E-ISSN:2583-1747

Research Article

Financial Literacy

Management Journal for Advanced Research

2026 Volume 6 Number 1 February
Publisherwww.singhpublication.com

Navigating the Digital Noise: A Primary Data Survey on Behavioural Drivers Effects on Individual Investment Behavior in Emerging Markets

Bhattacharyya S1*
DOI:10.54741/MJAR/6.1.2026.280

1* Sandip Bhattacharyya, Research Scholar, Department of Commerce, University of Calcutta, Kolkata, West Bengal, India.

This research paper explores how financial literacy of retail investors and behavioural biasness may influence individual’s portfolio investment decisions. The biasness in behavioural activities include herding behavior, overconfidence and probable losses of aversion. Financial literacy represents knowledge of finance and financial market functioning. The study also considers age and demographic factors of the respondents. The research aims to examine which factors may properly predict investment decisions and how knowledge can reduce biasness in investment. A total of eighty (80) retail investors participated. In this paper through structured questionnaires, data were collected. The data analysis included descriptive statistics, reliability tests, correlations, ANOVA and multiple regression.
In this research article Descriptive statistics showed Investor’s moderate herding behavior, overconfidence, and loss aversion. Financial literacy and investment decisions are having higher mean values. Reliability tests confirmed all construct variables are consistent and dependable. Analysis of Correlation demonstrates that strong positive linkage in between financial literacy and investment decisions of retail investors. The probable aversion of losses has a negative relationship. ANOVA indicates significant differences in investment decisions across age groups. Age affects the influence of biases. Multiple regression identifies financial literacy as the strongest predictor. Loss aversion negatively influences investment decisions of investors. Collecting behavior of Investors’ and overconfidence are the contributing factors but not a noteworthy predictor. Multicollinearity tests indicated no biasness in variables. Test of Residuals confirms that there is no autocorrelation.
The study concludes that knowledge and awareness improve to predict trend in financial market investment decisions. The most important factors such as investors’ education and creating awareness. Stock market regulators can use these perspectives to frame market policies that reduce market volatility. Financial experts can provide guidance tailored to psychological tendencies.

Keywords: behavioural biases, financial literacy, investment decisions, herding behavior, loss aversion.

Corresponding Author How to Cite this Article To Browse
Sandip Bhattacharyya, Research Scholar, Department of Commerce, University of Calcutta, Kolkata, West Bengal, India.
Email: 		Bhattacharyya S, Navigating the Digital Noise: A Primary Data Survey on Behavioural Drivers Effects on Individual Investment Behavior in Emerging Markets. Manag J Adv Res. 2026;6(1):40-47.
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https://mjar.singhpublication.com/index.php/ojs/article/view/280

Manuscript Received Review Round 1 Review Round 2 Review Round 3 Accepted
2026-01-12 2026-01-28 2026-02-24
Conflict of Interest Funding Ethical Approval Plagiarism X-checker Note
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© 2026 by Bhattacharyya S and Published by Singh Publication. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/ unported [CC BY 4.0].

Manag J Adv Res 2026;6(1)40
Download PDFBack To Article1. Introduction2. Statement
of the Problem3. Significance
of the Study4. Objectives
of the Study5. Literature
Review6. Research
Gap7. Research
Methodology8. Data
Analysis9. ConclusionReferences
Bhattacharyya S. Navigating the Digital Noise

1. Introduction

The financial world is changing faster than ever. In this study, traditional views saw markets as cold and logical. The rational investors made decisions based only on numbers and expected rewards. The two factors’ Emotions and Psychology were not yet considered sometimes. But in the real scenario, especially in emerging markets, it is different. Nowadays investors are becoming a part of the digital environment. Mobile trading apps and financial social media platforms have made trading easy and fast. Millions of investors can invest within a few moments. Market access has not automatically made them wise. Instead of that investors are surrounded with different types of information. Financial news, social media news, expert analysis and real-time data flows nonstop. But more information does not equal better investment decisions. Investors and potential investors tend to rely on mental shortcuts, habits, and emotional factors. The fear of missing out, herding behavior patterns, overconfidence and loss aversion of risk factors dominate choices. Social factors and Emotional factors of Investors often overshadow rational analysis. This study explores the human gap in emerging markets. It focuses on what is going on in an investor's mind, not on stock charts and technical analysis. The augment of digital trading platforms has brought convenience. But it has also increased pressure on the cognitive environment. Investors are making thoughtless judgments under the level of stress. The biases of behavioural factors influence risk perception and decision-making. Investors now are following online crowds. The investors trust social trends over analysis of data. It is essential for understanding for advisors, regulators and investors.

