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Abstract

Investors’ financial literacy entails making sound investment decisions and the behavioural biases or irrational behaviour in decision-making that are collectively formed by heuristic bias, framing effect, cognitive illusions and herd mentality factors. The present study examines the combined impact of financial literacy and behavioural biases on investment decisions. A questionnaire was developed using Likert scaling technique to elicit study variables and collected data was analysed using SEM technique. The results showed that heuristic bias had a significant positive association with the creation of behavioural bias in decision-making. However, the framing effect, cognitive illusions and herd mentality have negative associations in the formation of behavioural biases. Further, investors often practice and follow heuristic biases rather than other irrational techniques for making investment decisions. Therefore, the financial literacy of individual investors has a significant impact on affecting stock market investment decisions.

Keywords

Financial literacy behavioural bias investment decisions framing effect cognitive illusions heuristics herd mentality

Introduction

In the investment world, investors often make rational or irrational decisions based on their understanding and it was widely discussed in traditional finance and behavioural finance. Traditional finance asserts that investors are rational and formulate wise investment decisions. Investors try to maximize profit or gain by selecting the best investment alternative even in uncertain times (Kumar & Goyal, 2015). According to efficient market hypothesis, the stock market is always perfect and efficient and the stock prices reflect all prevailing information (Fama, 1970). Behavioural finance contradicts such a reality of rational investment decisions among investors. Behavioural finance however, focuses on the behavioural facets of irrational decision-making among individual investors (Semenov, 2009). Behavioural biases are believed to have a direct impact on investment decisions, which ultimately leads to less investment gains in the stock market. Behavioural finance is an area that captures the irrationality of investors and the biases to which investors are prone. These cognitive biases are due to the inability of investors to predict market movements, which pushes them to make biased investment decisions (Stanovich & West, 2008). Behavioural finance is a relatively new school of thought that controls the management of psychology about investor behaviour and its influence on decision-making. Behavioural finance has gained massive impetus recently in stock markets investments.

Behavioural finance is a sub-field of behavioural economics, linking psychological, cognitive and behavioural premises with traditional economics and finance, to describe why investors make irrational decisions. Unlike traditional finance, the behavioural approach assumes that there are restrictions to arbitrage leverage and that not all investors are rational. Superfluous information and emotional issues play a key role among individual investors in investment decision-making (Banerjee, 2011). The basic postulation of traditional theory is that investors are rational and continually seek to capitalize on their own benefit by improving wealth (Shapira & Venezia, 2001). In fact, more often, the investor considers the thumb rule rather than long and laborious mental calculations that can lead to suboptimal options and can create friction in the market. However, investors are not rational always and are not able to equip infinite computing capacity to believe in all potential circumstances to make sound investment decisions. Traditional theory assumes that irrational investors are eliminated from the market by arbitrage and it can also be challenged in many aspects (Hirshleifer & Teoh, 2003).

Decision-making is a psychological and cognitive process that results in the selection of investments. The approach to behavioural finance divulges the reality that individual investors are not often highlighted and does not take into account fundamental and technical investigations in investment decisions. It focuses on how individual investors gather and recognize information to make evidence-based decisions and discover the influence of emotional aspects on investment decisions (Waweru et al., 2008). Believers of the behavioural theory argue that investors fundamentally behave in a ridiculous way on investment decision-making. The phenomenon certifies that investors often buy stocks when prices go up and sell them when prices go down. Recently, academicians and experts have tried to find how the emotions and biases drive overall behaviour of an individual investor. They highlighted the main role of heuristics, cognitive illusions, framing effect and herd mentality as a guide to make such irrational investment decisions (Economou et al., 2010). Put together, financial literacy of individual investors has had a strong impact on investment decisions.

The aim of the study is to examine how financial literacy correlates with behavioural biases in investment decisions. Financial literacy guides individual investors to avoid irrational decisions to some extent. In earlier days, investors did not have enough education to search for the fundamentals of the broad dimensions of the company, industry and economy. Ideally, they would adopt technical dimensions and irrational behaviour in investment decision-making. However, investors now have sufficient education with extensive financial literacy and often use these phenomena in investment decisions. Therefore, the impact of financial literacy and behavioural biases is the true motivation to initiate such seminal research in an empirical manner. This study contributes to quantify the investment decision-making tendency of individual investors in India, to what extent they place financial literacy in decision-making and how they influenced with behavioural biases can estimate. It also helps determine if individual investors take a rational or irrational approach to investment decisions due to financial illiteracy.

Background of the Study

Individual investors are partially rational and partially irrational in making investment decisions. They have mental biases and cognitive restrictions, which prevent them from making rational decisions. Various behavioural biases often forbid individual investors from taking rational decisions (Zamri et al., 2017). Behavioural biases are categorized into heuristics, framing effect, cognitive illusions and herd mentality, and have effect on investment performance of individual investors’ portfolio. They are either inherent in investor psychology or in view of emotional decisions existing in general understanding of investment market. As per heuristic phenomenon, anchoring bias, representativeness and overconfidence have been involved in transforming individual investors as to stock market investments. Framing effect includes regret aversion, mental accounting and endowment effect (Lin, 2011). Cognitive illusions are explained by conservatism, confirmation and hindsight bias (Lad & Tailor, 2017). Herd mentality which is associated with information processing, social group and bandwagon effect has influence on stock market decisions (Bekiros et al., 2017). Further, financial literacy dimensions in terms of financial competency, financial proficiency and financial opportunity streamline their decisions in stock market. The study will support the finding of the various kinds of behavioural biases and its influence on investment decision-making.

