Article Sidebar

Submitted
July 16, 2024
Accepted
July 21, 2025
Published
July 21, 2025
Download
Full text PDF
Statistic
Read Counter : 928 Download : 1268

Downloads

Download data is not yet available.

Main Article Content

Abstract

Purpose – This research consists of Behavioral Finance where it is focused on cognitive bias factors influence on Investment Decision with using the scope of research in three countries which are Indonesia, Thailand, and Japan.
Design/methodology/approach – the method of research is categorized as quantitative research where it uses a questionnaire with 232 respondents. Then, the data is processed and analyzed using software SmartPLS 3.0.
Findings – The findings reveal that overconfidence and availability bias have a significant positive effect on investment decisions, while herding behavior has a negative effect and anchoring bias shows no significant influence.
Research limitations/implications – This research is limited by its relatively small sample size of 232 respondents across three culturally and economically diverse countries, which may affect the generalizability of the findings.
Practical implications – The strong influence of overconfidence and availability bias highlights the need for improved investor education focused on risk awareness and critical analysis, especially in the digital era. Also, to prevent irrational behavior driven by herding, financial institutions and regulators should enhance collective financial literacy and promote transparent, data-driven decision-making.
Originality/value – This result provides reasonable insight into why there is a difference in results between each country supported with the data and results from the previous research that have been done before.

Keywords

Overconfidence behavior herding behavior Availability bias Anchoring bias Investment Decision

Article Details

License

Copyright (c) 2025 Deddy Marciano, Liliana Inggrit Wijaya, Laurenco Lingguardi Sugianto, Zunairoh Zunairoh

