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数据分析与知识发现  2018, Vol. 2 Issue (4): 20-28     https://doi.org/10.11925/infotech.2096-3467.2017.1172
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
媒介信息与投资者过度交易*——基于微博舆论、行业新闻与公司公告的实证
岑咏华1(), 张灿1, 吴承尧2
1南京理工大学经济管理学院 南京 210094
2南京农业大学金融学院 南京 210095
Media Information and Overtrading——An Empirical Study on Micro-Blog Posts, Industry News and Company Announcements
Cen Yonghua1(), Zhang Can1, Wu Chengyao2
1School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
2College of Finance, Nanjing Agricultural University, Nanjing 210095, China
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摘要 

目的】考察在不同类型媒介信息影响下股票投资者过度交易现象是否更加显著。【方法】采用事件分析法考察微博舆论、行业新闻、公司公告信息涉及的公司股票在信息发布前后各时间窗口内异常换手率的差异, 检验媒介信息类型的影响差异和信息传播效应。【结果】媒介信息披露后, 股票过度交易现象显著; 媒介信息传播过程中存在显著内幕效应、信息披露效应以及持续性影响; 相比利好信息, 投资者更容易受到利空信息的影响而过度交易; 投资者过度交易背后体现了其有限关注、选择性关注以及情绪波动等有限理性特征。【局限】样本规模可以进一步扩大, 数据依据财经媒介所提供的概念股体系构建信息与股票的关联, 但这种关联可能并不完全为投资者所意识, 可能会对实证结果产生干扰。【结论】媒介信息对于市场过度交易具有催化作用。该研究在一定意义上可为理解媒介信息、投资者有限理性及其决策偏差等提供证据和视角。

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岑咏华
张灿
吴承尧
关键词 媒介信息过度交易换手率事件分析法有限理性    
Abstract

[Objective] This paper tries to discover the impacts of micro-blog posts, industry news and company announcements on investors’ overtrading. [Methods] We examined the variations of the abnormal stock turnover rates before and after information disclosure from different channels. [Results] Media information posed significant impacts on overtrading, including the insider effect, information disclosure effect and lasting effect before, during and after the disclosure. Besides, investors were more likely to overtrade once exposed to negative news than positive news. The limited attention, selective attention, and mood fluctuation, were correlated to the investors’ trading behaviors. [Limitations] The sample size could be expanded. Individual investors may not be aware of the findings. [Conclusions] Media information catalyzes the overtrading of irrational investors.

Key wordsMedia Information    Over-trading    Turnover Rate    Event Study    Bounded Rationality
收稿日期: 2017-11-22      出版日期: 2018-05-11
ZTFLH:  F832.51  
基金资助:*本文系国家自然科学基金项目“社会化影响下个体信息认知处理中的扭曲与偏见机制研究”(项目编号: 71471089)和国家自然科学基金项目“投资者有限关注与证券市场监管: 基于大数据和计算实验的方法”(项目编号: 71503130)的研究成果之一
引用本文:   
岑咏华, 张灿, 吴承尧. 媒介信息与投资者过度交易*——基于微博舆论、行业新闻与公司公告的实证[J]. 数据分析与知识发现, 2018, 2(4): 20-28.
