Social media sentiment, behavioral biases, and abnormal returns: Evidence from Indonesian Non-Cyclical Consumer Stocks

Authors

DOI:

https://doi.org/10.26905/afr.v9i2.17464

Keywords:

Abnormal returns, Behavioral finance, Herding, Overconfidence, Social media sentiment

Abstract

Social media has become an important source of information for investors, while stock price anomalies continue to challenge market efficiency. However, evidence on the combined role of social media sentiment, overconfidence, and herding behavior in the Indonesian stock market remains limited. This study examines the associations of social media sentiment and behavioral biases, particularly overconfidence and herding, with abnormal returns (AR) of Indonesia’s non-cyclical consumer sector stocks listed on the Indonesia Stock Exchange (IDX) during 2021–2025. Through a quantitative approach, this study employs panel data gathered from 119 firms selected through purposive sampling, comprising daily abnormal returns, a social-media-based sentiment index, overconfidence and herding proxies, and firm size as a control variable. The proposed model is tested using fixed-effects panel regression selected through Chow and Hausman tests. The reported main model shows positive and statistically significant associations for social media sentiment and herding, while firm size is also significant while included as a control variable. In contrast, overconfidence shows no statistically significance results after the reported robust standard-error adjustment. An alternative treatment is provided which excludes no-comment days yields consistent positive directions for sentiment and herding, but reduces the sample from 129,457 to 3,955 firm-day observations. The findings indicate statistically significant associations between social media sentiment, the herding proxy, and abnormal returns; they do not establish causal effects or a ranking of economic importance.

Downloads

Download data is not yet available.

References

Agarwal, S., Kumar, S., & Goel, U. (2021). Social media and the stock markets: An emerging market perspective. Journal of Business Economics and Management (JBEM), 22(6), 1614-1632. https://doi.org/10.3846/jbem.2021.15619

Ahn, K., Cong, L., Jang, H., & Kim, D. S. (2024). Business cycle and herding behavior in stock returns: Theory and evidence. Financial Innovation, 10(1), 6. https://doi.org/10.1186/s40854-023-00540-z

Akin, I., & Akin, M. (2026). Behavioral finance impacts on US stock market volatility: An analysis of market anomalies. Behavioural Public Policy, 10(2), 337-361. https://doi.org/10.1017/bpp.2024.13

Awad, A., Aziz, A. F., & Shma, T. R. (2025). Investment behavior in the Egyptian stock market: The impact of social media on investor decision-making. Investment Management & Financial Innovations, 22(1), 203-212. https://doi.org/10.21511/imfi.22(1).2025.16

Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261-292. https://doi.org/10.1162/003355301556400

Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics, 49(3), 307-343. https://doi.org/10.1016/S0304-405X(98)00027-0

Bouteska, A., & Regaieg, B. (2020). Loss aversion, overconfidence of investors and their impact on market performance evidence from the US stock markets. Journal of Economics, Finance and Administrative Science, 25(50), 451-478. https://doi.org/10.1108/JEFAS-07-2017-0081

Brooks, C. (2008). Introductory econometrics for finance (2nd Edition). Cambridge University Press.

Costa, F., Fortuna, N., & Lobão, J. (2024). Herding states and stock market returns. Research in International Business and Finance, 68, 102163. https://doi.org/10.1016/j.ribaf.2023.102163

Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor psychology and security market under‐and overreactions. The Journal of Finance, 53(6), 1839-1885. https://doi.org/10.1111/0022-1082.00077

De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98(4), 703-738. https://doi.org/10.1086/261703

Divernois, M. A., & Filipović, D. (2024). StockTwits classified sentiment and stock returns. Digital Finance, 6(2), 249-281. https://doi.org/10.1007/s42521-023-00102-z

Dutta, A. (2024). Social media sentiment and stock market volatility: Evidence from the us hi-tech companies. International Journal of Professional Business Review, 9(10), 1–17. https://doi.org/10.26668/businessreview/2024.v9i10.4978

Enow, S. T. (2022). Overreaction and underreaction during the covid-19 pandemic in the outh African stock market and its implications. Eurasian Journal of Business and Management, 10(1), 19-26. https://doi.org/10.15604/ejbm.2022.10.01.002

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417. https://doi.org/10.2307/2325486

Fan, R., Talavera, O., & Tran, V. (2023). Social media and price discovery: The case of cross‐listed firms. Journal of Financial Research, 46(1), 151-167. https://doi.org/10.1111/jfir.12310

Gan, B., Alexeev, V., & Yeung, D. (2024). Tweets versus broadsheets: Sentiment impact on stock markets around the world. Journal of Financial Research, 47(3), 601-633. https://doi.org/10.1111/jfir.12380

Glaser, M., & Weber, M. (2007). Overconfidence and trading volume. The Geneva Risk and Insurance Review, 32(1), 1-36. https://doi.org/10.1007/s10713-007-0003-3

Glez-Peña, D., Lourenço, A., López-Fernández, H., Reboiro-Jato, M., & Fdez-Riverola, F. (2014). Web scraping technologies in an API world. Briefings in Bioinformatics, 15(5), 788-797. https://doi.org/10.1093/bib/bbt026

Gujarati, D. N. (2004). Basic econometrics (4th Edition). McGraw−Hill Companies.

