Borsa Istanbul Review最新文献

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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。

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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

The reaction of cryptocurrencies to the approval of spot Bitcoin and Ethereum ETFs: An intraday event study 加密货币对现货比特币和以太坊etf批准的反应：一项日内事件研究

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-11-01 DOI: 10.1016/j.bir.2025.10.002

Seyed Mehdian , Ștefan Cristian Gherghina , Ovidiu Stoica

This paper examines the market reaction to the approval of spot Bitcoin and Ethereum exchange-traded funds (ETFs), focusing on the return dynamics of a functionally diverse types of leading cryptocurrencies, including coins (BTC, BCH, LTC, XRP), smart contract platforms (ETH, ADA, AVAX), and utility tokens (LINK, MATIC). Using high-frequency intraday data, we perform an event study to assess the abnormal returns around the ETF approval dates. This study makes a significant contribution to the literature on event studies by being the first to examine investors’ reactions to information arrival in a “primary market.” Both the market model and the capital asset pricing model (CAPM) are applied to evaluate the effects of ETF approval on individual asset returns. Our results reveal that spot Bitcoin ETF approval by the US Securities and Exchange Commission leads to significant positive abnormal returns, along with heightened market volatility. In contrast, spot Ethereum ETF approval has had more modest effects. Moreover, we observe considerable shifts in the volatility spillovers among Bitcoin, Ethereum, and other major cryptocurrencies after the ETF approval, reflecting a change in market sentiment and interconnectedness. This analysis enhances understanding of how institutional products, such as ETFs, shape cryptocurrency market behavior, offering valuable insights for regulatory frameworks and investor strategies.

本文研究了市场对现货比特币和以太坊交易所交易基金（etf）批准的反应，重点关注功能不同类型的领先加密货币的回报动态，包括硬币（BTC， BCH， LTC， XRP），智能合约平台（ETH， ADA， AVAX）和实用代币（LINK， MATIC）。利用高频日内数据，我们进行了一项事件研究，以评估ETF批准日期前后的异常回报。本研究首次考察了投资者对“一级市场”信息到达的反应，对事件研究的文献做出了重大贡献。本文采用市场模型和资本资产定价模型（CAPM）来评估ETF批准对个人资产收益的影响。我们的研究结果表明，美国证券交易委员会批准的现货比特币ETF导致显著的正异常收益，同时市场波动加剧。相比之下，现货以太坊ETF批准的影响更为温和。此外，我们观察到，在ETF获批后，比特币、以太坊和其他主要加密货币之间的波动性溢出效应发生了相当大的变化，反映了市场情绪和互联性的变化。这一分析增强了对机构产品（如etf）如何影响加密货币市场行为的理解，为监管框架和投资者策略提供了有价值的见解。

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引用次数: 0

Greenness and capital investment decisions: Evidence from NYSE firms 绿色与资本投资决策：来自纽交所公司的证据

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-11-01 DOI: 10.1016/j.bir.2025.07.013

Berna N. Yılmaz, Seza Danışoğlu

This study explores the impact of greenness on real capital investment on all New York Stock Exchange (NYSE) firms between 2002 and 2021. We measure the greenness of a firm by adjusting the environmental component of its environmental, social, and governance (ESG) score for industry and market effects. The relationship between greenness and investment is examined using two different methodologies. The dynamic panel regression results show that green firms invest more, regardless of how greenness is defined. The quantile regression results imply that companies that have lower levels of capital investment tend to invest more when they are greener, compared to companies that have higher levels of capital investment. The findings of the study are consistent with Pastor, Stambaugh, and Taylor's (2021) prediction that the market will become greener over time because greener firms have higher levels of capital investment compared to brown firms.

