Review

Online public opinion and asset prices: a literature review

  • Received: 01 May 2021 Accepted: 07 June 2021 Published: 15 June 2021
  • JEL Codes: G15, C81, G41

  • We review the research work undertaken to explore how online public opinion information through social media and news media affects asset prices. First, it summarizes the measurement of online public opinion from three aspects: data source of online public opinion, textual sentiment analysis, and measurement of online public opinion index. Second, it puts forward the related theoretical basis of the research on online public opinion and asset price such as the noise trading theory, arbitrage limitation demonstrations, limited attention assumption and divergence models, etc. Third, it summarizes the three transmission channels through which online public opinion affects asset prices: investor attention, investor perceptions, and investor sentiment. Last, it looks deeply into the area and classifies the empirical literature according to various sources of online public opinion chosen by the researcher. Therefore, this exploratory work contributes to the existing literature by introducing the first systematic review.

    Citation: Yaya Su, Yi Qu, Yuxuan Kang. Online public opinion and asset prices: a literature review[J]. Data Science in Finance and Economics, 2021, 1(1): 60-76. doi: 10.3934/DSFE.2021004

    Related Papers:

  • We review the research work undertaken to explore how online public opinion information through social media and news media affects asset prices. First, it summarizes the measurement of online public opinion from three aspects: data source of online public opinion, textual sentiment analysis, and measurement of online public opinion index. Second, it puts forward the related theoretical basis of the research on online public opinion and asset price such as the noise trading theory, arbitrage limitation demonstrations, limited attention assumption and divergence models, etc. Third, it summarizes the three transmission channels through which online public opinion affects asset prices: investor attention, investor perceptions, and investor sentiment. Last, it looks deeply into the area and classifies the empirical literature according to various sources of online public opinion chosen by the researcher. Therefore, this exploratory work contributes to the existing literature by introducing the first systematic review.



    加载中


    [1] Ackert LF, Jiang L, Lee HS, et al. (2016) Influential investors in online stock forums. Int Rev Financ Anal 45: 39-46. doi: 10.1016/j.irfa.2016.02.001
    [2] Alanyali M, Moat HS, Preis T (2013) Quantifying the relationship between financial news and the stock market. Sci Reports 3: 1-6.
    [3] Al-Nasseri A, Ali FM (2018) What does investors' online divergence of opinion tell us about stock returns and trading volume? J Bus Res 86: 166-178.
    [4] Aman H, Moriyasu H (2017) Volatility and public information flows: Evidence from disclosure and media coverage in the Japanese stock market. Int Rev Econ Financ 51: 660-676. doi: 10.1016/j.iref.2017.07.029
    [5] Andrei D, Hasler M (2015) Investor attention and stock market volatility. Rev Financ Stud 28: 33-72. doi: 10.1093/rfs/hhu059
    [6] Antweiler W, Frank MZ (2004) Is all that talk just noise? The information content of internet stock message boards. J Financ 59: 1259-1294. doi: 10.1111/j.1540-6261.2004.00662.x
    [7] Ap Gwilym O, Kita A, Wang Q (2014) Speculate against speculative demand. Int Rev Financ Anal 34: 212-221. doi: 10.1016/j.irfa.2014.03.001
    [8] Audrino F, Sigrist F, Ballinari D (2020) The impact of sentiment and attention measures on stock market volatility. Int J Forecast 36: 334-357. doi: 10.1016/j.ijforecast.2019.05.010
    [9] Awan TM, Khan MS, Haq IU, et al. (2021) Oil and stock markets volatility during pandemic times: a review of G7 countries. Green Financ 3: 15-27. doi: 10.3934/GF.2021002
    [10] Bandhakavi A, Wiratunga N, Massie S, et al. (2016) Emotion-corpus guided lexicons for sentiment analysis on Twitter, International Conference on Innovative Techniques and Applications of Artificial Intelligence, Springer, Cham, 71-85.
