Archives
Current issue
About
About us
Aims and Scope
Abstracting and Indexing
Editorial Office
Open Access Policy
Publication Ethics
Journal History
Publisher
Follow us: Twitter
Contact
For Authors
Editorial Policy
Peer Review Policy
Manuscript Preparation Guidelines
Copyright & Licencing
Publication Fees
Fee Waiver Policy for Doctoral Students
EJMSTE Language Editing Service
More
Statistics
Special Issues
Special Issue Announcements
Published Special Issues
SPECIAL ISSUE PAPER
Heterogeneous Beliefs, Institutional Investors and Stock Returns – Evidence from China
1
School of Economics and Management, Southwest Jiaotong University, Chengdu, Sichuan 610031, CHINA
2
School of Economics and Finance, Chongqing University of Technology, Banan District, Chongqing 400054, CHINA
Online publication date: 2017-11-21
Publication date: 2017-11-21
EURASIA J. Math., Sci Tech. Ed 2017;13(12):7783-7790
KEYWORDS
TOPICS
ABSTRACT
Using the turnover decomposition model, I extract unexpected trading volume from the institutional investors’ trading activity to measure the institutional investors’ heterogeneous beliefs and explore the explanatory power of that on stock returns. Portfolios built according to the magnitude of institutional investors’ heterogeneous beliefs are significantly profitable. The expected returns of portfolios with higher heterogeneous beliefs are significantly higher than other portfolios, particularly for small companies, and the influence of institutional investors’ heterogeneous beliefs on stock returns during the current month is significantly positive, but it is significantly negative for the next month. When considering beta, bm, size and short-sales constraints, the conclusion is still valid.
REFERENCES (27)
1.
Ajinkya, B. B., Atiase, R. K., & Gift, M. J. (1991). Volume of trading and the dispersion in financial analysts’ earnings forecasts. Accounting Review, 389-401.
2.
Allen, F., Litov, L., & Mei, J. (2006). Large investors, price manipulation, and limits to arbitrage: An anatomy of market corners. Review of Finance, 10(4), 645-693.
3.
Bamber, L. S., Barron, O. E., & Stober, T. L. (1999). Differential interpretations and trading volume. Journal of Financial and Quantitative Analysis, 34(3), 369-386.
4.
Barber, B. M., & Odean, T. (2007). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. The Review of Financial Studies, 21(2), 785-818.
5.
Basak, S. (2005). Asset pricing with heterogeneous beliefs. Journal of Banking & Finance, 29(11), 2849-2881.
6.
Boehme, R. D., Danielsen, B. R., & Sorescu, S. M. (2006). Short-sale constraints, differences of opinion, and overvaluation. Journal of Financial and Quantitative Analysis, 41(2), 455-487.
7.
Chen, J., Hong, H., & Stein, J. C. (2002). Breadth of ownership and stock returns. Journal of financial Economics, 66(2), 171-205.
8.
Chen, L., Qin, L., & Zhu, H. (2015). Opinion divergence, unexpected trading volume and stock returns: Evidence from China. International Review of Economics & Finance, 36, 119-127.
9.
Chopra, N., Lakonishok, J., & Ritter, J. R. (1992). Measuring abnormal performance: do stocks overreact?. Journal of financial Economics, 31(2), 235-268.
10.
De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of political Economy, 98(4), 703-738.
11.
Detemple, J., & Murthy, S. (1997). Equilibrium asset prices and no-arbitrage with portfolio constraints. The Review of Financial Studies, 10(4), 1133-1174.
12.
Diether, K. B., Malloy, C. J., & Scherbina, A. (2002). Differences of opinion and the cross section of stock returns. The Journal of Finance, 57(5), 2113-2141.
13.
Garfinkel, J. A. (2009). Measuring investors’ opinion divergence. Journal of Accounting Research, 47(5), 1317-1348.
14.
Garfinkel, J. A., & Sokobin, J. (2006). Volume, opinion divergence, and returns: A study of post–earnings announcement drift. Journal of Accounting Research, 44(1), 85-112.
15.
Goetzmann, W. N., & Massa, M. (2001). Heterogeneity of trade and stock returns: Evidence from index fund investors. ICF Working Paper No. 00-28.
16.
Goetzmann, W. N., & Massa, M. (2005). Dispersion of opinion and stock returns. Journal of Financial Markets, 8(3), 324-349.
17.
Hirshleifer, D., Subrahmanyam, A., & Titman, S. (1994). Security analysis and trading patterns when some investors receive information before others.The Journal of Finance, 49(5), 1665-1698.
18.
Hong, H., & Stein, J. C. (2007). Disagreement and the stock market. Journal of Economic Perspectives, 21(2), 109-128.
19.
Kaniel, R., Saar, G., & Titman, S. (2008). Individual investor trading and stock returns. The Journal of Finance, 63(1), 273-310.
20.
Karpoff, J. M. (1987). The relation between price changes and trading volume: A survey. Journal of Financial and quantitative Analysis, 22(1), 109-126.
21.
Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica: Journal of the Econometric Society, 1315-1335.
22.
Lee, B. S., Li, W., & Wang, S. S. (2010). The dynamics of individual and institutional trading on the Shanghai Stock Exchange. Pacific-Basin Finance Journal, 18(1), 116-137.
23.
Liu, P. H. (2016). Sociocultural Perspectives on the Internationalization of Research in Mathematics Education: A Survey Based on JRME, ESM, and MTL. Eurasia Journal of Mathematics, Science and Technology Education, 13(3), 911-927.
24.
Liu, X. G., & Natarajan, R. (2012). The effect of financial analysts’ strategic behavior on analysts’ forecast dispersion. The Accounting Review, 87(6), 2123-2149.
25.
Miller, E. M. (1977). Risk, uncertainty, and divergence of opinion. Journal of Finance, 32(4), 1151-1168.
26.
Qin, L., & Zhu, H. (2015). Efficiency of heterogeneity measures: an asset pricing perspective. China Finance Review International, 5(4), 371-385.
27.
Zhong, X. M., & Fan, K. K. (2016). A New Perspective on Design Education: A" Creative Production-Manufacturing Model" in" The Maker Movement" Context. Eurasia Journal of Mathematics, Science & Technology Education, 12(5).
| eISSN: | 1305-8223 |
| ISSN: | 1305-8215 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
