Borsa Istanbul Review最新文献_第5页

The price of backlash: Performance of Israeli firms Post-Gaza War 反弹的代价：加沙战争后以色列公司的表现

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-11-01 DOI: 10.1016/j.bir.2025.10.006

Ihlas Sovbetov

This study examines the financial price of reputational backlash against Israeli firms following the Gaza War on October 7, 2023. We develop a sentiment–trade interaction framework that integrates Google Trends hostility queries, GDELT media tone, and a composite sentiment index with Israel's bilateral trade exposure. Using a panel of 516 Israeli listed firms, we estimate a triple-interaction model that separates direct war effects from reputational backlash transmitted through bilateral trade linkages. Results show that a one-standard-deviation rise in backlash erased one to two months of typical equity gains, with effects most pronounced in Muslim-majority countries. Sectoral regressions reveal severe penalties in industrials, financials, basic materials, energy, and consumer-facing sectors, while defense and technology were comparatively insulated. Firm-level heterogeneity highlights stronger losses among firms with high foreign institutional ownership, insider concentration, ESG risk, and leverage. A step-dummy approach confirms persistence, underscoring how moral backlash imposes market penalties absent formal sanctions.

本研究考察了2023年10月7日加沙战争后以色列公司声誉反弹的财务代价。我们开发了一个情绪-贸易互动框架，该框架集成了谷歌趋势敌意查询，GDELT媒体语气以及与以色列双边贸易敞口的综合情绪指数。利用516家以色列上市公司的面板，我们估计了一个三重互动模型，该模型将直接战争影响与通过双边贸易联系传播的声誉反弹分离开来。结果显示，反弹的一个标准差上升抹去了一到两个月的典型股票收益，影响在穆斯林占多数的国家最为明显。行业回归显示，工业、金融、基础材料、能源和面向消费者的行业受到严重打击，而国防和科技行业相对不受影响。公司层面的异质性表明，在外资机构持股、内部人集中度、ESG风险和杠杆率较高的公司中，损失更大。step-dummy方法证实了持久性，强调了道德反弹如何在没有正式制裁的情况下施加市场惩罚。

引用次数: 0

Family businesses in the GCC: What drives their capital structure? 海湾合作委员会的家族企业：是什么驱动了它们的资本结构？

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-11-01 DOI: 10.1016/j.bir.2025.06.012

Yousif Abdelbagi Abdalla , Ibrahim Elsiddig Ahmed , Adam Yahya Jafeel

This study examines the determinants of the capital structure at family-owned businesses in the member countries of the Gulf Cooperation Council (GCC), focusing on internal company characteristics and their impact on leverage decisions. It analyzes panel data on 99 family-owned companies in the London Stock Exchange Group (LSEG) database (2015–2023), using fixed-effects regression and instrumental variable–two-stage least squares (IV-2SLS) approaches to address potential endogeneity. The findings reveal that profitability and sales growth negatively impact leverage, supporting the pecking order theory, while asset tangibility and firm size positively influence leverage. Liquidity, the market-to-book value, and firm age become significant, with different effects in addressing endogeneity. The interest rate negatively predicts leverage, whereas regulatory quality contributes to an increase in the size of leverage. This study contributes to the sparse research on the determinants of the capital structure at GCC family businesses by providing insights into how family ownership influences financing decisions in gulf countries and examining the relevance of capital-structure theories in this context. The findings offer valuable insights for family business owners, managers, and policy makers in the GCC, contributing to effective financial management, succession planning, and the long-term sustainability of family businesses.

本研究考察了海湾合作委员会（GCC）成员国家族企业资本结构的决定因素，重点关注内部公司特征及其对杠杆决策的影响。它分析了伦敦证券交易所集团（LSEG）数据库中99家家族企业的面板数据（2015-2023），使用固定效应回归和工具变量两阶段最小二乘（IV-2SLS）方法来解决潜在的内结构性问题。研究结果显示，盈利能力和销售增长对杠杆产生负向影响，支持优先顺序理论，而资产有形性和公司规模对杠杆产生正向影响。流动性、市净率和公司年龄变得重要，在解决内生性方面有不同的效果。利率负向预测杠杆，而监管质量有助于杠杆规模的增加。本研究提供了家族所有权如何影响海湾国家融资决策的见解，并考察了资本结构理论在此背景下的相关性，从而有助于对海湾合作委员会家族企业资本结构决定因素的稀疏研究。研究结果为海湾合作委员会的家族企业所有者、管理者和政策制定者提供了有价值的见解，有助于有效的财务管理、继任规划和家族企业的长期可持续性。

