors and borders, and the lack of a rigorous framework on their cost-benefit profile. This paper presents a comprehensive cost-benefit analysis (CBA) of an AI-driven operational digital platform (ODP) designed for holistic, cross-sectoral and cross-border optimization. The ODP targets improvements in energy efficiency, grid reliability, and environmental sustainability. We develop a seven-step CBA framework that links each benefit stream to the platform's architecture and quantifies incremental gains relative to the existing system, while accounting for investment and operating costs. The framework is demonstrated with case studies for Austria, Hungary, and Slovenia. Results indicate a benefit-cost ratio (BCR) of about 1.41 and a net present value (NPV) exceeding euro 356 million over 2026-2035. Robustness is examined through extensive sensitivity analyses that vary discount rates, cost components, and adoption trajectories, as well as through Monte Carlo simulations that capture uncertainty in BCR, NPV, data availability, and AI accuracy. The findings support the viability of investing in digital platforms for cross-sectoral and cross-border integration, and highlight the role of ODPs in advancing decarbonization and efficiency in the mobility-power nexus.

[46] arXiv:2507.18229 (替换) [中文pdf, pdf, html, 其他]

标题： 从个体学习到市场均衡：校正经济模型强化学习模拟中的结构和参数偏差 显示英文标题

标题： From Individual Learning to Market Equilibrium: Correcting Structural and Parametric Biases in RL Simulations of Economic Models

Ruxin Chen, Zeqiang Zhang

主题： 一般经济学 (econ.GN) ; 人工智能 (cs.AI)

强化学习（RL）在经济建模中的应用揭示了均衡理论假设与学习代理产生的行为之间的根本冲突。 尽管经典的经济模型假设原子化代理作为“市场条件的接受者”行动，但一个简单的单智能体RL模拟会激励代理成为其环境的“操控者”。 本文首先在一个具有凹生产函数的搜索与匹配模型中展示了这一差异，表明标准的RL代理学习到了非均衡的、买方垄断的策略。 此外，我们发现了一种参数偏差，这是由于经济贴现与RL对时间成本处理之间的不匹配引起的。 为了解决这两个问题，我们提出了一种校准后的平均场强化学习框架，该框架将代表性代理嵌入固定的宏观经济场中，并调整成本函数以反映经济机会成本。 我们的迭代算法收敛到一个自洽的固定点，其中代理的策略与竞争性均衡一致。 这种方法为在计算社会科学更广泛的领域内对经济系统中的学习代理进行建模提供了一种可行且理论上有根据的方法。 显示英文摘要

The application of Reinforcement Learning (RL) to economic modeling reveals a fundamental conflict between the assumptions of equilibrium theory and the emergent behavior of learning agents. While canonical economic models assume atomistic agents act as `takers' of aggregate market conditions, a naive single-agent RL simulation incentivizes the agent to become a `manipulator' of its environment. This paper first demonstrates this discrepancy within a search-and-matching model with concave production, showing that a standard RL agent learns a non-equilibrium, monopsonistic policy. Additionally, we identify a parametric bias arising from the mismatch between economic discounting and RL's treatment of intertemporal costs. To address both issues, we propose a calibrated Mean-Field Reinforcement Learning framework that embeds a representative agent in a fixed macroeconomic field and adjusts the cost function to reflect economic opportunity costs. Our iterative algorithm converges to a self-consistent fixed point where the agent's policy aligns with the competitive equilibrium. This approach provides a tractable and theoretically sound methodology for modeling learning agents in economic systems within the broader domain of computational social science.