Cen Yonghua,Zhang Can,Wu Chengyao. Media Information and Overtrading——An Empirical Study on Micro-Blog Posts, Industry News and Company Announcements. Data Analysis and Knowledge Discovery, 2018, 2(4): 20-28.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.1172      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I4/20
  事件分析法窗口示意图
媒介信息类型 事件前期[T-10,T-6] 内幕期[T-5,T-1] 发生期[T0,T4] 延伸期[T5,T9]
真实换手率 异常换手率 真实换手率 异常换手率 真实换手率 异常换手率 真实换手率 异常换手率
微博-可预知 3.159 0.213 2.823 0.301 3.033 0.193 3.242 0.262
微博-突发 2.392 0.023 2.551 -0.114 2.355 0.934 2.503 0.381
行业新闻 2.855 0.121 2.671 0.322 2.794 0.923 3.620 0.965
公司公告 2.828 0.092 2.851 0.330 3.072 0.680 3.183 0.110
利空 3.252 0.098 3.045 0.294 2.828 0.649 3.735 0.276
利好 2.467 0.087 2.258 0.130 2.503 0.624 2.527 0.370
  不同时期的平均真实换手率和异常换手率(%)
  异常换手率(微博舆论信息影响)
  异常换手率(行业新闻影响)
  异常换手率(公司公告影响)
  异常换手率(利空VS利好)
媒介信息类型 前期与
内幕期
内幕期与
发生期
发生期与
延伸期
延伸期与
内幕期
微博-可预知 0.410 0.259 0.450 0.468
微博-突发 0.082 0.001** 0.004** 0.014*
行业新闻 0.065 0.003** 0.416 0.012*
公司公告 0.035* 0.133 0.004** 0.179
利空 0.080 0.043* 0.021* 0.466
利好 0.355 0.007** 0.103 0.017*
  异常换手率显著性对比(P值)
[1] Li J, Yu J. Investor Attention, Psychological Anchors,Stock Return Predictability[J]. Journal of Financial Economics, 2012, 104(2): 401-419.
doi: 10.1016/j.jfineco.2011.04.003
[2] Siganos A, Vagenas-Nanos E, Verwijmeren P.Facebook’s Daily Sentiment and International Stock Markets[J]. Journal of Economic Behavior & Organization, 2014, 107: 730-743.
doi: 10.1016/j.jebo.2014.06.004
[3] Deaves R, Lüders E, Luo G Y.An Experimental Test of the Impact of Overconfidence and Gender on Trading Activity[J]. Review of Finance, 2009, 13(3): 373-397.
doi: 10.1093/rof/rfn023
[4] Odean T. Volume, Volatility, Price, and Profit When All Traders are Above Average[J]. The Journal of Finance, 1998, 53(6): 1887-1934.
doi: 10.1111/0022-1082.00078
[5] Barber B, Odean T.Do Investors Trade Too Much?[J]. American Economic Review, 1999, 89(5): 1279-1298.
doi: 10.1257/aer.89.5.1279
[6] De Sousa Y F, Munro A. Truck, Barter and Exchange Versus the Endowment Effect: Virtual Field Experiments in an Online Game Environment[J]. Journal of Economic Psychology, 2012, 33(3): 482-493.
doi: 10.1016/j.joep.2011.12.011
[7] Zhang J, Wang H, Wang L, et al.Is There Any Overtrading in Stock Markets? The Moderating Role of Big Five Personality Traits and Gender in a Unilateral Trend Stock Market[J]. PLoS One, 2014, 9(1): e87111.
doi: 10.1371/journal.pone.0087111 pmid: 3903640
[8] 王玉宝, 沈杰. 投资者过度交易行为的一种解释——基于股票交易日资金流数据的研究[J]. 南方经济, 2013, 32(8): 31-44.
[8] (Wang Yubao, Shen Jie.An Explanation of Investors’ Trading Behavior in Chinese A-Shares Stock Market[J]. South China Journal of Economics, 2013, 32(8): 31-44.)
[9] 廖理, 贺裴菲, 张伟强, 等. 中国个人投资者的过度自信和过度交易研究[J]. 投资研究, 2013, 28(8): 35-46.
[9] (Liao Li, He Peifei, Zhang Weiqiang, et al.The Study on Overconfident and Overtrading of Chinese Individual Investors[J]. Review of Investment Studies, 2013, 28(8): 35-46.)
[10] Glaser M, Weber M.Which Past Returns Affect Trading Volume?[J]. Journal of Financial Markets, 2009, 12(1): 1-31.
doi: 10.1016/j.finmar.2008.03.001
[11] 谭松涛, 王亚平. 股民过度交易了么?——基于中国某证券营业厅数据的研究[J]. 经济研究, 2006, 41(10): 83-95.
