Borsa Istanbul Review最新文献_第9页

A retrospective evaluation of Borsa Istanbul review using a machine learning data analytical approach 使用机器学习数据分析方法对Borsa Istanbul审查进行回顾性评估

IF 6.3 2区经济学

Borsa Istanbul Review Pub Date : 2025-02-01 DOI: 10.1016/j.bir.2024.12.019

Hassnian Ali , Ahmet Faruk Aysan , Hasmet Gokirmak

{"title":"A retrospective evaluation of Borsa Istanbul review using a machine learning data analytical approach","authors":"Hassnian Ali , Ahmet Faruk Aysan , Hasmet Gokirmak","doi":"10.1016/j.bir.2024.12.019","DOIUrl":"10.1016/j.bir.2024.12.019","url":null,"abstract":"<div><div>This study conducts a detailed examination of <em>Borsa Istanbul Review</em> (<em>BIR</em>) from 2013 to 2023, employing bibliometric analysis, regression analysis, and structural topic modeling (STM) to explore its scholarly impact, authorship patterns, and thematic evolution. Our bibliometric analysis reveals a significant increase in <em>BIR</em>'s publication volume and citation count, as well as a marked expansion in its author collaboration network, with notable contributions from Turkish and East Asian scholars. Through regression analysis, we identify several factors—such as article length, age, position in the issue (lead article status), regional author affiliation, title characteristics (length and novelty), and the presence of multiple authors, keywords, figures, and tables—as significant determinants of citation rates. Furthermore, STM reveals ten dominant themes in <em>BIR</em>, highlighting key focus areas, such as firm dynamics, market and country growth, financial health, and stock market returns. This comprehensive analysis sheds light on <em>BIR</em>'s evolving scholarly landscape and offers valuable insights for its editorial board, stakeholders, and the broader academic community interested in finance and economics. This enhanced understanding of <em>BIR</em>'s trends and themes is a crucial resource for navigating the wider finance research domain.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"25 1","pages":"Pages 1-20"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Population aging and corporate cash holdings: Empirical evidence from Chinese listed companies 人口老龄化与企业现金持有量：来自中国上市公司的经验证据

IF 6.3 2区经济学

Borsa Istanbul Review Pub Date : 2025-02-01 DOI: 10.1016/j.bir.2024.12.007

Yue Zhang , Qizhen Zhang

引用次数: 0

The impact of personality, behavior, and geography on participation in the private pension system in Türkiye: A machine learning approach 个性、行为和地理因素对个人养老金制度参与的影响：一种机器学习方法

IF 6.3 2区经济学

Borsa Istanbul Review Pub Date : 2025-02-01 DOI: 10.1016/j.bir.2024.12.010

Can Verberi , Muhittin Kaplan

{"title":"The impact of personality, behavior, and geography on participation in the private pension system in Türkiye: A machine learning approach","authors":"Can Verberi , Muhittin Kaplan","doi":"10.1016/j.bir.2024.12.010","DOIUrl":"10.1016/j.bir.2024.12.010","url":null,"abstract":"<div><div>This study examines regional disparities in the factors that affect participation in the Private Pension System (PPS) in Türkiye, focusing on sociodemographic characteristics, personality traits and behavior, and pension and financial literacy. The behavioral factors identified encompass procrastination, locus of control, pessimism, compulsive buying, and time perspective, and the personality traits include openness, agreeableness, extraversion, neuroticism, and conscientiousness. The study employs data on two provinces in Türkiye, Şırnak and Istanbul, and uses XGBoost and Tree SHAP algorithms and a probit model. Our findings indicate that personality traits such as openness, agreeableness, and conscientiousness have a positive influence on individual engagement in pension plans, whereas extraversion has a negative impact. Additionally, basic pension literacy is more influential than advanced pension literacy. The results also show that regional geography significantly influences personality and behavioral factors. Finally, a perception of protection is a critical factor in PPS participation.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"25 1","pages":"Pages 149-162"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Does sentiment in Fed governors’ speeches shape US equity market sectors? 美联储理事讲话中的情绪是否左右美国股市板块？

