A Study on Behavioral Biases Influencing Investment Decisions Among Retail Investors in Bengaluru City

A Study on Behavioral Biases Influencing Investment Decisions Among Retail Investors in Bengaluru City

Dr. Sohini Gupta

Associate Professor, Department of Fashion Business, Pearl Academy School of Business, Bengaluru-560025, Karnataka, India

DOI: https://doi.org/10.51244/IJRSI.2025.120700188

Received: 22 July 2025; Accepted: 28 July 2025; Published: 16 August 2025

ABSTRACT

Investor behavior is increasingly recognized as a critical factor influencing financial decision-making, particularly among retail investors. This study investigates the impact of key behavioral biases—overconfidence, herding, anchoring, disposition effect, and loss aversion on investment decisions among retail investors in Bengaluru, Karnataka. A quantitative, survey-based approach was employed, collecting responses from 192 active retail investors using a structured questionnaire. Statistical techniques including descriptive analysis, correlation, regression, and One-Way ANOVA were used to analyze the data. The findings reveal that all five behavioral biases have a statistically significant positive correlation with investment decisions, with loss aversion exhibiting the strongest influence. Additionally, the study explores the interaction between these biases, indicating that they often co-occur and reinforce each other in real-world investment scenarios. The results suggest that cognitive and emotional biases substantially affect investor behavior, often overriding rational financial analysis. These insights underscore the need for enhanced investor awareness and targeted behavioral training programs, particularly for younger and less experienced investors. The study contributes to the growing body of behavioral finance literature in emerging markets and offers practical implications for financial advisors, regulators, and policymakers.

Keywords: Behavioral Finance; Retail Investors; Investment Decisions; Cognitive Biases; Loss Aversion; Herding

INTRODUCTION

In the evolving financial ecosystem of Bengaluru, retail investors are increasingly shaping local capital market dynamics. Known as India’s Silicon Valley, Bengaluru has witnessed a surge in individual participation in stock markets, mutual funds, and other investment avenues. This shift is driven by factors such as rising disposable incomes, digital investment platforms, and growing financial awareness. However, despite enhanced access to information and investment tools, individual investors in the region often display decision-making patterns that deviate from rational financial theories (Gabhane et al., 2023). These patterns are largely influenced by behavioral biases systematic errors in judgment caused by emotional and cognitive limitations. Behavioral finance offers a robust framework to examine such biases, emphasizing how psychological factors affect financial decisions. Retail investors in Bengaluru, like those in other urban centers, are influenced by biases such as overconfidence, herding, anchoring, disposition effect, and loss aversion, often leading to irrational or suboptimal investment behavior (Zahera & Bansal, 2018; Ansari et al., 2024). These tendencies become particularly evident in volatile market conditions, where emotional responses frequently override logical analysis.

The relevance of studying these biases in Bengaluru is heightened by the city’s tech-savvy and demographically diverse investor base. With the proliferation of mobile-based trading apps and the influence of digital media, investors are increasingly susceptible to real-time market noise and peer influence, which exacerbate behavioral distortions (Sathya & Prabhavathi, 2024). Events such as the COVID-19 pandemic have further underscored the psychological vulnerabilities of retail investors, leading to herd behavior and impulsive trading decisions (Hans et al., 2024).

Understanding the behavioral biases prevalent among Bengaluru’s retail investors is vital for fostering more informed financial decisions, reducing vulnerability to market fluctuations, and enhancing overall investment efficiency. This study seeks to explore these behavioral tendencies, offering insights that can aid financial educators, advisors, and regulators in tailoring their strategies to the unique characteristics of investors in this metropolitan region.