[47] arXiv:2509.06510 (替换) [中文pdf, pdf, html, 其他]

标题： 流动性提供者在自动做市商中的最优退出时间 显示英文标题

标题： Optimal Exit Time for Liquidity Providers in Automated Market Makers

Philippe Bergault, Sébastien Bieber, Leandro Sánchez-Betancourt

主题： 交易与市场微观结构 (q-fin.TR) ; 数学金融 (q-fin.MF)

我们研究了代表性的流动性提供者（LP）在自动做市商（AMM）中最优流动性撤出问题。 LPs 从交易活动中获得费用，但由于价格波动而面临暂时性损失（IL）。 虽然现有研究集中在静态提供和外生退出策略上，但我们将其最优退出时间表征为具有内生停止时间的随机控制问题。 数学上，LP 的价值函数被证明满足一个哈密顿-雅可比-贝尔曼拟变分不等式，在粘性意义下我们建立了其唯一性。 为了数值求解该问题，我们开发了两种互补的方法：基于算子分裂的欧拉方案和 Longstaff-Schwartz 回归方法。 校准的模拟突显了 LP 的最优退出策略如何依赖于预言机价格波动率、费用水平以及套利者和噪音交易者的行为。 我们的结果表明，虽然套利会产生费用和 IL，但 LP 的最优决策会根据池状态变量和价格错配来平衡这些相反的影响。 最后，我们发现当代表性的 LP 采用我们推导出的退出策略时，其最优费用水平。 这项工作有助于更深入地理解 AMM 中的动态流动性提供，并提供了在不同市场制度下被动 LP 策略可持续性的见解。 显示英文摘要

We study the problem of optimal liquidity withdrawal for a representative liquidity provider (LP) in an automated market maker (AMM). LPs earn fees from trading activity but are exposed to impermanent loss (IL) due to price fluctuations. While existing work has focused on static provision and exogenous exit strategies, we characterise the optimal exit time as the solution to a stochastic control problem with an endogenous stopping time. Mathematically, the LP's value function is shown to satisfy a Hamilton-Jacobi-Bellman quasi-variational inequality, for which we establish uniqueness in the viscosity sense. To solve the problem numerically, we develop two complementary approaches: a Euler scheme based on operator splitting and a Longstaff-Schwartz regression method. Calibrated simulations highlight how the LP's optimal exit strategy depends on the oracle price volatility, fee levels, and the behaviour of arbitrageurs and noise traders. Our results show that while arbitrage generates both fees and IL, the LP's optimal decision balances these opposing effects based on the pool state variables and price misalignments. Lastly, we find the optimal fee level for the representative LP when they play the exit strategy we derived. This work contributes to a deeper understanding of dynamic liquidity provision in AMMs and provides insights into the sustainability of passive LP strategies under different market regimes.

[48] arXiv:2510.14108 (替换) [中文pdf, pdf, html, 其他]

标题： 时间次分数布朗运动过程在金融市场中的应用 显示英文标题

标题： On Time-subordinated Brownian Motion Processes for Financial Markets

Rohan Shenoy, Peter Kempthorne

主题： 数学金融 (q-fin.MF) ; 统计理论 (math.ST)

在时间次序布朗运动模型的背景下，提出了傅里叶理论和方法来建模时间增量的随机分布。 高斯方差-均值混合和时间次序模型被回顾，其中关键例子是方差-伽马过程。 提出了次序布朗运动的非参数特征函数分解。 该理论需要将某些特征函数的实数域扩展到复平面，其有效性在此得到证明。 这使得能够直接从整个过程中表征和研究随机时间变换。 提供了对S&P对数收益的实证分解以说明该方法。 显示英文摘要

In the context of time-subordinated Brownian motion models, Fourier theory and methodology are proposed to modelling the stochastic distribution of time increments. Gaussian Variance-Mean mixtures and time-subordinated models are reviewed with a key example being the Variance-Gamma process. A non-parametric characteristic function decomposition of subordinated Brownian motion is presented. The theory requires an extension of the real domain of certain characteristic functions to the complex plane, the validity of which is proven here. This allows one to characterise and study the stochastic time-change directly from the full process. An empirical decomposition of S\&P log-returns is provided to illustrate the methodology.

