Information Transfer between Stock Market Sectors: A Comparison between the USA and China

被引:26
作者
Yue, Peng [1 ]
Fan, Yaodong [2 ,3 ]
Batten, Jonathan A. [4 ]
Zhou, Wei-Xing [1 ,5 ,6 ]
机构
[1] East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China
[2] Univ Technol Sydney, Sch Business, Sydney, NSW 2007, Australia
[3] Shanghai Univ Finance & Econ, Coll Business, Shanghai 200433, Peoples R China
[4] Univ Utara Malaysia, Sch Econ Finance & Banking, Coll Business, Sintok 06010, Kedah, Malaysia
[5] East China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R China
[6] East China Univ Sci & Technol, Res Ctr Econophys, Shanghai 200237, Peoples R China
基金
中国国家自然科学基金;
关键词
information transfer; transfer entropy; stock markets; econophysics; LEAD-LAG RELATIONSHIP; TIME-SERIES; GRANGER CAUSALITY; TRANSFER ENTROPY; PREDICTABILITY; NETWORK; FLOW; PREDICTION; INDEX; PATH;
D O I
10.3390/e22020194
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Information diffusion within financial markets plays a crucial role in the process of price formation and the propagation of sentiment and risk. We perform a comparative analysis of information transfer between industry sectors of the Chinese and the USA stock markets, using daily sector indices for the period from 2000 to 2017. The information flow from one sector to another is measured by the transfer entropy of the daily returns of the two sector indices. We find that the most active sector in information exchange (i.e., the largest total information inflow and outflow) is the non-bank financial sector in the Chinese market and the technology sector in the USA market. This is consistent with the role of the non-bank sector in corporate financing in China and the impact of technological innovation in the USA. In each market, the most active sector is also the largest information sink that has the largest information inflow (i.e., inflow minus outflow). In contrast, we identify that the main information source is the bank sector in the Chinese market and the energy sector in the USA market. In the case of China, this is due to the importance of net bank lending as a signal of corporate activity and the role of energy pricing in affecting corporate profitability. There are sectors such as the real estate sector that could be an information sink in one market but an information source in the other, showing the complex behavior of different markets. Overall, these findings show that stock markets are more synchronized, or ordered, during periods of turmoil than during periods of stability.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Inferring a Drive-Response Network from Time Series of Topological Measures in Complex Networks with Transfer Entropy
    Ai, Xinbo
    [J]. ENTROPY, 2014, 16 (11) : 5753 - 5776
  • [2] [Anonymous], 2000, APPL FINANCIAL EC, DOI DOI 10.1080/0960310003316992-S2.0-0033948195
  • [3] [Anonymous], 2000, An Introduction to Econophysics: Correlations and Complexity in Finance
  • [4] Transfer Entropy as a Log-Likelihood Ratio
    Barnett, Lionel
    Bossomaier, Terry
    [J]. PHYSICAL REVIEW LETTERS, 2012, 109 (13)
  • [5] Information Feedback in Temporal Networks as a Predictor of Market Crashes
    Begusic, Stjepan
    Kostanjcar, Zvonko
    Kovac, Dejan
    Stanley, H. Eugene
    Podobnik, Boris
    [J]. COMPLEXITY, 2018,
  • [6] On the predictability of energy commodity markets by an entropy-based computational method
    Benedetto, F.
    Giunta, G.
    Mastroeni, L.
    [J]. ENERGY ECONOMICS, 2016, 54 : 302 - 312
  • [7] The dynamics of information-driven coordination phenomena: A transfer entropy analysis
    Borge-Holthoefer, Javier
    Perra, Nicola
    Goncalves, Bruno
    Gonzalez-Bailon, Sandra
    Arenas, Alex
    Moreno, Yamir
    Vespignani, Alessandro
    [J]. SCIENCE ADVANCES, 2016, 2 (04):
  • [8] Large shocks and the September 11th terrorist attacks on international stock markets
    Charles, Amelie
    Darne, Olivier
    [J]. ECONOMIC MODELLING, 2006, 23 (04) : 683 - 698
  • [9] Understanding the Subprime Mortgage Crisis
    Demyanyk, Yuliya
    Van Hemert, Otto
    [J]. REVIEW OF FINANCIAL STUDIES, 2011, 24 (06) : 1848 - 1880
  • [10] Using transfer entropy to measure information flows between financial markets
    Dimpfl, Thomas
    Peter, Franziska Julia
    [J]. STUDIES IN NONLINEAR DYNAMICS AND ECONOMETRICS, 2013, 17 (01) : 85 - 102