IF 6.3 2区经济学

Borsa Istanbul Review Pub Date : 2025-02-01 DOI: 10.1016/j.bir.2024.12.009

Asma Arshad , Muhammad Suhail Rizwan

{"title":"Does sentiment in Fed governors’ speeches shape US equity market sectors?","authors":"Asma Arshad , Muhammad Suhail Rizwan","doi":"10.1016/j.bir.2024.12.009","DOIUrl":"10.1016/j.bir.2024.12.009","url":null,"abstract":"<div><div>This paper investigates the responsiveness of US equity sectors to the sentiments conveyed by the speeches of the governors of the US Federal Reserve (the Fed). Using principal component analysis of the scores of four Lexicon dictionaries to analyze speeches from June 1, 1996, to September 30, 2023, we find convincing evidence of a significant reaction to the sentiment index by the consumer discretionary, financial, information technology, raw materials, real estate, and utilities sectors. This reaction is asymmetric, as negative sentiments affect the US sectors more quickly than positive sentiments. Among the sectors, communication services, industrial, consumer staples, and energy sectors only respond significantly to negative sentiments. Furthermore, speeches by the Fed chairman have a greater effect, as shown by the significant reaction of eight sectors, followed by speeches by the vice chairman affecting five and speeches by the governor influencing only three. These results showing the market's asymmetrical response in terms of the types and sources of sentiment have valuable implications for a broad group of market participants.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"25 1","pages":"Pages 79-93"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

US Treasury market default risk and global interbank liquidity risk 美国国债市场违约风险和全球银行间流动性风险

IF 6.3 2区经济学

Borsa Istanbul Review Pub Date : 2025-02-01 DOI: 10.1016/j.bir.2024.12.011

Simon Cottrell , Jinghua Lei , Yihong Ma , Sarath Delpachitra

引用次数: 0

Determinants of financial inclusion in sub-Saharan Africa and OECD countries 撒哈拉以南非洲和经合组织国家普惠金融的决定因素

IF 6.3 2区经济学

Borsa Istanbul Review Pub Date : 2025-02-01 DOI: 10.1016/j.bir.2024.11.004

Samuel Fiifi Eshun , Evžen Kočenda

引用次数: 0

The impact of ESG rating disagreement on the financial performance of environmentally sensitive industry companies worldwide ESG评级分歧对全球环境敏感行业公司财务绩效的影响

IF 6.3 2区经济学

Borsa Istanbul Review Pub Date : 2025-01-31 DOI: 10.1016/j.bir.2025.01.013

Miray Tabur , Recep Bildik

{"title":"The impact of ESG rating disagreement on the financial performance of environmentally sensitive industry companies worldwide","authors":"Miray Tabur , Recep Bildik","doi":"10.1016/j.bir.2025.01.013","DOIUrl":"10.1016/j.bir.2025.01.013","url":null,"abstract":"<div><div>Many studies have examined the relationship between environmental, social and governance performance (ESGP) and financial performance (FP) across various sectors. However, the statistical results of previous studies exhibit variability, including significant and insignificant positive or negative relationships. Moreover, the effects of using ESG ratings from different databases are rarely considered. This paper investigates the sensitivity of data results from different ESG rating agencies by analyzing the ESG ratings of the same companies from the Bloomberg and Refinitiv databases over a 10-year period, covering 464 companies in environmentally sensitive sectors worldwide. Through examining the relationship between ESGP and FP, our research offers potentially valuable insights into the consequences of using different ESG ratings from different rating agencies. The results indicate that using different ESG scores from different rating agencies significantly alters the main results of such studies in the literature and, consequently, influences researchers' and investors' decision-making processes, depending on which ESG data source is utilized.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"25 3","pages":"Pages 435-448"},"PeriodicalIF":6.3,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859794","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