    [11] Bank M, Larch M, Peter G (2011) Google search volume and its influence on liquidity and returns of German stocks. Financ Mark Portf Manage 25: 239-264. doi: 10.1007/s11408-011-0165-y
    [12] Barber BM, Odean T (2001) The Internet and the investor. J Econ Perspect 15: 41-54. doi: 10.1257/jep.15.1.41
    [13] Barberis N, Shleifer A, Wurgler J (2005) Comovement. J Financ Econ 75: 283-317. doi: 10.1016/j.jfineco.2004.04.003
    [14] Behrendt S, Schmidt A (2018) The Twitter myth revisited: Intraday investor sentiment, Twitter activity and individual-level stock return volatility. J Bank Financ 96: 355-367. doi: 10.1016/j.jbankfin.2018.09.016
    [15] Berry TD, Howe KM (1994) Public information arrival. J Financ 49: 1331-1346. doi: 10.1111/j.1540-6261.1994.tb02456.x
    [16] Campbell MC (1999) Pricing strategy & practice "Why did you do that?" The important role of inferred motive in perceptions of price fairness. J Product Brand Manage.
    [17] Chan WS (2003) Stock price reaction to news and no-news: drift and reversal after headlines. J Financ Econ 70: 223-260. doi: 10.1016/S0304-405X(03)00146-6
    [18] Chang EC, Mcqueen GR, Pinegar JM (1999) Cross-autocorrelation in Asian stock markets. 7: 471-493.
    [19] Chatrath A, Miao H, Ramchander S, et al. (2014) Currency jumps, cojumps and the role of macro news. J Int Money Financ 40: 42-62. doi: 10.1016/j.jimonfin.2013.08.018
    [20] Chatterjee S, John K, Yan A (2012) Takeovers and divergence of investor opinion. Rev Financ Stud 25: 227-277. doi: 10.1093/rfs/hhr109
    [21] Chen W, Lai K, Cai Y (2018) Topic generation for Chinese stocks: a cognitively motivated topic modeling method using social media data. Quant Financ Econ 2: 279-293. doi: 10.3934/QFE.2018.2.279
    [22] Chen WH, Xu GX (2018) Prediction accuracy of stock market volatility based on deep learning and stock forum data. Manage World 34: 180-181.
    [23] Chen XG, Duan S, Wang L (2017) Research on trend prediction and evaluation of network public opinion. Concurrency Comput Pract Experi 29: e4212.
    [24] Converse PE (1987) Changing conceptions of public opinion in the political process. Public Opinion Q 51: S12-S24.
    [25] Coqueret G (2020) Stock-specific sentiment and return predictability. Quant Financ 20: 1531-1551. doi: 10.1080/14697688.2020.1736314
    [26] Corwin SA, Coughenour JF (2008) Limited attention and the allocation of effort in securities trading. J Financ 63: 3031-3067. doi: 10.1111/j.1540-6261.2008.01420.x
    [27] 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
    [28] Danbolt J, Siganos A, Vagenas-Nanos E (2015) Investor sentiment and bidder announcement abnormal returns. J Corp Financ 33: 164-179. doi: 10.1016/j.jcorpfin.2015.06.003
    [29] Dastgir S, Demir E, Downing G, et al. (2019) The causal relationship between Bitcoin attention and Bitcoin returns: Evidence from the Copula-based Granger causality test. Financ Res Lett 28: 160-164. doi: 10.1016/j.frl.2018.04.019
    [30] De Long JB, Shleifer A, Summers LH, et al. (1989) The size and incidence of the losses from noise trading. J Financ 44: 681-696. doi: 10.1111/j.1540-6261.1989.tb04385.x
    [31] Delort JY, Arunasalam B, Milosavljevic M, et al. (2009) The impact of manipulation in Internet stock message boards.
    [32] Malkiel BG, Fama EF (1970) Efficient capital markets: A review of theory and empirical work. J Financ 25: 383-417. doi: 10.1111/j.1540-6261.1970.tb00518.x
    [33] Fang J, Gozgor G, Lau CKM, et al. (2020) The impact of Baidu Index sentiment on the volatility of China's stock markets. Financ Res Lett 32: 101099.