{"title":"Family businesses in the GCC: What drives their capital structure?","authors":"Yousif Abdelbagi Abdalla , Ibrahim Elsiddig Ahmed , Adam Yahya Jafeel","doi":"10.1016/j.bir.2025.06.012","DOIUrl":"10.1016/j.bir.2025.06.012","url":null,"abstract":"<div><div>This study examines the determinants of the capital structure at family-owned businesses in the member countries of the Gulf Cooperation Council (GCC), focusing on internal company characteristics and their impact on leverage decisions. It analyzes panel data on 99 family-owned companies in the London Stock Exchange Group (LSEG) database (2015–2023), using fixed-effects regression and instrumental variable–two-stage least squares (IV-2SLS) approaches to address potential endogeneity. The findings reveal that profitability and sales growth negatively impact leverage, supporting the pecking order theory, while asset tangibility and firm size positively influence leverage. Liquidity, the market-to-book value, and firm age become significant, with different effects in addressing endogeneity. The interest rate negatively predicts leverage, whereas regulatory quality contributes to an increase in the size of leverage. This study contributes to the sparse research on the determinants of the capital structure at GCC family businesses by providing insights into how family ownership influences financing decisions in gulf countries and examining the relevance of capital-structure theories in this context. The findings offer valuable insights for family business owners, managers, and policy makers in the GCC, contributing to effective financial management, succession planning, and the long-term sustainability of family businesses.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"25 6","pages":"Pages 1128-1136"},"PeriodicalIF":7.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The dilemma of growth: High growth expectation and borrower discouragement 增长的困境：高增长预期和借款人气馁

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-11-01 DOI: 10.1016/j.bir.2025.07.011

Vu Tuan Chu, Trang Hanh Lam Pham

This paper investigates the dilemma of growth in which the expectation of high growth is the source of discouragement among borrowers. Using the panel probit model on a cross-country panel of firms studies in waves of the Survey on Access to Finance of Enterprises, we do not find any evidence that high-growth firms can be considered discouraged borrowers. However, firms that expect to grow rapidly in the future are discouraged from borrowing because those that are growth oriented understand the uncertainty of their growth plans and do not want to send negative signals to stakeholders if their loan applications are scaled back or rejected. This applies to all forms of financing and includes both first-time rapidly growing aspirants and enterprises looking for their next spurt of high growth. Finally, improvement in credit relationships with banks reduces information asymmetry and increases the frequency of interaction between banks and firms planning for high growth. Therefore, better banking relationships increase the borrowing discouragement of firms planning for high growth. The paper proposes that growth expectation (not necessarily high-growth performance) is an underexplored source of financial constraints. This distinction introduces a new theoretical perspective: the expectation of growth, rather than its realization, play a critical role in discouragement behavior. Policy makers and financial institutions should design tailored financial instruments for high-growth aspirants.

本文研究了高增长预期是借款人气馁的来源的增长困境。在“企业融资渠道调查”中，我们对跨国公司研究小组使用了面板概率模型，我们没有发现任何证据表明高增长公司可以被视为气馁的借款人。然而，那些期望在未来快速增长的公司不鼓励借款，因为那些以增长为导向的公司了解其增长计划的不确定性，并且不希望在贷款申请缩减或被拒绝时向利益相关者发出负面信号。这适用于所有形式的融资，包括首次快速增长的有志者和寻求下一个高增长井喷的企业。最后，改善与银行的信贷关系减少了信息不对称，并增加了银行与计划高增长的企业之间的互动频率。因此，更好的银行关系增加了企业计划高增长的借贷积极性。本文提出，增长预期（不一定是高增长业绩）是一个未被充分发掘的金融约束来源。这种区别引入了一种新的理论视角：对增长的预期，而不是其实现，在阻碍行为中起着关键作用。政策制定者和金融机构应该为有志于实现高增长的企业设计量身定制的金融工具。