本研究探讨了绿色对2002年至2021年间所有纽约证券交易所（NYSE）公司实际资本投资的影响。我们通过调整其环境、社会和治理（ESG）得分的环境成分对行业和市场影响来衡量企业的绿色度。绿色和投资之间的关系使用两种不同的方法进行检验。动态面板回归结果表明，无论绿色度如何定义，绿色企业的投资都更多。分位数回归结果表明，与资本投资水平较高的公司相比，资本投资水平较低的公司在更环保的情况下往往会投资更多。该研究的结果与Pastor、Stambaugh和Taylor（2021）的预测一致，即随着时间的推移，市场将变得更加绿色，因为绿色企业的资本投资水平高于棕色企业。

引用次数: 0

Dynamic responses of Bitcoin, gold, and green bonds to geopolitical risk: A quantile wavelet analysis 比特币、黄金和绿色债券对地缘政治风险的动态响应：分位数小波分析

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-11-01 DOI: 10.1016/j.bir.2025.07.002

Sami Mejri , Arturo Leccadito , Ramazan Yildirim

This study investigates the heterogeneous responses of Bitcoin (BTC), gold (GOLD), and green bonds (GBOND) to geopolitical risk (GPR) shocks across different market regimes and investment horizons. Using a triadic empirical framework that encompasses wavelet quantile-on-quantile regression (QQR), wavelet cross-quantilogram (WCQ), and advanced portfolio optimization strategies, our analysis captures asymmetric dependence, tail risks, and time-frequency dynamics from January 2015 to December 2024. Our results show that BTC consistently has strong hedging potential at lower quantiles, particularly during short-term stress, whereas GOLD and GBOND offer greater stability over medium- and long-term horizons. Conditional expected shortfall (CES) and extreme downside correlation (EDC) analyses highlight BTC's resilience to extreme downside risks, whereas GOLD and GBOND serve primarily as long-term defensive assets. Portfolio optimization confirms BTC's critical role in diversification under minimum correlation and connectedness strategies, and GBOND dominates variance-minimizing portfolios. These findings offer practical guidance for constructing robust, adaptive portfolios under geopolitical uncertainty.

本研究探讨了比特币（BTC）、黄金（gold）和绿色债券（GBOND）在不同市场制度和投资视野下对地缘政治风险（GPR）冲击的异质反应。利用包含小波分位数-分位数回归（QQR）、小波交叉量化图（WCQ）和高级投资组合优化策略的三合一经验框架，我们的分析捕获了2015年1月至2024年12月期间的不对称依赖性、尾部风险和时频动态。我们的研究结果表明，比特币在较低的分位数下始终具有强大的对冲潜力，特别是在短期压力下，而黄金和GBOND在中长期内提供了更大的稳定性。条件预期缺口（CES）和极端下行相关性（EDC）分析强调了比特币对极端下行风险的抵御能力，而黄金和GBOND主要作为长期防御性资产。投资组合优化证实了最小关联和连通性策略下BTC在多元化中的关键作用，GBOND在方差最小化投资组合中占主导地位。这些发现为在地缘政治不确定性下构建稳健的适应性投资组合提供了实践指导。

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引用次数: 0

High-frequency dynamics of Bitcoin futures: An examination of market microstructure 比特币期货的高频动态：市场微观结构的检验

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-11-01 DOI: 10.1016/j.bir.2025.07.016

Mateus Gonzalez de Freitas Pinto

We investigate the high-frequency dynamics of Bitcoin and Ethereum perpetual futures traded on Binance from January 2020 to December 2024. After a thorough discussion of the stylized facts and particularities of Bitcoin perpetual futures, based on previous research in futures markets, we evaluate the fit of two competing models of market microstructure: the Mixture of Distributions Hypothesis (MDH) and the Intraday Trading Invariance Hypothesis (ITIH). Using intraday data at different levels of aggregation, we investigate the relationship between return volatility per transaction and trade size. We find evidence favoring the MDH in the crypto futures market.

