INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
Prev
Next
|
|
|
Network correlation between investor's herding behavior and overconfidence behavior |
Mao Zhang(张昴)1, Yi-Ming Wang(王一鸣)1,2 |
1 School of Economics, Peking University, Beijing 100871, China; 2 Key Laboratory of Mathematical Economics and Quantitative Finance, Peking University, Beijing 100871, China |
|
|
Abstract It is generally accepted that herding behavior and overconfidence behavior are unrelated or even mutually exclusive. However, these behaviors can both lead to some similar market anomalies, such as excessive trading volume and volatility in the stock market. Due to the limitation of traditional time series analysis, we try to study whether there exists network relevance between the investor's herding behavior and overconfidence behavior based on the complex network method. Since the investor's herding behavior is based on market trends and overconfidence behavior is based on past performance, we convert the time series data of market trends into a market network and the time series data of the investor's past judgments into an investor network. Then, we update these networks as new information arrives at the market and show the weighted in-degrees of the nodes in the market network and the investor network can represent the herding degree and the confidence degree of the investor, respectively. Using stock transaction data of Microsoft, US S&P 500 stock index, and China Hushen 300 stock index, we update the two networks and find that there exists a high similarity of network topological properties and a significant correlation of node parameter sequences between the market network and the investor network. Finally, we theoretically derive and conclude that the investor's herding degree and confidence degree are highly related to each other when there is a clear market trend.
|
Received: 02 December 2019
Revised: 13 January 2020
Accepted manuscript online:
|
PACS:
|
89.65.Gh
|
(Economics; econophysics, financial markets, business and management)
|
|
64.60.aq
|
(Networks)
|
|
Fund: Project supported by the Youth Program of the National Social Science Foundation of China (Grant No. 18CJY057). |
Corresponding Authors:
Mao Zhang
E-mail: zhangmao@pku.edu.cn
|
Cite this article:
Mao Zhang(张昴), Yi-Ming Wang(王一鸣) Network correlation between investor's herding behavior and overconfidence behavior 2020 Chin. Phys. B 29 048901
|
[1] |
Donner R V, Small M, Donges J F, Marwan N, Zou Y, Xiang R X and Kurths J 2011 Int. J. Bifurcat. Chaos 21 1019
|
[2] |
Zhang J, Luo X and Small M 2006 Phys. Rev. E 73 016216
|
[3] |
Zhang J, Sun J F, Luo X D, Zhang K, Nakamura T and Small M 2008 Physica D 237 2856
|
[4] |
Marwan N, Donges J F, Zou Y, Donner R V and Kurths J 2009 Phys. Lett. A 373 4246
|
[5] |
Gao Z K and Jin N D 2009 Chaos 19 033137
|
[6] |
Eroglu D, Marwan N, Prasad S and Kurths J 2014 Nonlinear Proc. Geoph. 21 1085
|
[7] |
Xu X K, Zhang J and Small M 2008 Proc. Natl. Acad. Sci. USA 105 19601
|
[8] |
Lacasa L, Luque B, Ballesteros F, Luque J and Nuño J C 2008 Proc. Natl. Acad. Sci. USA 105 4972
|
[9] |
Lacasa L, Luque B, Luque J and Nuno J C 2009 Europhys. Lett. 86 30001
|
[10] |
Luque B, Lacasa L, Ballesteros F J and Luque J 2009 Phys. Rev. E 80 046103
|
[11] |
Luque B, Lacasa L, Ballesteros F J and Robledo A 2012 Chaos 22 013109
|
[12] |
Yu L 2013 Physica A 392 3374
|
[13] |
Zhuang E Y, Small M and Feng G 2014 Physica A 410 483
|
[14] |
Shirazi A H, Jafari G R, Davoudi J, Peinke J, Tabar M R R and Sahimi M 2009 J. Stat. MechTheory E 7 P07046
|
[15] |
Campanharo A S L O, Sirer M I, Malmgren R D, Ramos F M and Amaral L A N 2011 Plos One 6 e23378
|
[16] |
Zhao Y, Weng T F and Ye S K 2014 Phys. Rev. E 90 012804
|
[17] |
Yang Y and Yang H 2008 Physica A 387 1381
|
[18] |
Yue Y, Wang J, Yang H and Mang J S 2009 Physica A 388 4431
|
[19] |
Pang M B and Huang Y M 2018 Chin. Phys. B 27 118902
|
[20] |
Chen D, Shi D D and Pan G J 2019 Acta Phys. Sin. 68 118901 (in Chinese)
|
[21] |
Wu L R, Li J J and Qi J Y 2019 Acta Phys. Sin. 68 078901 (in Chinese)
|
[22] |
Wang N, Li D and Wang Q 2012 Physica A 391 6543
|
[23] |
Sias R W 2004 Rev. Financ. Stud. 17 165
|
[24] |
Barber B, Odean T and Zhu N 2009 Rev. Financ. Stud. 22 151
|
[25] |
Gontis V, Havlin S, Kononovicius A, Podobnik B and Stanley H E 2016 Physica A 462 1091
|
[26] |
Kremer S and Nautz D 2013 J. Bank. Financ. 37 1676
|
[27] |
Galariotis E C, Rong W and Spyrou S I 2015 J. Bank. Financ. 50 589
|
[28] |
Chiang T C and Zheng D 2010 J. Bank. Financ. 34 1911
|
[29] |
Bikhchandani S and Sharma S 2000 IMF Staff Papers 47 279
|
[30] |
Gervais S and Odean T 2001 Rev. Financ. Stud. 14 1
|
[31] |
Park A and Sabourian H 2011 Econometrica 79 973
|
[32] |
Blasco N, Corredor P and Ferreruela S 2012 Quantum Finance 12 311
|
[33] |
Hoelzl E and Rustichini A 2005 Econ. J. 115 305
|
[34] |
Benoȋt J P and Dubra J 2011 Econometrica 79 1591
|
[35] |
Barber B and Odean T 2001 Quart. J. Econ. 116 261
|
[36] |
Chuang W I and Susmel R 2011 J. Bank. Financ. 35 1626
|
[37] |
Odean T 1998 J. Financ. 53 1887
|
[38] |
Daniel K, Hirshleifer D and Subrahmanyam A 1998 J. Financ. 53 1839
|
[39] |
Hirshleifer D, Subrahmanyam A and Titman S 1994 J. Financ. 49 1665
|
[40] |
Jegadeesh N and Kim W 2010 Rev. Financ. Stud. 23 901
|
[41] |
Tauchen G E and Pitts M 1983 Econometrica 51 485
|
[42] |
Lux T 1995 Econ. J. 105 881
|
[43] |
Yook S H, Jeong H, Barabási A L and Tu Y 2001 Phys. Rev. Lett. 86 5835
|
[44] |
Zheng B, Ren F, Trimper S and Zheng D F 2004 Physica A 343 653
|
[45] |
Avery C and Zemsky P 1998 Amer. Econ. Rev. 88 724
|
[46] |
Statman M, Thorley S and Vorkink K 2006 Rev. Financ. Stud. 19 1531
|
[47] |
Karpoff J M 1987 J. Financ. Quant. Anal. 22 109
|
No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|