{"title":"The dilemma of growth: High growth expectation and borrower discouragement","authors":"Vu Tuan Chu, Trang Hanh Lam Pham","doi":"10.1016/j.bir.2025.07.011","DOIUrl":"10.1016/j.bir.2025.07.011","url":null,"abstract":"<div><div>This paper investigates the dilemma of growth in which the expectation of high growth is the source of discouragement among borrowers. Using the panel probit model on a cross-country panel of firms studies in waves of the Survey on Access to Finance of Enterprises, we do not find any evidence that high-growth firms can be considered discouraged borrowers. However, firms that expect to grow rapidly in the future are discouraged from borrowing because those that are growth oriented understand the uncertainty of their growth plans and do not want to send negative signals to stakeholders if their loan applications are scaled back or rejected. This applies to all forms of financing and includes both first-time rapidly growing aspirants and enterprises looking for their next spurt of high growth. Finally, improvement in credit relationships with banks reduces information asymmetry and increases the frequency of interaction between banks and firms planning for high growth. Therefore, better banking relationships increase the borrowing discouragement of firms planning for high growth. The paper proposes that growth expectation (not necessarily high-growth performance) is an underexplored source of financial constraints. This distinction introduces a new theoretical perspective: the <em>expectation</em> of growth, rather than its realization, play a critical role in discouragement behavior. Policy makers and financial institutions should design tailored financial instruments for high-growth aspirants.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"25 6","pages":"Pages 1293-1301"},"PeriodicalIF":7.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145527778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Biodiversity risk and global stock markets: A cross-national heterogeneity analysis based on quantile-on-quantile methods 生物多样性风险与全球股票市场：基于分位数对分位数方法的跨国异质性分析

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-11-01 DOI: 10.1016/j.bir.2025.10.013

Hongjun Zeng , Huifang Liu , Han Yan , Shenglin Ma

This paper uses quantile-on-quantile kernel-regularised least squares (QQKRLS) and quantile-on-quantile Granger causality (QQGC) methods to examine how the S&P Global Large MidCap Biodiversity Index (GBI) heterogeneously influences the stock markets of the G7 countries and China. Our empirical analysis of data from 2019 to 2025 demonstrates that the risk of biodiversity loss has a significant, quantile-dependent, and non-linear effect on these national stock markets. The findings reveal that the stock markets of developed European economies display highly sensitive positive responses to GBI fluctuations, whilst the United States' stock market exhibits complex cross-causality structures. Conversely, the Chinese stock market shows pronounced asymmetric and stage-specific characteristics, demonstrating significant vulnerability during periods of heightened risk. Robustness tests using quantile regression and ordinary least squares (OLS) regression further validate the reliability of these principal findings. This research provides substantial empirical evidence on the cross-national transmission mechanisms of sustainable finance. It holds significant theoretical value and offers practical implications for investors developing quantile-sensitive investment strategies and for policymakers refining green finance regulatory frameworks. The results forecast that biodiversity-related financial risks will continue to have heterogeneous effects across different market conditions and geographical regions.

本文采用分位数对核正则化最小二乘（QQKRLS）和分位数对格兰杰因果关系（QQGC）方法，研究了标普全球大盘股生物多样性指数（GBI）对七国集团和中国股市的异质性影响。我们对2019 - 2025年数据的实证分析表明，生物多样性丧失风险对这些国家的股票市场具有显著的、分位数依赖的非线性影响。研究结果表明，欧洲发达经济体的股票市场对GBI波动表现出高度敏感的积极反应，而美国股票市场则表现出复杂的交叉因果关系结构。相反，中国股市表现出明显的不对称和阶段性特征，在高风险时期表现出明显的脆弱性。使用分位数回归和普通最小二乘（OLS）回归的稳健性检验进一步验证了这些主要发现的可靠性。本研究为可持续金融的跨国传导机制提供了大量的实证证据。这对投资者制定分位数敏感的投资策略和政策制定者完善绿色金融监管框架具有重要的理论价值和实践意义。研究结果预测，生物多样性相关金融风险在不同的市场条件和地理区域将继续具有异质效应。