我们研究了从2020年1月到2024年12月在币安交易的比特币和以太坊永久期货的高频动态。在深入讨论了比特币永续期货的风规化事实和特殊性之后，基于之前对期货市场的研究，我们评估了两个相互竞争的市场微观结构模型：混合分布假设（MDH）和即日交易不变性假设（ITIH）。利用不同聚合水平的日内数据，我们研究了每笔交易的收益波动率与交易规模之间的关系。我们在加密货币期货市场中发现了有利于MDH的证据。

引用次数: 0

Hybrid forecasting of agricultural commodity prices: Integrating machine learning, time series, and stochastic simulation models 农产品价格的混合预测：整合机器学习、时间序列和随机模拟模型

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-11-01 DOI: 10.1016/j.bir.2025.10.004

Busra Agan Celik , Serdar Celik

This study examines a hybrid forecasting framework to evaluate the predictive performance of time series models (ARIMA, VAR), deep learning (LSTM), and stochastic simulations (GBM, FBM, BB) in forecasting agricultural commodity prices during global crises. Using daily data from 1985 to 2024, the analysis spans nine crisis periods, including the Global Financial Crisis, COVID-19, and the Russia–Ukraine conflict, and focuses on seven major agricultural commodities. Forecasting accuracy (MAPE, RMSE), risk (VaR), and return metrics are used to evaluate model performance. Results show that LSTM outperforms other models in capturing nonlinear dynamics during volatile episodes, whereas ARIMA provides stable results in shorter-term, low-volatility settings. GBM offers the best balance of forecast precision and risk-adjusted returns among stochastic models. In contrast, FBM captures memory effects but produces higher volatility. The findings highlight the importance of adaptive, context-specific forecasting models to enhance policy responses in food security, trade resilience, and agricultural risk management.

本研究考察了一个混合预测框架，以评估时间序列模型（ARIMA、VAR）、深度学习（LSTM）和随机模拟（GBM、FBM、BB）在预测全球危机期间农产品价格方面的预测性能。利用1985年至2024年的日常数据，该分析跨越了9个危机时期，包括全球金融危机、2019冠状病毒病和俄罗斯-乌克兰冲突，并重点关注7种主要农产品。预测精度（MAPE， RMSE），风险（VaR）和回报度量被用来评估模型的性能。结果表明，LSTM在捕获波动时段的非线性动力学方面优于其他模型，而ARIMA在短期、低波动环境下提供稳定的结果。在随机模型中，GBM提供了预测精度和风险调整收益的最佳平衡。相比之下，FBM捕获了记忆效应，但产生了更高的波动性。研究结果强调了适应性的、针对具体情况的预测模型对于加强粮食安全、贸易抵御力和农业风险管理方面的政策响应的重要性。

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引用次数: 0

Knowledge, attitude or risk? What drives the financial literacy gaps of university staff? 知识、态度还是风险？是什么导致了大学员工的金融知识差距？

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-10-27 DOI: 10.1016/j.bir.2025.10.032

Ibrahim Ayoade Adekunle , Tolulope Oyakhilome Williams , Robin Maialeh , Muiz Adeniji Adegbenro

This study examines the extent to which variations in financial knowledge, attitudes and risk preferences shape the educational investment behaviour of academic and non-academic university staff. Our findings revealed distinct patterns. We documented that being armed with a sound financial knowledge significantly influences the educational investment decisions of academic staff, thus facilitating informed decision-making. We also documented that financial attitudes and risk tolerance exert positive impacts, thus indicating that confidence in terms of one's financial management informs educational advancement. Conversely, a sound financial knowledge does not predict the take-up of further education for non-academic staff, while financial attitudes and risk aversion exert negative impacts, thus suggesting that financial constraints and limited careers inhibit investment in higher education. Institutional dynamics and socio-economic conditions further moderate these relationships and expose structural barriers to financial literacy. The findings identified the necessity for tailored financial literacy intervention measures that address the occupational stratifications and institutional constraints that hinder educational progression.