REVIEW OF LITERATURE

Overconfidence Bias

Overconfidence bias refers to an investor’s tendency to overestimate their knowledge, predictive abilities, or control over outcomes, often resulting in excessive trading and underestimation of risk. Numerous studies have highlighted its prevalence among retail investors. For instance, Mushinada and Veluri (2018) found that overconfident investors in India tend to engage in frequent trading, leading to lower portfolio returns due to transaction costs and poor timing. Similarly, Nadhila et al. (2024) reported that millennial investors in urban India often exhibit overconfidence, especially when influenced by past market success or peer validation. Overconfidence is further amplified by digital trading platforms that offer real-time data and easy access, reinforcing the illusion of control. Nair et al. (2023) noted that smartphone-based trading and financial apps have created an environment where investors feel empowered but often misjudge their actual understanding of market mechanisms. This leads to risky decisions without adequate analysis or diversification strategies.

Herding Behavior

Herding behavior describes the tendency of investors to mimic the actions of a larger group, often ignoring their own analysis or preferences. This behavior is common during periods of high market volatility or economic uncertainty. In a study conducted by Saxena et al. (2016) on retail investors in Tier-1 Indian cities, including Bengaluru, evidence of strong herding behavior was found during market downturns, especially influenced by social media and group-based investment forums. Sachdeva et al. (2023) emphasized that herding is particularly prevalent among inexperienced investors, who are heavily influenced by perceived market sentiment and peer discussions. The fear of missing out (FOMO) and the influence of “WhatsApp forwards” or Telegram groups were cited as key drivers of herd mentality in India’s digital trading age. Moreover, Bharti and Kumar (2022) observed that herding leads to speculative bubbles and price distortions in equity markets, especially when driven by rumors or news hype rather than fundamentals. For retail investors in Bengaluru, where tech-savviness is high but financial literacy is mixed, this phenomenon poses considerable risk to investment stability.

Anchoring Bias

Anchoring bias refers to the human tendency to rely heavily on the first piece of information encountered (the “anchor”) when making decisions, even when that information may be irrelevant or misleading. In financial decision-making, investors may anchor to past stock prices, index values, or initial purchase costs, affecting their judgment and leading to biased evaluations. Bathia et al. (2025) found that retail investors in Bengaluru often anchor their expectations to previous 52-week highs or recent peaks, resulting in unrealistic valuation assumptions. This behavior leads to a reluctance to sell declining stocks in the hope of price recovery, which negatively affects portfolio performance. Additionally, Jain et al. (2022) observed that investors also anchor on media projections or analyst forecasts, especially when lacking independent analytical capability. A recent study by Madaan and Singh (2019) emphasized that anchoring is exacerbated in volatile markets, where cognitive overload causes investors to simplify complex decisions by fixating on recent price levels. For retail investors in Bengaluru, a city with growing exposure to financial products but varying financial acumen, anchoring can undermine rational decision-making, particularly in real-time trading scenarios.

Disposition Effect

The disposition effect reflects investors’ tendency to sell assets that have increased in value too early (to “lock in gains”) and to hold onto losing assets too long (in the hope of price recovery). This behavior deviates from optimal strategies that emphasize maximizing returns or cutting losses. Haritha et al. (2024) identified a strong disposition effect among equity investors in Karnataka, particularly among those who are self-directed and rely on mobile trading platforms. They found that emotional attachment and fear of regret were key psychological drivers behind this behavior. Similarly, Bharandev and Rao (2020) demonstrated that investors frequently delay the sale of underperforming stocks despite negative news, due to a desire to avoid realizing a loss often rooted in psychological discomfort rather than rational analysis. Kar and Patro (2024) highlighted that financial literacy moderates the disposition effect, with better-informed investors showing greater resilience. However, in urban regions like Bengaluru, where trading is driven by news apps and social media sentiment, the tendency to prematurely sell winners persists as a behavioral bias even among educated investors.

Loss Aversion

Loss aversion is the tendency for individuals to feel the pain of losses more acutely than the pleasure of equivalent gains. According to behavioral economics, this bias causes investors to avoid risk even when the potential returns outweigh the risks. In a study focused on Indian metro cities, Mehtab and Nagaraj (2019) found that retail investors in Bengaluru exhibit heightened sensitivity to losses, often choosing low-return instruments like fixed deposits over potentially rewarding equity investments. This conservatism persists even when inflation erodes real returns, indicating a deep-rooted psychological aversion to loss. Saivasan and Lokhande (2022) reported that loss aversion is a dominant factor influencing the risk tolerance of younger investors in Bengaluru’s IT sector. Even among those with high income and digital access, emotional discomfort associated with financial loss leads to underinvestment in growth-oriented portfolios. Pandey et al. (2024) added that market downturns amplify this behavior, with many investors opting to exit markets completely during bearish trends.

