Research article

Measuring the effects of investor attention on China's stock returns

  • Received: 07 December 2021 Accepted: 19 December 2021 Published: 24 December 2021
  • JEL Codes: G15, C22

  • The increasing abundance of information leads to the scarcity of investor attention, which has become an important factor affecting the financial market. Search engines play the role of information retrieval and record the search behavior of investors, which is a direct and accurate measure of investor attention. This paper investigates the relationship between investor attention and China's stock market. Considering the relationship with stock returns as the mainline, we take the Baidu index as a substitute variable of investor attention to deeply study the correlation and the time-varying nature between investor attention and China's stock returns. To this end, we used quantile regression to examine the relationship over the period 2006–2021 to capture its evolution during calm and turbulent times. We thus investigated the effect of investor attention on the mean and other quantiles. Our findings show that the relationship between investor attention and China's stock returns exhibits time-variation as investor attention significantly impacts the dynamics of China's stock returns, but its sign and effect vary per quantile: investor attention is negatively correlated with stock returns at low quantiles, but it turns positive at high quantiles. In addition, to test the model's robustness, variable replacement method and model replacement method are used to conduct significance tests, respectively. The results are equally significant.

    Citation: Yi Chen, Zhehao Huang. Measuring the effects of investor attention on China's stock returns[J]. Data Science in Finance and Economics, 2021, 1(4): 327-344. doi: 10.3934/DSFE.2021018

    Related Papers:

  • The increasing abundance of information leads to the scarcity of investor attention, which has become an important factor affecting the financial market. Search engines play the role of information retrieval and record the search behavior of investors, which is a direct and accurate measure of investor attention. This paper investigates the relationship between investor attention and China's stock market. Considering the relationship with stock returns as the mainline, we take the Baidu index as a substitute variable of investor attention to deeply study the correlation and the time-varying nature between investor attention and China's stock returns. To this end, we used quantile regression to examine the relationship over the period 2006–2021 to capture its evolution during calm and turbulent times. We thus investigated the effect of investor attention on the mean and other quantiles. Our findings show that the relationship between investor attention and China's stock returns exhibits time-variation as investor attention significantly impacts the dynamics of China's stock returns, but its sign and effect vary per quantile: investor attention is negatively correlated with stock returns at low quantiles, but it turns positive at high quantiles. In addition, to test the model's robustness, variable replacement method and model replacement method are used to conduct significance tests, respectively. The results are equally significant.



    加载中


    [1] Akerlof G, Shiller R (2009) Animal Spirits—How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism, London: Pearson.
    [2] Aouadi A, Arouri M, Teulon F (2013) Investor attention and stock market activity: Evidence from France. Econ Model 35: 674–681. doi: 10.1016/j.econmod.2013.08.034
    [3] Aboody D, Lehavy R, Trueman B (2010) Limited attention and the earnings announcement returns of past stock market winners. Rev Account Stud 15: 317–344. doi: 10.1007/s11142-009-9104-9
    [4] Bank M, Larch M, Peter G (2011) Google search volume and its influence on liquidity and returns of German stocks. Financ Mark Portfolio Manage 25: 1–26. doi: 10.1007/s11408-011-0165-y
    [5] Barber BM, Odean T (2007) The effect of attention and news on the buying behavior of individual and institutional investors. Rev Financ Stud 21: 394–422.
    [6] Da Z, Engelberg J, Gao P (2011) In search of attention. J Financ 66: 1461–1499. doi: 10.1111/j.1540-6261.2011.01679.x
    [7] Fang L, Peress J (2009) Media coverage and the cross-section of stock returns. J Financ 64: 2023–2052. doi: 10.1111/j.1540-6261.2009.01493.x
    [8] Glosten L, Milgrom P (1985) Bid-ask and transaction prices in a specialist market with heterogeneously informed traders. J Financ Econ 14: 70–100. doi: 10.1016/0304-405X(85)90044-3
    [9] Goddard J, Kita A, Wang Q (2015) Investor attention to market volatility. J Int Financ Mark Inst Money 38: 79–96. doi: 10.1016/j.intfin.2015.05.001
    [10] Gervais S, Dean T (2001) Learning to be overconfident. Rev Financ Stud 14: 1–27. doi: 10.1093/rfs/14.1.1
    [11] Herzog B (2012) Asset prices and Google's search data. Ssrn Electron J 4: 25–51.
    [12] Jawadi F, Namouri H, Ftiti Z (2017) An analysis of the effect of investor sentiment in a heterogeneous switching transition model for G7 stock markets. J Econ Dyn Control 91: 469–484. doi: 10.1016/j.jedc.2017.10.004
    [13] Jawadi N, Jawadi F, Cheffou A I (2020) Computing the time-Varying effects of investor attention in Islamic stock returns. Comput Econ 56: 131–143. doi: 10.1007/s10614-020-09988-y
    [14] Jin L, Myers S C (2006) R2 around the World: New Theory and New Tests. J Financ Econ 79: 257–292. doi: 10.1016/j.jfineco.2004.11.003
    [15] Kahneman D (1973) Attention and Effort. Englewood Cliffs, NJ: Prentice–Hall.
    [16] Koenker R, Hallock K F (2001) Quantile regression. J Econ Perspect 15: 143–156. doi: 10.1257/jep.15.4.143
    [17] Koenker R, Bassett G (1978) Regression quantile. Econometrica 46: 33–50. doi: 10.2307/1913643
    [18] Ng L, Wu F (2006) Revealed stock preferences of individual investors: Evidence from Chinese equity markets. Pacific Basin Financ J 14: 175–192. doi: 10.1016/j.pacfin.2005.10.001
    [19] Nguyen CP, Schinckus C, Nguyen TVH (2019) Google search and stock returns in emerging markets. Borsa Istanbul Rev 19: 288–296. doi: 10.1016/j.bir.2019.07.001
    [20] Peltomaki J, Graham M, Hasselgren A (2018) Investor attention to market categories and market volatility: The case of emerging markets. Res Int Bus Financ 44: 532–546. doi: 10.1016/j.ribaf.2017.07.124
    [21] Peng L, Xiong W (2006) Investor Attention, Overconfidence and category learning. J Financ Econ 80: 563–602. doi: 10.1016/j.jfineco.2005.05.003
    [22] Seasholes MS, Guojun W (2007) Predictable behavior, profits, and attention. J Empir Financ 14: 590–610. doi: 10.1016/j.jempfin.2007.03.002
    [23] Seyhun N (1986) Insiders' profits, costs of trading, and market efficiency. J Financ Econ 16: 189–212. doi: 10.1016/0304-405X(86)90060-7
    [24] Stephan P, von Nitzsch R (2013) Do individual investors' stock recommendations in online communities contain investment value? Financ Mark Portfolio Manage 27: 149–186. doi: 10.1007/s11408-013-0208-7
    [25] Simon HA (1995) A behavioral model of rational choice. Q J Econ 69: 99–118.
    [26] Vlastakisa N, Markellos RN (2012) Information demand and stock market volatility. J Bank Financ 36: 1808–1821. doi: 10.1016/j.jbankfin.2012.02.007
    [27] Wang X, Qiang Y, Zhao F, et al. (2017) Investor sentiment and the Chinese index futures market: Evidence from the internet search. J Futures Mark, 468–477.
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(988) PDF downloads(79) Cited by(0)

Article outline

Figures and Tables

Figures(3)  /  Tables(12)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog