Behavioral Biases and Investment Decision-Making in the Indian Stock Market: The Moderating Role of Financial Literacy and Investor Experience.

2.1 Heuristics

In investment decision-making, heuristics are frequently employed to simplify the evaluation of financial information, asset performance, and market trends. Most investors rely on recent price movements, familiar company names, or initial stock values to make quick judgements, often without conducting a thorough analysis (Ricciardi & Simon, 2000).

Heuristics can also lead to systematic errors or cognitive biases, such as the anchoring effect, availability heuristics, framing effect, and confirmation bias (Yasseri & Reher, 2019; Lyons & Kass-Hanna, 2021). These biases can cause investors to make suboptimal investment decisions, as they may rely on incomplete or biased information, fail to consider alternative perspectives, or exhibit overconfidence in their abilities (Kumar & Goyal, 2015).

Dangol & Manandhar (2020) found that heuristics such as representativeness, availability, and anchoring are significantly associated with irrational investment decisions. According to Haritha (2023) revealed that investor sentiment mediates the influence of heuristics on stock selection (Haritha, 2023). According to Parkash & Parkash (2024) found that institutional investors, such as fund managers, are prone to heuristics like overconfidence and anchoring, which skew asset selection and market timing decisions. According to Ahmad et al. (2025) empirically examined the influence of heuristic-driven cognitive biases among investors in emerging markets. They found that overconfidence, anchoring, and availability biases significantly disrupted rational investment behavior and led to poorly diversified portfolios.

Empirical studies have found that individual investors rely heavily on overconfidence and availability heuristics, which can lead to excessive trading and risk underestimation, particularly among young and active traders in markets such as Mumbai (Kakkar & Hariharan, 2022).

2.2 Prospect theory

The prospect theory was developed by Kahneman and Tversky. This explains how investors perceive gains and losses, emphasizing their loss aversion. Investment decision making highlights that investors prioritize avoiding losses over acquiring equivalent gains, influencing their financial choices and risk assessments (Morais Rodopoulos & Silveira Júnior, 2024). It challenges traditional economic models, particularly the expected utility theory, by incorporating psychological factors into the decision-making process (Reyna and Brainerd, 2012).

Prospect theory challenges the traditional notion of rationality by illustrating how individuals make decisions under risk, not based on objective outcomes, but on perceived gains and losses relative to a reference point (Tversky & Kahneman, 1991). It challenges traditional economic models, particularly the expected utility theory, by incorporating psychological factors into the decision-making process (Reyna and Brainerd, 2012).

An empirical analysis across U.S. and European markets found that Prospect Theory based portfolios consistently outperformed traditional mean-variance allocations. This highlights the practical value of behavioral considerations in portfolio construction (Fortin & Hlouskova, 2024). Yadav & Daga (2023) found that biases derived from Prospect Theory particularly loss aversion and risk aversion significantly influence irrational investment decisions among Indian stock market participants. Khairunnisa (2024) applied Prospect Theory to study financial decision-making among university students. The results revealed strong behavioral tendencies such as loss aversion and temporal discounting, influencing both short- and long-term investment planning.

As a result, prospect theory provides a valuable framework for analyzing the psychological underpinnings of investor behavior, particularly in uncertain or volatile market conditions.

2.3 Emotions

Emotions and psychological factors play a crucial role in shaping investment decisions and often influence financial choices in ways that traditional economic models fail to account. Emotions play a significant role in investment decision making, often leading investors to deviate from purely rational analyses (Lerner et al., 2015).

Traditional finance theory often assumes that investors are rational actors who make decisions based on objective information and logical analysis. However, this assumption overlooks the powerful influence of emotions on human behavior (Singh & Dhami, 2024). Emotions can shape how investors perceive risk, evaluate potential returns, and react to market fluctuations (Brooks & Williams, 2022).

Furthermore, literature reviews reveal that external factors such as weather and religious observances can modulate investor mood and behavior, subtly shifting risk perception and investment appetite (Annapurna & Basri, 2024). According to Sutejo et al. (2024) positive emotions significantly enhanced investment decisions, while negative emotions had mixed or insignificant effects. The study also revealed that financial risk tolerance mediates the influence of these emotions on portfolio choices. Theoretical work underscores those successful investors not only recognize their emotional states, but also regulate them to align with strategic objectives, merging affective awareness with cognitive rigor (Das & Panja, 2020).

2.4 Market impact

In modern financial markets, investment decisions are increasingly influenced by external factors and real-time market dynamics. The term market impact broadly refers to the influence of market-related factors such as price trends, volatility, trading volume, economic indicators, and media coverage on investor behavior. Rather than relying solely on intrinsic valuations or fundamental analysis, investors often react to what is happening in the market, especially when faced with uncertainty or rapidly changing conditions (Barberis & Thaler, 2003; Shiller, 2000).