    [34] 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
    [35] Feng L, Seasholes MS (2004) Correlated trading and location. J Financ 59: 2117-2144. doi: 10.1111/j.1540-6261.2004.00694.x
    [36] Fisk RP, Patrício L, Ordanini A, et al. (2011) Crowd‐funding: transforming customers into investors through innovative service platforms. J Serv Manage.
    [37] Fung GPC, Yu JX, Lam W (2003) Stock prediction: Integrating text mining approach using real-time news, IEEE International Conference on Computational Intelligence for Financial Engineering, Proceedings, IEEE, 395-402.
    [38] Füss R, Guidolin M, Koeppel C (2020) Sentiment Risk Premia in the Cross-Section of Global Equity. University of St. Gallen, School of Finance Research Paper, (2019/13).
    [39] Gao C, Rong X, Chen Y (2011) Research on Public Opinion Monitoring Index-system in Micro-blogging. J Intell 9.
    [40] Hamid A, Heiden M (2015) Forecasting volatility with empirical similarity and Google Trends. J Econ Behav Organ 117: 62-81. doi: 10.1016/j.jebo.2015.06.005
    [41] Hart RP (2001) Redeveloping DICTION: theoretical considerations. Progress Commun Sci 2001: 43-60.
    [42] Hoffmann AOI, Post T, Pennings JME (2015) How investor perceptions drive actual trading and risk-taking behavior. J Behav Financ 16: 94-103. doi: 10.1080/15427560.2015.1000332
    [43] Hoffmann AOI, Post T, Pennings JME (2013) Individual investor perceptions and behavior during the financial crisis. J Bank Financ 37: 60-74. doi: 10.1016/j.jbankfin.2012.08.007
    [44] Hong H, Stein JC (1999) A unified theory of underreaction, momentum trading, and overreaction in asset markets. J Financ 54: 2143-2184. doi: 10.1111/0022-1082.00184
    [45] Hubalek F, Schachermayer W (2001) The limitations of no-arbitrage arguments for real options. Int J Theor Appl Financ 4: 361-373. doi: 10.1142/S0219024901001024
    [46] Ivković Z, Weisbenner S (2007) Information diffusion effects in individual investors' common stock purchases: Covet thy neighbors' investment choices. Rev Financ Stud 20: 1327-1357. doi: 10.1093/revfin/hhm009
    [47] Jegadeesh N, Wu D (2013) Word power: A new approach for content analysis. J Financ Econ 110: 712-729. doi: 10.1016/j.jfineco.2013.08.018
    [48] Jiao P, Veiga A, Walther A (2020) Social media, news media and the stock market. J Econ Behav Organ 176: 63-90. doi: 10.1016/j.jebo.2020.03.002
    [49] Jin F, Self N, Saraf P, et al. (2013) Forex-foreteller: Currency trend modeling using news articles, Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 1470-1473.
    [50] Jones AL (2006) Have internet message boards changed market behavior? Info 8: 67-76.
    [51] Joseph K, Wintoki MB, Zhang Z (2011) Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search. Int Forecast 27: 1116-1127. doi: 10.1016/j.ijforecast.2010.11.001
    [52] Kaustia M, Knüpfer S (2012) Peer performance and stock market entry. J Financ Econ 104: 321-338. doi: 10.1016/j.jfineco.2011.01.010
    [53] Kemp S (2020) Digital 2020: October Global Statshot. Datareportal. Hootsuite.
