Behavioral Biases in Investment Decisions: A Mixed-Methods Study on Retail Investors in Emerging Markets

Ahmad Ashifuddin  Aqham; Eny Endaryati; Vivi Kumalasari Subroto; Robby Andika  Kusumajaya

doi:10.51903/jmi.v3i3.63

Authors

Ahmad Ashifuddin Aqham Fakultas Studi Akademik, Universitas Sains dan Teknologi Komputer, Semarang City, Indonesia, 50192
Eny Endaryati Fakultas Studi Akademik, Universitas Sains dan Teknologi Komputer, Semarang City, Indobesia, 50192
Vivi Kumalasari Subroto Fakultas Studi Akademik, Universitas Sains dan Teknologi Komputer, Semarang City, Indonesia, 50192 https://orcid.org/0009-0005-3141-6249
Robby Andika Kusumajaya Fakultas Studi Akademik, Universitas Sains dan Teknologi Komputer, Semarang City, Indonesia, 50192 https://orcid.org/0000-0003-2953-6063

DOI:

https://doi.org/10.51903/jmi.v3i3.63

Keywords:

Behavioral Biases, Retail Investors, Emerging Markets, Investment Decisions

Abstract

Retail investment in emerging markets has experienced rapid growth, driven by technological advancements and increasing public awareness of financial management. However, behavioral biases often influence retail investment decision-making, such as overconfidence and herding, which can undermine portfolio performance. This study aims to identify the key behavioral biases affecting retail investors' decision-making in emerging markets and analyze their impact on investment performance. A mixed-methods approach was employed, combining a quantitative survey of 200 retail investors with in-depth interviews to gain qualitative insights. The results reveal that overconfidence bias dominates, with a prevalence of 70%, followed by herding bias at 50%, anchoring at 40%, and loss aversion at 60%. Overconfidence bias showed a significant positive correlation with investment returns (r = 0.65, p < 0.01), while herding (r = -0.48, p < 0.03), anchoring (r = -0.35, p < 0.05), and loss aversion (r = -0.60, p < 0.02) had negative impacts on portfolio performance. This research contributes to the behavioral finance literature by highlighting the unique conditions of emerging markets, such as low financial literacy and limited access to information, which exacerbate the effects of behavioral biases. As a practical implication, the development of behavioral finance-based educational programs is recommended to help investors understand and manage these biases. Furthermore, future research is encouraged to explore the use of alternative data, such as social media, to monitor behavioral trends in real-time

References

Aljifri, R. (2023). Investor Psychology in the Stock Market: An Empirical Study of the Impact of Overconfidence on Firm Valuation. Borsa Istanbul Review, 23(1), 93–112. https://doi.org/10.1016/j.bir.2022.09.010

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

Bhattacharya, A., & Sardashti, H. (2022). The Differential Effect of New Product Preannouncements in Driving Institutional and Individual Investor Ownership. Journal of Business Research, 149, 811–823. https://doi.org/10.1016/j.jbusres.2022.05.080

Cascão, A., Quelhas, A. P., & Cunha, A. M. (2023). Heuristics and Cognitive Biases in the Housing Investment Market. International Journal of Housing Markets and Analysis, 16(5), 991–1006. https://doi.org/10.1108/ijhma-05-2022-0073

Chen, X., & Tongurai, J. (2023). Informational Linkage and Price Discovery Between China’s Futures and Spot Markets: Evidence from the US–China Trade Dispute. Global Finance Journal, 55, 100750. https://doi.org/10.1016/J.GFJ.2022.100750

Chwolka, A., & Raith, M. G. (2023). Overconfidence as a Driver of Entrepreneurial Market Entry Decisions: A Critical Appraisal. In Review of Managerial Science (Vol. 17, Issue 3). Springer Berlin Heidelberg. https://doi.org/10.1007/s11846-022-00552-6

Espinosa, V. I., Wang, W. H., & Huerta de Soto, J. (2022). Principles of Nudging and Boosting: Steering or Empowering Decision-Making for Behavioral Development Economics. Sustainability, 14(4), 1–18. https://doi.org/10.3390/su14042145

García-Monleón, F., González-Rodrigo, E., & Bordonado-Bermejo, M. J. (2024). Investor Behavior in Crisis: A Comparative Study of Fear-Driven Downtrends and Confidence-Led Recoveries. Journal of Risk Finance, 25(5), 894–914. https://doi.org/10.1108/jrf-07-2024-0189

Gavrilakis, N., & Floros, C. (2024). Volatility and Herding Bias on ESG Leaders’ Portfolios Performance. Journal of Risk and Financial Management, 17(2), 77. https://doi.org/10.3390/jrfm17020077

Giannikos, C. I., Kakolyris, A., & Suen, T. S. (2023). Prospect Theory and A Manager’s Decision to Trade a Blind Principal Bid Basket. Global Finance Journal, 55, 100806. https://doi.org/10.1016/j.gfj.2023.100806