[49] arXiv:2410.23587 (替换) [中文pdf, pdf, html, 其他]

标题： 矩通过积分矩生成函数得到 显示英文标题

标题： Moments by Integrating the Moment-Generating Function

Peter Reinhard Hansen, Chen Tong

主题： 计量经济学 (econ.EM) ; 计算金融 (q-fin.CP) ; 计算 (stat.CO)

我们引入一种新方法，用于获得具有明确定义的矩生成函数（MGF）的任意随机变量的各种矩。 我们推导了分数矩和分数绝对矩的新表达式，包括中心矩和非中心矩。 这些表达式是相对简单的积分，涉及MGF，但不需要其导数。 我们将新方法称为CMGF，因为它使用MGF的复数扩展，并可用于获得复数矩。 我们通过三个应用来说明新方法，其中MGF以闭合形式给出，而相应的密度函数以及MGF的导数要么不可用，要么非常难以获得。 显示英文摘要

We introduce a novel method for obtaining a wide variety of moments of any random variable with a well-defined moment-generating function (MGF). We derive new expressions for fractional moments and fractional absolute moments, both central and non-central moments. The expressions are relatively simple integrals that involve the MGF, but do not require its derivatives. We label the new method CMGF because it uses a complex extension of the MGF and can be used to obtain complex moments. We illustrate the new method with three applications where the MGF is available in closed-form, while the corresponding densities and the derivatives of the MGF are either unavailable or very difficult to obtain.

[50] arXiv:2505.18297 (替换) [中文pdf, pdf, html, 其他]

标题： 一种用于倒向随机伏尔泰拉积分方程的深度求解器 显示英文标题

标题： A deep solver for backward stochastic Volterra integral equations

Kristoffer Andersson, Alessandro Gnoatto, Camilo Andrés García Trillos

评论： 25页，10图

主题： 数值分析 (math.NA) ; 机器学习 (cs.LG) ; 概率 (math.PR) ; 数学金融 (q-fin.MF)

我们提出了第一个用于倒向随机Volterra积分方程（BSVIEs）及其完全耦合的前后向变体的深度学习求解器。 该方法通过一个阶段训练神经网络来近似两个解场，避免了限制经典算法的嵌套时间步进循环。 对于解耦情况，我们证明了一个非渐近误差界，由一个后验残差加上对时间步长的常见平方根依赖组成。 数值实验与这一速率一致，并揭示了两个关键特性：\emph{可扩展性}，在此意义上，准确性从低维到500个空间变量保持稳定，同时GPU批处理使壁钟时间几乎保持不变；以及\emph{普遍性}，因为同一方法可以处理前向动力学依赖于后向解的耦合系统。 这些结果为随机控制和量化金融中的高维、时间不一致问题家族提供了实际的解决方案。 显示英文摘要

We present the first deep-learning solver for backward stochastic Volterra integral equations (BSVIEs) and their fully-coupled forward-backward variants. The method trains a neural network to approximate the two solution fields in a single stage, avoiding the use of nested time-stepping cycles that limit classical algorithms. For the decoupled case we prove a non-asymptotic error bound composed of an a posteriori residual plus the familiar square root dependence on the time step. Numerical experiments are consistent with this rate and reveal two key properties: \emph{scalability}, in the sense that accuracy remains stable from low dimension up to 500 spatial variables while GPU batching keeps wall-clock time nearly constant; and \emph{generality}, since the same method handles coupled systems whose forward dynamics depend on the backward solution. These results open practical access to a family of high-dimensional, time-inconsistent problems in stochastic control and quantitative finance.

