Trading Volume Concentration across S&P 500 Index Constituents—A Gini-Based Analysis and Concentration-Driven (Daily Rebalanced) Portfolio Performance Evaluation: Is Chasing Concentration Profitable?

Q4 Business, Management and Accounting
Dominik Metelski, Janusz Sobieraj
{"title":"Trading Volume Concentration across S&P 500 Index Constituents—A Gini-Based Analysis and Concentration-Driven (Daily Rebalanced) Portfolio Performance Evaluation: Is Chasing Concentration Profitable?","authors":"Dominik Metelski, Janusz Sobieraj","doi":"10.3390/jrfm17080325","DOIUrl":null,"url":null,"abstract":"The period from January 2020 to December 2022 was marked by a confluence of major events, including the COVID-19 pandemic, the Russia–Ukraine war, the energy crisis, surging inflation, Federal Reserve policy shifts, and banking turmoil, which collectively fueled heightened market volatility, risk management needs, and speculative trading opportunities, leading to volatile swings in trading volume concentration across financial markets, with periods of significant increases followed by rapid declines. This paper examines the variation in the concentration of trading volume across the full spectrum of S&P 500 companies, with a focus on explaining the reasons behind the stochastic changes in trading volume concentration. We analyze different concentration measurement methods, including the power law exponent, the Herfindahl–Hirschman Index, and the Gini-based Trading Concentration Index (TCI). The research employs a novel experimental design, comparing a concentration-driven portfolio, rebalanced daily based on the top 30 stocks by trading volume, against the S&P 500 benchmark. Our findings reveal that the Gini-based TCI fluctuated between 55.98% and 77.35% during the study period, with significant variations coinciding with major market events. The concentration-driven portfolio outperformed the S&P 500, achieving an annualized return of 10.66% compared to 5.89% for the index, with a superior Sharpe ratio of 0.325 versus 0.19. This performance suggests that following trading volume concentration can yield above-average results. However, this study also highlights the importance of understanding and managing the risks associated with concentrated portfolios. This study contributes to the literature on market dynamics and offers practical insights for investors and fund managers on optimizing portfolio strategies in response to evolving concentration patterns in financial markets.","PeriodicalId":47226,"journal":{"name":"Journal of Risk and Financial Management","volume":"70 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Risk and Financial Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/jrfm17080325","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Business, Management and Accounting","Score":null,"Total":0}
引用次数: 0

Abstract

The period from January 2020 to December 2022 was marked by a confluence of major events, including the COVID-19 pandemic, the Russia–Ukraine war, the energy crisis, surging inflation, Federal Reserve policy shifts, and banking turmoil, which collectively fueled heightened market volatility, risk management needs, and speculative trading opportunities, leading to volatile swings in trading volume concentration across financial markets, with periods of significant increases followed by rapid declines. This paper examines the variation in the concentration of trading volume across the full spectrum of S&P 500 companies, with a focus on explaining the reasons behind the stochastic changes in trading volume concentration. We analyze different concentration measurement methods, including the power law exponent, the Herfindahl–Hirschman Index, and the Gini-based Trading Concentration Index (TCI). The research employs a novel experimental design, comparing a concentration-driven portfolio, rebalanced daily based on the top 30 stocks by trading volume, against the S&P 500 benchmark. Our findings reveal that the Gini-based TCI fluctuated between 55.98% and 77.35% during the study period, with significant variations coinciding with major market events. The concentration-driven portfolio outperformed the S&P 500, achieving an annualized return of 10.66% compared to 5.89% for the index, with a superior Sharpe ratio of 0.325 versus 0.19. This performance suggests that following trading volume concentration can yield above-average results. However, this study also highlights the importance of understanding and managing the risks associated with concentrated portfolios. This study contributes to the literature on market dynamics and offers practical insights for investors and fund managers on optimizing portfolio strategies in response to evolving concentration patterns in financial markets.
标准普尔 500 指数成分股的交易量集中度--基于基尼系数的分析和集中度驱动的(每日再平衡)投资组合绩效评估:追逐集中度是否有利可图?
2020 年 1 月至 2022 年 12 月期间,COVID-19 大流行病、俄乌战争、能源危机、通胀飙升、美联储政策转变和银行业动荡等重大事件交织在一起,共同加剧了市场波动、风险管理需求和投机交易机会,导致整个金融市场的交易量集中度波动剧烈,先是大幅上升,然后迅速下降。本文研究了标准普尔 500 指数所有公司交易量集中度的变化,重点是解释交易量集中度随机变化背后的原因。我们分析了不同的集中度测量方法,包括幂律指数、赫芬达尔-赫希曼指数和基于基尼系数的交易集中度指数(TCI)。研究采用了一种新颖的实验设计,将每天根据交易量排名前 30 位的股票进行再平衡的集中度驱动型投资组合与标准普尔 500 指数基准进行比较。我们的研究结果表明,在研究期间,基于基尼系数的 TCI 在 55.98% 和 77.35% 之间波动,重大市场事件发生时会出现显著变化。集中度驱动型投资组合的表现优于标准普尔 500 指数,实现了 10.66% 的年化收益率,而该指数的年化收益率为 5.89%,夏普比率为 0.325,优于 0.19。这一业绩表明,跟踪交易量集中度可以产生高于平均水平的结果。不过,本研究也强调了了解和管理集中投资组合相关风险的重要性。本研究为有关市场动态的文献做出了贡献,并为投资者和基金经理提供了针对金融市场不断变化的集中模式优化投资组合策略的实用见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
4.50
自引率
0.00%
发文量
512
审稿时长
11 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信