“Impact of personality traits on investment decision-making: Mediating role of investor sentiment in India”

The behavior of investors and their investment decision-making process in the financial markets are guided by psychological (sentiments) and personal characteristics (personality traits). Research in recent years has shown the connection between in-vestor sentiment and personality traits and investment decisions. Though academic works in the field of behavioral finance are growing, studies on personality traits and investment decision-making with investor sentiment as a mediator are sparse. To this end, the paper aims to analyze the effects of Indian retail investors’ Big-five personality traits (Neuroticism, Extraversion, Openness to experience, Agreeableness, and Conscientiousness) on their short-term and long-term investment decision-making with the mediating effect of investor sentiment. The study employs the Partial Least Square-Structural Equation Model to test the framed hypotheses. The findings of the study reveal that Neuroticism has a significant positive effect (β=0.352, p<0.05) on investor sentiment. It further shows that Extraversion has a significant positive effect (β=0.186, p<0.05) on long-term decision-making. On the contrary, the consciousness trait has a significant negative effect (β=-0.335, p<0.05) on short-term investment decision-making. Furthermore, the Openness trait demonstrates a significant effect on both short-term and long-term investment decision-making (β=0.357, p<0.05; β=0.007, p<0.05). However, the findings reveal no significant intervening effect of investor sentiment between personality traits and investment decision-making. Thus, the study strongly exerted the impact of investors’ personality traits on their investment decision-making due to the high influence of personal characteristics over sentiment effects.

Impact of personality traits on investment decision-making: Mediating role of investor sentiment in India

INTRODUCTION
Over the past decade, the irrationality of individual investors is recognized as a key factor in the financial markets due to the repeat occurrence of crises and crashes. This questions the assumptions of the classical finance theory of Efficient Market Hypothesis (EMH) (Fama, 1965). It is likely noted that investors' irrationality is the result of changes in their personalities and sentiments which tends to have an impact on their decision-making (Lo et al., 2005). It is well observed that "people's decision-making is based on losses and gains rather than results" (Kahneman & Tversky, 1979). With this view, behavioral finance has emerged from the mainstream of finance, which tests the irrationality of investors and their psychological phenomena in the financial markets.
Investment Decision-Making (IDM) is a process of investing funds in the available alternatives after conducting an effective risk-return analysis. It is evident to note that the investment decision is always guided by Personality Traits (PT) and Investor Sentiment (IS). Personality is referred as the thoughts, attitudes, and behavior patterns that distinguish one individual from another (Baker et al., 2021). Investor Sentiment (IS) is the overall attitude of investors toward the financial market (Baker & Wurgler, 2006). As the financial markets and economy of the country are interlinked, a favorable effect on the advancement and growth of a country is witnessed with an expanding market. Similarly, due to the increased participation of retail investors, financial markets also evidenced a remarkable influence of IS and personalities (Kengatharan & Kengatharan, 2014).
Past scholarly works have demonstrated the direct association between IS and decision-making (Haritha & Uchil, 2020). Further studies attempted to show the association between PT and IS (Baker et al., 2021). In addition, the nexus between IS and decision-making is also well documented (Sachdev & Lehal, 2023). Though it is well observed the importance of including mediators in behavioral finance (Nigam et al., 2018), no attempts have been made to examine the mediating effect of IS between PT and IDM. Moreover, it is crucial for market participants such as investors and financial advisors to realize the influence of PT and IS on IDM.

LITERATURE REVIEW AND HYPOTHESES
Classical finance theories assume that investors are rational decision-makers and that markets are informationally efficient based on the theory of the EMH (Fama, 1965) Investors' personality plays a crucial role in influencing the behavior of investors resulting in errors and biases in their decision-making (Kumar & Goyal, 2016). Previous scholarly works also confirm the linkage between behavioral finance and psychological biases (Durand et al., 2008;Oehler et al., 2018). Notably, the model with five key dimensions, namely, the "Big Five PT model" of Costa and McCrae (1992), attained essential support among personality psychologists (Jhon & Srivatsava, 1999). This model measures personality based on orthogonal dimensions which include NEU, EX, CON, OP, and AG (Alderotti et al., 2023). The score of each respondent concerned with these dimensions depicts an even pattern of thoughts and emotions (Rustichini et al., 2016).
Neuroticism: It is defined as a person who is more emotional, unpredictable, or "testy" than others (Oehler et al., 2018), they engage in unsteady decision-making as a result of their emotional instability and depressive behavior. A person with NEU exerts the feelings such as anger, fear, and anxiety (Camgoz et al., 2017).
Extroversion: It refers to an individual who is joyful, highly active, full of life, also friendly, and social with a great sense of humor ( Investment is defined as "the process of purchasing assets out of available resources with an aim to reap greater future benefits" (Ahmed, 2021). Investors make a commitment towards their resources in short-term investments as well as long-term investments. A short-term investment is a temporary investment in various securities which can be converted into cash between 3 to 12 months (Kenton, 2019) whereas, long-term investments are assets held for more than one year to generate revenue (Twin, 2019

METHOD
The study employed a quantitative research design using a questionnaire to gather primary data. The study performed a power analysis using G-power, a computer-based statistical software to estimate the minimum sample size (Faul et al., 2009

