Tóm tắt
Purpose – The author investigates whether investors’ online information demand measured by Google search query and the changes in the numbers of Wikipedia page view can explain and predict stock return, trading volume and volatility dynamics of companies listed on the Nigerian Stock Exchange. Design/methodology/approach – The multiple regression model which encompasses both the univariate and multivariate regression framework was employed as the research methodology. As part of our preanalysis, we test for multicollinearity and applied the Wu/Hausman specification test to detect whether endogeneity exist in the regression model. Findings – We provide novel and robust evidence that Google searches neither explain the contemporaneous nor predict stock return, trading volume and volatility dynamics. Similarly, results also indicate that trading volume and volatility dynamics have no relationship with changes in the numbers of Wikipedia pages view related to stock activities. Originality/value – This study opens new strand of empirical literature of “ investors’ attention” in the context of African stock markets as empirical evidence. No evidence from previous studies on investors’ attention exist, whether in Google search query or Wikipedia page view, with respect to African stock markets, particularly the Nigerian stock market. This study seeks to bridge these knowledge gaps by examining these relations.
Chủ đề
Google search, Wikipedia page view, Investors’ attention, Information demand, Volatility, Stock returns
Nhà xuất bản
Kinh Tế Quốc Dân???dc.relation.reference???
Adachi, Y., Masuda, M. and Takeda, F. (2017), “Google search intensity and its relationship to the returns and liquidity of Japanese startup stocks”, Pacific Basin Finance Journal, Vol. 46, pp. 243-257. Alizadeh, S., Brandt, W.M. and Diebold, X.F. (2002), “Range-based estimation of stochastic volatility models”, Journal of Finance, Vol. 57, pp. 1047-1091. Antweiler, W. and Frank, M.Z. (2004), “Is all that talks just noise? The information content of internet stock message boards”, The Journal of Finance, Vol. 59, pp. 1259-1294. Aouadi, A., Arouri, M. and Teulon, F. (2013), “Investor attention and stock market activity: evidence from France”, Economic Modelling, Vol. 35, pp. 674-681. Audrino, F., Sigrist, F. and Ballinari, D. (2019), “The impact of sentiment and attention measures on stock market volatility”, International Journal of Forecasting, doi: 10.1016/j.ijforecast.2019.05.010. Bank, M., Larch, M. and Peter, G. (2011), “Google search volume and its influence on liquidity and returns of German stocks”, Financial Markets and Portfolio Management, Vol. 253, pp. 239-264. Batista, G. and Monard, M. (2003), “An analysis of four missing data treatment methods for supervised learning”, Applied Artificial Intelligence, Vol. 17 No. 5-6, pp. 519-533. Bijl, L., Kringhaug, G., Moln ar, P. and Sandvik, E. (2016), “Google searches and stock returns”, International Review of Financial Analysis, Vol. 45, pp. 150-156. Brandt, W.M. and Kinlay, J. (2005), Estimating Historical Volatility, available at: http://www. investment-analytics.com. Cergol, B. and Omladi c, M. (2015), “What can Wikipedia and Google tell us about stock prices under different market regimes?”, Ars Mathematica Contemporanea, Vol. 9 No. 2, pp. 301-320. Chesney, M., Crameri, R. and Mancini, L. (2015), “Detecting abnormal trading activities in option markets”, Journal of Empirical Finance, Vol. 33, pp. 263-275. Chung, K.H. and Chuwonganant, C. (2018), “Market volatility and stock returns: the role of liquidity provider”, Journal of Financial Markets, Vol. 37, pp. 17-34. Da, Z., Engelberg, J. and Gao, P. (2011), “ In search of attention” , The Journal of Finance, Vol. 66 No. 5, pp. 1461-1499. Dimpfl, T. and Jank, S. (2016), “Can internet search queries help to predict stock market volatility?”, European Financial Management, Vol. 22 No. 2, pp. 171-192. Dimpfl, T. and Kleiman, V. (2019), “Investor pessimism and the German stock market: exploring google search queries”, German Economic Review, Vol. 20 No. 1, pp. 1-28. Fama, E.F. and French, K.R. (1993), “Common risk factors in the returns on stocks and bonds”, Journal of Financial Economics, Vol. 33 No. 1, pp. 3-56. Fama, E.F. and French, K.R. (2015), “A five-factor asset pricing model”, Journal of Financial Economics, Vol. 116 No. 1, pp. 1-22. Fang, L. and Peress, J. (2009), “Media coverage and the cross-section of stock returns”, The Journal of Finance, Vol. 645, pp. 2023-2052. Fehle, F., Tsyplakov, S. and Zdorovtsov, V. (2005), “Can companies influence investor behavior through advertising? super bowl commercials and stock returns”, European Financial Management, Vol. 11, pp. 625-647. Garman, M.B. and Klass, M.J. (1980), “On the estimation of security price volatilities from historical data”, Journal of Business, Vol. 53 No. 1, pp. 67-78. Herv e, F., Zouaoui, M. and Belvaux, B. (2019), “Noise traders and smart money: evidence from online searches”, Economic Modelling, Vol. 83, pp. 141-149. Huang, M.Y., Rojas, R.R. and Convery, P.D. (2019), “Forecasting stock market movements using google trend searches”, Empirical Economics, doi: 10.1007/s00181-019-01725-1. Kim, Y.H. and Meschke, F. (2011), “CEO interviews on CNBC”, Working Paper, available at: http:// ssrn.com/abstract5 1745085. Kim, N., Lucivjanska, K., Molnar, P. and Villa, R. (2019), “Google searches and stock market activities: evidence from Norway”, Finance Research Letters, Vol. 28, pp. 208-220. Moat, H.S., Curme, C., Avakian, A., Kenett, D.Y., Stanley, H.E. and Preis, T. (2013), “Quantifying wikipedia usage patterns before stock market moves”, Scientific Reports, Vol. 3, pp. 1801, doi: 10.1038/srep01801. Parkinson, M. (1980), “The extreme value method for estimating the variance of the rate of return”, Journal of Business, Vol. 53, pp. 61-68. Preis, T., Moat, H.S. and Stanley, H.E. (2013), “Quantifying trading behavior in financial markets using Google Trends”, Scientific Reports, Vol. 3, pp. 1684. Rogers, L.C.G. and Satchell, S.E. (1991), “Estimating variance from high, low and closing prices”, Annals of Applied Probability, Vol. 1, pp. 504-512. Takeda, F. and Wakao, T. (2014), “Google search intensity and its relationship with returns and trading volume of Japanese stocks”, Pacific-Basin Finance Journal, Vol. 27, pp. 1-18. Takeda, F. and Yamazaki, H. (2006), “Stock price reactions to public TV programs on listed Japanese companies”, Economics Bulletin, Vol. 137, pp. 1-7. Yang, D. and Zhang, Q. (2000), “Drift independent volatility estimation based on high, low, open and close prices”, Journal of Business, Vol. 73, pp. 477-491. Yoshida, M., Arase, Y., Tsunoda, T. and Yamamoto, M. (2015), “Wikipedia page view reflects web search trend”, Proceedings of the 2015 ACM Web Science Conference, November, California, CA, Article No. 65, pp. 1-2, doi: 10.1145/2786451.2786495. Zagoruyko, S. and Komodakis, N. (2019), “Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer”, 5th International Conference on Learning Representations, Iclr 2017 - Conference Track Proceedings, New Orleans, Louisiana. Zhang, J., Djajadikerta, H.G. and Zhang, Z. (2018), “Does sustainability engagement affect stock return volatility? Evidence from the Chinese financial market”, Sustainability, Vol. 10 No. 10, pp. 336
Adachi, Y., Masuda, M. and Takeda, F. (2017), “Google search intensity and its relationship to the returns and liquidity of Japanese startup stocks”, Pacific Basin Finance Journal, Vol. 46, pp. 243-257. Alizadeh, S., Brandt, W.M. and Diebold, X.F. (2002), “Range-based estimation of stochastic volatility models”, Journal of Finance, Vol. 57, pp. 1047-1091. Antweiler, W. and Frank, M.Z. (2004), “Is all that talks just noise? The information content of internet stock message boards”, The Journal of Finance, Vol. 59, pp. 1259-1294. Aouadi, A., Arouri, M. and Teulon,...See More