2. Statement of the Problem

Retail investors are sometimes underachieved in spite of access to downward trends in the market. The Markets reward patience, discipline and rational thinking power. But many fail to meet benchmarks. This is because there is not a lack of data. Instead of that, overburden of information creates stress and underprivileged judgment. Investors are retreated into mental shortcuts. This research has stated Investors’ biases like herding, overconfidence, and loss aversion. A real-time communication hardly reflected by the few research studies.

It can be seen losses in technical charts, but not the psychological factors. Some of the existing literature often treats biasness separately. In practical terms, it is often overlying. Overconfidence may push someone to lead a herd instinct. Loss of aversion factors that may amplify the impact of crowds. In emerging market trends these kinds of behaviors are being strengthened by digital platforms. This makes rational analysis firmer. The centralised problem is the Rationality Deficit. Investors are behaving rationally in theory but emotional movement in practice. The gap in research between knowledge and action is widening. Without understanding which biases are most damaging, interventions may fail in nature. This research aims to show some psychological tricks and their influence on investment behavior in emerging markets.

3. Significance of the Study

This research is important for three main groups:

1. For Investors Perspective: By recognizing Behavioral biasness of loss like aversion and herd-following investors can make better investment decisions. Investor awareness can prevent emotional decisions that lead to significant losses.
2. For Financial Experts’ Perspective: The study states that how people think, not just what they are investing. To understand the psychological tendencies which allows experts to provide sympathetic guidance. This approach might strengthen client’s trust and portfolio performance.
3. From Regulators’ Perspective: In the digital marketing perspective trends can spread quickly. Most influential financial advice can create instability. Market regulators can use these findings to design policies that might protect retail investors and improve market stability.

4. Objectives of the Study

The study has three main objectives:

1. This study may identify socio-economic characteristics, stock market trading experience and asset preferences of retail investors in developing markets.
2. To examine how Herding Behavior instinct, Over-confidence, and Aversion of loss that may influence investment decisions.
3. To determine how Financial Literacy moderates merely gives investors a false sense of confidence to the investors.


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5. Literature Review

Das and Das (2025) explored Investor’s psychological weight of overconfidence and herding. Their findings suggested that these are not just isolated error terms but are often triggered by incidental guilt which can be explained by them as an emotional state where previous market losses drive future irrationality. In their studies they identified Financial Literacy not restricted to human skill, but as a Behavioral Moderator that reduces these factors. This implied that education acts as a psychological barrier rather than a concept.

Sometimes traditional models prioritize Accounting Information Merikas, George, and Prasad (2024) stated that subjective variables are more influential. Their primary survey revealed Advocate Recommendations. These might often come from family or professional experts, peers which can carry more weight than formal financial factors. This creates a Subjective Reality for the investor phenomenon where social factor evidence overrides balance sheets. The research builds on this perception by investigative if this reliance on experts has shifted toward Digital Peers in the post-2024 landscape.

The contemporary appearance of social influence is most evident in emergent markets. Gupta et al. (2023) discovered a direct correlation in between Influencers of social media and the herding behavior of Gen Z investors. Their use of mediation factor analysis showed that Risk Perception is the bridge gap when it minimizes perceived risk. The term herding behaviour might follow mechanically in investors investment perspective.

The relationship between investor’s ego and data was examined by Nair and Antony (2025). They found that overconfidence of an investor creates a Confirmation Biases loop. Investors pursue a large amount of data, not to learn but to prove themselves in the right path. On the other hand they noted that conservatism bias creates a Paralysis in decision making where investors react too slowly to genuine market shifts.

From the market perspective it has assumed that wealth leads to better investing. However, Mohd Adil (2024) used empirical study.

The author demonstrated that income only impacts investment success when the data has gone through financial literacy. High-income individual networks without financial literacy were found to be just as susceptible to emotional trading as low-income groups. This finding justified the decision to include a wide range of income levels.