RESULTS
The study examined the relationship between the PT and IDM (SDM and LDM), by taking IS as an intervening variable. The study based on literature and hypotheses, depicts the structured model in Figure 1. Table 1 exhibits the socio-demographic profile of 181 participants. The sample comprised 62.98% of males and 37.02% of females. The majority of the respondents belong to the age group between 21 and 31 years (53.03%) with less than 1 (40.88%) and 1 to 3 years of experience (26%). More than half of the respondents were with an education background of post-graduation (63.53%), 44.19% of respondents were salaried, and 51.93% of respondents had an annual income under five lakhs.
The analysis for the study was carried out using Smart PLS-SEM 4. It is a two-step model including both structural and measurement models. Structural models cannot be evaluated without assessing the reliability and validity of the measurement model. To measure the reliability of the model, Cronbach alpha, composite reliability, and outer loading for each construct were used and the results are depicted in Table 2. The study found that the values of internal consistency reliability denoted through Cronbach alpha and composite reliability of each construct were above the threshold limit of 0.7. Hence, all the constructs were considered reliable. Further, the outer loadings of each construct were tested, and the constructs such as EX2 (0.682), CON1 (0.426), CON2 (0.624), CON3 (0.608), IS1 (0.239), IS2 (0.660), IS3 (0.497), and SDM1 (0.683) were removed due to low factor loadings. The constructs above the threshold limit of 0.708 were retained for the analysis. The Convergent validity is tested using AVE, and all the AVE values were above 0.5. The results depicted in Tables 3 and 4 provide evidence for the discriminant validity of the constructs. The discriminant validity for the study constructs was assessed using Fornell-Lacker and HTMT criteria. According to Fornell-Lacker, discriminant validity is said to be established when the square root of the AVE values are greater than the correlation coefficients with the other constructs. Furthermore, HTMT a stronger measure to determine discriminant validity was assessed. The study results reveal that all the HTMT values are below 0.85. Therefore, the discriminant validity for the study constructs was confirmed.
After validating the measurement models, to assess the structural model, hypothesis analysis was done using path coefficients and bootstrapping. The study extracts path coefficient values  H2a, H3a, H3c, H3d, and H3e were rejected, and H3b was accepted. Additionally, the direct impact of PT as well as IS on SDM depicts, NEU (H3f) (β= 0.007, p>0.05), EX (H3g) (β= 0.067, p>0.05), AG (H3j) (β= -0.058, p>0.05), and IS (H2b) (β= 0.020, p>0.05), which shows an insignificant effect. However, CON (H3h) (β= -0.335, p<0.05) shows that it has a significant negative effect on SDM at a 5% significance level. It posits that the 1% change in investor CON trait leads to a change in the sentiment of about 33.5%. On the contrary, the OP shows (H3i) (β= 0.007, p<0.05) a significant positive effect on SDM. Therefore, H3f, H3g, H3j, and H2b were rejected, and H3h and H3i were accepted. The result of R squared value for PT to IS is 0.201, representing 20.1% changes in the sentiment of investors explained by AG, CON, EX, NEU, and OP. The R squared value for PT to LDM and SDM is 0.283 and 0.265, respectively. This implies that the PT model accounts for 28.3% and 26.5% variability in LDM and SDM. The Q square value helps in understanding the generalizability of the model. According to Chin (1998), a predictive score of 2-15% indicates low power, 15-35% indicates good power, and more than 35% indicates strong power. The study shows Q square values from PT to IS, LDM, and SDM about 0.108, 0.156, and 0.142, respectively. This indicates that the Q square value of IS and SDM shows a low power, whereas LDM shows a high power.
As the current study examines the mediating effect of IS between Big Five PT and IDM. Table  6 exhibits the mediation results comparing the specific indirect and direct paths. The results depict that IS did not mediate the relationship between NEU (H4a) (α=0.007, β=-0.079), EX (H4b) (α=0.001, β=0.186), CON (H4c) (α=-0.003, β=-0.093), OP (H4d) (α=0.000, β=0.357), AG (H4e)    (Lin, 2011). Further, they are more inclined towards risky investment avenues to generate a positive return. Thus, the framed hypothesis (H3d and H3i) on OP and IDM is strongly supported by past evidence. The results of EX showed a significant positive effect on LDM. This finding is in tandem with Pan and Statman (2013), who found similar observations in their study, but in contrast to Durand et al. (2008). As extrovert people create a trade-off and capitalize their money more on the stock market, they tend to enjoy a higher return from their investment in the long run. Further, it can be inferred that extroverted people will not be overconfident but more cautious about their financial decisions. However, findings on NEU, CON, and AG show no significant relationship with LDM. These results are in contrast with Baker et al. (2021) and Sachdev and Lehal (2023). A possible explanation for this is that investors with NEU, CON, and AG are not long-term decision-makers, as they are mainly associated with emotions and follow other people's judgment.
The study provides evidence that IS does not have a direct relationship with LDM and SDM.

CONCLUSION
This study aimed to examine the effect of the Big Five PT on IDM with a mediating role of IS. Among the Big Five PT, NEU significantly affects IS, as investors with the NEU trait account for varying degrees of characteristics such as unstable minds anxiety, and depression. Interestingly, it is observed that the OP trait has a strong effect on both LDM and SDM as investors are more creative and risk-averse while making investment decisions. However, IS does not mediate the relationship between PT and IDM based on the argument that PT overweighs the effect of IS.
The study enables retail investors to understand their own PT and thereby guides them to make efficient investment decisions. As a result, investors can avoid potential bias in their investment decisions. In addition, investors based on their knowledge of personality types could predict and modify trading strategies to reap maximum benefits. Based on the findings, policymakers can form a new way of profiling investors in accordance with investors' PT. The study helps researchers and academicians to enhance their knowledge of PT and its impact on investment decisions.
It is concluded from the study that there is a direct relationship between PT and IDM. However, IS does not play a mediating role but has a significant relationship with PT. The study is not without any limitations. Even though the study sample is collected throughout India, there is a need for even distribution of the sample. Future researchers might consider other mediating or moderating variables such as the herding effect, disposition effect, overconfidence, and other behavioral biases.