Financial Literacy

Investors’ financial literacy is all about their awareness and understanding of investment ideas and information, thus ensuring the capability to make informed, safe and effective decisions on investments. In a broad sense, it is designated, as understanding the economy and how economic situations and events influence decisions (Worthington, 2006). In a narrow sense, it is defined as basic money management instrument used for savings, budgeting, investment and insurance (Gallery et al., 2010). Financial literacy is the capability to recognize, evaluate, manage and communicate on stock market investments (Vitt & Anderson, 2001). Financial literacy can be discussed in three ways such as financial competency, financial proficiency and financial opportunity. Financial competency is concerned with equipping relevant knowledge on different financial products (Shobha & Shalini, 2015). Financial proficiency deals with the capability to apply knowledge and correspond; creating financial literacy for effective decisions (Hilgert et al., 2003). Financial opportunity highlights that a financially knowledgeable investor should have the opportunity to invest funds and enjoy gain on investments.

Behavioural Biases

Investment decisions in daily life rely on the mix of various factors such as tendency, motive, passion and social interaction. Investors make investments according to available funds, periodical and financial objectives (Muhammad & Abdullah, 2009). Investors perform behavioural biases because of lack of technical proficiency and belief in their skills for effective decision-making. Behavioural biases, such as herd mentality, heuristics, cognitive illusions and the framing effect influence rational decision-making. Individual investors have less opportunity to evaluate stock due to lack of knowledge and interest, high cost and limited time. Therefore, they exercise simple heuristics or rule of thumb to make decisions (Lo, 2005). Framing effect describes the state of an individual investor’s mind in decision-making. Cognitive illusions are systematic patterns of deviation from a specific norm or rationality in evaluations. Cognitive illusions induce the investors to follow certain norms in selection of investments (Agarwal et al., 2016). Herd mentality is the tendency of investors to abandon self-belief and information but follow others’ action in decision-making. Herd mentality has tremendous effects like high volatility, bubbles and market imperfections (Braha, 2012). The combination of heuristic bias, framing effect, cognitive illusions and herd mentality cause irrationality and it is referred to as behavioural biases in investment decisions. Behavioural biases could establish a way to deviate from intrinsic worth and cause inefficiency in market (Babajide & Adetiloye, 2012).

Investment Decisions

Investors make decisions based on experience and knowledge on the stock market. Behavioural finance focuses on how individual investors act and interpret information to make investment decisions. Behavioural finance is getting a main element of decision-making, since it affects behaviour of investor significantly (Kannan & Vijayakumar, 2015). Investment decisions of investors project their intention to select better stocks. Intention is reflected in estimation of gain that is determined by using financial literacy level and biases in investments. Behavioural finance assists investors to make exclusive decisions and avoid mistakes in investments. Investment decisions are often surrounded by complexity and uncertainty; it can assist financial literacy and cognitive estimations to select suitable stocks (Kafayat, 2014). This standpoint makes investors’ prioritize their intuition over rationality assumed by modern finance approach. Behavioural finance is a peculiar science that combines financial sense and cognitive psychology in investment decisions. In this way, financial literacy on investment and behavioural strategy could be the antecedents in determining investment decisions of an investor (Sukanya & Thimmarayappa, 2015).

Literature Review

The literature on financial literacy, behavioural biases, and their effect on investment decision have been discussed in several previous studies in many ways. The construct financial literacy consists of competency, proficiency and opportunity as to stock selection. Behavioural biases are composed and explained by heuristic biases, framing effect, cognitive illusions and herd mentality components. Detailed discussions have been held from this point of view.

Behavioural Biases

Heuristic Bias

Heuristic bias is basically called as rule of thumb; it simplifies the decision-making process of investors, especially in uncertain and difficult circumstances by reducing the intricacy of assessing possibilities and forecasting benefits to make easy judgments (Kahneman & Tversky, 1974). Heuristic bias possibly impedes positive gain in investment which could simultaneously imitate in reduced portfolio gains. The heuristics bias affect the private investors’ portfolio construction and even professional investors influenced by the heuristic bias (Gavrilakis & Floros, 2021). Further the heuristics bias strongly influencing the investment decision making of investors with the mediating factor of risk tolerance (Salman et al., 2020). Hence based on the risk tolerance level of investors, heuristic bias plays significant role. Heuristic bias can be explained in three ways such as anchoring bias, representativeness and overconfidence. Anchoring bias is an emotional state of affairs that exists when investors give unwanted importance to statistically random and emotionally determined anchors which direct them to make decisions that are not basically rational (Tseng & Yang, 2011; Zahera & Bansal, 2018). Anchoring bias can also be described as the investor’s tendency to anchor their considerations to a rationally irrelevant point in investment decision-making (Pompian, 2006). Anchoring bias is concerned with riskless trading behaviour among the investors (Ofir & Wiener, 2012).

Representativeness is a dedicated tendency to relate a new event with an intention of knowing the event and through which only investors make investment (Anderson et al., 2005). Decisions are made by comparing the present event with previous events and making investments accordingly. It can create some bias in terms of investors placing more importance on recent incidents and undervalue the long-term gain in investments (Kubilay & Bayrakdaroglu, 2016; Zahera & Bansal, 2018). Overconfidence is essentially the over estimation on one’s own ability because investors believe that they can make better decisions than others can but in reality it is not achievable (Larrick et al., 2007). Further, in a study, Ahmad and Shah (2020) found that overconfidence can impair the quality of investment decisions of investors. There are two major implications of overconfidence with regard to investor perspective. The first is failure to generalize the information and the next is to do extra trading because of such failure (Shefrin & Statman, 2000). Overconfidence induces to trade more quantity and take high risks; it leads to market inefficiency due to more volatility and mispricing (Shah et al., 2012). Based on the above propositions, it is hypothesized in the following way.

