Impact of behavioral finance biases on investment decision
Table of Contents
Chapter-1 Introduction
Background
The goal of “behavioral finance” is to investigate how cultural norms and individual psychology impact economic behavior. The term “behavioral finance” refers to the study of the psychological and social influences on financial decisions. Because of the importance of behavioural elements in determining investors’ investment decisions, behavioral finance has become a crucial aspect of the development of return on investment (ROI) theories (Kanan, 2018).
Due to the merging of psychological and cognitive frameworks, “behavioral finance” is a relatively new field within the financial sector. The field of behavioral finance looks at the emotional and cognitive factors that influence financial decisions. The psychological, social, and cognitive theoretical intersections make investing a difficult and time-consuming endeavor (Kumari, 2015).
According to Sewell (2007), behavioral finance is the study of how the irrational decisions of investors and bankers impact international financial markets. He continued by saying that behavioral finance piques his interest since it may help to explain why certain markets are less efficient than others.
Behavioral Biases
(Agrawal, 2012) highlights the fact that investors’ decision-making has always been and always will be impacted by their own behavioral biases. Although it’s impossible for an investor to completely free oneself of biases, there are situations in which doing so is essential. (Rayenda Khresna Brahmana, 2012) reiterate the fact that irrational human psychology plays a role in stock price anomalies and economic decision making, and elaborate on the factors at play.
Heuristic Behavior
Due to the uncertainty and volatility of the market, investors are forced to make decisions via trial and error or by relying on accepted standards. The incorporation of cognitive and emotional components in evaluating investment possibilities may, however, remove rational behavior in the decision-making process.
(Kahneman D., 2003) defines decision-making is aided by heuristics, which are “cognitive shortcuts” or “rules of thumb” that reframe a complicated problem as a simpler one. Through trial and error or even just simple experiments, people create strategies for generating quick decisions and assessments. Heuristics are useful in certain contexts, but they are seldom the optimal approach when it comes to making financial choices since they overlook important details. Heuristics are susceptible to a variety of cognitive biases.
Representativeness
The financial market is rife with hasty assessments made by investors based on a small number of presumptions. The success of past financial decisions might lead investors to incorrectly infer a trend in the present and future. Investors are not placing wagers based on long-term trends or even considering the rule of averages. Short-term trends are given more weight, such as an increase in the price of a presently traded stock or an industry that has been exceeding the market averages recently. To ensure the smooth operation of completely rational markets, it is essential that previous stock price variations have no influence on the future performance of the stock in issue. (Jacob, 2017)
Adapting strategy based on investment returns If an investor’s earlier decisions were beneficial, that investor is more likely to make similar decisions in the future without giving much thought to the many uncertainty patterns that might have contributed to his or her success. This is the same as reaching conclusions with insufficient evidence. It is a type of representational bias on the part of investors to overreact and buy popular stocks despite their bad record. (Goyal, 2015).
Overconfidence
Adielyani and Mawardi (2020) stated that by considering their own investment portfolio and their own personal beliefs, as well as the results of the aforementioned study, an investor may be better able to make wise financial decisions. but (Rahman and Gan, 2020) argues that this bias has a negative effect on investment decisions by lowering returns, increasing the likelihood of making a poor investment choice, and decreasing portfolio diversification.
Gamblers Fallacy
Gamblers fallacy refers to the mistake made by investors who bet on a rapid reversal of the market’s present trend. Those involved are in a similar position to a gambler at a casino. A player at roulette can elect to bet on a red number if he or she detects that the die has been constantly landing on black numbers. Similarly, when allocating funds, it’s common to wager that a stock’s recent losing streak will end abruptly, allowing for a winning purchase. Investors lose money when they make decisions based on faulty predictions. However, when investors make accurate predictions, the returns may be substantial. (Bhattacharya, 2012)
Availability Bias
In order to execute trades with a lower anticipated rate of return, investors assign relative priority weights to different pieces of information before making decisions based on these weights, accepting the risk of suffering losses along with the potential for a lower expected rate of return. The ability of an event to influence thinking and action grows in proportion to its timeliness and relevance. (Qawi, 2010)
Conservatism
“Conservative bias” refers to the failure to adequately update one’s beliefs in light of fresh information. This indicates that people are slow to react to and adjust to new developments. Potentially risk-averse investors may be slow to respond to shifting market conditions because of their conservative nature. It’s possible for investors to misjudge the long-term average when they observe a long-term trend and overreact to it. (Singh S., 2012).
