INTRODUCTION:

Individuals approach everyday decisions armed with information. Recent research suggests that besides information, other factors also influence decision-making, including biases that develop over time due to psychological factors. These behavioral biases often lead to suboptimal financial decisions, exacerbated by humans' propensity for unnecessary risk-taking. Tversky and Kahneman (1991) introduced the concept of shortcuts or heuristics, proposing that investors may not always act rationally. Overconfidence, one such heuristic, is under scrutiny in this study. As defined by Dittrich et al. (2005), it entails "the constant overvaluation of one's own investment decisions," leading individuals to overestimate their abilities and undervalue other information, thereby adversely affecting decision-making. Numerous professionals exhibit excessive confidence in their actions (Cooper et al., 1988). The crashes in the US stock market in 1987 and 2008 underscore that investors are not always rational; behavioral biases such as overconfidence play a significant role in decision-making. Overconfidence is one of the most extensively studied behavioral biases (D'Acunto, 2015), with overconfident individuals often making overly severe decisions (Zacharakis and Shepherd, 2001). Traditional financial models are based on rationality, assuming that investors assess current situations and make decisions using subjective expected utility theory. While efficient capital markets theory suggests that professional traders exploit arbitrage opportunities while irrational investors distort prices, human intuition and overconfidence bias are pivotal in financial decision-making (Debondt et al., 2013).

Thoroughly examining pertinent literature concerning the current topic offers a foundation for the inquiry, facilitating the development of an appropriate research methodology to investigate the issues at hand and uncover relevant theories. Furthermore, it simplifies the comparison of results from previous investigations with those of the present study. Several studies exploring the impact of overconfidence bias on investment decisions can be categorized under the following headings.

· Investment Decisions:

Investors are primarily driven by investment performance when considering stock investments. Previous research suggests that the majority of investors achieve satisfactory returns on their common stock investments (Dai and Zhu, 2020). However, due to behavioral biases and a limited theoretical understanding of the stock market, some individual investors often face suboptimal outcomes (Chhapra et al., 2018). These biases occasionally contribute to market inefficiency, resulting in poor performance. Investors exhibit diverse investment styles and behaviors, as highlighted by Pompian and Longo (2004), who recommend tailoring client profiling to accommodate each investor's unique set of behaviors. They propose basing every aspect of an investment policy on the investor profile as a means to mitigate individual overconfidence bias. Investors can be classified into two main categories: passive and active (Pompian, 2008). Active investors take risks with their own capital to build wealth and typically exhibit higher levels of overconfidence bias compared to passive investors (Kudryavtsev et al., 2013). Therefore, the investor type should be considered when analyzing investment decision-making.

Various factors influence an investor's stock selection, with most favoring attractive equities for purchase. Selling decisions are often influenced by the performance of stocks, whereas purchasing actions are linked to both profitable and unprofitable stocks. Given the abundance of listed securities, investors tend to focus on stocks they are familiar with, regardless of whether their past performance was positive or negative (Barber and Odean, 1999). Buying decisions are relatively straightforward for individual investors as they consider stocks they already own. However, selling decisions are more complex due to the multitude of variables involved (Barberis and Thaler, 2003).

· Overconfidence Bias:

In this context, the overconfidence bias exhibited by individual investors holds significance. Overconfidence bias entails the inclination to overly trust in unjustified stock prices. As highlighted by Odean (1998), overconfident investors can cause the market to underreact to information provided by rational investors. Considering that investors are typically risk-averse, Kahneman and Tversky (1991) introduced prospect theory to elucidate decisions made under conditions of risk and uncertainty, where choices are framed in terms of gains and losses relative to a reference point. This concept is further elaborated upon by Moore and Healy (2008), who delineate overconfidence in terms of three aspects: 1) Overestimation, where individuals display excessive confidence in their ability to predict their own performance; 2) Over placement, wherein individuals compare their personal attributes favourably to those of others; and 3) Over precision, indicating the extent to which individuals can forecast uncertainty.

