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Understanding Investor Behavior: Evidence from Individual Investors in Varanasi, India
Subject area: Management and Commerce · Area of research: Behavioral Finance
Abstract
Behavioral finance challenges the classical idea that investors make rational decisions based on information and options by arguing that psychological biases systematically distort investors' financial judgment. This study examines five behavioral biases: overconfidence, anchoring, herding, representativeness, and regret aversion and their indirect effects on investment decision-making through perceived risk among individual investors in Varanasi, a tier-two city in India. The primary data were obtained from 209 individual investors through a structured, five-point Likert-scale questionnaire and a convenience sampling method; the model was estimated using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS. The measurement model demonstrated good internal consistency, convergent validity, and discriminant validity. The structural model was able to support all eleven hypotheses: the effects from anchoring, herding, representativeness and regret aversion were positive and significant in relation to perceived risk, whereas the effects from overconfidence were negative and significant in relation to perceived risk; the effect of perceived risk on investment decision-making was negative and significant, and perceived risk carried significant indirect effects from all five behavioral biases to investment decision-making. The highest positive path coefficient for perceived risk was for regret aversion, and the largest effect size in the model was for the path from perceived risk to decision. The results indicate that perceived risk is a significant pathway between cognitive and emotional biases and financial decisions and provide a valuable set of cues for investor education and advice.
Keywords
behavioral finance; behavioral biases; perceived risk; investment decision-making; individual investors
How to cite this paper
@article{1722422,
author = {Shakti Kant Sharma, Ajeet Kumar, Dr. Avadhesh Singh, Dr. Akhil Mishra},
title = {Understanding Investor Behavior: Evidence from Individual Investors in Varanasi, India},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {2},
pages = {2150-2166},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722422.pdf},
abstract = {Behavioral finance challenges the classical idea that investors make rational decisions based on information and options by arguing that psychological biases systematically distort investors' financial judgment. This study examines five behavioral biases: overconfidence, anchoring, herding, representativeness, and regret aversion and their indirect effects on investment decision-making through perceived risk among individual investors in Varanasi, a tier-two city in India. The primary data were obtained from 209 individual investors through a structured, five-point Likert-scale questionnaire and a convenience sampling method; the model was estimated using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS. The measurement model demonstrated good internal consistency, convergent validity, and discriminant validity. The structural model was able to support all eleven hypotheses: the effects from anchoring, herding, representativeness and regret aversion were positive and significant in relation to perceived risk, whereas the effects from overconfidence were negative and significant in relation to perceived risk; the effect of perceived risk on investment decision-making was negative and significant, and perceived risk carried significant indirect effects from all five behavioral biases to investment decision-making. The highest positive path coefficient for perceived risk was for regret aversion, and the largest effect size in the model was for the path from perceived risk to decision. The results indicate that perceived risk is a significant pathway between cognitive and emotional biases and financial decisions and provide a valuable set of cues for investor education and advice.},
keywords = {behavioral finance; behavioral biases; perceived risk; investment decision-making; individual investors},
month = {August},
}