Role of Personality
We make decisions based in part on our personalities and our innate prejudices. Decision-makers seldom agree on a course of action when confronted with comparable situations because of their divergent personalities (Shanmugasundaram, 2011). A person’s evaluation, knowledge, and experience matter more than their personality attributes while making decisions.
Extroversion
When making investments, an extrovert is affected by things like what other people know about potential prospects. Extraverted people are more likely to place more stock in the judgments and views of others around them and less on their own abilities for introspective analysis. Extroverted individuals are more likely to make snap decisions because they live in the moment rather than according to a set of predetermined guidelines. Extroverts may suffer financial setbacks because they lack the self-control, intellect, and desire necessary to succeed in the investment world. (Rasoul, 2011)
Agreeableness
A person’s agreeableness may be measured by how much they value the input of others who have a personal stake in the outcomes of their decisions. In making judgments, he always prioritizes the needs of others around him, as seen by his candor and openness. The presence of these positive qualities is critical when selecting whether or not to invest in a business partnership. (Rasoul, 2011)
Neuroticism
Investors who are prone to anxiety act according to their wants and requirements. They are so self-absorbed and focused on themselves that they never consider anybody else while making decisions. Those people are fully capable of acting independently of cultural pressures and making decisions based only on their own self-evaluations and self-interests. (Rasoul, 2011)
One’s Capacity to Gain Knowledge from Past Errors Investors who have a taste for risk tend to reap more rewards. Investors were enticed to put money into high-risk new businesses by the prospect of a substantial financial return on their investment. A person who is eager to learn and grow will always be on the lookout for fresh experiences. People with this outlook are involved in many different aspects of society, including the political, ideological, perceptual, cultural, and economic spheres, and as a consequence, they bring a variety of knowledge and experience from these areas to bear on the decisions they make in their daily lives. (Rasoul, 2011)
Feelings and Emotions
Emotions may have a big role in the decisions of investors, according to recent research. Moods integrate environmental factors like temperature, the body’s biorhythms, and sociocultural influences, and this translates into stock price swings. Emotional cues from the environment, such as the weather, the body’s biorhythms, and the social milieu, facilitate the best decision-making (Brian and Lucey, 2005).
Problem Statement
To what extend Heuristic Behavior, role of personality and feeling &emotions can influence the investment decisions making
Research objectives
The objective of this study is to critically analyze the behavioral finance Biases and its effect on investment decision-making. Specifically, this study investigates the impact of heuristics and Role of personality, feeling and emotion on investment decision
Research questions
- What is the influence of Heuristic Behavior on investment decision?
- What is the influence of Role of personality on investment decision?
- What is the influence of feeling and emotion on investment decision?
Significance
The Pakistan stock market is one of the most important emerging markets in Asia due to its high trading volume, and researchers hope that by identifying the most important behavioral finance factors that may affect their decisions when investing in stocks, they can help individual investors make better choices.
Chapter 2
Literature Review and Theoretical Framework
While the focus of this literature review will be on behavioral finance and related topics, a solid understanding of the “Standard Finance” that paved the way for this area of research is nevertheless required. Those that invest in the market are assumed to be rational individuals, the market is assumed to be fully efficient, and complete and accurate information is assumed to be easily available to all market participants. This section will provide a concise overview of the financial concepts and limits discussed.
‘Behavioral finance is a discipline of finance that analyses how the conduct of agents in the financial market are impacted by psychological aspects,’ writes Subash (2012). Stock prices are influenced in turn by the actions of individual investors in the market for buying and selling securities. “Behavioral finance shows that investors do not always behave rationally when making investment choices,” as suggested by Babajide and Adetiloye (2012). This is true even if the investor has all the information, knowledge, and understanding necessary to make a rational decision. Behavioral finance, as defined by Huckle (2007), is the subfield of finance that uses empirical evidence to explain why and how individuals choose certain investment strategies. Alquraan, Alqisie, and Al Shorafa (2016) conducted research on the influence of behavioral finance determinants on private investors’ stock investing choices using the Saudi Stock Exchange as a natural experiment. The hypotheses were tested using the original data using multiple linear regression and analysis of variance. Conclusions: loss aversion, overconfidence, and risk perception all mattered for Saudi Stock Market traders, however herd mentality didn’t play a role. Only education matters significantly when it comes to investing, regardless of gender, age, money, or experience.
