Abstract

Stock market volatility significantly impacts investors, policymakers, and industry development. While previous studies have identified key influencing factors, they have largely overlooked the unique volatility of the Chinese Baijiu stock market. This study adopts a complex network perspective, integrating transfer entropy, Peter and Clark momentary conditional independence (PCMCI), and interpretable machine learning methods to reveal the key drivers and mechanisms behind the market's volatility. The research identifies 10 critical factors spanning four dimensions: resources and agriculture, industry and manufacturing, services and consumption, and cross-domain sustainable development. Our findings indicate that the volatility of the Chinese Baijiu stock market is driven by a combination of agricultural, industrial, and sustainable development factors, highlighting the importance of industrial synergies. In addition to confirming the significance of traditional factors, this study also reveals the direct positive causal relationships of emerging industry variables, such as construction decoration, environmental protection, and energy, with the Chinese Baijiu stock market. These findings not only enhance the understanding of the dynamics of the Chinese Baijiu stock market but also provide a transferable research framework and methodology for other industries.

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