本研究考察了金融知识、态度和风险偏好的变化在多大程度上影响了学术和非学术大学员工的教育投资行为。我们的发现揭示了不同的模式。我们证明，拥有良好的金融知识会显著影响学术人员的教育投资决策，从而促进明智的决策。我们还证明了财务态度和风险承受能力会产生积极影响，从而表明个人财务管理方面的信心会影响教育进步。相反，良好的财务知识并不能预测非学术人员继续接受教育，而财务态度和风险规避则会产生负面影响，从而表明财务约束和有限的职业限制了高等教育的投资。制度动态和社会经济条件进一步缓和了这些关系，并暴露了金融知识的结构性障碍。研究结果表明，有必要采取量身定制的金融素养干预措施，解决阻碍教育进步的职业分层和制度限制问题。

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引用次数: 0

The impact of financial literacy on financial development: A cross-country analysis 金融素养对金融发展的影响：一个跨国分析

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-10-25 DOI: 10.1016/j.bir.2025.10.017

Asuman Koç Yurtkur , Yunus Kilic , Mehmet Fatih Bugan , Sel Dibooglu , Emrah I. Cevik

This study investigates the impact of financial literacy on financial development across a large set of countries, utilizing data from the Global Financial Inclusion and Consumer Protection (GFICP) surveys conducted in 2017 and 2022. Using baseline OLS regressions, the results reveal that financial literacy significantly enhances financial development, particularly in upper-middle-, lower-middle-, and low-income countries. The study also highlights regional variations, with the East Asia and Pacific region demonstrating the most substantial positive relationship. Additionally, the quantile regression analysis indicates that the effect of financial literacy on financial development is more pronounced in countries with either highly developed or underdeveloped financial systems. These results underscore the importance of designing context-specific financial education policies, particularly in developing economies, where improvements in foundational financial knowledge can play a catalytic role in strengthening financial systems.

本研究利用2017年和2022年进行的全球金融普惠和消费者保护（GFICP）调查的数据，调查了金融素养对许多国家金融发展的影响。使用基线OLS回归，结果显示金融知识显著促进了金融发展，特别是在中高、中低和低收入国家。该研究还强调了地区差异，东亚和太平洋地区表现出最实质性的积极关系。此外，分位数回归分析表明，金融素养对金融发展的影响在金融体系高度发达或不发达的国家更为明显。这些结果强调了设计针对具体情况的金融教育政策的重要性，特别是在发展中经济体，基础金融知识的改善可以在加强金融体系方面发挥催化作用。

引用次数: 0

Financial citizenship beyond borders: Validation of a model between Brazil and France 超越国界的金融公民：巴西和法国模式的验证

IF 7.1 2区经济学 Q1 BUSINESS, FINANCE

Borsa Istanbul Review

Pub Date : 2025-10-16 DOI: 10.1016/j.bir.2025.10.015

Ana Luiza Paraboni , Ani Caroline Grigion Potrich , Kelmara Mendes Vieira

This study proposed and validated a model of financial citizenship that integrates financial literacy, inclusion, and protection, comparing its applicability and mean differences between Brazilians and French. A sample from both countries was investigated. The data were analyzed using descriptive statistics, structural equation modeling, model invariance testing, and mean differences analysis. The invariance analysis confirmed that the model has an equivalent factor structure in both countries, enabling valid and consistent comparisons. The results indicated no significant differences in the mean scores for financial literacy and financial protection between the groups, possibly due to similarities in national financial education strategies. However, Brazilians scored significantly higher in financial inclusion and citizenship, suggesting some advances in financial practices in Brazil (e.g., the adoption of PIX) and thus expand access to digital banking services. This work advances the cross-cultural validity of the financial citizenship model and demonstrates its usefulness for international research.

本研究提出并验证了一个集金融素养、包容性和保护于一体的金融公民模型，并比较了该模型在巴西人和法国人之间的适用性和均值差异。对两国的样本进行了调查。采用描述性统计、结构方程建模、模型不变性检验和均值差异分析对数据进行分析。不变性分析证实，该模型在两国具有等效的因子结构，使比较有效和一致。结果显示，两组学生在金融素养和金融保护方面的平均分没有显著差异，这可能是由于各国的金融教育策略相似所致。然而，巴西人在金融包容性和公民身份方面的得分明显更高，这表明巴西在金融实践方面取得了一些进步（例如，采用PIX），从而扩大了数字银行服务的可及性。本研究提高了金融公民模型的跨文化有效性，并证明了其对国际研究的有用性。

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