The link between fear and groupthink behaviour was established by Din et al. (2025). The author used PLS-SEM analysis to prove that aversion to losses , that is fear of realizing a loss, drives people toward following the crowd. Inconsistently, investors feel that if they lose their money alongside everyone else, the emotional stigma of failure is reduced.

6. Research Gap

Most of the studies focus on individual biases such as overconfidence, herding or in isolation. But it often treats investor’s behaviors as separate phenomena. Though in the context of emerging real-world markets these biases rarely act alone. Herding is not just passive sometimes it has been driven by those investors, who overestimate their own judgment. Another important research gap is using primary data. Most of the studies depend on older datasets or secondary information. Very few studies examine the current phase, when digital platforms and social media began strongly influencing investor behavior. The fastest spread of social media trends, online expert influencers and mobile trading has changed how biases interact with one another. From this relevance the study addresses these gaps by exploring the synergy of Behavioral biases. Despite analyzing each bias separately it examines how overconfidence, loss aversion and herding combine to influence investment decisions. It also investigates whether the financial literacy moderately effects in a digital emerging-market context.

7. Research Methodology

Research Design: The substance of this study is rooted in a deductive approach, quantitative. However, we proceed with the algorithmic nature of traditional finance by adopting a Behavioral Finance perspective. In this study a cross-sectional survey design has been selected because it captures a psychological behaviour of investors during current market conditions.


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This research design is particularly effective for identifying research design is particularly effective for identifying how internal biases rather than external market numbers to decree of investment behavior. By convergence with a single point in time the long-term market fluctuations may be avoided and focus purely on the investor’s immediate cognitive state.

Sample Population: The target population sample comprises retail investors active in the stock market, mutual fund markets for at least twelve months’ time span. This study utilized Purposive Sampling followed by a Snowballing technique. The sample size of the dataset of Eighty (80) responses. It allows for more deeper insight analysis of specific behavioral patterns. These significant steps to ensure Zero Biasness in this group. Respondents from investment groups of different age brackets actively sought out individuals across different income levels and professional sectors. It can ensure that the overconfidence or herding observed is genuine investors, not just a byproduct of a specific demographic variable. The data was gathered via a structured questionnaire.

  • Segment A (Demographics): It is Capturing age, income, and market experience of investors.
  • Segment B (Behavioral Core Concept): Utilized a Five-point Likert Scale. To counter Inventors’ rationality, it has been phrased questions indirectly. Instead of asking about herding, it was asked if respondents felt more comfortable buying an asset that is trending on social media.
  • Segment C (Outcomes): This interlinkage of investor’s feelings in Segment B to real actions such as portfolio diversification strategy.

Operationalizing the Variables: To transform human abstract and emotions into measurable data for SPSS:

1. Herding Behavior (HB): It can be focused on social impression and the fear of missing out the information.
2. Overconfidence (OC): It can be assessed the gap between a respondent's perceived expertise experience and their actual reliability on external data.

3. Loss Aversion (LA): It can be measured through the disposition effect that is investor’s tendency to adhere to losing stocks in hopes of breaking even.
4. Financial Literacy (FL): It has been treated here as a moderating variable.

Validation and the SPSS Protocol: In this study Cronbach's Alpha has been used to check for internal reliability of the dataset. The final analysis was performed using SPSS software. A logical progression has been followed in this analytical path. Descriptive statistics have been used to frame an overall picture of 80 investors. Pearson Correlation, to measure the overconfidence and herding move in the same direction or in different directions. Further Multiple Regression has been used to determine which bias is the strongest parameter of a rational portfolio. Single factor ANOVA, to check if youth investors are mostly disposed to Digital Herding than older generations.