{"title":"Biodiversity risk and global stock markets: A cross-national heterogeneity analysis based on quantile-on-quantile methods","authors":"Hongjun Zeng , Huifang Liu , Han Yan , Shenglin Ma","doi":"10.1016/j.bir.2025.10.013","DOIUrl":"10.1016/j.bir.2025.10.013","url":null,"abstract":"<div><div>This paper uses quantile-on-quantile kernel-regularised least squares (QQKRLS) and quantile-on-quantile Granger causality (QQGC) methods to examine how the S&P Global Large MidCap Biodiversity Index (GBI) heterogeneously influences the stock markets of the G7 countries and China. Our empirical analysis of data from 2019 to 2025 demonstrates that the risk of biodiversity loss has a significant, quantile-dependent, and non-linear effect on these national stock markets. The findings reveal that the stock markets of developed European economies display highly sensitive positive responses to GBI fluctuations, whilst the United States' stock market exhibits complex cross-causality structures. Conversely, the Chinese stock market shows pronounced asymmetric and stage-specific characteristics, demonstrating significant vulnerability during periods of heightened risk. Robustness tests using quantile regression and ordinary least squares (OLS) regression further validate the reliability of these principal findings. This research provides substantial empirical evidence on the cross-national transmission mechanisms of sustainable finance. It holds significant theoretical value and offers practical implications for investors developing quantile-sensitive investment strategies and for policymakers refining green finance regulatory frameworks. The results forecast that biodiversity-related financial risks will continue to have heterogeneous effects across different market conditions and geographical regions.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"25 6","pages":"Pages 1518-1529"},"PeriodicalIF":7.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145527780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How does local government debt expansion affect firm value in China? 中国地方政府债务扩张如何影响企业价值？

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-11-01 DOI: 10.1016/j.bir.2025.07.014

Rui Wan , Lingbing Feng

In recent years, the rapid expansion of local government debt has raised critical concerns regarding its implications for the real economy, particularly firm value. Using data from publicly listed companies in China, for the period 2009–2023, this study presents an analytical framework to investigate the relationship between local government debt and firm value, emphasizing the roles of corporate financing constraints and tax liabilities. The results reveal that local government debt exerts a non-linear, inverted-U effect on firm value, driven by heightened financing constraints and increased tax liabilities. This relationship remains robust across multiple sensitivity tests. Further, heterogeneity analyses show that the inverted-U effect is attenuated in regions with higher GDP per capita, in state-owned enterprises, and in firms with abundant internal funds. Additionally, fiscal pressure positively moderates the relationship, amplifying the impact of debt on firm value. These findings offer novel micro-level evidence on the effects of local government debt, providing actionable policy insights to mitigate debt risks and safeguard firm value.

近年来，地方政府债务的迅速扩张引发了人们对其对实体经济（尤其是企业价值）影响的严重担忧。本研究利用2009-2023年中国上市公司的数据，提出了一个分析框架来考察地方政府债务与企业价值之间的关系，并强调了企业融资约束和税收负债的作用。结果表明，在融资约束加剧和税收负债增加的驱动下，地方政府债务对企业价值产生了非线性的倒u型效应。这种关系在多个敏感性测试中仍然很牢固。此外，异质性分析表明，在人均GDP较高的地区、国有企业和内部资金充裕的企业中，倒u型效应减弱。此外，财政压力正向调节了这一关系，放大了债务对企业价值的影响。这些发现为地方政府债务的影响提供了新的微观证据，为减轻债务风险和维护企业价值提供了可操作的政策见解。

{"title":"How does local government debt expansion affect firm value in China?","authors":"Rui Wan , Lingbing Feng","doi":"10.1016/j.bir.2025.07.014","DOIUrl":"10.1016/j.bir.2025.07.014","url":null,"abstract":"<div><div>In recent years, the rapid expansion of local government debt has raised critical concerns regarding its implications for the real economy, particularly firm value. Using data from publicly listed companies in China, for the period 2009–2023, this study presents an analytical framework to investigate the relationship between local government debt and firm value, emphasizing the roles of corporate financing constraints and tax liabilities. The results reveal that local government debt exerts a non-linear, inverted-U effect on firm value, driven by heightened financing constraints and increased tax liabilities. This relationship remains robust across multiple sensitivity tests. Further, heterogeneity analyses show that the inverted-U effect is attenuated in regions with higher GDP per capita, in state-owned enterprises, and in firms with abundant internal funds. Additionally, fiscal pressure positively moderates the relationship, amplifying the impact of debt on firm value. These findings offer novel micro-level evidence on the effects of local government debt, providing actionable policy insights to mitigate debt risks and safeguard firm value.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"25 6","pages":"Pages 1337-1347"},"PeriodicalIF":7.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145527784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Are parametric models still useful to measure the market risk of bank securities holdings? 参数化模型对于衡量银行证券持有的市场风险是否仍然有用？