[51] arXiv:2506.20930 (替换) [中文pdf, pdf, html, 其他]

标题： 台湾股市行业轮动的量子强化学习交易代理 显示英文标题

标题： Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market

Chi-Sheng Chen, Xinyu Zhang, Ya-Chuan Chen

主题： 量子物理 (quant-ph) ; 机器学习 (cs.LG) ; 计算金融 (q-fin.CP)

我们提出了一种混合量子-经典强化学习框架，用于台湾股市的行业轮动。 我们的系统采用近端策略优化（PPO）作为核心算法，并将经典架构（LSTM，Transformer）和量子增强模型（QNN，QRWKV，QASA）作为策略和价值网络进行整合。 一个自动化的特征工程流程从资本份额数据中提取财务指标，以确保所有配置下的模型输入一致。 实证回测揭示了一个关键发现：尽管量子增强模型在训练奖励上始终表现更好，但在实际投资指标如累计收益和夏普比率方面表现不如经典模型。 这种差异凸显了将强化学习应用于金融领域的一个核心挑战——即代理奖励信号与真实投资目标之间的不匹配。 我们的分析表明，当前的奖励设计可能鼓励对短期波动的过拟合，而不是优化风险调整后的收益。 这一问题在噪声中等规模量子（NISQ）约束下量子电路的固有表达能力和优化不稳定性的影响下变得更加严重。 我们讨论了这一奖励-性能差距的含义，并提出了未来改进的方向，包括奖励塑造、模型正则化和基于验证的早停策略。 我们的工作提供了一个可复现的基准和关于在现实金融中部署量子强化学习的实际挑战的关键见解。 显示英文摘要

We propose a hybrid quantum-classical reinforcement learning framework for sector rotation in the Taiwan stock market. Our system employs Proximal Policy Optimization (PPO) as the backbone algorithm and integrates both classical architectures (LSTM, Transformer) and quantum-enhanced models (QNN, QRWKV, QASA) as policy and value networks. An automated feature engineering pipeline extracts financial indicators from capital share data to ensure consistent model input across all configurations. Empirical backtesting reveals a key finding: although quantum-enhanced models consistently achieve higher training rewards, they underperform classical models in real-world investment metrics such as cumulative return and Sharpe ratio. This discrepancy highlights a core challenge in applying reinforcement learning to financial domains -- namely, the mismatch between proxy reward signals and true investment objectives. Our analysis suggests that current reward designs may incentivize overfitting to short-term volatility rather than optimizing risk-adjusted returns. This issue is compounded by the inherent expressiveness and optimization instability of quantum circuits under Noisy Intermediate-Scale Quantum (NISQ) constraints. We discuss the implications of this reward-performance gap and propose directions for future improvement, including reward shaping, model regularization, and validation-based early stopping. Our work offers a reproducible benchmark and critical insights into the practical challenges of deploying quantum reinforcement learning in real-world finance.

[52] arXiv:2507.07935 (替换) [中文pdf, pdf, html, 其他]

标题： 与人工智能合作：测量生成式人工智能在职业中的适用性 显示英文标题

标题： Working with AI: Measuring the Applicability of Generative AI to Occupations

Kiran Tomlinson, Sonia Jaffe, Will Wang, Scott Counts, Siddharth Suri

评论： 42页

主题： 人工智能 (cs.AI) ; 计算机与社会 (cs.CY) ; 一般经济学 (econ.GN)

鉴于生成式AI的迅速采用及其对广泛任务的潜在影响，了解AI对经济的影响是社会最重要的问题之一。 在本研究中，我们通过分析人们使用AI进行的工作活动，这些活动的成功程度和广泛性，并结合从事这些活动的职业数据，向这一目标迈进了一步。 我们分析了20万条用户与微软Bing Copilot（一个公开可用的生成式AI系统）之间的匿名化和隐私清理后的对话数据集。 我们发现，人们寻求AI帮助的最常见工作活动涉及信息收集和写作，而AI本身执行的最常见活动包括提供信息和协助、写作、教学和建议。 将这些活动分类与任务成功度和影响范围的测量结果相结合，我们为每个职业计算了一个AI适用性评分。 我们发现，知识型职业群体如计算机和数学类，以及办公室和行政支持类职业，以及销售类职业（其工作活动涉及提供和沟通信息）的AI适用性评分最高。 此外，我们描述了最成功执行的工作活动类型，工资和教育水平与AI适用性的关系，以及现实世界使用情况与职业AI影响预测的比较。 显示英文摘要