Hong, H., & Stein, J. C. (1999). A unified theory of underreaction, momentum trading, and overreaction in asset markets. The Journal of Finance, 54(6), 2143-2184. https://doi.org/10.1111/0022-1082.00184

Ikram, B., Fouad, B. E. H., & Sara, C. (2023). An exploration of overconfidence and the disposition effect in the stock market. International Journal of Financial Studies, 11(2), 78. https://doi.org/10.3390/ijfs11020078

Irawan, R. L. (2025). Pengaruh overconfidence bias terhadap pengambilan keputusan investasi di pasar saham selama perang Rusia-Ukraina. Coopetition: Jurnal Ilmiah Manajemen, 16(1), 233–242. https://doi.org/10.32670/coopetition.v16i1.5011

Irawan, R. L., & Gurhanawan, G. (2024). Analisis efek underreaction pada saham indofood: pengaruh sentimen pasar dan perilaku investor. Journal of Business and Economics Research (JBE), 5(3), 452–460. https://doi.org/10.47065/jbe.v5i3.5716

Jin, C., Lu, X., & Zhang, Y. (2022). Market reaction, COVID-19 pandemic and return distribution. Finance Research Letters, 47, 102701. https://doi.org/10.1016/j.frl.2022.102701

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185

Karki, U., Bhatia, V., & Sharma, D. (2024). A systematic literature review on overconfidence and related biases influencing investment decision making. Economic and Business Review, 26(2), 130-150. https://doi.org/10.15458/2335-4216.1338

Kaya, H. D., Maramraju, A., & Nallapu, A. (2023). The impact of social media related events on the price volatility of mega-cap technology stocks. Financial Markets, Institutions and Risks, 7(4), 14–23. https://doi.org/10.61093/fmir.7(4).14-23.2023

Khasawneh, M., McMillan, D. G., & Kambouroudis, D. (2024). Left-tail risk and UK stock return predictability: Underreaction, overreaction, and arbitrage difficulties. International Review of Financial Analysis, 95, 103333. https://doi.org/10.1016/j.irfa.2024.103333

Kresnawati, E., Sofia, L., & Utami, E. R. (2024). Herding behavior, information type, and overconfidence bias: An experimental study on novice investors’ investment decisions. Journal of Accounting and Investment, 25(1), 369-386. https://doi.org/10.18196/jai.v25i1.14823

Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1-24. https://doi.org/10.1016/S0304-4076(01)00098-7

Loang, O. K. (2025). Overreaction, investor sentiment and market sentiment of COVID-19. Vision, 29(5), 636-650. https://doi.org/10.1177/09722629221087386

Madaan, G., & Singh, S. (2019). An analysis of behavioral biases in investment decision-making. International Journal of Financial Research, 10(4), 55-67. https://doi.org/10.5430/ijfr.v10n4p55

Mustofa, A. A., Berniz, Y. M., & Susanto, A. (2022). The effect of financial performance, company size, and gross domestic product on abnormal returns. Jurnal Penelitian, 19(2), 75-83. https://doi.org/10.26905/jp.v19i2.9046

Padmavathy, M. (2024). Behavioral finance and stock market anomalies: Exploring psychological factors influencing investment decisions. Shanlax International Journal of Management, 11(1), 191-197. https://doi.org/10.34293/management.v11iiS1-Jan.7164

Pujiati, D. (2025). The moderating role of herding behavior in investment decision-making. RISET: Jurnal Aplikasi Ekonomi Akuntansi dan Bisnis, 7(2), 295-310. https://doi.org/10.37641/riset.v7i2.2716

Shleifer, A., & Summers, L. H. (1990). The noise trader approach to finance. Journal of Economic Perspectives, 4(2), 19-33. https://doi.org/10.1257/JEP.4.2.19

Shukla, A., Rushdi, D. N. J., & Katiyar, D. R. C. (2020). Impact of behavioral biases on investment decisions ‘a systematic review’. International Journal of Management, 11(4), 68–76.

Siska, E., Aryal, N. P., Singh, G. K., & Indra, N. (2025). Market response to the launch of Indonesia ’ s sovereign wealth fund. Ekonomi, Keuangan, Investasi dan Syariah (Ekuitas), 6(4), 604–611. https://doi.org/10.47065/ekuitas.v6i4.7184

Siska, E., Lestari, N. P., & Amalia, M. M. (2021). Satu tahun Virus Corona: Analisis pergerakan harga saham bank syariah di lantai bursa. Jurnal Ilmiah Ekonomi Islam, 7(3), 1253-1260. https://doi.org/10.29040/jiei.v7i3.3051

Syafitri, W., Effendi, J., & Irfany, M. I. (2022). Is there a short-term overreaction to pandemic COVID-19? A case study of the Indonesia Islamic capital market. al-Uqud: Journal of Islamic Economics, 6(2), 177-191. https://doi.org/10.26740/aluqud.v6n2.p211-228

Vo, D. H., Ho, C. M., & Dang, T. H. N. (2022). Stock market volatility from the COVID-19 pandemic: New evidence from the Asia-Pacific region. Heliyon, 8(9), 1–21. https://doi.org/10.1016/j.heliyon.2022.e10763

Wang, X., Xiang, Z., Xu, W., & Yuan, P. (2022). The causal relationship between social media sentiment and stock return: Experimental evidence from an online message forum. Economics Letters, 216, 110598. https://doi.org/10.1016/j.econlet.2022.110598

Yunita, S., Komalasari, A., & Suhendro, S. (2023). The effect of timeliness of financial reporting on abnormal return during the Covid-19 period with profitability and company size as moderating variables (Study on the food and beverages sector companies listed on the IDX in 2020-2021). International Journal of Business and Applied Economics (IJBAE), 2(3), 391–404. https://doi.org/10.55927/ijbae.v2i3.4304

Additional Files

Published

2026-10-10

How to Cite

Irawan, R. L., Siska, E., & Lestari, T. (2026). Social media sentiment, behavioral biases, and abnormal returns: Evidence from Indonesian Non-Cyclical Consumer Stocks. AFRE (Accounting and Financial Review), 9(2), 297–312. https://doi.org/10.26905/afr.v9i2.17464

Similar Articles

<< < 1 2 3 4 > >> 

You may also start an advanced similarity search for this article.