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-11-01 DOI: 10.1016/j.bir.2025.10.007

Michele Leonardo Bianchi , Leonardo Del Vecchio , Federico Maria Stara

This paper estimates the daily market risk of Italian bank securities portfolios under different model assumptions, using granular data on all banks and exposures from 2008 to 2023. Market risk is measured via value-at-risk and expected shortfall, estimated with three approaches: (1) non-parametric historical simulation, (2) multivariate normal GARCH, and (3) a multivariate parametric model capturing heavy tails, negative skewness, asymmetric dependence, and volatility clustering. We empirically examine the characteristics of each approach and compare them through extensive backtesting. Results show that parametric models generally outperform the non-parametric method, though the latter remains viable. Using actual bank portfolio data introduces unique challenges, requiring careful treatment in risk analysis. Finally, we discuss which approach is most suitable for financial stability purposes, informing system-wide market risk indicators and stress-testing frameworks.

本文利用2008年至2023年所有银行和敞口的粒度数据，在不同模型假设下估计了意大利银行证券投资组合的日常市场风险。市场风险是通过风险价值和预期不足来衡量的，通过三种方法来估计：(1)非参数历史模拟，(2)多元正态GARCH，以及(3)捕获重尾、负偏度、不对称依赖和波动聚类的多元参数模型。我们从经验上考察了每种方法的特点，并通过广泛的回溯测试对它们进行了比较。结果表明，参数模型总体上优于非参数方法，尽管后者仍然可行。使用实际的银行投资组合数据带来了独特的挑战，需要在风险分析中仔细处理。最后，我们讨论了哪种方法最适合金融稳定的目的，为全系统的市场风险指标和压力测试框架提供信息。

引用次数: 0

Predicting mutual fund performance with machine learning: Are ESG pillar scores relevant predictors of fund return? 用机器学习预测共同基金业绩：ESG支柱得分与基金回报相关吗？

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-11-01 DOI: 10.1016/j.bir.2025.07.019

Francisco Climent , Alexandre Momparler , Pedro Carmona

The growing emphasis on sustainability within the business community has led to a progressive integration of Environmental, Social, and Governance values (ESG) into the investment process. This study aims to identify those key fund characteristics that best predict financial performance, with a special focus on the individual impact of ESG Pillar scores. Using the Extreme Gradient Boosting (XGBoost) algorithm, a machine learning technique known for enhancing predictive accuracy, we analyze cross-sectional data on Euro-denominated equity mutual funds with a global scope over a five-year period (2020–2024). In addition, this paper evaluates the advantages of the XGBoost algorithm in predicting mutual fund returns by comparing its performance against two benchmark models: OLS regression and a deep learning architecture. Our findings reveal that ESG Pillar Social score is the second most important predictive factor and that it is positively associated with fund performance, whereas ESG Pillar Environmental score ranks fifth in predictive power and it shows a negative relationship with performance. These insights offer practical value for values-driven investors, financial advisors, and fund managers by supporting investment decisions that align financial performance with sustainability considerations. This study addresses a critical gap in the literature by analyzing the individual effects of each ESG pillar, rather than relying on aggregated ESG scores as commonly done in prior research. The use of cross-sectional data provides a detailed representation of these relationships over a five-year span. Our approach expands the literature by using advanced machine learning to show links between sustainability and fund returns.

商界对可持续发展的日益重视，导致了环境、社会和治理价值观（ESG）逐步融入投资过程。本研究旨在确定最能预测财务业绩的关键基金特征，并特别关注ESG支柱得分的个人影响。使用极端梯度提升算法（XGBoost），一种以提高预测准确性而闻名的机器学习技术，我们分析了五年期（2020-2024年）全球范围内欧元计价股票共同基金的横截面数据。此外，本文通过比较XGBoost算法与两种基准模型（OLS回归和深度学习架构）的表现，评估了XGBoost算法在预测共同基金回报方面的优势。我们的研究结果表明，ESG支柱社会得分是第二重要的预测因素，它与基金业绩呈正相关，而ESG支柱环境得分在预测能力上排名第五，与业绩呈负相关。这些见解通过支持将财务绩效与可持续性考虑相结合的投资决策，为价值驱动型投资者、财务顾问和基金经理提供了实用价值。本研究通过分析每个ESG支柱的个体影响，而不是像以前的研究那样依赖于ESG总分，解决了文献中的一个关键空白。横断面数据的使用提供了五年跨度内这些关系的详细表示。我们的方法通过使用先进的机器学习来扩展文献，以显示可持续性与基金回报之间的联系。