H₁: Anchoring bias, representativeness and overconfidence collectively form the heuristic bias in investment decision-making.

H₂: Heuristic bias has significant positive influence with behavioural biases.

Framing Effect

Framing effect is often called as prospect theory. It describes how investors manage uncertainty and risk in investments. According to framing effect, investment decisions can be segregated into two phases that is framing phase and evaluation phase (Dhar & Zhu, 2006). Furthermore, it is believed to be irregular and contradictory in decision-making. Framing effect emphasizes that investors formulate decisions based on possible value of loss and return rather than final results and consequently make decisions on perceived gains than perceived loss (Kahneman & Tversky, 1979). Further a study found that the framing effect has a very strong effect on investors’ decision-making (Tabesh et al., 2019). In keeping with Thaler (1999), mental accounting is composed of three components. The first component deals with how outcomes are received and experienced; the second discusses the fund inflow and outflow from an account; and the third evaluates the regularity of account. Investors normally assess the inflow and outflow, and the reaction will be uncertain to organize investments with regular returns. However, components of mental accounting decrease the financial standard of substitutability (Carlin, 2009). Excess return will stimulate investors because of the assessment between part information, to continue until enhanced returns are realized. Further a recent study states that the understanding of mental accounting significantly improve the decision-making quality and in turn increase the profitability (Mahapatra & Mishra, 2020). Furthermore, in general investors use to assess the trade-off between the cost and return of investment and the mental accounting is influencing in the process of assessing the trade-off. Hence, ultimately the mental accounting is influencing the investment decision-making of investors (Zhang & Sussman, 2018).

Endowment effect reveals that investors desire more in selling than purchasing. It has strong implications and claims that investors treat different costs differently (Shefrin & Statman, 1985). The cost of selling stock from a portfolio of an investor is considered as a loss and opportunity cost is considered as a prior gain. The former should carry more weight as investors refuse to accept change when they hold a stock. A study stating that a significant impact of endowment effect on the investors risk behaviour and therefore endowment effect have effect on investment decision-making process of investors (Holden & Tilahun, 2021). Regret aversion is experienced by the investors who commit mistakes in the process of judgement. Investors regret certain processes in investment decisions because of failure of bringing expected return and this is termed as regret aversion (Talha et al., 2015). However, avoiding regret is an emotional state. Regret is expressed when an investor observes that their decision is wrong, even if it was initially believed correct. Regret is linked with the logic of accountability for choice, and therefore differs from the aggravation of assigning responsibility to external components for poor results (Michenaud & Solnik, 2008). Further the regret averse investors are more sensitive hence they use to invest in stocks which have lesser risk than investing in the stocks which have less return (Awais & Estes, 2019). It helps to determine the following hypotheses.

H₃: Mental accounting, endowment effect and regret aversion collectively form framing effect in investment decision-making.

H₄: Framing effect has significant positive influence with behavioural biases.

Cognitive Illusions

Cognitive illusions are associated with investors’ acceptance, understanding and assessment in making investment decisions (Lei & Seasholes, 2005). Cognitive illusions can affect the investors’ decision-making process and it facilitates to avoid making mistakes in financial decisions. Therefore, knowing and recognizing cognitive illusions helps to improve the allocation of investments. There is a self-belief in the capability to make the right decisions based on available information, but in fact the right decisions are not able to be made (Kim & Nofsinger, 2008). A recent study stating that the investors’ investment decisions not fully rational and it to be prone towards bias like cognitive illusions (Singh et al., 2016). Conservatism is a mental process where individual investors depend on their earlier viewpoint to forecast information to acquire new ideas. Here, investors depend mainly on experience than on learning new techniques in making investments. Preferences based on past experiences were not consistent enough; investors with rigorous conservatism bias will be determined by their preferences. Conservatism shows that investors failed to integrate new information by continuing to clutch their past prediction (Alwathainani, 2012) and a recent study also state that the conservatism bias has negative impact on the investment decision-making if investors’ financial literacy is lesser (Ahmed at al., 2020). Further a study found that overconfidence, conservatism and availability bias have an impact on the investment decisions of investors (Bakar & Yi, 2016).

Investment decisions rely on availability and information gathering of stocks and its features along with anticipation of what others will perform so as to make correct valuation of stocks in the future. Confirmation bias leads to a tendency for investors to focus on information that corresponds to their opinions, avoiding them from reacting to any other information. Confirmation bias can lead to the formation of bubbles in the financial markets (Pouget et al., 2017). Hindsight refers to an investor’s tendency to find past events more predictable than previous results (Biais & Weber, 2009). It makes a person convinced that the circumstances of an event after it has occurred were expected and based on historical data. There is a faith in predictability, as it is considered as a capability to make investment decisions (De Miguel Guzman et al., 2018). The reason why forecasting is easy, is concerned with the reality that investors are biased by their knowledge of what occurred. In this way, the following hypotheses are proposed.

H₅: Conservatism, confirmation and hindsight bias collectively form cognitive illusions in investment decision-making.

H₆: Cognitive illusions have significant positive influence with behavioural biases.