8. Data Analysis

Reliability Analysis

Research Questions:

  • Does the socio-economic characteristics, investment experience and asset preferences of all investors impact the emergent market?
  • Do behavioural biases such as Herding Behavior, Overconfidence, and Loss Aversion may influence investment decisions?
  • Does it ensure that Financial Literacy moderate the relationship between behavioral biases and investment decisions of investors?
Table 1: Showing Reliability Analysis of all Constructs
ConstructItemsCronbach’s AlphaMeanStd. Deviation
Herding Behavior (HB)HB1, HB2, HB3, HB4, HB50.7332.731.42
Overconfidence (OC)OC1, OC2, OC3, OC4, OC50.8783.260.86
Loss Aversion (LA)LA1, LA2, LA3, LA4, LA50.8693.190.70
Financial Literacy (FL)FL1, FL2, FL3, FL4, FL50.7953.561.22
Investment Decision (ID)ID1, ID2, ID3, ID4, ID50.7493.581.33

Source: Author’s calculation and compilation


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Interpretation:

1. Investor’s Herding Behavior (HB): In the above table Cronbach’s alpha of 0.733 indicates that the items are reliably consistent while measuring consistent while measuring investors’ tendency to follow the traditional way of investing. The mean value of 2.73 suggests a reasonable level of herding behavior of investors among respondents. It is showing that some investors are being influenced by others’ behaviour but not all rely heavily on market trends.
2. Investor’s Overconfidence (OC): In the above table Cronbach’s alpha of 0.878 which can be shown that the overconfidence of the investor validates strong internal reliability. The mean value of 3.26 indicates that investors may trust their own perspective and feel definite to identify profitable opportunities. This may affect their predisposition to take risks and their responsiveness.
3. Investor’s Aversion of Loss (LA): In the above table the alpha value 0.869 that can be reflected in a strong reliability. This analysis confirms that all are logically measuring the emotional impact of probable losses on investor behavior. The mean value of 3.19 shows that investors are moderately experiencing risk of loss aversion.
4. Retail investor’s Financial Literacy (FL): In the above table the alpha value of 0.795. That can be confirmed that the financial literacy scale is reliable. The mean value shown in the above table is 3.56, which suggests that respondents own moderate to high understanding of key financial concepts, such as risk-return trade-off, portfolio diversification and emotional behavioural impacts on investment decisions. This can potentially mitigate the influence of investor’s behavioral biases regarding investment decisions.
5. Dependent variable Investment Decision: In the above table alpha value 0.749 indicates internal consistency. All items consistently reflect the investment decision-making behavioral activity. The mean value of 3.58 shows that respondents tend to make diversification, well-informed and objective-oriented investment decisions.

Descriptive Statistics

In this study the descriptive statistics for all constructs variables such as Herding Behavior (HB), Overconfidence (OC), Aversion of Losses (LA), Investor’s Financial Literacy (FL) and Investment Decision (ID).

Table 2: Showing Descriptive Statistics
ConstructNumber of RespondentsMinimumMaximumMeanStd. DeviationSkew-
ness		Kurtosis
HB801.005.002.731.4170.304-1.317
OC802.004.603.260.862-0.256-1.337
LA802.004.403.190.6990.424-0.368
FL801.205.003.561.225-0.431-0.835
ID801.005.003.581.329-0.763-0.466

Source: Author’s calculation and compilation

Interpretation:

1. Investor’s Herding Behavior (HB): In the above table, the mean value of 2.73 states that moderate tendency among all investors to follow the instinct of others regarding investment decisions. The positive skewed value such as 0.304 shows a small concentration of responses toward lower values. It may be suggested that a few respondents have a strong herding propensity. The negative kurtosis value such as -1.317 indicates a relatively flat distribution across the range.
2. Overconfidence of Investors’ (OC): Mean value of 3.26 investors generally exhibits moderate confidence in their investment judgment. The slight negative skewness that is -0.256 suggested that more participants tend towards higher confidence levels. The negative kurtosis value such as -1.337 reflects a relatively flatter distribution with no extreme outlier values.
3. Investor’s Loss of Aversion (LA): In this study an average of 3.19 on loss aversion indicates that investors can moderately weigh potential losses than their return on investment. Positive skewness value such as 0.424) shows that a small number of participants demonstrates a lesser loss aversion. Simultaneously the value of kurtosis -0.368, that might be indicated, tends to a normal distribution but flatter.
4. Financial Literacy of Investors’ (FL): In the above analysis the mean value of 3.56 indicates a sound foundation of financial concepts in between respondents. The negative skewness value such as -0.431) shows that the higher end of the scale. Kurtosis (-0.835) which is negative suggests a slightly flattened distribution. This means that respondents are spread throughout the range rather than clustered in one area.
5. Investor’s Investment Decision (ID): In our study, a mean value of 3.58, shows that the participants are generally rational in behaving pattern and goal-oriented in their investment decisions.