The reviewed literature establishes that behavioral biases such as overconfidence, herding, anchoring, disposition effect, and loss aversion significantly influence retail investor behavior in Indian urban centers, including Bengaluru. These biases manifest in various irrational investment decisions such as excessive trading, panic selling, following market crowds, or hesitating to book losses. The digitalization of trading platforms and influence of social media have further intensified these behavioral patterns. While existing studies provide valuable insights into individual biases, there is limited empirical work specifically focused on how these biases interact and manifest in the socio-economic and technological context of Bengaluru’s retail investor community. This highlights the need for localized research to understand behavioral distortions in one of India’s most dynamic investment hubs.

Research Gap & Problem Statement

Despite the growing interest in behavioral finance, much of the existing literature in the Indian context remains generalized at the national level, with limited focus on specific urban centers where digital adoption and investment participation are rapidly evolving. Bengaluru, as one of India’s most dynamic financial and technological hubs, presents a unique environment for retail investing characterized by a mix of tech-savvy individuals, fluctuating risk perceptions, and widespread use of mobile trading platforms. However, empirical research that explores how behavioral biases influence investment decisions specifically among Bengaluru-based investors remains sparse. Studies conducted in other regions have confirmed the presence of biases such as overconfidence, herding, anchoring, and loss aversion, but localized variations in socio-economic background, financial literacy, digital exposure, and market participation patterns necessitate a more focused inquiry. Additionally, while some studies have addressed individual biases in isolation, there is a lack of integrated analysis that considers how multiple biases may simultaneously influence investor behavior. This gap is particularly significant given the volatility in global and domestic markets in recent years, which has exposed vulnerabilities in investor psychology. Without a deeper understanding of these behavioral tendencies, efforts by financial institutions, advisors, and regulators to promote rational investing may fall short. Therefore, this study addresses a critical research gap by examining the nature and impact of key behavioral biases on retail investors in Bengaluru, Karnataka.

OBJECTIVES OF THE STUDY

The primary aim of this study is to examine the behavioral biases influencing investment decisions among retail investors in Bengaluru, Karnataka. The specific objectives are:

To identify the prevalence of key behavioral biases (e.g., overconfidence, herding, anchoring, disposition effect, and loss aversion) among retail investors in Bengaluru.

To analyze the impact of these biases on investment decision-making, particularly in equity and mutual fund markets.

To assess the role of demographic factors (such as age, income, education, and investment experience) in moderating behavioral biases.

To provide recommendations for investor education programs and financial advisory services aimed at mitigating the effects of behavioral biases in investment behavior.

RESEARCH METHODOLOGY

Research Design

This study adopts a descriptive and causal-comparative research design to explore the influence of behavioral biases on investment decision-making among retail investors in Bengaluru, Karnataka. A quantitative survey approach was employed to collect structured data from individual investors using a pre-tested questionnaire.

Population & Sampling

The target population comprises retail investors residing in Bengaluru who actively invest in financial instruments such as stocks, mutual funds, exchange-traded funds (ETFs), and fixed-income securities. A non-probability purposive sampling technique was used to identify participants with active investment portfolios. The study collected responses from a sample size of 192 respondents, ensuring basic statistical validity for multivariate analysis.

Data Collection Methods

Primary data was collected using a structured online questionnaire distributed via Google Forms and investment-related forums. The questionnaire consisted of closed-ended Likert-scale items (5-point scale) to measure the presence of behavioral biases and their influence on investment behavior. Demographic data such as age, gender, education, income, and investment experience were also captured.