Figure 1 Theoretical Framework
| Investment Decision Making |
| Heuristic Behavior |
| Role of Personality |
| Feeling and Emotions |
Hypothesis
H1. There is a significant influence of Heuristic Behavior on Investment Decision Making
H2. There is a significant influence of Role of Personality on Investment Decision Making
H3. There is a significant influence of Feeling and Emotion on Investment Decision Making
Chapter 3 Research methodology
Kothari (2004) defined methodology consists of the steps taken and tools used to delve further into a subject. The difference between research methodology and research methods, he went on to explain, lies in the former’s emphasis on providing background information on the research problem and the latter’s focus on identifying the most appropriate ways for solving it. To help answer these concerns and choose the best strategies for tackling this research difficulty, the research onion developed by Mark Saunders, Philip Lewis, and Adrian Thornhill (2015) might serve as a useful framework. Each part of Figure 1 would be given the care it needs.
Figure 1. The research ‘onion’ (Saunders, Lewis and Thornhill, 2015)
Research Philosophy
According to Saunders et al. (2007), in order for research to advance the state of knowledge, it is necessary for researchers to follow a certain “philosophy,” or guiding set of ideas and assumptions. As mentioned by Burrell and Morgan (2016), assumptions must be formed throughout the whole of a study effort. The writers then elaborated on the three cornerstone presuppositions of each research project. Presupposition of the real world for this field of research, proposed theory in the area of epistemology concerned with the development and dissemination of reliable knowledge. The researcher’s value, whether it be neutral or not, is included in the axiological responsibility of values and ethics in a scientific activity. Human values, as outlined by Heron (1996), serves as a guide for all endeavors, including scientific inquiry. In light of these presumptions, Positivism will serve as the guiding philosophical framework for this investigation. Philosophy Under its ontological assumptions, positivism explains that there is only one reality, which is independent of any internal directions or perspectives, and under its epistemological assumptions, positivism explains that results are drawn by standard approach and processes based on relationships between two variables through observations and facts (Saunders et al., 2018). With a positivist axiology, the researcher is expected to be objective in all of their interactions with others and their quest of knowledge, with an emphasis on using a technique that is “scientific empiricist” in nature and free from prejudice.
Research Approach
The methods used in an investigation are the next layer in the inductive inquiry’s framework. Ragab and Arisha detail both a deductive and an inductive approach (2018). This investigation will make use of the deductive strategy. The authors argue that there is a certain framework upon which the deductive approach rests in order to articulate the causal link between variables. One may easily find credible sources to support or refute the study of behavioral finance, giving one enough material to provide a case for or against the field. The hypotheses were based on findings from the literature review. In Trochim and Donnelly, this kind of approach is referred to as “top-down” (2008). Figure depicts the procedure for implementing this approach.
Figure 2: Deductive approach (Research Methods Knowledge Base, 2021)
Methodological choices
Next up in the research “onion” are the techniques you ultimately settle on. Kothari (2004) defines “research methodology” as “the set of procedures and strategies employed in conducting a study.” It is an integral part of the methodology used in the investigation. As Ragab and Arisha (2018) point out, while choosing a study approach, it’s crucial to bear in mind the underlying assumptions, philosophies, and methods being used. In conducting studies, one may utilize either quantitative methods or qualitative methods, or even combine the two. According to Bryman (2012), the researcher’s positivist and logical stances make quantitative methods well suited to this investigation. Williams (2011) chimed in to say that quantitative methods may be used to investigate the direction and impact of relationships between factors. Specifically, we will utilize the quantitative data and the statistical approaches and models given by Creswell to (2002).
Among the quantitative methods described by Ragab and Arisha (2018) are controlled experiments, questionnaires, structured observations, and in-depth interviews To get at a more informed appraisal and knowledge of the phenomena under investigation, qualitative methodologies, as opposed to quantitative analysis, depend on the researcher’s personal stories and observations (Williams, 2011). The terms “interpretivism” and “induction” are often used in (Guest, Namey, & Mitchell, 2012). Mixing quantitative and qualitative methods is the third strategy, and it is grounded on the pragmatic paradigm, which maintains that the two should work together rather than against one another. According to Azorn and Cameron’s (2010) logic, this method is superior since it identifies and eliminates any issues ahead of time. Despite the fact that a mixed-methods approach would first appear like the best option for this research, time constraints mean that a quantitative one is really more appropriate. To conduct this study, the researcher will use qualitative techniques. Given that there are quantitative studies out there (Kengatharan and Kengatharan, 2014), it seems prudent to utilize such a strategy. This data is essential for comparing outcomes.