The landscape of the financial industry has undergone significant transformation due to globalization, resulting in a competitive, intricate, unpredictable, and volatile business environment (Shepherd et al., 2015). Behavioural finance, as elucidated by Barberis and Thaler (2003), assists investors in making prudent investment decisions while minimizing the influence of behavioural biases. Research has demonstrated that a majority of overconfident investors tend to overreact to private signals, whereas those engaged in excessive trading tend to underreact to public signals (Biais et al., 2004). Additionally, according to Barber and Odean (2001), increased trading activity contributes to overconfidence as investors' confidence and experience grow with each transaction, leading to inflated forecasts and an overconfidence bias. Both corporate and individual investment decisions are impacted by overconfidence bias. As noted by Spengler et al. (2015), overconfidence pertains to individuals' ability to recognize their own skills and the limitations of their knowledge.

RESEARCH GAP:

A comprehensive review of the literature was conducted to examine the impact of overconfidence bias on stock market investors. Subsequently, a research gap was identified, prompting further analysis. While numerous studies explore the effect of overconfidence bias on investment patterns, scant attention has been paid to its impact on women stock market investors, particularly regarding the role of risk aversion in the decision-making process of individual women investors in developing nations, with a focus on Asian countries such as India, China, Malaysia, Korea, and Japan. This study addresses this gap in the literature. Moreover, measuring an investor's risk tolerance is an intriguing and burgeoning area of behavioural finance. Each individual investor's decision-making is influenced by their utility function, with risk tolerance being a critical factor shaping their willingness to assess risk when making choices. Some investors exhibit comfort in challenging situations and are inclined to take risks when presented with favourable opportunities.

RESEARCH OBJECTIVE:

In pursuit of the research goals, the following objectives were delineated:

The primary objective of the research is outlined as follows:

· Investigating the impact of overconfidence bias on investment decision-making.

· Assessing the degree of correlation between risk tolerance and investment behaviour.

RESEARCH METHODOLOGY:

To address the research objectives, the following hypotheses were formulated:

H1a: Overconfidence Bias does not exert a significant impact on investment decisions.

H1b: Overconfidence Bias does exert a significant impact on investment decisions.

H2a: Risk Tolerance does not have a significant impact on investment decisions.

H2b: Risk Tolerance does have a significant impact on investment decisions.

The primary data for this study were collected through a survey questionnaire, which was adapted from various research articles authored by different writers across distinct time periods. Participants within the relevant demographic were presented with a range of statements in the questionnaire. Both online and offline formats of the questionnaire were distributed to stock market investors who were actively involved in investment activities. Closed-ended survey questions were utilized to assess pertinent factors. The overall internal consistency of the data, indicated by the combined Cronbach's alpha coefficient, stands at .909, suggesting high reliability. Furthermore, individual variables demonstrated satisfactory internal consistency, as evidenced by their item-wise Cronbach's alpha values, all of which were within an acceptable range.

Upon gathering all required data, the subsequent step involves analysing the material to address the research questions and derive solutions. The questionnaire was distributed to 450 women stock market investors for evaluation, with 315 responses received, yielding an overall response rate of 70%. The main stages of analysis were conducted using SPSS 23.0, encompassing data screening, simple regression analysis, and multiple regression analysis. During the data screening phase, statistical assumptions were scrutinized to ensure data integrity. Regression analysis was employed to test hypotheses concerning the influence of overconfidence bias on investment decisions. Additionally, multiple regression analysis was utilized to explore the collective impact of independent variables on the dependent variable.

ANALYSIS AND FINDINGS:

Table 1 Demographic profile of the collected sample.