[11] (Tan Songtao, Wang Yaping.Do Investors Trade Too Much? Evidence from China’s Stock Markets[J]. Economic Research Journal, 2006, 41(10): 83-95.)
[12] Bregu K. Overconfidence and (Over) Trading: The Effect of Feedback on Trading Behavior [R/OL]. University of Arkansas, 2016. .
[13] 何诚颖, 陈锐, 蓝海平, 等. 投资者非持续性过度自信与股市反转效应[J]. 管理世界, 2014, 29(8): 44-54.
[13] (He Chengying, Chen Rui, Lan Haiping, et al.Non-Standing Investors Overconfidence and Reversal Effect in Stock Market[J]. Management World, 2014, 29(8): 44-54.)
[14] Peress J.The Media and the Diffusion of Information in Financial Markets: Evidence from Newspaper Strikes[J]. The Journal of Finance, 2014, 69(5): 2007-2043.
doi: 10.1111/jofi.12179
[15] Tumasjan A, Sprenger T O, Sandner P G, et al.Election Forecasts with Twitter: How 140 Characters Reflect the Political Landscape[J]. Social Science Computer Review, 2011, 29(4): 402-418.
doi: 10.1177/0894439310386557
[16] Fang L H, Peress J, Zheng L.Does Media Coverage of Stocks Affect Mutual Funds’ Trading and Performance?[J]. Review of Financial Studies, 2014, 27(12): 3441-3466.
doi: 10.2139/ssrn.1572047
[17] Barber B M, Odean T.All that Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors[J]. Review of Financial Studies, 2008, 21(2): 785-818.
doi: 10.1093/rfs/hhm079
[18] Nassirtoussi A K, Aghabozorgi S, Wah T Y, et al.Text Mining for Market Prediction: A Systematic Review[J]. Expert Systems with Applications, 2014, 41(16): 7653-7670.
doi: 10.1016/j.eswa.2014.06.009
[19] Chen H, De P, Hu Y J, et al.Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media[J]. Review of Financial Studies, 2014, 27(5): 1367-1403.
doi: 10.2139/ssrn.1807265
[20] Nguyen T H, Shirai K, Velcin J.Sentiment Analysis on Social Media for Stock Movement Prediction[J]. Expert Systems with Applications, 2015, 42(24): 9603-9611.
doi: 10.1016/j.eswa.2015.07.052
[21] Li Q, Wang T, Gong Q, et al.Media-Aware Quantitative Trading Based on Public Web Information[J]. Decision Support Systems, 2014, 61: 93-105.
doi: 10.1016/j.dss.2014.01.013
[22] Bollen J, Mao H, Zeng X.Twitter Mood Predicts the Stock Market[J]. Journal of Computational Science, 2011, 2(1): 1-8.
doi: 10.1016/j.jocs.2010.12.007
[23] Chan S W K, Chong M W C. Sentiment Analysis in Financial Texts[J]. Decision Support Systems, 2017, 94: 53-64.
doi: 10.1016/j.dss.2016.10.006
[24] Wu D D, Zheng L, Olson D L.A Decision Support Approach for Online Stock Forum Sentiment Analysis[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2014, 44(8): 1077-1087.
doi: 10.1109/TSMC.2013.2295353
[25] Kahneman D.Attention and Effort[M]. Englewood Cliffs: Prentice-Hall, 1973.
[26] Schwartzstein J.Selective Attention and Learning[J]. Journal of the European Economic Association, 2014, 12(6): 1423-1452.
doi: 10.1111/jeea.12104
[27] Duffie D.Presidential Address: Asset Price Dynamics with Slow-Moving Capital[J]. The Journal of Finance, 2010, 65(4): 1237-1267.
doi: 10.1111/j.1540-6261.2010.01569.x
[28] Andrei D, Hasler M.Investor Attention and Stock Market Volatility[J]. Review of Financial Studies, 2015, 28(1): 33-72.
doi: 10.2139/ssrn.2336073
[29] 俞庆进, 张兵. 投资者有限关注与股票收益——以百度指数作为关注度的一项实证研究[J]. 金融研究, 2012, 33(8): 152-165.
