Browse > Article
http://dx.doi.org/10.13106/jafeb.2021.vol8.no10.0147

A Characteristic Analysis and Countermeasure Study of the Hedging of Listed Companies in China Stock Markets  

WU, Guo-Hua (School of Management, Hefei University of Technology)
JIANG, Xiao-Ling (Huishang Futures Co., Ltd)
DENG, Su-Ya (Huishang Futures Co., Ltd)
Publication Information
The Journal of Asian Finance, Economics and Business / v.8, no.10, 2021 , pp. 147-158 More about this Journal
Abstract
Due to COVID-19, the risk of price volatility in commodity and equity markets increases. The research and application of hedging is the most effective way to reduce the market risk. Hedging is a risk management strategy employed to offset losses in investments by taking an opposite position in a related asset. We use K-means and hierarchical clustering methods to cluster companies and futures products respectively, and analyze the relationship between the number of hedging firms, regional distribution, nature of firms, capital distribution, company size, profitability, number of local Futures Commission Merchants (FCMs), regional location, and listing time. The study shows that listed companies with large scale and good profitability invest more money in hedging, while state-owned enterprises' participation in hedging is more likely to be affected by the company size and the number of local futures commission merchants, and private enterprises are more likely to be affected by the company profitability and the regional location. Listed companies are more willing to choose long-listed and mature futures products for hedging. We also provide policy advice based on our conclusion. So far, there is no study on the characteristics of hedging. This paper fills the gap. The results provide a basis and guidance for people's investment and risk management. Using clustering analysis in hedging study is another innovation of this paper.
Keywords
Characteristic Analysis; Countermeasure Study; Hedging; Listed Companies; China Stock Markets;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Chang, C. L., McAleer, M., & Tansuchat, R. (2011). Crude oil hedging strategies using dynamic multivariate GARCH. Energy Economics, 33(5), 912-923. https://doi.org/10.1016/j.eneco.2011.01.009   DOI
2 Smith, C. W., & Stulz, R. M. (1985). The determinants of firms' hedging policies. Journal of Financial and Quantitative Analysis, 20(4), 391-405. https://doi.org/10.2307/2330757   DOI
3 Di Lascio, F. M. L., Giammusso, D., & Puccetti, G. (2018). A clustering approach and a rule of thumb for risk aggregation. Journal of Banking & Finance, 96, 236-248. https://doi.org/10.1016/j.jbankfin.2018.07.002   DOI
4 He, X. (2008). Multivariate statistical analysis. Beijing, China: Renmin University of China Press.
5 Lee, J. W., Becker, K., & Potluri, R. M. (2016). Antecedents of corporate adoption of social media and the role of the technology acceptance model in the path. Journal of Asian Finance, Economics, and Business, 3(2), 67-76. https://doi.org/10.13106/jafeb.2016.vol3.no2.67   DOI
6 Lee, J. W., & Mendlinger, S. (2011). An empirical investigation of the relationship between the operational competence of service providers and the use and adoption of mobile commerce. Journal of Distribution Science, 9(2), 5-12. https://doi.org/10.2139/ssrn.3089058   DOI
7 Sopranzetti, B. J., & Datar, V. (2002). Price clustering in foreign exchange spot markets. Journal of Financial Markets, 5(4), 411-417. https://doi.org/10.1016/S1386-4181(01)00032-5   DOI
8 Li, C. (2019). Fundamental futures and other derivatives: Beijing: China Fortune Press.
9 Liu, G. (2019). Technical trading behavior: Evidence from Chinese rebar futures market. Computational Economics, 54(2), 669-704. https://doi.org/10.1007/s10614-018-9851-4   DOI
10 Rahman, M. M, Meah, M. R, & Chaudhory, N. U (2019). The impact of audit characteristics on firm performance: An empirical study from an emerging economy. The Journal of Asian Finance, Economics, and Business, 6(1), 59-69. https://doi.org/10.13106/jafeb.2019.vol6.no1.59   DOI
11 Buehler, H., Gonon, L., Teichmann, J., & Wood, B. (2019). Deep hedging. Quantitative Finance, 19(8), 1271-1291. https://doi.org/10.1080/14697688.2019.1571683   DOI
12 Tola, V., Lillo, F., Gallegati, M., & Mantegna, R. (2008). Cluster analysis for portfolio optimization. Journal of Economic Dynamics and Control, 32(1), 235-258. https://doi.org/10.1016/j.jedc.2007.01.034   DOI
13 Tran, T. T.T., Do, N. H., & Nguyen, Y. T. (2020) Impact of board characteristics on bank risk: The case of Vietnam. The Journal of Asian Finance, Economics, and Business, 7(9), 377-388. https://doi.org/10.13106/jafeb.2020.vol7.no9.377   DOI
14 Ward Jr, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236-244. https://doi.org/10.1080/01621459.1963.10500845   DOI
15 Schwartz, A. L., Van Ness, B. F., & Van Ness, R. A. (2004). Clustering in the futures market: Evidence from S&P 500 futures contracts. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 24(5), 413-428. https://doi.org/10.1002/fut.10129   DOI
16 Arouri, M. E. H., Jouini, J., & Nguyen, D. K. (2012). On the impacts of oil price fluctuations on European equity markets: Volatility spillover and hedging effectiveness. Energy Economics, 34(2), 611-617. https://doi.org/10.1016/j.eneco.2011.08.009   DOI