{"title":"Predicting mutual fund performance with machine learning: Are ESG pillar scores relevant predictors of fund return?","authors":"Francisco Climent , Alexandre Momparler , Pedro Carmona","doi":"10.1016/j.bir.2025.07.019","DOIUrl":"10.1016/j.bir.2025.07.019","url":null,"abstract":"<div><div>The growing emphasis on sustainability within the business community has led to a progressive integration of Environmental, Social, and Governance values (ESG) into the investment process. This study aims to identify those key fund characteristics that best predict financial performance, with a special focus on the individual impact of ESG Pillar scores. Using the Extreme Gradient Boosting (XGBoost) algorithm, a machine learning technique known for enhancing predictive accuracy, we analyze cross-sectional data on Euro-denominated equity mutual funds with a global scope over a five-year period (2020–2024). In addition, this paper evaluates the advantages of the XGBoost algorithm in predicting mutual fund returns by comparing its performance against two benchmark models: OLS regression and a deep learning architecture. Our findings reveal that ESG Pillar Social score is the second most important predictive factor and that it is positively associated with fund performance, whereas ESG Pillar Environmental score ranks fifth in predictive power and it shows a negative relationship with performance. These insights offer practical value for values-driven investors, financial advisors, and fund managers by supporting investment decisions that align financial performance with sustainability considerations. This study addresses a critical gap in the literature by analyzing the individual effects of each ESG pillar, rather than relying on aggregated ESG scores as commonly done in prior research. The use of cross-sectional data provides a detailed representation of these relationships over a five-year span. Our approach expands the literature by using advanced machine learning to show links between sustainability and fund returns.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"25 6","pages":"Pages 1403-1419"},"PeriodicalIF":7.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Impulsive Approach to procyclicality; measuring the reactiveness of risk-based initial margin models to changes in market conditions using impulse response functions 顺周期的冲动性分析方法利用脉冲响应函数测量基于风险的初始保证金模型对市场条件变化的反应性

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-11-01 DOI: 10.1016/j.bir.2025.06.006

Pedro Gurrola-Pérez , David Murphy

In recent years, many derivatives market participants received large margin calls in episodes of elevated market volatility such as the onset of the Covid-19 global pandemic and the illegal Russian invasion of Ukraine. The lack of some market participants’ preparedness to meet these calls resulted in liquidity stress and reinvigorated the policy debate about how reactive margin should be to changes in market conditions. This debate has been hampered by the lack of a generally accepted way of measuring the reactiveness of the models used to calculate initial margin. The first contribution of this paper is to provide such a measure. We consider a step function in volatility, and examine the responses of various initial margin models to paths of risk factor returns consistent with this impulse, introducing the impulse response function as a convenient means of presenting this reaction.

The results presented demonstrate that a model's impulse response is a robust and useful measure of its reactiveness. This approach could be used both to measure initial margin model reactiveness, or procyclicality as it is often termed, and to capture the uncertainty in this measurement. It also provides significant, novel insights into the behaviour of some economically important margin models. In particular, the tendency of some filtered historical simulation value at risk models to over-react to sharp stepwise increases in volatility is demonstrated and the reasons for it are explored. The behaviour of two widely-used anti-procyclicality tools, the buffer and the use of a stressed period, are also analysed: the latter is found to be more successful at mitigating procyclicality than the former. The paper concludes with a discussion of the policy implications of the results presented.