Investors in established markets such as India actively consider macroeconomic conditions such as GDP growth, inflation, interest rates, and political stability when forming their investment strategies (Varshini and Vinayalaxmi, 2024). Karimi & Nasieku (2024) examined the Nairobi Securities Exchange and revealed that behavioural biases intensify market volatility by encouraging reactionary trading patterns and premature selling or holding decisions. Zain Ui Abideen et al. (2023) revealed that herding, overconfidence, and loss aversion biases interact with market anomalies, amplifying price volatility and mispricing indicating that biases can destabilize markets unless actively managed.

Empirical research indicates that dynamic market variables, such as past stock performance, customer preferences, and general market information are central to shaping investor behavior, especially in developing financial markets such as Nepal, where youth participation is rapidly increasing (Dhungana et al., 2023).

2.5 Herding

Herding behavior, the tendency of investors to follow the actions of a larger group, can drive market trends and create bubbles or crashes (Kellard et al., 2016). This behavior is especially prominent in financial markets, where investors faced with uncertainty, time pressure, or informational ambiguity, and tend to follow the perceived consensus rather than act independently.

Herding behavior leads to suboptimal investment performance among individual investors (Zafar et al., 2024). This imitation can be seen in both bullish and bearish market conditions which may lead to overpricing and speculative bubbles while in downturns, it may trigger panic selling and excessive pessimism (Shiller, 2000; Devenow & Welch, 1996).

Chang, Cheng, and Khorana (2000) report significant herding effects in emerging markets, where informational asymmetry is higher and investor sophistication tends to be lower. In retail investing, herding has been linked to limited financial literacy, fear of missing out, and the growing influence of social media on investment sentiment (Kliger & Levy, 2003).

2.6 Financial literacy and Investment decision

Behavioral biases are detrimental to investment decision making and recent studies have increasingly focused on the moderate effect of financial literacy. Financial literacy is defined as the ability to understand and apply basic financial concepts to financial decisions. It can significantly mitigate the adverse effects of behavioral biases (Lusardi & Mitchell, 2014). A high level of financial literacy and skills help investors to analyze information critically, make informed decisions, and thus reduce reliance on heuristics that might lead to biased decisions (Hirshleifer & Teoh, 2003) and better investment outcomes, including improved portfolio diversification and reduced impulsive trading behaviors (Subnani & Todwal, 2024). However, the moderating effect of financial literacy may vary depending on the type of bias and investor gender, with some studies suggesting that financial literacy is more effective at moderating the impact of cognitive biases among male investors (Adil et al., 2021).

In a study of millennial investors in Jakarta, financial literacy moderated the effect of overconfidence on investment decisions, indicating that well-informed investors are better equipped to avoid overestimating their decision-making abilities (Nadhila et al., 2024). A similar study conducted in the Iraqi stock market, shows that financial literacy strengthened the predictive relationship between behavioral factors such as firm image, advocate recommendations, financial needs and investment outcomes, suggesting that informed investors are better at integrating diverse inputs into their investment decisions (Abdulridha & Hussin, 2024).

In the Indian context, financial literacy is considerably low, particularly among young and less educated populations (Lusardi & Mitchell, 2011). According to Sood and Jain (2021), increased financial education programs can enhance an individual’s understanding of investment products, thus empowering investors to counteract biases such as overconfidence and loss aversion. The authors argue that financial literacy serves as a buffer against psychological factors, enabling investors to remain rational and resulting in better portfolio performance.

Financially literate investors are more likely to conduct independent research, scrutinizing market trends rather than simply following market sentiments. Such investors demonstrate a stronger capacity to withstand social pressures inherent in investment environments (Prakash & Gupta, 2021).

2.7 Investor experience and Investment decision

Investor experience moderates the impact of behavioral biases on investment decision and portfolio management decisions. Novice investors tend to be more susceptible to biases, such as herding and anchoring, whereas experienced investors may utilize their insights to mitigate these effects (Agarwal & Tiwari, 2018). Additionally, Malmendier, Pouzo & Vanasco (2020) found that while biases persist in all investor categories, the degree of their influence diminishes with increasing experience level.

Past research in the Indian context indicates that experienced investors are better equipped to deal with market volatility and may demonstrate greater resilience to emotional biases (Agarwal & Tiwari, 2018). Investment experience helps individuals better recognize and manage their own behavioral patterns, leading to improved decision-making through diversified portfolios and realistic goal setting (Atiq, 2024).