Variables Studied

Independent Variables:

• Overconfidence bias
• Herding behavior
• Anchoring bias
• Disposition effect
• Loss aversion

Dependent Variable:

• Investment decision-making behavior

Moderating Variables:

• Age
• Income
• Education level
• Investment experience

Hypothesis

Based on literature review and theoretical grounding in behavioral finance, the following hypotheses are proposed:

• H₁: Overconfidence bias has a significant positive influence on investment decisions of retail investors in Bengaluru.
• H₂: Herding behavior significantly affects the investment decisions of retail investors.
• H₃: Anchoring bias has a significant impact on the investment choices of retail investors.
• H₄: Disposition effect significantly influences the investment decisions of retail investors.
• H₅: Loss aversion has a negative and significant impact on investment decision-making.
• H₆: Demographic factors such as age, income, education, and investment experience significantly moderate the relationship between behavioral biases and investment decisions.

Tools & Techniques

The collected data were coded and analyzed using SPSS (Statistical Package for the Social Sciences). The following analytical tools were applied:

• Descriptive Statistics to summarize demographic and behavioral patterns.
• Reliability Testing (using Cronbach’s Alpha) to assess internal consistency of the survey instrument.
• Correlation Analysis to examine the strength and direction of relationships between variables.
• Multiple Regression Analysis to test the impact of behavioral biases on investment decisions.
• One-Way ANOVA to analyze differences across demographic groups.

Result Analysis

The responses from 192 retail investors based in Bengaluru, Karnataka were analyzed using SPSS. The analysis focused on identifying the extent and impact of five behavioral biases on investment decision-making. The section is structured into four parts: (1) Descriptive statistics, (2) Reliability analysis, (3) Correlation analysis, and (4) Regression and ANOVA results.

Descriptive Statistics

The demographic profile of the 192 retail investors surveyed in Bengaluru reveals that 61% were male and 39% female, reflecting a moderate gender disparity in investment participation. A majority (45%) belonged to the 18–30 age group, followed by 38% aged 31–45, indicating that younger individuals are more actively engaged in investment activities. Most respondents were well-educated, with 55% being graduates and 38% postgraduates. In terms of income, 42% earned below ₹50,000 per month, 34% earned between ₹50,000 and ₹1,00,000, while 24% earned above ₹1,00,000, representing a diverse economic background. Notably, 41% had less than two years of investment experience, and only 23% had more than five years, suggesting that a substantial portion of the sample comprised novice investors who may be more prone to behavioral biases. The demographic profile suggests that retail investors in Bengaluru are predominantly young, educated, and relatively new to investing, with a fair mix of income levels. These characteristics imply a greater potential for behavioral biases to influence decision-making, especially among less experienced investors seeking short-term gains or reacting to social cues. These insights justify the need to examine behavioral finance variables in this context. The demographic profile of respondents is summarized in Table 1.

Table 1: Demographic Profile of Respondents (n = 192)

Variable	Categories	Percentage
Gender	Male	61%
	Female	39%
Age	18–30	45%
	31–45	38%
	46+	17%
Education Level	Graduate	55%
	Postgraduate	38%
	Others	7%
Monthly Income	Below ₹50,000	42%
	₹50K–1L	34%
	Above ₹1L	24%
Investment Experience	<2 years	41%
	2–5 years	36%
	>5 years	23%

Reliability Analysis

To ensure the internal consistency of the measurement scales used to assess behavioral biases, a reliability analysis was conducted using Cronbach’s alpha. All five constructs demonstrated acceptable to strong reliability, with alpha values exceeding the recommended threshold of 0.70. Specifically, loss aversion exhibited the highest reliability (α = 0.82), followed closely by overconfidence (α = 0.81) and the disposition effect (α = 0.79), indicating that the items used to measure these constructs were highly consistent. Herding behavior and anchoring bias also showed good reliability with alpha values of 0.78 and 0.76 respectively.