Given the rapid adoption of generative AI and its potential to impact a wide range of tasks, understanding the effects of AI on the economy is one of society's most important questions. In this work, we take a step toward that goal by analyzing the work activities people do with AI, how successfully and broadly those activities are done, and combine that with data on what occupations do those activities. We analyze a dataset of 200k anonymized and privacy-scrubbed conversations between users and Microsoft Bing Copilot, a publicly available generative AI system. We find the most common work activities people seek AI assistance for involve gathering information and writing, while the most common activities that AI itself is performing are providing information and assistance, writing, teaching, and advising. Combining these activity classifications with measurements of task success and scope of impact, we compute an AI applicability score for each occupation. We find the highest AI applicability scores for knowledge work occupation groups such as computer and mathematical, and office and administrative support, as well as occupations such as sales whose work activities involve providing and communicating information. Additionally, we characterize the types of work activities performed most successfully, how wage and education correlate with AI applicability, and how real-world usage compares to predictions of occupational AI impact.

[53] arXiv:2509.19151 (替换) [中文pdf, pdf, html, 其他]

标题： 阈值模型在投资组合信用风险中的尖锐大偏差和吉布斯条件化 显示英文标题

标题： Sharp Large Deviations and Gibbs Conditioning for Threshold Models in Portfolio Credit Risk

Fengnan Deng, Anand N. Vidyashankar, Jeffrey F. Collamore

主题： 概率 (math.PR) ; 统计理论 (math.ST) ; 数学金融 (q-fin.MF) ; 投资组合管理 (q-fin.PM) ; 风险管理 (q-fin.RM)

我们获得了在具有发散潜在因子数量的依赖三角阵列阈值模型中超过概率的精确大偏差估计。 前因子量化了潜在因子相关性和尾部几何如何在主导阶中出现，产生三种情形：高斯或指数幂尾部产生Bahadur-Rao $n^{-1/2}$ 定律的对数多项式修正；规则变化尾部产生由指数驱动的多项式标度；有界支撑（端点）情形导致一个 $n^{-3/2}$ 前因子。 我们通过指数积分的Laplace-Olver渐近和三角阵列的条件Bahadur-Rao估计推导出这些结果。 利用这些估计，我们在总变差下建立了Gibbs条件化原理：在大型超过事件条件下，违约指标渐近独立同分布，违约损失分布是指数倾斜的（边界情况通过端点分析处理）。 作为例证，我们得到了风险价值和预期损失的二阶近似，澄清了投资组合何时处于真正的大偏差情形。 这些结果提供了一套可转移的技术——定位、曲率和倾斜识别——用于依赖阈值系统中的精确稀有事件分析。 显示英文摘要

We obtain sharp large deviation estimates for exceedance probabilities in dependent triangular array threshold models with a diverging number of latent factors. The prefactors quantify how latent-factor dependence and tail geometry enter at leading order, yielding three regimes: Gaussian or exponential-power tails produce polylogarithmic refinements of the Bahadur-Rao $n^{-1/2}$ law; regularly varying tails yield index-driven polynomial scaling; and bounded-support (endpoint) cases lead to an $n^{-3/2}$ prefactor. We derive these results through Laplace-Olver asymptotics for exponential integrals and conditional Bahadur-Rao estimates for the triangular arrays. Using these estimates, we establish a Gibbs conditioning principle in total variation: conditioned on a large exceedance event, the default indicators become asymptotically i.i.d., and the loss-given-default distribution is exponentially tilted (with the boundary case handled by an endpoint analysis). As illustrations, we obtain second-order approximations for Value-at-Risk and Expected Shortfall, clarifying when portfolios operate in the genuine large-deviation regime. The results provide a transferable set of techniques-localization, curvature, and tilt identification-for sharp rare-event analysis in dependent threshold systems.