According to Chalissery (2023), financial literacy enhances this moderating effect, particularly in emerging markets such as India and Pakistan. Their findings show that while behavioral biases such as anchoring, overconfidence, and herding are prevalent, experienced investors with higher financial literacy are less influenced by them, suggesting a strong interplay between knowledge and experiential learning. Investor experience acts as a critical buffer against distortions caused by behavioral biases, helping investors make more informed and rational decisions.

Based on the above literature the study develops the following conceptual framework (Figure 1).

Figure 1. Conceptual framework.

Source(s): Authors own creation.

4.1 Descriptive analysis

Table 1 presents the summary statistics of the study’s respondents; majority of the respondents were male 65% and 35% were female. Among the respondents, A majority 45% were from the age group of 26-35 and 41% respondents were from the age group of 36-54 it indicates that investments are mainly focused on the economically active adults. There is a lack of interest in young investors and senior citizens. The finding indicates that nearly 64% of the respondents were postgraduates are the investors and 15% were PhD holders, which shows that majority of the respondents were highly educated sample. marital status of the 65% respondents was married and unmarried, or singles are 29%. Regarding to the employment status, more than half of the respondents 57% are employed in private sector, 17% in government sector, 15% were self-employed and remaining 11% have belonged to other categories. Occupation wise professions were the majority in the group 42%, followed by technical roles 15% and administrative and clerical jobs 14%. Finally, it shows the majority of the respondents 53% invest 10% of their income to investment, 25% of the respondents are investing 11%-20% of their income to investments and only 9% investors are aggressively investing their income.

4.2 Factor loading, average variance extracted, composite reliability and Cronbach’s alpha

The reliability and validity of the variables used in this study were evaluated using outer loadings, Average Variance Extracted (AVE), composite reliability, and Cronbach’s alpha are key metrics used to evaluate reflective measurement models (Hair, 2010).

The factor loadings for the constructs varied as shown in Table 2. For heuristics, loadings ranged from 0.644 to 0.78. The prospect theory items had loadings between 0.631 and 0.772. Their emotions ranged from 0.616 to 0.78. The market impact ranged from 0.682 to 0.84. herding ranged from 0.615 to 0.877, investment decisions ranged from 0.737 to 0.807, Financial Literacy ranged from 0.731 to 0.817 and Investor Experience ranged from 0.766 to 0.846. The value of the constructs was greater than the threshold of 0.50. This suggests that each item adequately represents its respective construct (Tang, 2024).

An AVE value of 0.5 or higher suggests adequate convergent validity, indicating that the indicators represent the underlying construct well (Hair et al., 2019). The Table 2 shows that the AVE values for each construct are above the threshold of 0.5, indicating that all constructs are typically considered acceptable (Hair et al., 2010) and indicates that the indicators used to measure the constructs are strongly related to the underlying concepts.

According to Hair et al. (2019) and Hair et al. (2021), a Composite reliability value between 0.70 and 0.90 is considered acceptable and indicates satisfactory internal consistency. Conversely, values below 0.70 are considered inadequate and reflect poor reliability, suggesting the need for model refinement. The Table 2 shows that CR values are for each construct are above the threshold of 0.7, indicating that all constructs are typically considered acceptable (Hair et al., 2019) and this indicates that all constructs have high internal consistency suggesting that the items consistently measure the intended constructs (Henseler, Ringle & Sarstedt 2009).

4.3 Cronbach’s alpha

This assesses how well a set of items measures a single underlying construct. The Table 2 shows that the values of Cronbach’s alpha for all the constructs were above 0.70, indicating good internal consistency and reliability (Hair et al., 2010).

As all evaluation criteria have been met with acceptance, it can be concluded that there is greater support for reliability and validity.

The figure below shows the results of Confirmatory Factor Analysis by using SmartPLS software.

Figure 2. Measurement model for CFA.

Sources: Smart PLS Output, Authors calculation.

Based on the Fornell–Larcker criterion, the discriminant validity of the construct was established when the square root of the Average Variance Extracted (AVE) for each construct exceeded the construct’s highest correlation with any other construct in the model (Hair et al. 2019). As shown in Table 3, the square roots of AVE were greater than the inter-construct correlations indicating that discriminant validity is generally supported except between constructs E (emotions) and M (market impact), where the correlation (0.755) exceeded the square root of AVE for E (0.73) indicating that there is an overlap between these constructs and a lack of discriminant validity.

Furthermore, the discriminant validity of the constructs was established using the Heterotrait - Monotrait Ratio (HTMT) of the correlations. Discriminant validity is established when the HTMT values are below 0.85 for conceptually distinct constructs (Hair et al., 2021). The HTMT ratio is a more sensitive method than the Fornell-Larcker criterion for detecting discriminant validity issues using variance-based structural equation modeling (Henseler, Ringle, & Sarstedt, 2009; Hair et al., 2021). As shown in Table 4, most constructs met the threshold of less than 0.85, indicating discriminant validity. However, certain sub constructs HTMT values exceed the threshold of 0.85, such as the constructs of emotions (E) and heuristics (HU) at 0.948 and emotions (E) and market impact (M) at 1.018.