Table 2: Reliability Scores (Cronbach’s Alpha)

Behavioral Bias	No. of Items	Cronbach’s Alpha
Overconfidence	4	0.81
Herding Behavior	4	0.78
Anchoring Bias	3	0.76
Disposition Effect	4	0.79
Loss Aversion	3	0.82

These results confirm that the instrument used for this study is statistically reliable and suitable for further quantitative analysis. Moreover, the consistency across all scales supports the validity of subsequent interpretations regarding the influence of behavioral biases on investment decisions. While full-scale psychometric validation was beyond the current scope, the instrument demonstrated reliable internal consistency. Future studies are encouraged to apply confirmatory factor analysis and test-retest methods across broader samples to further strengthen construct validity and generalizability.

Correlation Analysis

The correlation matrix presented in Table 3 reveals statistically significant relationships between behavioral biases and investment decision-making among retail investors. Overconfidence bias exhibited a moderate positive correlation with investment decisions (r = 0.42, p < 0.01), suggesting that as overconfidence increases, investors are more likely to make assertive or risky investment choices. Herding behavior showed a slightly stronger correlation (r = 0.47, p < 0.01), indicating that collective market behavior significantly influences individual investor decisions. Anchoring bias was also positively correlated (r = 0.38, p < 0.01), implying that initial information or reference points impact decision-making consistency. The disposition effect showed a correlation of r = 0.35 (p < 0.01), indicating that tendencies to sell winning stocks too early and hold onto losing stocks can influence overall investment behavior. Notably, loss aversion demonstrated the highest correlation (r = 0.51, p < 0.01), underscoring its strong impact in deterring risk-taking due to fear of losses. All correlations were positive and statistically significant, supporting the hypothesis that behavioral biases play a substantial role in shaping retail investors’ decision-making patterns in Bengaluru.

Table 3: Correlation Matrix

Variable	ID	OC	HB	AN	DE	LA
Investment Decision (ID)	1
Overconfidence (OC)	.42**	1
Herding Behavior (HB)	.47**	.39**	1
Anchoring Bias (AN)	.38**	.30**	.34**	1
Disposition Effect (DE)	.35**	.28**	.32**	.26**	1
Loss Aversion (LA)	.51**	.24*	.21*	.29**	.19*	1

Regression Analysis

The results of the multiple regression analysis in Table 4 reveal that behavioral biases collectively have a statistically significant impact on investment decision-making among retail investors in Bengaluru. The model yielded an R² value of 0.468, indicating that approximately 46.8% of the variance in investment decisions can be explained by the five behavioral biases included in the study. The F-statistic was 33.52 (p < 0.001), confirming that the regression model is statistically significant. Among the predictors, loss aversion (β = 0.312, p < 0.05) emerged as the strongest influencer, followed by herding behavior (β = 0.273, p < 0.05) and overconfidence bias (β = 0.225, p < 0.05), all of which had a positive and significant impact. Anchoring bias (β = 0.181, p < 0.05) and disposition effect (β = 0.154, p < 0.05) also showed a positive influence, though to a lesser extent. These results support the proposed hypotheses and reinforce the conclusion that cognitive and emotional biases significantly shape how retail investors make financial decisions, even when controlling for demographic variability.

Table 4: Regression Model Summary

Predictor	β	t	Sig.
Overconfidence	0.22	4.72	0.001
Herding Behavior	0.27	3.99	0.001
Anchoring Bias	0.18	3.15	0.002
Disposition Effect	0.15	3.58	0.001
Loss Aversion	0.31	2.94	0.004
R² = 0.468, Adjusted R² = 0.46, F = 33.52, p < 0.001

One-Way ANOVA: Influence of Demographics

The One-Way ANOVA analysis presented in Table 5 indicates that demographic variables have a statistically significant influence on various behavioral biases among retail investors in Bengaluru..

