An Examination of Sectoral Herding in the French Stock Market
Keywords:
Herding behavior, Sectoral herding, Asymmetric herding, CSAD model, Quantile regressions, COVID-19, Russia-Ukraine warAbstract
This paper aims to detect the presence of herding behavior in the French stock market at the industry level and under several market states. Methodology: The authors use closing prices of 270 companies listed on Paris Stock Exchange and pertaining to 9 industries, namely Industrials, Technology, Utilities, Basic Materials, Financials, Consumer Good, Health Care, Oil & Gas and Consumer Services. The study period ranges from January 1, 2017 to October 31, 2022, covering the COVID-19 crisis and the Russia-Ukraine war. The CrossSectional Absolute Deviation of returns (CSAD) model, proposed by Chang, Cheng and Khorana (2000), and the quantile regressions (QR) are used to examine the occurrence of herding behavior in different market states. GARCH model is applied to obtain robust results. Findings: The results indicate that herding behavior occurs only in some industries. We report evidence of herding behavior in 2 sectors out 9 (Technology and Financials). In addition, some market conditions such as downturn, volatility and trading volumes appear to cause the formation of herding behavior. Additionally, investors in some sectors are likely to herd during crisis periods and high uncertainty levels. The results of the quantile regression indicate that herding behavior varies across quantiles. Originality: Prior studies on herding behavior have primarily focused on global stock markets, while this behavior may occur within specific activity sectors. The novelty of this paper lies in examining herding behavior at the industry level and under various market conditions. Also, we conducted a comparative study between the results found by each of the two methods: CSAD and QR. While the CSAD model is useful for detecting aggregate patterns, the quantile regression approach offers a richer and more detailed understanding of herding behavior, especially under extreme market conditions and within specific sectors. Research implications: Our findings could aid investors, portfolio managers and policymakers to understand the causes of mispricing in some sectors. The presence of herding in the Technology and Financials sectors implies that policymakers should remove information asymmetry in these two sectors to avoid mispricing and ensure market efficiency. The anti-herding behavior documented in the sectors of Basic materials, Consumer goods and Oil & Gas may be attributed to a good information environment in these sectors. The presence of herding in some sectors and anti-herding in others imply that investors and portfolio managers ought to adopt an effective diversification strategy by spreading their investments across a number of sectors to reduce loss due to herding. This diversification strategy should be revised depending on market conditions since our findings reveal that herding behavior has an asymmetric characteristic. Policymakers are called to monitor the efficiency of the financial market, particularly for some activity sectors.
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