    [54] Kim HY, Mei JP (2001) What makes the stock market jump? An analysis of political risk on Hong Kong stock returns. J Int Money Financ 20: 1003-1016. doi: 10.1016/S0261-5606(01)00035-3
    [55] Kim N, Lučivjanská K, Molnár P, et al. (2019) Google searches and stock market activity: Evidence from Norway. Financ Res Lett 28: 208-220. doi: 10.1016/j.frl.2018.05.003
    [56] Klemola A, Nikkinen J, Peltomä ki J (2016) Changes in Investors' Market Attention and Near-Term Stock Market Returns. J Behav Financ 17: 18-30. doi: 10.1080/15427560.2016.1133620
    [57] Kruse P (2020) Spreading entrepreneurial news—investigating media influence on social entrepreneurial antecedents. Green Financ 2: 284-301. doi: 10.3934/GF.2020016
    [58] Kudryavtsev A (2017) Absolute Stock Returns and Trading Volumes: Psychological Insights. Quant Financ Econ 1: 186-204. doi: 10.3934/QFE.2017.2.186
    [59] Kumar A, Lee CMC (2006) Retail investor sentiment and return comovements. J Financ 61: 2451-2486. doi: 10.1111/j.1540-6261.2006.01063.x
    [60] Lee R, Kim J (2021) Developing a Social Index for Measuring the Public Opinion Regarding the Attainment of Sustainable Development Goals. Social Indicators Res, 1-21.
    [61] Leitch D, Sherif M (2017) Twitter mood, CEO succession announcements and stock returns. J Comput Sci 21: 1-10. doi: 10.1016/j.jocs.2017.04.002
    [62] LeRoy SF, Porter RD (1981) The present-value relation: Tests based on implied variance bounds. Econometrica 49: 555-574. doi: 10.2307/1911512
    [63] Leung H, Ton T (2015) The impact of internet stock message boards on cross-sectional returns of small-capitalization stocks. J Bank Financ 55: 37-55. doi: 10.1016/j.jbankfin.2015.01.009
    [64] Li F (2010) The information content of forward‐looking statements in corporate filings—A naï ve Bayesian machine learning approach. J Account Res 48: 1049-1102. doi: 10.1111/j.1475-679X.2010.00382.x
    [65] Li J, Chen Y, Shen Y, et al. (2019) Measuring China's Stock Market Sentiment. Available at SSRN 3377684.
    [66] Li X, Shen D, Zhang W (2018) Do Chinese internet stock message boards convey firm-specific information? Pacific-Basin Financ J 49: 1-14.
    [67] Li ZH, Hu ZH (2018) The impact of Internet public opinion on financial asset prices: a literature review. Financ Rev 10: 110-117+122.
    [68] Liew JKS, Budavári T (2016) Do tweet sentiments still predict the stock market? Available at SSRN 2820269.
    [69] Liu B, McConnell JJ (2013) The role of the media in corporate governance: Do the media influence managers' capital allocation decisions? J Financ Econ 110: 1-17.
    [70] Liu F, Ye Q, Li YJ (2014) Impacts of interactions between news attention and investor attention on stock returns: Empirical investigation on financial shares in China. J Manage Sci China 17: 72-85.
    [71] Liu F, Ye Q, Li YJ (2014) The interaction of media attention and investor attention on Stock Returns: An Empirical Study Based on Chinese financial stocks. J Manage Sci 17: 72-85.
    [72] Liu L, Wu J, Li P, et al. (2015) A social-media-based approach to predicting stock comovement. Expert Syst Appl 42: 3893-3901. doi: 10.1016/j.eswa.2014.12.049
    [73] Liu R, Xie Y, Xie Y (2017) A Study of Online Public Opinion in New Media Environment, New Media and China's Social Development, Springer, Singapore, 73-103.
    [74] Loughran T, McDonald B (2011) When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. J Financ 66: 35-65. doi: 10.1111/j.1540-6261.2010.01625.x
    [75] Lugmayr A, Gossen G (2013) Evaluation of Methods and Techniques for Language Based Sentiment Analysis for DAX 30 Stock Exchange A First Concept of a â € œLUGOâ € Sentiment Indicator. International SERIES on Information Systems and Management in Creative eMedia (CreMedia), 69-76.