[54] arXiv:2510.11013 (替换) [中文pdf, pdf, html, 其他]

标题： 差分法中的时空边界：来自纳维-斯托克斯方程的框架 显示英文标题

标题： Spatial and Temporal Boundaries in Difference-in-Differences: A Framework from Navier-Stokes Equation

Tatsuru Kikuchi

评论： 56页，4张图

主题： 计量经济学 (econ.EM) ; 一般经济学 (econ.GN) ; 统计理论 (math.ST) ; 应用 (stat.AP) ; 方法论 (stat.ME)

本文提出了一种统一的框架，用于在双重差分设计中识别处理效应的空间和时间边界。 从基本的流体动力学方程（纳维-斯托克斯方程）出发，我们推导了处理效应在空间和时间上呈指数衰减的条件，使研究人员能够计算出效应变得不可检测的明确边界。 该框架涵盖了线性（纯扩散）和非线性（带有化学反应的对流扩散）情形，其可检验的范围条件基于物理学中的无量纲数（佩克莱特数和雷诺数）。 我们通过燃煤电厂的空气污染案例展示了该框架的诊断能力。 分析2019-2021年美国西部791个地面PM$_{2.5}$监测站和189,564个基于卫星的NO$_2$网格单元，我们发现显著的区域异质性：在煤矿电厂100公里范围内，两种污染物均表现出正空间衰减（PM$_{2.5}$：$\kappa_s = 0.00200$，$d^* = 1,153$公里；NO$_2$：$\kappa_s = 0.00112$，$d^* = 2,062$公里），验证了该框架。 超过100公里后，负衰减参数正确表明城市源主导，扩散假设失效。 地表PM$_{2.5}$的衰减速率大约是卫星柱状NO$_2$的两倍，这与大气传输物理一致。 该框架成功诊断了其在八个分析区域中的四个区域的有效性，为研究人员提供了基于物理的工具，以评估其空间差分中的差异设置是否在应用估计器之前满足扩散假设。 我们的结果表明，严格的边界检测需要从基本原理进行理论推导，并对基础物理假设进行实证验证。 显示英文摘要

This paper develops a unified framework for identifying spatial and temporal boundaries of treatment effects in difference-in-differences designs. Starting from fundamental fluid dynamics equations (Navier-Stokes), we derive conditions under which treatment effects decay exponentially in space and time, enabling researchers to calculate explicit boundaries beyond which effects become undetectable. The framework encompasses both linear (pure diffusion) and nonlinear (advection-diffusion with chemical reactions) regimes, with testable scope conditions based on dimensionless numbers from physics (P\'eclet and Reynolds numbers). We demonstrate the framework's diagnostic capability using air pollution from coal-fired power plants. Analyzing 791 ground-based PM$_{2.5}$ monitors and 189,564 satellite-based NO$_2$ grid cells in the Western United States over 2019-2021, we find striking regional heterogeneity: within 100 km of coal plants, both pollutants show positive spatial decay (PM$_{2.5}$: $\kappa_s = 0.00200$, $d^* = 1,153$ km; NO$_2$: $\kappa_s = 0.00112$, $d^* = 2,062$ km), validating the framework. Beyond 100 km, negative decay parameters correctly signal that urban sources dominate and diffusion assumptions fail. Ground-level PM$_{2.5}$ decays approximately twice as fast as satellite column NO$_2$, consistent with atmospheric transport physics. The framework successfully diagnoses its own validity in four of eight analyzed regions, providing researchers with physics-based tools to assess whether their spatial difference-in-differences setting satisfies diffusion assumptions before applying the estimator. Our results demonstrate that rigorous boundary detection requires both theoretical derivation from first principles and empirical validation of underlying physical assumptions.