近年来，在新冠肺炎（Covid-19）全球大流行爆发和俄罗斯非法入侵乌克兰等市场波动加剧的情况下，许多衍生品市场参与者收到了巨额追加保证金通知。一些市场参与者缺乏应对这些要求的准备，导致了流动性压力，并重新引发了有关保证金应如何应对市场状况变化的政策辩论。由于缺乏一种普遍接受的方法来衡量用于计算初始保证金的模型的反应性，这场辩论受到了阻碍。本文的第一个贡献就是提供了这样一个度量。我们考虑波动率中的阶跃函数，并检查各种初始边际模型对与该脉冲一致的风险因子回报路径的响应，并引入脉冲响应函数作为表示该反应的方便手段。结果表明，模型的脉冲响应是衡量其反应性的一种鲁棒性和实用性的方法。这种方法既可以用来衡量初始边际模型的反应性，也可以用来衡量通常所说的顺周期性，也可以用来衡量这种测量中的不确定性。它还为一些具有重要经济意义的利润率模型的行为提供了重要的、新颖的见解。特别是，在风险模型中，一些过滤的历史模拟值倾向于对波动率的急剧逐步增加过度反应，并探讨了其原因。还分析了两种广泛使用的反顺周期性工具——缓冲和压力期的使用——的行为：发现后者在缓解顺周期性方面比前者更成功。本文最后讨论了所提出结果的政策含义。

{"title":"The Impulsive Approach to procyclicality; measuring the reactiveness of risk-based initial margin models to changes in market conditions using impulse response functions","authors":"Pedro Gurrola-Pérez , David Murphy","doi":"10.1016/j.bir.2025.06.006","DOIUrl":"10.1016/j.bir.2025.06.006","url":null,"abstract":"<div><div>In recent years, many derivatives market participants received large margin calls in episodes of elevated market volatility such as the onset of the Covid-19 global pandemic and the illegal Russian invasion of Ukraine. The lack of some market participants’ preparedness to meet these calls resulted in liquidity stress and reinvigorated the policy debate about how reactive margin should be to changes in market conditions. This debate has been hampered by the lack of a generally accepted way of measuring the reactiveness of the models used to calculate initial margin. The first contribution of this paper is to provide such a measure. We consider a step function in volatility, and examine the responses of various initial margin models to paths of risk factor returns consistent with this impulse, introducing the impulse response function as a convenient means of presenting this reaction.</div><div>The results presented demonstrate that a model's impulse response is a robust and useful measure of its reactiveness. This approach could be used both to measure initial margin model reactiveness, or procyclicality as it is often termed, and to capture the uncertainty in this measurement. It also provides significant, novel insights into the behaviour of some economically important margin models. In particular, the tendency of some filtered historical simulation value at risk models to over-react to sharp stepwise increases in volatility is demonstrated and the reasons for it are explored. The behaviour of two widely-used anti-procyclicality tools, the buffer and the use of a stressed period, are also analysed: the latter is found to be more successful at mitigating procyclicality than the former. The paper concludes with a discussion of the policy implications of the results presented.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"25 6","pages":"Pages 1166-1182"},"PeriodicalIF":7.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From headlines to stock trends: Natural language processing and explainable artificial intelligence approach to predicting Türkiye's financial pulse 从头条新闻到股票趋势：自然语言处理和可解释的人工智能方法来预测<s:1> rkiye的金融脉搏

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-11-01 DOI: 10.1016/j.bir.2025.06.013

Mahat Maalim Ibrahim, Asad Ul Islam Khan, Muhittin Kaplan

The dynamic field of financial markets is constantly in search of new ways to understand complex market dynamics. The increasing availability of vast amounts of text data offers new avenues for investigation (Botchway et al., 2020). This study aims to shed light on the dynamics between stock market movements and news narratives in Türkiye. To address this issue, the study will include the analysis of business, financial, and economic news from four major news journals (The Economist, The New York Times, The Guardian, and Yeni Şafak) along with local tweets. Yeni Şafak and local tweets serve as proxies for local news sentiment. The analysis rests on daily Turkish stock market data from January 1, 2015, to February 27, 2024, obtained from Yahoo Finance. The issue was addressed using state-of-the-art Natural Language Processing (NLP), machine learning, and explainable AI techniques. The findings reveal that international news significantly predicts the Turkish Stock market, with the majority of machine learning models yielding approximately 80 percent predictive accuracy. The Explainable AI methods demonstrate that traditional international news media have a significant impact on the Turkish stock market in comparison to local news sources such as Yeni Şafak and Twitter which serve as less effective predictors. Notably, the ensemble algorithms, comprising Random Forest, Gradient Boosting, and XGBoost, demonstrate robust performance across all datasets.