    [76] Luo X, Zhang J (2013) How do consumer buzz and traffic in social media marketing predict the value of the firm? J Manage Infor Syst 30: 213-238.
    [77] Mao Y, Wei W, Wang B, et al. (2012) Correlating S & P 500 stocks with twitter data. Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research, 69-72.
    [78] Martin R (2019) Examination and implications of experimental research on investor perceptions. J Account Liter 43: 145-169. doi: 10.1016/j.acclit.2019.11.001
    [79] McQueen G, Pinegar M, Thorley S (1996) Delayed reaction to good news and the cross‐autocorrelation of portfolio returns. J Financ 51: 889-919. doi: 10.1111/j.1540-6261.1996.tb02711.x
    [80] Meng XJ, Meng XL, Hu YY (2016) Research on investor sentiment index based on text mining and Baidu Index. Macroecon Res, 144-153.
    [81] Meng Y, Chang J (2019) The influence of investor sentiment on scale effect. Stat Inf Forum 34: 98-104.
    [82] Merton RC (1973) An intertemporal capital asset pricing model. Econometrica, 867-887.
    [83] Mitchell ML, Mulherin JH (1994) The impact of public information on the stock market. J Financ 49: 923-950. doi: 10.1111/j.1540-6261.1994.tb00083.x
    [84] Mittermayer MA (2004) Forecasting intraday stock price trends with text mining techniques, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the IEEE, 10.
    [85] Mukwazvure A, Supreethi KP (2015) A hybrid approach to sentiment analysis of news comments, 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO), (Trends and Future Directions), IEEE, 1-6.
    [86] Nofer M, Hinz O (2015) Using twitter to predict the stock market. Bus Infor Syst Eng 57: 229-242. doi: 10.1007/s12599-015-0390-4
    [87] Peramunetilleke D, Wong RK (2002) Currency exchange rate forecasting from news headlines. Aust Comput Sci Commun 24: 131-139.
    [88] Qiao H, Su Y (2020) Media coverage and decomposition of stock market volatility: Based on the generalized dynamic factor model. Emerging Mark Financ Trade 56: 613-625. doi: 10.1080/1540496X.2019.1686974
    [89] Rao T, Srivastava S (2012) Twitter Sentiment Analysis: How To Hedge Your Bets In The Stock Markets. Computence, 227-247.
    [90] Ruan X, Zhang JE (2016) Investor attention and market microstructure. Econ Lett 149: 125-130. doi: 10.1016/j.econlet.2016.10.032
    [91] Sabherwal S, Sarkar SK, Zhang Y (2011) Do Internet stock message boards influence trading? Evidence from heavily discussed stocks with no fundamental news. J Bus Financ Account 38: 1209-1237. doi: 10.1111/j.1468-5957.2011.02258.x
    [92] Savor P, Wilson M (2013) How much do investors care about macroeconomic risk? Evidence from scheduled economic announcements. J Financ Quant Anal, 343-375.
    [93] Schneider G, Troeger VE (2006) War and the world economy: Stock market reactions to international conflicts. J Conflict Resolut 50: 623-645. doi: 10.1177/0022002706290430
    [94] Schumaker RP, Zhang Y, Huang CN, et al. (2012) Evaluating sentiment in financial news articles. Decis Support Syst 53: 458-464. doi: 10.1016/j.dss.2012.03.001
    [95] Shen D, Li X, Zhang W (2017) Baidu news coverage and its impacts on order imbalance and large-size trade of Chinese stocks. Financ Res Lett 23: 210-216. doi: 10.1016/j.frl.2017.06.008
    [96] Shen D, Li X, Zhang W (2018) Baidu news information flow and return volatility: Evidence for the Sequential Information Arrival Hypothesis. Econ Model 69: 127-133. doi: 10.1016/j.econmod.2017.09.012
    [97] Shiller RJ (1981) The use of volatility measures in assessing market efficiency. J Financ 36: 291-304.