[29] (Yu Qingjin, Zhang Bing.Investors Limited Attention and Stock Returns: An Empirical Study of Attention Based on Baidu Index[J]. Journal of Financial Research, 2012, 33(8): 152-165.)
[30] 汪昌云, 武佳薇. 媒体语气、投资者情绪与IPO定价[J]. 金融研究, 2015, 36(9): 174-189.
[30] (Wang Changyun, Wu Jiawei. Media Tone, Investor Sentiment and IPO Pricing[J]. Journal of Financial Research, 2015, 36(9): 174-189. )
[31] Kahneman D.Judgment Under Uncertainty: Heuristics and Biases[J]. Science, 1974, 185: 1124-1131.
doi: 10.1126/science.185.4157.1124
[32] De Bondt W F M, Thaler R H. Financial Decision-Making in Markets and Firms: A Behavioral Perspective[J]. Handbooks in Operations Research and Management Science, 1995, 9: 385-410.
doi: 10.1016/S0927-0507(05)80057-X
[33] Miller D T, Ross M.Self-serving Biases in the Attribution of Causality: Fact or Fiction[J]. Psychological Bulletin, 1975, 82(2): 213-225.
doi: 10.1037/h0076486
[34] Loewenstein G, O’donoghue T, Rabin M.Projection Bias in Predicting Future Utility[J]. The Quarterly Journal of Economics, 2003, 118(4): 1209-1248.
doi: 10.1162/003355303322552784
[35] Kahneman D, Tversky A.Prospect Theory: An Analysis of Decision Under Risk[J]. Econometrica: Journal of The Econometric Society, 1979, 47(2): 263-291.
doi: 10.2307/1914185
[36] Thaler R.Toward a Positive Theory of Consumer Choice[J]. Journal of Economic Behavior & Organization, 1980, 1(1): 39-60.
doi: 10.1016/0167-2681(80)90051-7
[37] Shiller R J.Speculative Asset Prices[J]. American Economic Review, 2014, 104(6): 1486-1517.
doi: 10.1257/aer.104.6.1486
[38] Brown S J, Warner J B.Using Daily Stock Returns: The Case of Event Studies[J]. Journal of Financial Economics, 1985, 14(1): 3-31.
doi: 10.1016/0304-405X(85)90042-X
[39] Kepplinger M H.Reciprocal Effects: Toward a Theory of Mass Media Effects on Decision Makers[J]. Harvard International Journal of Press/Politics, 2007, 12(2): 3-23.
doi: 10.1177/1081180X07299798
[40] Li Q, Wang T J, Li P, et al.The Effect of News and Public Mood on Stock Movements[J]. Information Sciences, 2014, 278: 826-840.
doi: 10.1016/j.ins.2014.03.096
[41] 饶育蕾, 王建新, 苏燕青. 上市公司盈余信息披露是否存在时机择?——基于投资者有限注意的实证分析[J]. 管理评论, 2012, 24(12): 146-155.
[41] (Rao Yulei, Wang Jianxin, Su Yanqing.Limited Investor Attention and the Timing of Earnings Report Announcement: Evidence from Chinese Listed Companies[J]. Management Review, 2012, 24(12): 146-155.)
[42] Benartzi S, Thaler R H.Myopic Loss Aversion and the Equity Premium Puzzle[J]. The Quarterly Journal of Economics, 1995, 110(1): 73-92.
doi: 10.2307/2118511
[43] Antoniou C, Doukas J A, Subrahmanyam A. Investor Sentiment, Beta, and the Cost of Equity Capital[J]. Management Science, 2016, 62(2): 347-367.
doi: 10.1287/mnsc.2014.2101
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