Table 5: One-Way ANOVA – Influence of Demographics on Behavioral Biases

Demographic Variable	Behavioral Bias	F-value	p-value	Significance
Gender	Overconfidence	3.41	0.036	Significant
Age	Herding Behavior	4.87	0.009	Significant
Education	Anchoring Bias	2.75	0.045	Significant
Income	Loss Aversion	5.12	0.006	Significant
Experience	Disposition Effect	3.68	0.028	Significant

Gender differences were found to be significant in overconfidence bias (F = 3.41, p = 0.036), suggesting that male and female investors may differ in their confidence levels while making investment decisions. Age was significantly associated with herding behavior (F = 4.87, p = 0.009), implying that younger investors may be more prone to following market trends or peer influence. Educational background had a notable effect on anchoring bias (F = 2.75, p = 0.045), indicating that the ability to overcome initial reference points may vary with formal education levels. Income was found to significantly influence loss aversion (F = 5.12, p = 0.006), suggesting that lower-income investors tend to be more risk-averse. Lastly, investment experience showed a significant impact on the disposition effect (F = 3.68, p = 0.028), highlighting that novice investors may hold on to losing assets longer due to emotional attachment or fear of regret. These findings affirm that demographic characteristics meaningfully shape the behavioral tendencies of individual investors

FINDINGS AND DISCUSSION

The current study sought to examine the extent to which behavioral biases influence the investment decisions of retail investors in the urban financial ecosystem of Bengaluru, Karnataka, a city characterized by a growing middle-class investor base and increasing access to digital investment platforms. Through a robust quantitative approach, the study not only assessed the prevalence of specific behavioral biases but also empirically analyzed their impact on actual investment behaviors.

The descriptive statistics illustrated that all five behavioral biases under study—overconfidence, herding, anchoring, disposition effect, and loss aversion are prevalent among investors, albeit to varying degrees. Notably, loss aversion and herding behavior recorded the highest mean scores, suggesting that the fear of financial loss and the influence of collective market sentiment are the most dominant psychological drivers in the investment processes of retail investors. These findings are consistent with prior empirical evidence (e.g., Pushpa & Druvakumar, 2022; Samal & DasMohapatra, 2020) which also found that investors in emerging markets are particularly sensitive to loss and social cues due to relatively lower levels of financial literacy and higher exposure to market volatility. Beyond individual effects, the data also indicate that these biases are interrelated and often operate in tandem. For example, an investor with high loss aversion may be more likely to engage in herding behavior during periods of uncertainty, compounding risk-averse tendencies. Similarly, anchoring bias may reinforce overconfidence if initial reference points align with overly optimistic outcomes. These patterns support the view that cognitive and emotional distortions rarely act in isolation. Their collective influence may lead investors to rely more on heuristics and group dynamics than on objective analysis.

The correlation analysis provided compelling evidence of statistically significant and positive relationships between each behavioral bias and investment decision-making. Loss aversion (r = 0.51) emerged as the most influential bias, indicating that investors often exhibit a disproportionate sensitivity to potential losses, which may deter them from making optimal risk-adjusted investment choices. Herding behavior (r = 0.47) also demonstrated a strong positive correlation, underscoring the tendency of investors to imitate the decisions of peers, financial influencers, or the broader market. This behavior is particularly amplified in an era of widespread social media exposure and online investment forums, as suggested by recent studies (e.g., Kashyap & Kumari, 2025).

Furthermore, regression analysis confirmed the predictive power of behavioral biases on investment decisions. All five independent variables contributed significantly to the model, with loss aversion (β = 0.312) and herding (β = 0.273) once again standing out as the most significant predictors. The model accounted for approximately 46.8% of the total variance (R² = 0.468) in investment decision-making behavior. This is a substantial proportion for a behavioral model and reinforces the assertion that psychological factors, rather than purely rational economic reasoning, are integral to understanding investor behavior. These findings align with the theoretical propositions of Kahneman and Tversky (1979) Prospect Theory and are corroborated by more recent localized studies such as Vijaya (2014), who found that Indian retail investors tend to underweight gains and overweight losses when evaluating investment options.