Gender	Age				Marital Status			Educational Level
Female	18-33	34-49	50-65	Above 60	Married	Single	Widow	Diploma	Under graduate	Postgraduate	Others
315	105	95	76	39	147	125	43	75	112	103	25

Based on the data presented in Table 2, investing decisions appear to be benefiting from overconfidence. This positive association stems from the fact that investors sometimes gain from their overconfidence when engaging in stock investments. The model summary reveals that the Adjusted R Square stands at .341, indicating that overconfidence bias explains 34.1% of the variances in investing decisions. Consequently, the null hypothesis is rejected, and H1b is accepted. According to the coefficient table, investment choices are expected to increase by 1.764 when the overconfidence bias is zero, as evidenced by the unstandardized coefficient for overconfidence bias being 0.627. Conversely, investment decisions are projected to rise by 0.627 with a 1-unit increase in overconfidence bias, suggesting a robust correlation.

Table 2. Results of regression analysis for overconfidence bias and investment decisions

Model Summary
ANOVA Results
Model		Sum of Squares	Df	Mean Squares	F	Sig.
1	Regression	68.312	1	68.312	162.261	.000^b
	Residue	131.781	314	.421
	Total	200.093	315
a. Dependent Variable: Investment Decision b. Predictors: Over-confidence Bias
Regression Co-efficient
Model		Unstandardized Coefficients		Standardized Coefficients	T	Sig
Model		B	Standard Error	Beta	T	Sig
1	Constant	1.764	.183		8.767	.000
1	OC	.627	.047	.509	13.675	.000
a. Dependent Variable: Investment Decision

Based on the findings in Table 3, H2b is affirmed, and the null hypothesis is refuted. With an adjusted R square of 0.44014, it's evident that approximately 44.01% of variations in investment decisions result from changes in risk tolerance levels. The coefficient table indicates an unstandardized coefficient of 0.728 for risk tolerance, suggesting that if risk tolerance were zero, investment decisions would increase by 1.121. Conversely, a 1-unit increase in risk tolerance would lead to a 0.728 increase in investment decisions, highlighting a substantial relationship.

Table 3. Results of regression analysis for risk tolerance and investment decisions

Model Summary
ANOVA Results
Model		Sum of Squares	Df	Mean Squares	F	Sig.
1	Regression	104.877	1	104.877	395.762	.000^b
	Residue	83.048	314	.265
	Total	187.925	315
a. Dependent Variable: Investment Decision b. Predictors: Over-confidence Bias
Regression Co-efficient
Model		Unstandardized Coefficients		Standardized Coefficients	T	Sig
Model		B	Standard Error	Beta	T	Sig
1	Constant	1.121	.144		7.779	.000
1	RT	.728	.037	.738	18.321	.000
a. Dependent Variable: Investment Decision

RESULTS AND DISCUSSIONS:

Based on the analysis of our collected data, we draw several conclusions discussed below.

The H1b hypothesis delves into the individual relationship between Overconfidence Biases and Investment Decisions. It posits that Overconfidence Bias significantly influences Investment Decisions. Following data analysis, the results substantiate this hypothesis, indicating that overconfidence significantly affects investment decisions, with a p-value of -000 falling within the acceptable range. This suggests a highly significant impact of overconfidence bias on investment decisions, aligning well with previous studies. The H2b hypothesis suggests that Risk Tolerance also significantly affects Investment Decisions. Both hypotheses are confirmed, with p-values of 0.000 falling within the acceptable range. Consequently, both hypotheses are upheld, affirming the significant relationship between Risk Tolerance and Investment Decisions.

CONCLUSION AND LIMITATION:

The research highlighted Overconfidence and risk tolerance as the primary determinants influencing investment decision-making, in line with existing literature. By exclusively examining female stock market investors, the study contributes to narrowing existing research gaps. However, limitations exist, such as the omission of exploring the impact of overconfidence on risk tolerance and the potential mediating role of risk tolerance between overconfidence and investment decisions.

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Received on 18.05.2024 Revised on 02.08.2024

Accepted on 20.09.2024 Published on 18.12.2024

Available online on December 27, 2024

Int. J. of Reviews and Res. in Social Sci. 2024; 12(4):228-232.

DOI: 10.52711/2454-2687.2024.00039

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