U.S. options exchange-traded funds: Performance dynamics and managerial expertise 美国期权交易所交易基金：业绩动态和管理经验

IF 6.3 2区经济学

Borsa Istanbul Review Pub Date : 2025-01-24 DOI: 10.1016/j.bir.2025.01.012

Elroi Hadad , Davinder Malhotra , Robert McLeod

{"title":"U.S. options exchange-traded funds: Performance dynamics and managerial expertise","authors":"Elroi Hadad , Davinder Malhotra , Robert McLeod","doi":"10.1016/j.bir.2025.01.012","DOIUrl":"10.1016/j.bir.2025.01.012","url":null,"abstract":"<div><div>This study examines the performance dynamics of U.S. options exchange-traded funds (ETFs), whose investment strategy involves options contracts. Analyzing monthly returns data from February 2014 to April 2023, we evaluate the risk-adjusted performance, volatility, and market sensitivity of U.S. options ETFs relative to U.S. and global equities. Using Carhart's four-factor model, we find that U.S. options ETFs yield lower monthly returns than those of U.S. equities but outperform global equities, suggesting potential diversification benefits. While U.S. options ETFs underperformed during the COVID-19 pandemic, they demonstrated resilience thereafter, offering higher rewards for downside risk. We also find that managerial expertise may not consistently improve performance or market timing. These results remain robust across various checks, including analyzing lagged public information, adopting multiple estimation methods, and investigating diverse market conditions. This study contributes to understanding the performance dynamics of options ETFs, emphasizing the importance of market conditions and managerial strategies in investment decisions.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"25 3","pages":"Pages 423-434"},"PeriodicalIF":6.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859793","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 good, the better and the challenging: Insights into predicting high-growth firms using machine learning 好的，更好的和具有挑战性的：使用机器学习预测高增长公司的见解

IF 6.3 2区经济学

Borsa Istanbul Review Pub Date : 2024-12-01 DOI: 10.1016/j.bir.2024.12.001

Sermet Pekin, Aykut Şengül

{"title":"The good, the better and the challenging: Insights into predicting high-growth firms using machine learning","authors":"Sermet Pekin, Aykut Şengül","doi":"10.1016/j.bir.2024.12.001","DOIUrl":"10.1016/j.bir.2024.12.001","url":null,"abstract":"<div><div>This study aims to classify high-growth firms using several machine learning algorithms, including K-Nearest Neighbors, Logistic Regression with L1 (Lasso) and L2 (Ridge) Regularization, XGBoost, Gradient Descent, Naive Bayes and Random Forest. Leveraging a dataset composed of financial metrics and firm characteristics between 2009 and 2022 with 1,318,799 unique firms (averaging 554,178 annually), we evaluate the performance of each model using metrics such as MCC, ROC AUC, accuracy, precision, recall and F1-score. In our study, ROC AUC values ranged from 0.53 to 0.87 for employee-high growth and from 0.53 to 0.91 for turnover-high growth, depending on the method used. Our findings indicate that XGBoost achieves the highest performance, followed by Random Forest and Logistic Regression, demonstrating their effectiveness in distinguishing between high-growth and non-high-growth firms. Conversely, KNN and Naive Bayes yield lower accuracy. Furthermore, our findings reveal that growth opportunity emerges as the most significant factor in our study. This research contributes valuable insights to financial analysts and investors in identifying high-growth firms and underscores the potential of machine learning in economic prediction.</div></div>","PeriodicalId":46690,"journal":{"name":"Borsa Istanbul Review","volume":"24 ","pages":"Pages 47-60"},"PeriodicalIF":6.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143347779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network 利用沙猫群优化和改进的深度长短期记忆网络进行股票价格预测

IF 6.3 2区经济学

Borsa Istanbul Review Pub Date : 2024-12-01 DOI: 10.1016/j.bir.2024.12.002

Burak Gülmez

引用次数: 0