Goodell, J. W., Kumar, S., Rao, P., & Verma, S. (2023). Emotions and Stock Market Anomalies: A Systematic Review. Journal of Behavioral and Experimental Finance, 37, 100722. https://doi.org/10.1016/j.jbef.2022.100722

Hasan, F., Kayani, U. N., & Choudhury, T. (2023). Behavioral Risk Preferences and Dividend Changes: Exploring the Linkages with Prospect Theory Through Empirical Analysis. Global Journal of Flexible Systems Management, 24(4), 517–535. https://doi.org/10.1007/s40171-023-00350-3

Jain, J., Walia, N., Singla, H., Singh, S., Sood, K., & Grima, S. (2023). Heuristic Biases as Mental Shortcuts to Investment Decision-Making: A Mediation Analysis of Risk Perception. Risks, 11(4), 1–22. https://doi.org/10.3390/risks11040072

Kaur, M., Jain, J., & Sood, K. (2024). All Are Investing in Crypto, I Fear of Being Missed Out: Examining the Influence of Herding, Loss Aversion, and Overconfidence in the Cryptocurrency Market with the Mediating Effect of FOMO. Quality and Quantity, 58(3), 2237–2263. https://doi.org/10.1007/s11135-023-01739-z

Kumar, J., & Prince, N. (2022). Overconfidence Bias in the Indian Stock Market in Diverse Market Situations: An Empirical Study. International Journal of System Assurance Engineering and Management, 13(6), 3031–3047. https://doi.org/10.1007/s13198-022-01792-1

Leal, C. C., & Oliveira, B. (2024). Nudging Financial Behavior in the Age of Artificial Intelligence. Artificial Intelligence in Production Engineering and Management, 1, 115–144. https://doi.org/10.1016/B978-0-12-819471-3.00002-1

Li, X., Chen, L., Hao, Y., Wang, Z., Changxing, Y., & Mei, S. (2023). Sharing Hydrogen Storage Capacity Planning for Multi-Microgrid Investors with Limited Rationality: A Differential Evolution Game Approach. Journal of Cleaner Production, 417, 138100. https://doi.org/10.1016/j.jclepro.2023.138100

Loang, O. K. (2025). Can Machine Learning Surpass Human Investors? Evidence from Adaptive Herding Behaviour in US, China and India. Journal of Applied Economics, 28(1), 2435796. https://doi.org/10.1080/15140326.2024.2435796

Lu, S., & Li, S. (2023). Is Institutional Herding Efficient? Evidence from an Investment Efficiency and Informational Network Perspective. Journal of Behavioral and Experimental Finance, 39, 100828. https://doi.org/10.1016/j.jbef.2023.100828

Mittal, S. K. (2022). Behavior Biases and Investment Decision: Theoretical and Research Framework. Qualitative Research in Financial Markets, 14(2), 213–228. https://doi.org/10.1108/qrfm-09-2017-0085

Niculaescu, C. E., Sangiorgi, I., & Bell, A. R. (2023). Does Personal Experience with COVID-19 Impact Investment Decisions? Evidence from a Survey of US Retail Investors. International Review of Financial Analysis, 88, 102703. https://doi.org/10.1016/j.irfa.2023.102703

Owusu, S. P., & Laryea, E. (2023). The Impact of Anchoring Bias on Investment Decision-Making: Evidence from Ghana. Review of Behavioral Finance, 15(5), 729–749. https://doi.org/10.1108/rbf-09-2020-0223

Rieger, M. O., Wang, M., Huang, P. K., & Hsu, Y. L. (2022). Survey Evidence on Core Factors of Behavioral Biases. Journal of Behavioral and Experimental Economics, 100, 101912. https://doi.org/10.1016/j.socec.2022.101912

Sin Huei, N., Zhuang, Z., Toh, M. Y., Ong, T. S., & Teh, B. H. (2022). Exploring Herding Behavior in an Innovative-Oriented Stock Market: Evidence from ChiNext. Journal of Applied Economics, 25(1), 523–542. https://doi.org/10.1080/15140326.2022.2050992

Ul Abdin, S. Z., Qureshi, F., Iqbal, J., & Sultana, S. (2022). Overconfidence Bias and Investment Performance: A Mediating Effect of Risk Propensity. Borsa Istanbul Review, 22(4), 780–793. https://doi.org/10.1016/j.bir.2022.03.001

Zang, D. G., Paudel, K. P., Liu, Y., Liu, D., & He, Y. (2023). Financial Decision-Making Behaviors of Ethnic Tibetan Households Based on Mental Accounting. Financial Innovation, 9(1), 1–26. https://doi.org/10.1186/s40854-023-00487-1

Zhao, S., Wang, Y., & Cao, G. (2025). Overconfident Investors, Predictable Returns, and Optimal Consumption-Portfolio Rules. The North American Journal of Economics and Finance, 75, 102284. https://doi.org/10.1016/j.najef.2024.102284

Behavioral Biases in Investment Decisions: A Mixed-Methods Study on Retail Investors in Emerging Markets

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