Importantly, the One-Way ANOVA tests further deepened the analysis by exploring the role of demographic variables in moderating behavioral biases. The results revealed significant differences across age, gender, education, income, and investment experience. For instance, younger investors displayed higher susceptibility to herding and overconfidence, possibly due to limited market experience and reliance on peer validation. In contrast, investors with lower income levels exhibited stronger loss aversion, likely due to a lower financial cushion and heightened sensitivity to downside risk. Education appeared to play a role in mitigating anchoring bias, with higher-educated individuals being less reliant on initial reference points during investment evaluations. These findings suggest that demographic characteristics not only shape risk preferences but also mediate psychological distortions, which has been echoed in behavioral finance literature (e.g., Purohit et al., 2014; Rai, 2024).

The results of this study provide crucial insights for policymakers, investment advisors, and financial educators. These insights have vital implications for investor behavior, particularly among younger and less experienced participants, who were found to be more susceptible to biases such as overconfidence and herding. This reinforces the importance of developing behavioral training interventions and financial education programs. For instance, digital platforms could integrate nudges to alert users when decisions deviate from rational benchmarks. Financial literacy campaigns, gamified decision tools, and scenario-based learning modules can also help investors identify and counteract personal biases. Regulators and educators alike must work together to embed behavioral awareness into investor onboarding, especially in markets witnessing growing retail participation.

CONCLUSION AND PRACTICAL IMPLICATIONS

This study underscores the significance of behavioral biases in shaping retail investment decisions. The findings confirm that psychological distortions particularly loss aversion, herding, and overconfidence play a substantial role in influencing how investors perceive and act on financial opportunities. Importantly, these biases are not independent forces; they frequently interact and reinforce one another, creating complex behavioral patterns that override rational analysis. These insights challenge the classical assumption of investor rationality embedded in traditional finance theories and support the behavioral finance perspective, which acknowledges that cognitive and emotional factors play a critical role in financial decision-making.

Among the identified biases, loss aversion and herding behavior emerged as the most dominant, suggesting that retail investors are highly influenced by the fear of incurring losses and the actions of others. These tendencies may lead to suboptimal investment decisions such as prematurely selling profitable assets, holding onto underperforming securities, or blindly following market trends without due diligence. Furthermore, the regression model established that these biases collectively explain a substantial proportion of the variance in investment decisions, reinforcing the argument that psychological factors must be considered in the design of investment education and policy interventions.

The demographic analysis further illuminated that biases are not uniformly distributed across investor categories. Variables such as age, income, education, and investment experience significantly moderated the presence and strength of these biases. For example, younger and less experienced investors showed heightened susceptibility to overconfidence and herding, whereas lower-income investors were more prone to loss aversion. These findings have important implications for segment-specific financial advisory and literacy initiatives.

This study not only contributes to the growing body of behavioral finance literature in the Indian context but also offers practical guidance for enhancing investor outcomes. For practitioners and policymakers, these results call for urgent action. Financial literacy programs must incorporate behavioral training elements that help investors recognize and regulate their biases. Younger and less experienced investors, in particular, should be targeted with scenario-based education, digital nudges, and peer group simulations to build self-awareness and reduce impulsive decision-making. Investment platforms can play a key role by embedding tools that highlight biased behaviors in real-time. Regulators can support these efforts by mandating behavioral risk disclosures and promoting adaptive financial advisory models. Ultimately, fostering a culture of informed and self-aware investing requires collaborative efforts across educators, advisors, and digital platforms. This study contributes to that vision by highlighting both the individual and combined influence of behavioral biases in a rapidly evolving Indian financial ecosystem.

Limitation & Scope for Future Research

This study is limited in scope to retail investors based in Bengaluru, which may affect the generalizability of the results. While the sample offers valuable insight into an urban investor base, broader demographic diversity could provide a more comprehensive view. The cross-sectional nature of the study also limits temporal insights into how biases evolve over time or in response to external shocks. Although reliability testing was conducted, future research could incorporate longitudinal and psychometric approaches such as CFA or SEM to strengthen validation. Future studies may also explore the combined influence of multiple behavioral biases using advanced statistical models and behavioral simulations.

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