    [98] Siganos A, Vagenas-Nanos E, Verwijmeren P (2017) Divergence of sentiment and stock market trading. J Bank Financ 78: 130-141. doi: 10.1016/j.jbankfin.2017.02.005
    [99] Smailović J, Grčar M, Lavrač N, et al. (2014) Stream-based active learning for sentiment analysis in the financial domain. Infor Sci 285: 181-203. doi: 10.1016/j.ins.2014.04.034
    [100] Soni A, van Eck NJ, Kaymak U (2007) Prediction of stock price movements based on concept map information, 2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, IEEE, 205-211.
    [101] Stone PJ, Dunphy DC, Smith MS (1966) The general inquirer: A computer approach to content analysis.
    [102] Su Y, Liao G (2019) The impact of macroeconomic news on stock returns of energy firms—evidence from China. Green Financ 1: 297-311. doi: 10.3934/GF.2019.3.297
    [103] Tauni MZ, Fang H, Mirza SS, et al. (2017) Do investor's Big Five personality traits influence the association between information acquisition and stock trading behavior? China Financ Rev Int.
    [104] Tetlock PC, Saar‐Tsechansky M, Macskassy S (2008) More than words: Quantifying language to measure firms' fundamentals. J Financ 63: 1437-1467. doi: 10.1111/j.1540-6261.2008.01362.x
    [105] Tetlock PC (2007) Giving content to investor sentiment: The role of media in the stock market. J Financ 62: 1139-1168. doi: 10.1111/j.1540-6261.2007.01232.x
    [106] Uddin MA, Hoque ME, Ali MH (2020) International economic policy uncertainty and stock market returns of Bangladesh: evidence from linear and nonlinear model. Quant Financ Econ 4: 236-251. doi: 10.3934/QFE.2020011
    [107] Wang CY, Wu JW (2015) Media tone, investor sentiment and IPO pricing. Financ Res 423: 174-189.
    [108] Wang Fl, Wang XY (2017) Does social emotion affect stock market returns? Evidence from Sina Weibo. J Shanxi Univ Financ Econ 39: 35-46.
    [109] Wuthrich B, Cho V, Leung S, et al. (1998) Daily stock market forecast from textual web data, SMC'98 Conference Proceedings, 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 98CH36218), IEEE, 3: 2720-2725.
    [110] Wysocki PD (1998) Cheap talk on the web: The determinants of postings on stock message boards. University of Michigan Business School Working Paper, 1998 (98025).
    [111] Xu YM, Gao YM (2017) Construction and application of CPI public opinion index based on Internet big data—Taking Baidu Index as an example. Res Quante Econ Technol Econ 034: 94-112.
    [112] Yang C, Zhang R (2013) Sentiment asset pricing model with consumption. Econ Model 30: 462-467. doi: 10.1016/j.econmod.2012.11.004
    [113] Yang SY, Mo SYK, Liu A (2015) Twitter financial community sentiment and its predictive relationship to stock market movement. Quant Financ 15: 1637-1656. doi: 10.1080/14697688.2015.1071078
    [114] Yang X, Zhu Y, Cheng TY (2020) How the individual investors took on big data: The effect of panic from the internet stock message boards on stock price crash. Pacific-Basin Financ J 59: 101245.
    [115] Yu GM (2013) The big data method of constructing the overall judgment of social public opinion—Taking the processing of Baidu's massive search data as an example. News Writing, 67-69.
    [116] Zhai Y, Hsu A, Halgamuge SK (2007) Combining news and technical indicators in daily stock price trends prediction, International symposium on neural networks, Springer, Berlin, Heidelberg, 1087-1096.
    [117] Zhang W, Yan K, Shen D (2021) Can the Baidu Index predict realized volatility in the Chinese stock market? Financ Innovation 7: 1-31.
    [118] Zhang Y, Qi J, Fang B, et al. (2011) Research on the Index System of Public Opinion on Internet for Unexpected Emergency. Inf Sci 9.
  • 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(4789) PDF downloads(216) Cited by(2)

Article outline

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog