Investor Sentiment and Tech-Sector Stock Returns: An Empirical Analysis Based on Guba Comment Volume
Keywords:
investor sentiment, stock returns, portfolio optimization, time-series analysisAbstract
Purpose - This study investigates whether investor sentiment carries causal information about the future returns of five large-cap Chinese technology stocks.
Design/methodology/approach - High-frequency proxies for sentiment are built from the daily count of emotion-tagged posts on Guba and from daily return metrics. After cross-sectional correlation and ADF stationarity checks, two-stage residual regressions and pooled panel specifications with lagged sentiment terms are estimated. The sample is then split into training and validation windows; out-of-sample forecasts are benchmarked against the Efficient-Market-Hypothesis (EMH) null. Finally, mean–variance frontiers that blend the five equities with Chinese government bonds are constructed to obtain implementable portfolio weights.
Findings - Non-contemporaneous sentiment exerts a statistically and economically significant effect on next-day returns. Both the residual and pooled models deliver out-of-sample MSPEs that are materially lower than the EMH benchmark. Incorporating sentiment-adjusted expected returns shifts the efficient frontier north-west, yielding higher Sharpe ratios.
Originality - The paper is the first to fuse intraday social-media emotion data with conventional econometric tests of predictability for Chinese technology firms, and to show that sentiment-driven return forecasts can be translated into realizable portfolio allocations.
References
He, P., Sun, Y., Zhang, Y. and Li, T., 2021. COVID–19's impact on stock prices across different sectors—An event study based on the Chinese stock market. In Research on pandemics (pp. 66-80). Routledge.
Chebbi, K., Ammer, M.A. and Hameed, A., 2021. The COVID-19 pandemic and stock liquidity: Evidence from S&P 500. The Quarterly Review of Economics and Finance, 81, pp.134-142.
Hsu, P.H., Li, K. and Tsou, C.Y., 2023. The pollution premium. The Journal of Finance, 78(3), pp.1343-1392.
Pandey, D.K. and Kumari, V., 2021. Event study on the reaction of the developed and emerging stock markets to the 2019-nCoV outbreak. International Review of Economics & Finance, 71, pp.467-483.
Lee, W.Y., Jiang, C.X. and Indro, D.C., 2002. Stock market volatility, excess returns, and the role of investor sentiment. Journal of banking & Finance, 26(12), pp.2277-2299.
Baker, M. and Wurgler, J., 2006. Investor sentiment and the cross‐section of stock returns. The journal of Finance, 61(4), pp.1645-1680.
Wright, C. and Swidler, S., 2023. Abnormal trading volume, news and market efficiency: Evidence from the Jamaica Stock Exchange. Research in International Business and Finance, 64, p.101804.
Ph, H. and Uchil, R., 2020. Influence of investor sentiment and its antecedent on investment decision-making using partial least square technique. Management Research Review, 43(11), pp.1441-1459.
Loewenstein, G.F., Weber, E.U., Hsee, C.K. and Welch, N., 2001. Risk as feelings. Psychological bulletin, 127(2), p.267.
Lucey, B.M. and Dowling, M., 2005. The role of feelings in investor decision‐making. Journal of economic surveys, 19(2), pp.211-237.
See-To, E.W. and Yang, Y., 2017. Market sentiment dispersion and its effects on stock return and volatility. Electronic Markets, 27(3), pp.283-296.
Platt, M.L. and Huettel, S.A., 2008. Risky business: the neuroeconomics of decision making under uncertainty. Nature neuroscience, 11(4), pp.398-403.
Lam, K., Liu, T. and Wong, W.K., 2010. A pseudo-Bayesian model in financial decision making with implications to market volatility, under-and overreaction. European Journal of Operational Research, 203(1), pp.166-175.
Baker, M. and Wurgler, J., 2006. Investor sentiment and the cross‐section of stock returns. The journal of Finance, 61(4), pp.1645-1680.
Jiang, F., Lee, J., Martin, X. and Zhou, G., 2019. Manager sentiment and stock returns. Journal of Financial Economics, 132(1), pp.126-149.
Kahneman, D. and Tversky, A., 2013. Prospect theory: An analysis of decision under risk. In Handbook of the fundamentals of financial decision making: Part I (pp. 99-127).
Shleifer, A. and Vishny, R.W., 1997. The limits of arbitrage. The Journal of finance, 52(1), pp.35-55.
Tetlock, P.C., 2007. Giving content to investor sentiment: The role of media in the stock market. The Journal of finance, 62(3), pp.1139-1168.
Zhang, K., Ren, W., Zhang, Y., & You, J. (2017). Research on stock prediction method based on information of investors’ comments. Journal of Beijing University of Posts and Telecommunications, 42(5), 1-10. DOI: 10.16508/j.cnki.11-5866/n.2017.05.013.
Silva de Carvalho, R., da Fonseca Tonin, J.M. and Raimundini Sanches, S.L., 2023. Stock Return Predictability based on Textual Sentiment Analysis: a review. Revista de Educação e Pesquisa em Contabilidade, 17(2).
Lai, K., Chen, H., Le, G., & Dong, Y. (2014). Can Mood Predict Stock Market? South China Journal of Psychology, 30(007), 1770-1781. DOI: 10.3724/SP.J.1042.2014.01770.
Kim, K. and Ryu, D., 2020. Predictive ability of investor sentiment for the stock market. Romanian Journal of Economic Forecasting, 23(4), pp.33-46.
W. Zhang, X. Gong, C. Wang, and X. Ye, ‘Predicting stock market volatility based on textual sentiment: A nonlinear analysis’, Journal of Forecasting, vol. 40, no. 8, pp. 1479–1500, Dec. 2021, doi: 10.1002/for.2777.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