Herd Mentality

Herd mentality refers to the state of affairs where rational investors commence to perform irrationally to replicate the evaluations of other investors in investment decisions (Malik & Elahi, 2014). Herd mentality is the investor’s tendency to go after the crowd, because the decisions made by most investors are considered as right forever. A herding investor will make decisions as per the actions of a crowd in buying and selling of stocks. Herd mentality has been found in both upward and downward phases in market. Moreover, high volatility and volume are the outcome of herding effect. Decision-making process relies on first-hand information collected from reference groups rather than own estimation (Mahapatra & Mehta, 2015). Investors pay effort on information processing from the crowd so as to make investment decisions. Often investors ignore their own information regardless of its accuracy in decision-making and blindly follow the herd, even if the crowd may be wrong. Their information processing always follows the herd and then gets delight based on whole herd mistake rather than personal mistake.

Trading and investment psychology of an investor is affected by the bandwagon effect. Bandwagon effect is a situation in which investors feel safer and assured if they come to know that their decision is consistent with the decision of others (Coval & Tyler, 2005). The bandwagon effect, however, can often be the fundamental rationale. Investors fear being excluded from the stock when they observe it increase; rather than looking at the basics of the stock, they begin buying stocks since they think everybody else is undertaking the same (Pertiwi et al., 2019). Investors have personal attachment with certain groups. They often make decisions based on the movement of social groups. Social environment has determined the investor’s behaviour in investment decisions. If an investor is polluted with infectious thoughts once, their behaviour becomes irrational and they make decisions accordingly. Social groups have sizable control in the stock market volatility (Mittal, 2010). It has been checked with the following hypotheses.

H₇: Information processing, bandwagon effect and social groups collectively form herd mentality in investment decision-making.

H₈: Herd mentality has significant positive influence with behavioural biases.

H₉: Behavioural biases have significant positive influence on investment decisions of individual investor.

Financial Literacy

Financial literacy is the capability to recognize how money acts around the globe, how investors can earn money and skill to administer and get more returns from investment (Giesler & Veresiu, 2014). Financial literacy can be measured by means of financial attitude, knowledge and behaviour on different investment outlets and financial dimensions. Financial literacy enables the investor to navigate with informed investment decisions and reduces the possibility of being misled (Agarwal et al., 2015). An investor with enough financial literacy has familiarity on arithmetic, budgeting, money management and capability to predict expenses and incomes. Furthermore, financial competency on savings, expenditure, borrowings and investment is of supreme importance for every investor making investments in stock market. Competencies lead to select suitable stock for both speculative and long-term investments (Ganapathi, 2014). Adding on in a study Ahmed et al. (2020) found that the financial literacy has positive impact on the investors investment decision.

Financial proficiency is related to the quantum of investors’ financial knowledge. Financially strong investors can plan in advance, discover and utilize information. Moreover, it helps the investors to be get familiar with when to get advice and how to proceed on such advice. It helps them to make superior gains (Hastings et al., 2013). Financial opportunity is the chance to make investments. Financial inclusion, compulsory dematerialization of stocks and maintaining bank account permits an individual to take part in the capital market arena. Such financial opportunity motivates individual investors to take part in stock market investments. Therefore, application of financial knowledge and performance in financial market is mostly relied on financial opportunity (Pareek & Dixit, 2016). The following hypotheses has been proposed in this way.

H₁₀: Financial competency, financial proficiency and financial opportunity collectively form financial literacy in investment decision-making.

H₁₁: Financial literacy has significant positive influence on investment decisions of individual investors.

Research Objectives and Problem

The study sought to establish the impact of behavioural biases and financial literacy on investment decisions of individual investors. The study assists to recognize the various types of behavioural biases and their potential impact on investment decisions. Similarly, how financial literacy transforms their investment decisions is also examined. Investment decision-making differs from one investor to another due to the development of own tenets or the imitation of others. It develops irrational behaviour in investment decisions. Then how can financial literacy trade-off be measured in such a phenomenon?

Methodology

To examine the combined impact of financial literacy and behavioural bias on investment behaviour, the required data was collected from individual investors through questionnaire in the google form. The questionnaire consisted of 22 self-assessment questions, of which 12 questions were related to behavioural bias; three questions to elicit financial literacy and seven questions were related to the socio-demographic conditions of the respondents. The questionnaire was divided into three parts—the first part covers socio-economic and demographic variables of gender, age, educational qualification, occupation, income and experience in investment, followed by the second part dealing with behavioural bias and financial literacy and last part of the questionnaire was asking suggestions related to the subject.

The data was collected from 250 individual investors by adopting convenient sample method. The individual investors’ list were collected from various investment consultants, portfolio advisors and stock trading terminals in southern states of India. The main criteria assigned for sample selection was that an investor should have two years of trading experience and has to reside in any of southern states—Andhra Pradesh, Telangana, Karnataka, Kerala and Tamil Nadu. The questionnaire survey was conducted to increase efficiency in data precision and decrease interviewer bias because respondents can take their liberty while answering the questionnaire. Moreover, the content and face validity of the survey was pre-tested with 50 investors in the study area prior to the actual survey. The use of words, meaning and measurement items was discussed with financial experts, academic professionals and investment advisors in the financial market. Accordingly, clarification in questions and relevant instructions guided individual investors to effectively answer the questionnaire.

The sample size was determined by adopting Jackson’s (2003) suggestion to determine the sample size based on the parameters to estimate. As per Jackson’s (2003) suggestion, for each parameters at least 20 cases are required. In this study author estimating seven parameters hence one hundred and forty cases is enough, but for better result researcher intended to collect data from 250 individual investors. Initially researcher collected data from 267 respondent, later while screening the data it is found that 17 responses are had some missing or inappropriate responses. Hence that 17 responses were ignored for further analysis.

To concurrently assess and examine how the process of investors’ decision is associated with financial literacy and behavioural biases, the structural equation modelling (SEM) has been used after measuring the data quality using confirmatory factor analysis.