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Skewness value shows negative such as -0.763 suggests that a noteworthy portion of investors are recorded high in terms of rational decision-making. The negative kurtosis value such as -0.466 indicates a flatter distribution.

Correlation Analysis

Research Questions

1. How do investors' trading experience, investment instrument preferences and socio-demographic factors that can have an impact on investment decisions?
2. Does Investors’ behavioural biasness that is herding behavior of Investors, loss aversion and overconfidence may affect their rational investment decisions?
3. Does financial literacy impact or have a moderate effect on behavioural attitude regarding perception of investment decisions?

Table 3: Showing Correlation
VariablesAgeHBOCLAFLID
Age1-0.909**-0.733**-0.558**0.909**0.884**
HB-0.909**10.773**0.732**-0.968**-0.938**
OC-0.733**0.773**10.287**-0.750**-0.676**
LA-0.558**0.732**0.287**1-0.737**-0.777**
FL0.909**-0.968**-0.750**-0.737**10.972**
ID0.884**-0.938**-0.676**-0.777**0.972**1

Note: p < 0.01 (2-tailed)  Source: Author’s Calculation and compilation

Interpretation:

Age and Investment Behavior: In the above correlation analysis while concerning demographic and investment decisions certain relative factors are exhibited from the above table. Investor’s age shows a strong negative relationship with their herding behavior that is r = -0.909 which is interlinked by overconfidence that is r = -0.733 and loss aversion construct that is r = -0.558. Traditional investors are less likely to follow the trend and overestimate their skills that might be sensitive to losses. On the other hand, an investor's age is positively associated with a financial literacy variable that is r = 0.909 and their investment decisions construct that is r = 0.884 indicating that they are more experienced which makes them a proper decision of rational investment.

Herding Behavior of Investor: While concerning Investor’s behavioural biases and investment decisions it related correlates positively skewness with overconfidence that is r = 0.773,

and loss aversion in the context of Investor’s portfolio that is r = 0.732 that might be showing that investors who tend to follow market trends and also exhibit overconfidence of retail investors and are sensitive to potential losses. It is strongly negatively correlated with financial literacy (r = -0.968) and investment decisions that is r = -0.938 that might be suggesting that investor’s herding behaviour may reduce the positive impact of knowledge of investors and underlying the rational portfolio.

Overconfidence of Investors: In the able table it is positively associated with portfolio loss aversion such as r = 0.287. This underlines that overconfidence in investing patterns is marginally affected by the potential losses in the investment portfolio. While concerning behavioural biasness and Investment decisions, it is negatively correlated with financial literacy construct that is r = -0.750 and dependent variable investment decisions such as r = -0.676. That might be indicating that overconfidence of an Investors’ can negotiate with rational decision-making ability.

Investor’ Loss Aversion (LA): In the table it shows a negative correlation between financial literacy that is r = -0.737 and investment decision making portfolio such as r = -0.777. It is suggesting that the high sensitivity to losses in investment portfolios limited effective impact on decision-making though the investors are knowledgeable.

Financial Literacy of Investors: In the above table it is very strongly correlated with investment decisions that is r = 0.972 which can be confirming that investors possess higher financial literacy knowledge which is better to make them rational. While our study objective concerning moderating effect on financial literacy, it has also shown a strong negative correlation with the three constructs that is herding behaviour, overconfidence and loss of aversion that might be indicating that the knowledge in investment pattern reduces the impact of behavioural biases.

Single Factor ANOVA Analysis

Null hypothesis (H0): There are no statistically significant differences in investment decision behavior among groups depending on socio-economic characteristics, experience and financial literacy levels.


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Alternative hypothesis (H1): At least one of the construct variables that may affect investment construct variables that may affect investment decision behavior among groups depending on socio-economic characteristics, experience and financial literacy levels.