4.4 Structural model

After establishing the requirements of the measurement model, the structural model was used to test the direct effect of various constructs on investment decisions measured through path coefficient analysis using PLS-SEM. The results of the direct effect of H1 to H5 hypotheses are presented in Table 5, the results of acceptance or rejection of the hypotheses mentioned in column 7 of Table 5. T statistics and P values were used to measure the significance of the hypotheses. The evaluation was based on the significance of t-statistics and p-values as recommended by Hair et al. (2021). According to Hair et al. (2021), the t-statistic must exceed 1.645 by assuming that a one-tailed test at the 5% significance level and the corresponding p-value should be less than 0.05. These thresholds provide sufficient evidence to confirm the statistical significance of hypothesized relationships (Hair et al., 2021; Sarstedt et al., 2022; Henseler et al., 2009).

The empirical results are presented in Table 6. The p-value for the heuristics (HU) is 0.000, which is less than the conventional significance level of 0.05, and the t-statistic is 3.324, which exceeds the threshold of 1.645. These results indicated that the hypothesis (HU → ID) was accepted. This finding suggests a statistically significant and positive relationship between heuristics and investment decisions.

The p-value for prospect theory (P) is 0.121, which is higher than the conventional significance level of 0.05, The t-statistic is 1.168, which is below the significance threshold of 1.645. These results indicate that hypothesis (P → ID) is not supported. This finding indicates that there is no statistically significant relationship between prospect theory and investment decision.

The p-value for emotions (E) is 0.186, which is higher than the conventional significance level of 0.05, and the t-statistic is 0.893, which is below the significance threshold of 1.645. These results indicate that the hypothesis (E -> ID) is not accepted. This finding indicates that there is no statistically significant relationship between emotions and investment decisions.

The p-value for Market Impact (M) is 0.097, which is higher than the conventional significance level of 0.05, and the t-statistic is 1.297, which is below the significance threshold of 1.645. These results indicate that hypothesis (M -> ID) is not accepted. This indicates that there is no statistically significant relationship between the market impact and investment decision.

The p-value for herding (H) is 0.19, which is higher than the conventional significance level of 0.05, and the t-statistic is 0.878, which is below the significance threshold of 1.645. These results indicate that hypothesis (M -> ID) is not accepted. This finding indicates that there is no statistically significant relationship between herding and investment decisions.

4.5 Moderating role of investors experience between behavioral biases and investment decision making

The study used a Moderated Multiple Regression revealed that Investor Experience significantly moderates the relationship between the two behavioral biases and investment decisions such as Emotions and Market Impact. The interaction between Investor Experience and Emotions (IE × E) shows a positive and statistically significant effect (β = 0.331, t = 2.524, p = 0.006). Suggesting that market fluctuations are less likely to influence emotional biases provides a better investment decision when investors have more experience. Similarly, the interaction terms between Investor Experience and Market Impact (IE × M) are negative and significant (β = -0.291, t = 2.542, p = 0.006). Suggesting that the influence of market impact biases on the investment decisions of experienced investors is less likely to be influenced by market fluctuations. However, the moderating effects of Investor Experience on herding behavior, heuristics, and prospect theory were found to be statistically insignificant. The evaluation was based on the criteria recommended by Hair et al. (2021), where a t-statistic greater than 1.645 and a p-value less than 0.05 indicate statistical significance. These findings that investor experience does not significantly influence these factors in investment decisions.

4.6 Moderating role of financial literacy between behavioral biases and investment decision making

Table 7 shows the results of the moderating role of financial literacy between behavioral biases and investment decision making. Moderation analysis revealed that financial literacy significantly influences the relationship between behavioral biases and investment decision-making. Out of the six hypothesized moderation effects, two are statistically significant, indicating that financial literacy influences the relationship between certain behavioral biases and investment decisions.

Specifically, financial literacy significantly moderates the effect of emotions (β = 0.334, t = 2.376, p = 0.009) and the market impact (β = -0.322, p = 0.003) on investment decisions. Financially literate investors are in a better position to critically evaluate market factors and avoid herd-behavior or cognitive based errors (Baker et al., 2019).

However, the moderation analysis also revealed that financial literacy has not significant moderation effect of herding bias, heuristic, and prospect theory components on investment outcomes because interaction terms did not meet the threshold for significance (p > 0.05). The above results clearly show that financial literacy is not completely influential on all behavioral constructs but rather shows a conditional moderation.