Results and Discussion

Socio-economic Background

The questionnaire asked respondents to provide their socio-economic details like age, gender, education, income, experience and occupation. Table 1 describes the socio-economic background of individual investors

Table 1.

Socio-economic Background

S. No	Socio-economic Background	Variables	Frequency
1.	Gender	MaleFemale	84%16%
2.	Age	Less than 30 years30–50 yearsMore than 50 years	21%44%35%
3.	Educational qualification	School educationUG/DiplomaPG/Professional	33%42%25%
4.	Monthly income	Less than ₹50,000₹50,000–1,00,000More than ₹1,00,000	46%36%18%
5.	Experience in investments	Less than 2 years3–10 yearsMore than 10 years	34%43%23%
6.	Occupation	Business/ProfessionEmployedAgriculture/Others	52%31%17%

Source: Survey data.

With respect to gender, about 84% are male and only 16% are female investors. Age reveals that 21% are in less than 30 years of age group, 44% are in the 30–50 year age group, and 35% are more than 50 years of age. Educational qualification confirms that 33% have completed their school education, 42% have completed UG or diploma and 25% have completed their PG or professional education. Monthly income shows that 46% are in the income group of less than ₹50,000 per month, 36% are in ₹50,000–100,000 income per month and 18% are in the more than ₹100,000 income per month; 34% of investors have less than 2 years of experience, 43% of them have 3–10 years of experience and 23% of them have more than 10 years of experience in investments. Occupation discloses that 52% are engaged in business or profession, 31% employed in private or government sectors and 17% are agriculturists, retired, homemakers, and so on.

Impact of Financial Literacy and Behavioural Biases in Decision-making

Investment decision-making is the process that blends logical and psychological calculations of investors. However, investment decisions are rational according to traditional finance theories. Contrary to that, modern finance theories advocate that investment decision-making are not governed by rational deliberations. Investment decisions taken by the investors are also frequently spontaneous. Contrarily, decision-making is subject to four major behavioural biases in the form of heuristic bias, framing effect, cognitive illusions and herd mentality. However, financial literacy and behavioural biases aspect have strong influence on changing investment decisions. The association between financial literacy and behavioural biases in relation with stock investments employs structural equation modelling with observed variables and latent variables.

SEM is an extraordinary statistical technique for inspecting and measuring the causal relationship between different variables. However, latent variables are not directly observable, but mainly derived from other variables. However, other variables can be directly observed and measured. Structural equation modelling is used for confirmatory modelling rather than exploratory researches, and is therefore applied to theoretical tests rather than development. Structural equation modelling usually includes two parts, which are the measurement model that explains the association between the latent variables and their factor indications and the SEM that transmits the causal associations between the latent variables (Fornell & Larcker, 1981). To forecast the impact of financial literacy and behavioural biases of investment decisions in the stock market, the study has developed a conceptual model, which aims to represent the process of investment evaluation and support investment behaviour.

Equity share investing behaviour tries to detect the most essential and significant factors in decision-making through heuristic bias, framing effect, cognitive illusions and herd mentality. Behavioural biases are involved with careful intellectual action of equity investors in order to imply or not a behavioural bias. In the stock market, investors have to experience different forms of behavioural biases in investment decisions. Stock investment decisions of individual investors is shaped by the continuation of behavioural biases and financial literacy. The real investment decisions are the resultant activity; hence, it has been planned to test with the proposed hypotheses. Confirmatory factor analysis performed to create measurement model, which determines perfect fit of the model with data. Reliability of data is primarily estimated, after which the convergent validity is measured. Reliability is documented as the internal consistency of every factor that is employed to assess a latent construct.

Table 2 depicts that for all the factors, correlation amongst the factors are in the range of 0.781 to 0.919, and in Table 3 Cronbach’s alpha value has been ascertained and its values were found in the range of 0.83 to 0.92, which is greater than the lowest threshold value of 0.70. These results proving that multi-collinearity is not the main issue among the variables.

Table 2.

Results of Correlation Co-efficient Matrix

Factors	Heuristic Bias	Framing Effect	Cognitive Illusions	Herd Mentality	Financial Literacy
Heuristic bias	1	0.889*	0.897*	0.836*	0.792*
Framing effect	0.889*	1	0.919*	0.875*	0.781*
Cognitive illusions	0.897*	0.919*	1	0.883*	0.808*
Herd mentality	0.836*	0.875*	0.883*	1	0.846*
Financial literacy	0.792*	0.781*	0.808*	0.846*	1

Source: Survey data.

Note: * Significant at 0.01 level.

Measurement Model

SEM determines the impact of exogenous construct on investment decisions, as it institutes a path for the instantaneous examination of the entire model that seeks various hypothetical relationships. At this point, a two-step analysis is performed where a measurement model with latent construct is first analysed, and then a structural equation model with all constructions and their hypothetical relationships is analysed. Based on the proposed hypotheses, five latent constructions are formed, such as heuristic bias, framing effect, cognitive illusions, herd mentality and financial literacy from 15 observed variables. A measurement model has been developed to assess the reliability and validity of the latent variables. Accordingly, its results are depicted in Figure 1.

Figure 1.

Source: The author.

Table 3 depicts that the latent construct captures factor loadings that are in the range of 0.789 to 0.929, indicating a solid support for the validity of the construct. In this order, the AVE values extracted for heuristic bias, framing effect, cognitive illusions, herd mentality and financial literacy are greater than the standard level of 0.50. The composite reliability coefficient values are greater than 0.60 for all latent variables, therefore, high internal reliability of the model is assured.