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
How to Cite
Marciano, D., Wijaya, L. I., Sugianto, L. L. ., & Zunairoh, Z. (2025). The effect of behavioral factors on investment decision towards stock market between Indonesia, Japan, and Thailand. Jurnal Siasat Bisnis, 29(2), 179–193. https://doi.org/10.20885/jsb.vol29.iss2.art4
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Ahmad, M., & Wu, Q. (2024). Heuristic-driven biases as mental shortcuts in investment management activities: a qualitative study. Qualitative Research in Financial Markets, 16(2), 291–309. https://doi.org/10.1108/QRFM-10-2022-0167
  2. Athota, V. S., Pereira, V., Hasan, Z., Vaz, D., Laker, B., & Reppas, D. (2023). Overcoming financial planners’ cognitive biases through digitalization: A qualitative study. Journal of Business Research, 154, 113291. https://doi.org/10.1016/j.jbusres.2022.08.055
  3. Bennett, D., Mekelburg, E., & Williams, T. H. (2023). BeFi meets DeFi: A behavioral finance approach to decentralized finance asset pricing. Research in International Business and Finance, 65, 101939. https://doi.org/10.1016/j.ribaf.2023.101939
  4. Bihari, A., Dash, M., Kar, S. K., Muduli, K., Kumar, A., & Luthra, S. (2022). Exploring behavioural bias affecting investment decision-making: a network cluster based conceptual analysis for future research. International Journal of Industrial Engineering and Operations Management, 4(1/2), 19–43. https://doi.org/10.1108/IJIEOM-08-2022-0033
  5. Buana, D. R. (2020). Analisis Perilaku Masyarakat Indonesia dalam Menghadapi Pandemi Virus Corona (Covid-19) dan Kiat Menjaga Kesejahteraan Jiwa. SALAM: Jurnal Sosial Dan Budaya Syar-I, 7(3). https://doi.org/10.15408/sjsbs.v7i3.15082
  6. Chang, H.-W., & Lin, C. (2023). Currency portfolio behavior in seven major Asian markets. Economic Analysis and Policy, 79, 540–559. https://doi.org/10.1016/j.eap.2023.06.027
  7. Chen, X., Wang, J., Wang, Y., & Zhong, X. (2023). Extreme illiquidity and stock returns: Evidence from Thailand market. Pacific-Basin Finance Journal, 82, 102191. https://doi.org/10.1016/j.pacfin.2023.102191
  8. Dumohar, A., Aryotejo, D., Djohan, N., Tirto, P., & Mulia, R. (2022). Behavioral Factors Analysis in Investment Decision-Making. PERWIRA - Jurnal Pendidikan Kewirausahaan Indonesia, 5(1), 20–31. https://doi.org/10.21632/perwira.5.1.20-31
  9. Gupta, S., & Shrivastava, M. (2022). Herding and loss aversion in stock markets: mediating role of fear of missing out (FOMO) in retail investors. International Journal of Emerging Markets, 17(7), 1720–1737. https://doi.org/10.1108/IJOEM-08-2020-0933
  10. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
  11. Hiskey, J., Moseley, M., & Goldberg, J. (2011). Subnational electoral regimes and crisis recovery in Argentina and Mexico. Electoral Studies, 30(3), 468–480. https://doi.org/10.1016/j.electstud.2010.11.011
  12. Inghelbrecht, K., & Tedde, M. (2024). Overconfidence, financial literacy and excessive trading. Journal of Economic Behavior & Organization, 219, 152–195. https://doi.org/10.1016/j.jebo.2024.01.010
  13. Jain, J., Walia, N., & Gupta, S. (2019). Evaluation of behavioral biases affecting investment decision making of individual equity investors by fuzzy analytic hierarchy process. Review of Behavioral Finance, 12(3), 297–314. https://doi.org/10.1108/RBF-03-2019-0044
  14. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263. https://doi.org/10.2307/1914185
  15. Kiryanto, K., Kartika, I., & Zaenudin, Z. (2022). Stock price reaction on ISO 9001 certification announcement: evidence from Indonesia. International Journal of Quality & Reliability Management, 39(2), 612–629. https://doi.org/10.1108/IJQRM-04-2020-0127
  16. Kumar, S., Jain, R., Narain, Balli, F., & Billah, M. (2023). Interconnectivity and investment strategies among commodity prices, cryptocurrencies, and G-20 capital markets: A comparative analysis during COVID-19 and Russian-Ukraine war. International Review of Economics & Finance, 88, 547–593. https://doi.org/10.1016/j.iref.2023.06.039
  17. Maditinos, D. I., Šević, Ž., & Theriou, N. G. (2007). Investors’ behaviour in the Athens Stock Exchange (ASE). Studies in Economics and Finance, 24(1), 32–50. https://doi.org/10.1108/10867370710737373
  18. Majewski, S., & Majewska, A. (2022). Behavioral portfolio as a tool supporting investment decisions. Procedia Computer Science, 207, 1713–1722. https://doi.org/10.1016/j.procs.2022.09.229
  19. Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common Method Variance in IS Research: A Comparison of Alternative Approaches and a Reanalysis of Past Research. Management Science, 52(12), 1865–1883. https://doi.org/10.1287/mnsc.1060.0597
  20. Mensi, W., Shahzad, S. J. H., Hammoudeh, S., Zeitun, R., & Rehman, M. U. (2017). Diversification potential of Asian frontier, BRIC emerging and major developed stock markets: A wavelet-based value at risk approach. Emerging Markets Review, 32, 130–147. https://doi.org/10.1016/j.ememar.2017.06.002
  21. Metawa, N., Hassan, M. K., Metawa, S., & Safa, M. F. (2019). Impact of behavioral factors on investors’ financial decisions: case of the Egyptian stock market. International Journal of Islamic and Middle Eastern Finance and Management, 12(1), 30–55. https://doi.org/10.1108/IMEFM-12-2017-0333
  22. Moenjak, T., Kongprajya, A., & Monchaitrakul, C. (2020). Fintech, financial literacy, and consumer saving and borrowing: The case of Thailand (Issue 1100). Asian Development Bank Institute (ADBI). https://hdl.handle.net/10419/238457
  23. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
  24. Rahman, M., & Gan, S. S. (2020). Generation Y investment decision: an analysis using behavioural factors. Managerial Finance, 46(8), 1023–1041. https://doi.org/10.1108/MF-10-2018-0534
  25. Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial Least Squares Structural Equation Modeling. In Handbook of Market Research (pp. 1–47). Springer International Publishing. https://doi.org/10.1007/978-3-319-05542-8_15-2
  26. Soekarno, S., & Pranoto, S. (2020). Influence of Financial Literacy on the Stock Market Participation and Financial Behavior among Indonesian Millennials. In Advanced Issues in the Economics of Emerging Markets (International Symposia in Economic Theory and Econometrics (pp. 115–125). Emerald Publishing Limited. https://doi.org/10.1108/S1571-038620200000027009
  27. Suttipun, M., & Yordudom, T. (2022). Impact of environmental, social and governance disclosures on market reaction: an evidence of Top50 companies listed from Thailand. Journal of Financial Reporting and Accounting, 20(3/4), 753–767. https://doi.org/10.1108/JFRA-12-2020-0377
  28. Takahashi, H. (2013). Molecular neuroimaging of emotional decision-making. Neuroscience Research, 75(4), 269–274. https://doi.org/10.1016/j.neures.2013.01.011
  29. Yoshino, N., Morgan, P. J., & Long, T. Q. (2017). Financial Literacy in Japan: Determinants and Impacts (ADBI Working Papers). https://www.adb.org/publications/financial-literacy-japan-determinants-and-impacts