Table 4: Showing ANOVA Calculation for Investment Decision Making Behaviour
SourceSum of SquaresdfMean SquareFSig.
Between Groups130.017245.41731.2540.000
Within Groups9.533550.173
Total139.55079

Source: Author’s calculation and Compilation

Interpretation: In the above table F calculated value 31.254 which is greater than F critical or tabulated value that indicates a significant difference in investment decision-making across age groups. On the other hand, p value < 0.001 suggests that the model is significant, rejecting the Null hypothesis that Age of the Investors plays a significant role in how investment decisions are rational, These differences due to invariability of investor’s experiences, risk tolerance and exposure to financial market knowledge. Another test that is Post-hoc analysis that is Tukey test showed to identify which age groups significantly differs from one another

Multiple Regression Analysis

Regression Equation

InvestmentDecision(ID) = 0.162 + 0.021 *HB + 0.084 *OC - 0.196 *LA + 1.040 *FL

Interpretation of the Equation: In the above equation the Intercept value 0.162 indicated that when all the variable predictors are zero and the underlying value of investment decision is 0.162. Considering the Herding behavior of retail investors and the value is 0.021 indicates an increase in one-unit that increases the investment decisions by 0.021 but this effect may not be statistically significant that reflects p = 0.838. Construct like Overconfidence by Investor denoted in the above regression is 0.084 that is at least one unit increase in overconfidence that might also increases in investment decision by 0.084, but this may not be statistically significant such as p = 0.299. Considering in the above equation loss aversion -0.196 that stated that an increase in one-unit that may decreases retail investor’s investment decision by 0.196 which is significant statistically that is p = 0.040.

Another construct that is financial literacy showing coefficient value that is 1.040 states that increase in financial literacy that might increases in investor’s investment portfolio by 1.040 which is highly significant such as p value < 0.001.

Table 5: Showing Summary of Multiple Regression
ModelRR SquareAdjusted R SquareStd. Error of the EstimateDurbin-Watson
10.9760.9530.9510.2962.064

Source: Author’s calculation and Compilation

Interpretation: In the above table the regression model is significant with a p value < 0.001and with R² = 0.953. It reveals that 95.3% of variance in investment decision-making is explained by the explanatory factor that is Herding Behavior, Overconfidence, Aversion of Losses and Financial Literacy of Investors’. In the above table Durbin-Watson value that is 2.064 indicates there is no significant autocorrelation in the residuals value.

9. Conclusion

In the above table the impact of behavioural biases and financial literacy impact on investor’s investment decisions making criterions. Descriptive statistical analysis showed moderate herding behavior, overconfidence and loss aversion. Financial literacy and investment decisions had higher average scores. Reliability analysis confirmed that all scale variables were consistent. This specifies that the variable constructs were reliable. Correlation analysis showed that financial literacy is strong and positively related to investment decisions. Aversion of losses has a negative relationship. Herding behavior and overconfidence showed much weaker. ANOVA test results revealed that there were significant differences between investment decisions across age groups of retail investors. Investor’s age influences how biases affect choices. Multiple regression showed that financial literacy is the strongest predictor. Loss aversion negatively affects decisions. Apart from these, Multicollinearity factor tests among all variables showed acceptable variance inflation. Simultaneously Residual analysis confirmed no autocorrelation.

Future Recommendations

Improving technical knowledge among investors can create financial literacy. Focus on emotional control in investing.


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Reduce fear of losses through awareness. Study investor behavior on emotional control in investing. Reduce fear of losses through awareness. Study investor behavior over time. Use technology to improve learning. Regulators should protect retail investors. Promote stable and informed markets.

References

1. Das, R., & Das, S. (2025). Financial literacy as a moderator against overconfidence and herding. Journal of Academic Advancement, 4(1), 92–102.

2. Din, A., et al. (2025). Loss aversion and herding: The safety of the crowd in individual investment behavior. International Journal of Creative Research Thoughts, 13(5), 314–322.

3. Gupta, P., Sharma, R., & Verma, S. (2023). Influence of social media on herding behavior in investments. Journal of Informatics Education & Research, 3(2), 725–732.

4. Merikas, A., Georgiou, K., & Papadopoulos, T. (2024). Peer advice vs. press coverage in retail investment decisions. International Journal of Research & Analytical Reviews, 11(2), 119–125.

5. Mohd Adil, S. (2024). Socio-economic factors and financial literacy in investment decisions. Journal of Management Research & Analysis, 11(1), 160–168. https://doi.org/10.18231/2394-2770

6. Nair, V., & Antony, J. (2025). Overconfidence and selective information search in investor decision-making. Journal of Marketing & Social Research, 8(1), 40–55.

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