Table 3.

Confirmatory Factor Analysis Results of Measurement Model

Latent Variables	Variables	Factor Loadings	Cronbach Alpha	CR	AVE
Heuristic bias	Anchoring biasOverconfidenceRepresentativeness	0.9290.9080.887	0.921	0.921	0.764
Framing effect	Mental accountingEndowment effectRegret aversion	0.8790.8730.895	0.889	0.885	0.725
Cognitive illusions	ConservatismConfirmationHindsight bias	0.9120.8840.796	0.874	0.861	0.693
Herd mentality	Information processingBandwagon effectSocial groups	0.9280.9230.789	0.853	0.852	0.687
Financial literacy	Financial competencyFinancial proficiencyFinancial opportunity	0.9030.9110.826	0.832	0.864	0.691

Source: Survey data.

Note: AVE = Average variance extracted; CR = Composite reliability.

Table 4 depicts that all prerequisites for authorizing a first-order measurement model are practically met. The results of the measurement model represent that the chi-square value is 361.994, with p = .000, CFI = 0.903 and RMSEA = 0.067. Consequently, the goodness of fit test values advocates a practically high fit model. Subsequent to establishing the reliability and validity of each item, the model is tested.

Table 4.

CFA Results of Model Fit

Chi-square	df	p	CMIN/df	CFI	RMSEA
361.994	126	0.000	2.873	0.903	0.067

Source: Survey data.

Structural Equation Modelling

The research hypotheses are tested due to the consistency of proposed measurement model with data. The hypothetical relationship between the different constructs is presented in Figure 2. From the hypothetical relationship to the construct, 11 hypotheses have been maintained with path significant at p = .05 level. Consequently, the study attempted to prove that there is a positive and direct association between all the other hypotheses.

Figure 2 exhibits that in the model, amongst the different hypothetical paths, all paths are significant at p < .05. The research scrutinized the models demonstrated in the graph; it makes flow charts for models of equity share investment decisions. Usually, in the structural equation modelling, the model fit assessed by the chi-square measurement is not at all times easy, because it is extremely responsive to the sample size. Owing to these constraints, different types of fit indices have been computed and they denote independent sample size. Therefore, goodness of the fit and the exhaustive link between the different hypotheses are showed in Tables 5 and 6.

Figure 2.

Source: The author.

Table 5 depicts the goodness of fit test of the SEM; it proved the good fit with the data through the use of various indices. Accordingly the computed value of such indices, like CFI (0.903), NFI (0.912), TLI (0.919), PNFI (0.921), PCFI (0.922), RFI 0.913) and IFI (0.904) are higher than threshold value of 0.9. Further, the values of RMSEA = 0.067 is safely below the threshold value of 0.08, it has perfect fit with the data. SEM has made a significant progress with regard to its goodness of fit indices, since consistency is found on all the suggested values. The outcome validates the reliability of the statistical test data.

Table 5.

Goodness of Fit Test

S. No	Goodness-of-fit	Statistics
1.	Comparative fit index (CFI) - (>0.90)	0.903
2.	Normed-fit index (NFI) - (>0.90)	0.912
3.	Tucker–Lewis index (TLI) - (>0.90)	0.919
4.	Parsimonious normed fit index (PNFI) - (>0.90)	0.921
5.	Parsimony comparative fit index (PCFI) - (>0.90)	0.922
6.	Relative fit index (RFI) - (>0.90)	0.913
7.	Incremental fit index (IFI) - (>0.90)	0.904
8.	Mean square error of approximation (RMSEA) - (<0.08)	0.067

Source: Survey data.

Table 6 represents that heuristic bias brings coefficient value of 0.795 for anchoring, 0.572 for overconfidence and 0.773 for representativeness. The construct heuristic bias has positive and significant association with its three antecedents. Put together, all antecedents have contributed to the existence of heuristic biases in stock investment decisions among individual investors, hence hypothesis (H₁) is accepted. Heuristic bias has a coefficient value of 0.434 for behavioural biases and it supported that it has direct and positive association, hence hypothesis (H₂) could be accepted. Therefore, the results are parallel with the outcome of Jain et al. (2015) and Charles and Kasilingam (2018). However, heuristic biases are mainly formed with anchoring, overconfidence and representativeness, and it creates a certain thumb rule in investment decision-making. Framing effect has a coefficient value of 0.684 for mental accounting, 0.649 for endowment effect and 0.815 for regret aversion. The construct framing effect has positive and significant association with its antecedents and it enthusiastically contributes to form the framing effect in stock investment decisions, which then leads to accept hypothesis (H₃). Framing effect has a coefficient value of –0.450 for behavioural biases; however, hypothesis (H₄) could also be accepted. The results of framing effect is similar to the findings of Ritter (2003), however, mental accounting, endowment and regret aversion together form their own mental calculations in decisions-making.

Table 6.

