Decoding investor psychology : The role of robo-advisors in mitigating herding bias and fear of missing out
Manisha Manchandamanisha.mba@dcrustm.orgDepartment of Management StudiesDeenbandhu Chhotu Ram University of Science and TechnologyMurthal, Haryana, 131027, India0000-0003-3931-3699View full profile → , Ayushayushchanana0612@gmail.comDepartment of Management StudiesDeenbandhu Chhotu Ram University of Science and TechnologyMurthal, Haryana, 131027, India0009-0009-4129-917XView full profile → , *Himanshi BajajCorresponding authorhimanshibajaj612@gmail.comDepartment of Management StudiesDeenbandhu Chhotu Ram University of Science and TechnologyMurthal, Haryana, 131027, India0009-0005-2321-0486View full profile → , Swatiswati.jangra77@gmail.comDepartment of CommerceGovt. College Hathin (Palwal)Hathin, Palwal, Haryana, 121103, India000-0002-9321-6729View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Dec 2025
- Published Online:
- 06 Aug 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JSMS-1681
- Pages:
- 865–874
Abstract
In traditional finance, it is presumed that investors are rational, but behavioral finance totally contradicts the idea that investors are rational. In reality, investors are emotional, which influences investment choices, including sentiments, peer pressure, and informational noise. Investors’ decisions are affected by many biases that cause investors to act illogically, which often results in noise trading. With the evolution of modern finance, Artificial Intelligence (AI) and Behavioral Finance have gained significant attention, resulting increased in the trend of robo-advisors. Robo-advisors assist investors in making decisions by providing algorithm-driven, emotion-free investment recommendations, which are emerging as a potential buffer against such biases. However, very few studies portray robo-advisory platforms as moderators among behavioral biases. Therefore, to address this gap, data was collected from 393 investors with the help of Google Forms using purposive sampling technique. Structural Equation Modelling (SEM) using Analysis of Moment Structures 22 (AMOS 22) was employed for data analysis. Findings revealed that Fear of Missing Out (FOMO) and herding bias significantly influence investors’ Investment Decision-Making (IDM), while robo-advisory platforms are insufficient to eliminate these biases.
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References
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