Trade policy uncertainty and financial investment: Evidence from Chinese energy firms

Li, Mengzhe; Lin, Qianru; Lan, Fei; Zhan, Zhimin; He, Zhongshi

doi:10.1016/j.eneco.2022.106424

Cited by 21 publications

(7 citation statements)

References 41 publications

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“…This finding implies that different crisis events lead to changes in sectors affected by risk spillovers from the oil market. Our finding is consistent with [45], who show that the recipient sectors of risk spillovers from the oil market can vary depending on the turbulent environment.…”

Section: Extreme Risk Spillover Networksupporting

confidence: 93%

“…3) MSCI China Index is an index of securities compiled by Morgan Stanley Capital International that reflects the volatility of China's sectors. 4) GPR (Geopolitical Risk Index) stands for the geopolitical risk index, and it has previously been shown in the literature that as geopolitical risk increases, the level of risk in the energy market and the stock market increases [45]. 5) EPU (Economic Policy Uncertainty) describes China's economic policy uncertainty.…”

Section: Random Forestmentioning

confidence: 99%

“…OUT is used to measure the risk spillover Frontiers in Physics frontiersin.org from the oil market. Referring to the studies of [38,45], we select CPI, OVX, MSCI, GPR, EPU and TPU as the factors that may have an impact on OUT. First, the data is smoothed using logarithmic differencing of the OUT and factors.…”

Section: Risk Spillover Mechanisms In the Oil Marketmentioning

confidence: 99%

“…This would affect the production costs of the related sectors, affecting their profitability and share prices. For GPR, its increase may represent an increase in geopolitical tensions, and the spread of such tensions may affect the stable supply of oil-producing countries, triggering concerns about energy security and supply disruptions [45]. As a major importer of oil, China relies on steady supplies from the oil industry for a number of sectors, including EN and UL.…”

Section: Risk Spillover Mechanisms In the Oil Marketmentioning

confidence: 99%

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Extreme risk measurement for the oil and China’s sectors system—network-based approach and machine learning methods

Fang,

Wang,

Lin

et al. 2023

Front. Phys.

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China is a large oil-consuming country, and sharp fluctuations in oil prices are bound to be detrimental to the stable growth of its economy. Therefore, accurately grasping the impact of the oil market on China’s sectors is the key to ensuring its healthy economic development. The article aims to explore the extreme risk transfer link of the oil and China’s sectors system, focusing on uncovering the risk spillover mechanism of the oil and providing early warning on it. We apply the TENET method to discuss risk propagation relationships within the oil and sectors system at three levels. The TVP-VAR model is brought in to recognize the factors affecting risk spillover in the oil market from the network correlation perspective. Finally, early warning of oil risk spillover is provided by incorporating the influencing factors into a machine learning model. The outcomes indicate that the risk connectivity of the oil and China’s sectors system is highly correlated with extreme events. There are variations in the spillover effects of oil market risk on different sectors, with Telecommunication Services, Utilities, Financials and Major Consumer sectors being the main bearers of the oil risk shocks. Overall, oil risk spillovers are mainly driven by economic policy and geopolitics, but oil price uncertainty is found to have a persistent impact on oil market risk spillovers in the dynamic analysis. Random forest model can provide effective early warning of oil risk spillovers. In addition, the significance analysis shows that oil price uncertainty and inflation are important factors affecting oil risk spillovers and are nonlinearly correlated with them.

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Section: Extreme Risk Spillover Networksupporting

confidence: 93%

Section: Random Forestmentioning

confidence: 99%

Section: Risk Spillover Mechanisms In the Oil Marketmentioning

confidence: 99%

Section: Risk Spillover Mechanisms In the Oil Marketmentioning

confidence: 99%

See 2 more Smart Citations

Extreme risk measurement for the oil and China’s sectors system—network-based approach and machine learning methods

Fang,

Wang,

Lin

et al. 2023

Front. Phys.

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show abstract

“…Emerging market firms operate in environments characterized by various volatility and uncertainty conditions, such as ambiguous institutions, chaotic supply and demand-side changes [ 7 ]. From the real option theory aspect, the external environment has a significant impact on firm growth, because environmental uncertainty may influence these firm’s attitude toward their investments on innovation activities [ 70 ]. In the short term, Chinese NEV battery recycling is more likely to hold more cash and increase R&D activities when EPU increases, and the conclusion is the opposite in the long term.…”

Section: Empirical Analysis and Discussionmentioning

confidence: 99%

Impact of Environmental Uncertainties and Strategic Flexibility in Innovation Activities on NEV Battery Recycling Firms in China

Liu

2023

IJERPH

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Due to the popularization and development of new energy vehicles (NEVs) worldwide, power batteries that have been used are being retired and replaced. In China’s battery recycling industry, the legal NEV battery recycling enterprises are at a negative financial performance. Based on theory of organizational adaptation, the key to innovation performance and sustainable development is recognition of the environment and strengthening organizational flexibility. This study empirically explores the bidirectional dynamic relationships among heterogeneous environmental uncertainties, innovation activities, firm growth and strategic flexibility in Chinese NEV battery recycling firms. A total of 1040 sample data were collected from 2015 to 2021. The research results demonstrate that environmental uncertainty (EU), strategic flexibility (SF) and innovation activities (INNO) all had impacts on firm growth (FG). Specifically, INNO had strongly negative effects in the short term, and in the long term, it will bring a positive effect to FG; the impact of EPU was more important than market uncertainty (MU) to FG and innovation activities. This could be due to the dependence of the Chinese NEV battery recycling industry on government policy. However, MU has a strong impact on SF. Moreover, the levels of SF should be reasonable, otherwise it could be a burden to enterprises. There also exists the bidirectional dynamic relationships between FG and INNO. This study contributes a non-core perspective to strategic flexibility research by revealing the complex environmental mechanism, and to the Chinese NEV battery recycling industry we provide a theoretical basis and practical guidance for government and firms on how to apply SF to promote innovation and realize growth in the present business environment.

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A timely helping hand: China‐U.S. Trade War, government subsidy, and firm innovation

Chen,

Yang,

et al. 2024

Manage Decis Econ

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This paper investigates the impact and mechanism of the China‐U.S. trade war on firm innovation in China exploiting the trade war as a quasi‐natural experiment using firm‐level data from 2015 to 2020. We apply textual analysis to annual reports of listed firms to identify treatment and control groups. Our DID estimates show that while the trade war is a negative shock, it has increased the number of total patent applications, invention patent applications, and non‐invention patent applications by 6.0%, 3.7%, and 4.1%, respectively. We also observe that government subsidies could help firms resist adverse shocks on time with more innovation outcomes. Furthermore, we find the pertinence of government subsidy. The government tended to grant subsidies to firms that have trade connections with the U.S. and firms would use subsidies for R&D to increase innovation. Overall, we find that the trade war prompts the government to increase subsidies for firms, thereby boosting innovation.

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Trade policy uncertainty and financial investment: Evidence from Chinese energy firms

Cited by 21 publications

References 41 publications

Extreme risk measurement for the oil and China’s sectors system—network-based approach and machine learning methods

Extreme risk measurement for the oil and China’s sectors system—network-based approach and machine learning methods

Impact of Environmental Uncertainties and Strategic Flexibility in Innovation Activities on NEV Battery Recycling Firms in China

A timely helping hand: China‐U.S. Trade War, government subsidy, and firm innovation

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