Testing of Hypothesis

Hypotheses		Un. Coef.		Std. Coef.	t-value	P-value	Decision
Hypotheses		Beta	SE	Std. Coef.	t-value	P-value	Decision
H ₁	Anchoring à Heuristic bias	1.000	0.082	0.795	4.498	0.227	Accept
	Overconfidence à Heuristic bias	0.732	0.086	0.572	3.642	0.164	Accept
	Representativeness à Heuristic bias	1.022	0.089	0.773	5.203	0.197	Accept
H ₂	Heuristic bias àBehavioural biases	0.511	0.328	0.434	5.328	0.223	Accept
H ₃	Mental accounting àFraming effect	1.000	0.096	0.684	4.213	0.381	Accept
	Endowment effect à Framing effect	0.890	0.102	0.649	3.248	0.248	Accept
	Regret aversion à Framing effect	1.138	0.111	0.815	4.236	0.276	Accept
H ₄	Framing effect àBehavioural biases	–0.608	0.411	–0.450	2.985	0.426	Accept
H ₅	Conservatism àCognitive illusions	1.000	0.225	0.506	4.452	0.452	Accept
	Confirmation àCognitive illusions	0.777	0.231	0.311	2.299	0.423	Accept
	Hind sight àCognitive illusions	1.750	0.392	0.669	4.878	0.324	Accept
H ₆	Cognitive illusions àBehavioural biases	–0.684	0.497	–0.274	3.625	0.456	Accept
H ₇	Information processing à Herd mentality	1.000	0.171	0.582	3.544	0.462	Accept
	Bandwagon effect à Herd mentality	1.043	0.184	0.601	3.365	0.423	Accept
	Social groups à Herd mentality	1.085	0.193	0.581	5.163	0.274	Accept
H ₈	Herd mentality à Behavioural biases	–0.456	0.339	–0.256	3.025	0.473	Accept
H ₉	Behavioural biases à Investment decisions	0.036	0.058	0.039	4.106	0.247	Accept
H ₁₀	Financial competency à Financial literacy	0.044	0.058	0.048	3.524	0.312	Accept
	Financial proficiency à Financial literacy	–0.107	0.062	–0.108	3.651	0.128	Accept
	Financial opportunity à Financial literacy	0.005	0.061	0.005	3.574	0.145	Accept
H ₁₁	Financial literacy à Investment decisions	0.051	0.064	0.051	4.245	0.324	Accept

Source: Survey data.

Note: Un. Coef = Un-standardized coefficient; Std. Coef = Standardized coefficient; SE = Standard Error.

Cognitive illusions has a coefficient of 0.506 for conservatism, 0.311 for confirmation and 0.669 for hindsight bias. It has been confirmed that cognitive illusions have a substantial relationship with their antecedents and accept hypothesis (H₅). Cognitive illusions have a coefficient value of –0.274 for behavioural biases; however, hypothesis (H₆), which deals with cognitive illusions and behavioural biases is accepted. The findings are similar to Qawi (2010) and Bakar and Yi (2016), however, conservatism, confirmation and hind sight bias assists the investors to follow certain norms in investment decisions. Herd mentality has significant connection with its antecedents with coefficient value of 0.582 for information processing, 0.601 for bandwagon effect and 0.581 for social groups; which leads for acceptance of hypothesis (H₇). Herd mentality has coefficient value of –0.256 for behavioural biases; therefore, hypothesis (H₈) could be accepted. The results of herd mentality are consistent with Hwang and Salmon (2004), herd behaviour induces to follow actions of other in investment decisions. Altogether, behavioural biases have coefficient value of 0.039 for stock investment decisions, it directs to accept hypothesis (H₉), which deals with behavioural biases on investment decisions. This result is alien with the results of Ahmed et al. (2020)

Financial literacy has a coefficient value of 0.48 for financial competency, –0.108 for financial proficiency and 0.005 for financial opportunity. Financial literacy constructs have a direct effect on enough financial knowledge in making investment decisions (Rai et al., 2019). However, hypothesis (H₁₀) is accepted. Financial literacy has a coefficient value of 0.051 for investment decisions and has a positive impact on investment decisions, which leads to accepting hypothesis (H₁₁). The findings are similar to the findings of Xia et al. (2014) and Ahmed et al. (2020) that financial literacy can increase decision-making quality. The findings corroborated that financial literacy dimensions and behavioural biases have a direct influence on the investment decisions of individual investments. With the intention to test the extent of association among independent factors in stock market decision-making, the test of estimates of independent factors has been performed and is presented in Table 7.

Table 7.

Estimates of Independent Factors

Variables	Variables	Estimate	SE	C. R.	R²	P
Heuristics bias	Framing effect	0.630	0.089	7.103	0.845	.001
Heuristics bias	Cognitive illusions	0.075	0.041	1.842	0.186	.065
Heuristics bias	Herd mentality	–0.162	0.056	–2.918	–0.287	.004
Framing effect	Cognitive illusions	0.007	0.034	0.201	0.019	.840
Framing effect	Herd mentality	–0.165	0.051	–3.258	–0.335	.001
Cognitive illusions	Herd mentality	–0.161	0.043	–3.749	–0.604	.001

Source: Survey data.

Table 7 depicts that 84.5% differences have been found between heuristic bias and framing effect. It confirms that both heuristic bias and framing effect develop a distinct behavioural bias in investment decisions. It also divulges that an investor assumes either heuristics or frames while constructing their stock portfolio. Heuristic bias has 18.6% variation in relation to cognitive illusions. Heuristic bias and cognitive illusions have a considerable influence while selecting a particular stock for investment purpose. Herd mentality of investors can be controlled to the extent of 28.7% by heuristics bias. Heuristics often controls the investors to make investments based on the thumb rule rather than following herd. Framing effect has 2% variation in connection with cognitive illusions, it shows that an investor either follow frames or illusions in decision-making. Framing effect controls the herd mentality in stock selection to the extent of 33.5%. Similar to that cognitive illusions detain the investor to follow herding behaviour by 60.4% in selection of investments.