金融市场的动态领域不断寻求新的方法来理解复杂的市场动态。越来越多的大量文本数据的可用性为调查提供了新的途径（Botchway等人，2020）。本研究旨在阐明股票市场运动与新闻叙事之间的动态关系。为了解决这个问题，这项研究将包括对来自四家主要新闻期刊（《经济学人》、《纽约时报》、《卫报》和Yeni Şafak）的商业、金融和经济新闻以及当地推文的分析。Yeni Şafak和当地推文是当地新闻情绪的代理。该分析基于从雅虎财经获得的2015年1月1日至2024年2月27日的每日土耳其股市数据。使用最先进的自然语言处理（NLP）、机器学习和可解释的人工智能技术解决了这个问题。研究结果显示，国际新闻可以显著预测土耳其股市，大多数机器学习模型的预测准确率约为80%。可解释的人工智能方法表明，与Yeni Şafak和Twitter等当地新闻来源相比，传统的国际新闻媒体对土耳其股市产生了重大影响，后者的预测效果较差。值得注意的是，集成算法，包括随机森林、梯度增强和XGBoost，在所有数据集上都表现出强大的性能。

{"title":"From headlines to stock trends: Natural language processing and explainable artificial intelligence approach to predicting Türkiye's financial pulse","authors":"Mahat Maalim Ibrahim, Asad Ul Islam Khan, Muhittin Kaplan","doi":"10.1016/j.bir.2025.06.013","DOIUrl":"10.1016/j.bir.2025.06.013","url":null,"abstract":"<div><div>The dynamic field of financial markets is constantly in search of new ways to understand complex market dynamics. The increasing availability of vast amounts of text data offers new avenues for investigation (Botchway et al., 2020). This study aims to shed light on the dynamics between stock market movements and news narratives in Türkiye. To address this issue, the study will include the analysis of business, financial, and economic news from four major news journals (The Economist, The New York Times, The Guardian, and Yeni Şafak) along with local tweets. Yeni Şafak and local tweets serve as proxies for local news sentiment. The analysis rests on daily Turkish stock market data from January 1, 2015, to February 27, 2024, obtained from Yahoo Finance. The issue was addressed using state-of-the-art Natural Language Processing (NLP), machine learning, and explainable AI techniques. The findings reveal that international news significantly predicts the Turkish Stock market, with the majority of machine learning models yielding approximately 80 percent predictive accuracy. The Explainable AI methods demonstrate that traditional international news media have a significant impact on the Turkish stock market in comparison to local news sources such as Yeni Şafak and Twitter which serve as less effective predictors. Notably, the ensemble algorithms, comprising Random Forest, Gradient Boosting, and XGBoost, demonstrate robust performance across all datasets.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"25 6","pages":"Pages 1152-1165"},"PeriodicalIF":7.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The influence of public listing on bankruptcy prediction in India: An AI-ML approach 印度上市对破产预测的影响：一种AI-ML方法

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-11-01 DOI: 10.1016/j.bir.2025.10.003

Nagaraju Thota, Sreenivasulu Puli, A.C.V. Subrahmanyam, Sneha Yarala

This study predicts bankruptcy among Indian firms using artificial intelligence–machine learning (AI-ML) methods, demonstrating their superior performance over traditional statistical models. Addressing class imbalance through oversampling techniques such as the synthetic minority oversampling technique (SMOTE), the paper achieves higher accuracy rates with AI-ML models, such as random forest, neural networks, and gradient boosting. By leveraging the information value and weight of evidence of explanatory variables, the study designs early warning variables for bankruptcy risks, helping both internal and external stakeholders to monitor and mitigate these risks. The analytical framework thus extends the methodological application of AI-ML models and offers a management toolkit that practitioners can use to track and address bankruptcy risks effectively. Furthermore, the study finds that AI-ML models improve prediction accuracy, especially for listed firms, because of better information content in their financial statements.

这项研究使用人工智能-机器学习（AI-ML）方法预测了印度公司的破产，证明了它们比传统统计模型的优越性能。通过合成少数派过采样技术（SMOTE）等过采样技术解决类失衡问题，本文使用随机森林、神经网络和梯度增强等AI-ML模型实现了更高的准确率。通过利用解释变量的信息价值和证据权重，设计破产风险预警变量，帮助内外部利益相关者对破产风险进行监测和缓解。因此，分析框架扩展了AI-ML模型的方法论应用，并提供了一个管理工具包，从业者可以使用它来有效地跟踪和解决破产风险。此外，研究发现AI-ML模型提高了预测的准确性，特别是对于上市公司，因为它们的财务报表中有更好的信息内容。

引用次数: 0