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The rapid growing of algorithmic trading (AT) has been playing an increasingly important role in shaping financial market in recent years. Although many scholars have studied the impact of AT on market volatility, the evidence they provided is inconsistent. Whether and how AT influence on market volatility is still not clear, especially for emerging markets. In this paper, using level 2 quotations from Chinese market, we answered the question. By introducing multiple mediator model, we found that AT can significantly reduce market volatility. In addition to the influence of AT itself, the sentiment effect accounts for approximate 1/4 of the influence, and a very small portion, about 4%, of the influence can be explained by herd effect. Besides, it also illustrated that the influences of AT are more sensitive in main board, rather than in GEM board. Nevertheless, the sentiment effect has been playing plays more important role in the influence of AT in GEM board. These findings provide new insights into previous inconsistent evidence regarding the influence of AT on market volatility. They also have important implications for investment strategies and market regulations.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12358520 | PMC |
http://dx.doi.org/10.1038/s41598-025-15020-w | DOI Listing |
PLoS One
September 2025
China Aluminum International Trading Group Co., Ltd, Shanghai, China.
This study examines the volatility connectedness across 28 sectors in the Chinese stock market, aiming to discern the risk spillovers and their implications for financial security and economic stability. Employing a network connectedness approach, we analyze the volatility connectedness's characteristics and dynamic evolution among various sectors. The findings indicate that manufacturing industries exhibit a high degree of correlation among themselves and predominantly function as exporters of risk spillovers.
View Article and Find Full Text PDFChaos
September 2025
Department of Economics and Business, University of Almería, 04120 Almería, Spain.
Studying and comprehending the eigenvalue distribution of the correlation matrices of stock returns is a powerful tool to delve into the complex structure of financial markets. This paper deals with the analysis of the role of eigenvalues and their associated eigenvectors of correlation matrices within the context of financial markets. We exploit the meaningfulness of Random Matrix Theory with the specific aspect of the Marchenko-Pastur distribution law to separate noise from true signal, but with a special focus on giving an interpretation of what these signals mean in the financial context.
View Article and Find Full Text PDFRisk Anal
September 2025
School of Business, East China University of Science and Technology, Shanghai, China.
Stable and efficient food markets are crucial for global food security. However, international staple food markets are increasingly exposed to complex risks, including intensified risk contagion and increasing external uncertainties. This paper systematically investigates risk spillovers in global staple food markets and explores the key determinants of these spillover effects, combining innovative decomposition-reconstruction techniques, risk connectedness analysis, and random forest models.
View Article and Find Full Text PDFJ Environ Manage
September 2025
School of Management, China Institute for Studies in Energy Policy, Collaborative Innovation Center for Energy Economics and Energy Policy, Xiamen University, Fujian, 361005, China; Advanced Interdisciplinary Research Center, City University of Macau, Macao, China. Electronic address:
This study examines the dynamics of volatility spillover between financial assets and transport energy. Such spillovers are critical for implementing risk management strategies, supporting energy policymaking, and enabling more effective investment decisions in today's globally integrated financial-energy system. Our study includes financial assets (both non-green and green) and transport energy (fossil fuels and biofuels).
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