Discussions and Conclusions

The rationale of the study is to examine the influence of financial literacy and behavioural biases with respect to irrational behaviour in decision-making. Investors have a great level of behavioural biases in investment decisions. Financial literacy helps to assimilate such irrational behaviour and make effective decisions to attain the desired profit. Given this point, the study expands the behavioural approach in decision-making on investments among individual investors. The study proposed eleven hypotheses, of which five hypotheses dealt with the antecedents of heuristics bias, framing effect, cognitive illusions, herd mentality and financial literacy; four hypotheses measured its impact with behavioural biases and two hypotheses addressed the influence of financial literacy and behavioural biases in investment decisions.

Findings divulged that anchoring bias, representativeness and overconfidence direct to attain investment decisions based on earlier incorrect experiences. Heuristic bias can be highly helpful to make speculative decisions that may give possible gains or loss in stock investing. It can be verified that investors mainly rely on such heuristic information rather than potential investigation and on price movements of a particular stock. Heuristic bias provides a few ideas to invest in the stock market and had a positive impact in developing behavioural biases in investment decisions. Framing effect is also a prominent aspect in creating behavioural bias among individual investors and it is mostly influenced by mental accounting, endowment effect and regret aversion. This is the outcome of the investors’ own techniques and intuitions to buy or sell a stock. Framing effect makes impulsive behaviour than rational analysis while planning to purchase a stock. The premise support that framing effect has significant influence on behavioural bias.

Cognitive illusions extensively create behavioural bias in investment decisions. Cognitive illusions are formed by conservatism, confirmation and hindsight bias in selecting a stock for investment. The antecedents of cognitive illusions guide the investors to make a certain pattern of deviation from rationality in evaluation of investments. Such precursors have a close connection with creating cognitive illusions. Cognitive illusions have a strong impact on behavioural bias. Herd mentality is formed with information processing, bandwagon effect and social groups in investment decision-making. However, information processing provides sufficient information to follow herd and create bandwagon effect in their decisions. Social groups make a better environment to compare their own ideas with others and make decisions accordingly. Herd mentality of individual investors creates a strong impact in building behavioural biases. Financial literacy is formed with financial competency, financial proficiency and financial opportunity in stock market investments. Financial competency develops knowledge of investors in various financial investments. Financial proficiency enhances investor skills in stock market investments. Financial opportunity deals with the chance to make investments in stock market.

Therefore, financial literacy increases an investor’s knowledge in stock selection, time of purchase or sale, risk and return estimation without any hesitation. The presence of financial competency, proficiency and opportunity develop financial literacy of investors to make profitable investment decision-making. Behavioural biases are a mix of heuristic bias, framing effect, cognitive illusions and herd mentality. It corroborates that investors often follow heuristics than frames or illusions or herd behaviour in investment decisions. However, the intellectual potency of individual investors is mainly influenced by heuristic bias. That is why; investors often follow the thumb rule as a significant decision technique than any other technique. On a few occasions, investors make their own frames based on their investment knowledge in the market. Sometimes, investors believe in following existing plans than developing new ideas to pick shares. Successive failure or bad experience will induce them to follow herding rather than developing their own analysis. It is confirmed that behavioural biases have a strong impact on investment decision-making.

Both financial literacy and behavioural biases involve governing investment decisions of individual investors. Financial literacy guides investors to learn more about the investment environment, but behavioural biases provide simple and effortless ideas for decision-making. The independent relationship between variables found that heuristic bias has significant positive impact with the framing effect and cognitive illusions in decision-making, but it has negative impact with the herd mentality. The framing effect has a positive impact with cognitive illusions but has a negative impact with the herd mentality. Cognitive illusions have a negative impact on investors’ herd mentality in decision-making. It can be concluded that individual investors follow irrational behaviour in investment decisions with the active presence of financial literacy in the stock market.

Research Implications

The findings also provide the following implications for academics and experts. Individual investors find it difficult to value a security while deciding to buy it. The movements of stock market prices are unpredictable for everyone; investors develop their own ideas or imitate others to make better decisions in the stock market. Investors often develop their own ideas based on a thumb rule, personal calculations and follow past experiences to make better decisions. Effective decision-making helps individual investors maximize their return on their investment. The success of investing in the stock market is based on their ability to learn the dynamic market environment and develop appropriate investment strategies. Proper investor education through training can increase their ability to learn the stock market. Due to the lack of training, investors create an irrational approach to make investment decisions. However, irrational decisions guarantee short-term gains, but it is not good to make profitable decisions from a long-term perspective. Academics, policymakers and experts should strive to provide training and development measures to help investors make rational decisions.

Limitation and Future Scope of Study

Like other studies, this study also has its own limitations. First, this study is conducted in South Indian states, hence it may not be generalize to other parts of country. Second, this study collected data using google form hence the respondents may be wrong on recalling their behavioural bias while responding. Third, limitation is to examine each individual bias—say anchoring bias—was estimated using only one variable, while it can examine using number of variables like other study. This study used financial literacy factor as predicting variable rather than mediating variable, a similar kind of study can be made using the financial literacy as a mediating factor in between the behavioural bias and investment decision.

Footnotes

I thank Dr M. Banu Durukan Sali Dokuz,professor in Finance,Eylül Üniversitesi/Dokuz Eylul University,Turkey,and my colleagues Dr Anand Sankar Raja,Dr Akansha Kanna,from CHRIST (Deemed to be University),assistant professor in Marketing,who provided an insight and expertise that greatly assisted the research,although they may not agree with all of the interpretations and conclusions of this article. I would also like to show my gratitude to Dr Jothi Munusamy,associate professor,CHRIST university,for sharing his pearls of wisdom with me during the course of this research.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research,authorship and/or publication of this article.

Funding

The author received no financial support for the research,authorship and/or publication of this article.

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