Research Strategy and Time Horizon
The research strategy is the next layer in the research onion. Lewis et al (2015) defined systematic preparation for answering a research question. This research used a survey design since it is both practical and applicable to the situation at hand. In light of COVID 19, the government has implemented social distance limitations, and the survey technique is utilized to monitor compliance with these regulations. This approach, as stated by Lewis et al., has to be in line with the researcher’s stated alternatives, technique, and worldview.
Data Collection and Analysis
Surveys would be used to collect primary data. Kothari (2004) states that experiments and surveys are both valid methods for gathering primary data (using questionnaires). Despite the crucial role that surveys play, secondary data will also be consulted. Given that we anticipate that our results will close a gap in the literature and enlarge the body of knowledge. The primary data that would be used for this research.-
Sample Size and Survey Design
Saunders et al (2009) explained that time and resource constraints make it impossible for researchers to collect information from the whole population; instead, they must concentrate on a representative sample. Kothari (2004) also categorized the there are two primary categories of sampling techniques: probability sampling and non-probabilistic sampling. As a kind of non-probabilistic sampling, convenience sampling will be used in this investigation.
(i.e., selecting participant that would be conveniently available to be surveyed) would be used in accessing the participants that would take part in the survey. The Likert scale would be used for the questionnaires. Nemoto and Beglar (2014) explained that the Likert scale is ideal for gauging an individual’s emotional state. The sample size would 120 from individual investors and questionnaire will be used in this research.
In our research first we will gathered data and after that we will do analysis of data, and for analysis of we will use SPSS (statistical package for social sciences). In our research we will find out regression and we will get the results of regression from SPSS. We will use regression, and check that either our independent variable has any effect on our dependent variable. In our research we will use surveys and distribute questionnaire after distributing we will get back all the distributed forms and then we will do analysis of data and we will use descriptive research design.
Researchers might potentially infer causal relationships between variables using the statistical technique of regression analysis. How strongly a dependent variable is linked to a set of independent variables may be calculated through regression analysis. Accurate estimates of all model variables are provided by the analysis. Our results must be less than 0.05 out of 100 times to be considered statistically significant for our variable.
Using a correlation analysis, we can see whether our variables have a positive or negative relationship. Correlation studies may also be used to accurately determine the directions (positive or negative) in which two variables are oriented. Since Pearson correlation’s scale spans from -1.0 to +1.0, findings should fall within this range when investigating causation between variables. If the value is negative, then the two variables are moving in opposite directions, and if the value is positive, then the two variables are moving in the same way. When the correlation coefficient between two variables is 0, it is assumed that there is no connection between them
Chapter-4 Data Analysis and Discussion
Analytical Techniques and Tools
Data Reliability test, Descriptive test, Correlation, Regression analysis and Durbin Watson are the Statistical tests which were used in this study. Cronbach’s alpha was calculated to calculate the internal reliability.
Table 1 Demographic statistics Gender
| Gender | ||
| Frequency | ||
| Valid | Male | 107 |
| Female | 32 | |
| Total | 139 | |
Table 2 Demographic statistics Age
| Age | ||
| Frequency | ||
| Valid | 18-25 | 66 |
| 26-35 | 29 | |
| 36-45 | 4 | |
| 46 and Above | 40 | |
| Total | 139 | |
Frequency in gender wise shows that majority of the respondents were male. 107 out of 139 respondents are males which is 77 percent of the total respondents and on the other hand 32 out of 139 are females which are just 23 percentage of the total respondents of 139. The statistics mentioned in above table shows that most of the respondents are from between 18 years to 25 years which are 66 in numbers and 47.5 percentage of the total 139 respondents and this stage. 20.9 percent of respondents are from 26 years to 35 years out of 139 and which are 29 in numbers. From 36 to 45 years of investor decision making 2.9 percent of respondents and 4 in numbers. 40 respondents are from 46 above
| Reliability Statistics | ||
| Variables | Cronbach’s Alpha | No of Items |
| FE | 0.819 | 5 |
| RP | 0.670 | 5 |
| HB | 0.745 | 5 |
| IDM | 0.761 | 5 |
Descriptive Statistics
| Descriptive Statistics | |||
| Mean | Std. Deviation | N | |
| IDM | 3.66 | 0.83 | 139 |
| HB | 3.62 | 0.85 | 139 |
| RP | 3.66 | 0.71 | 139 |
| FE | 3.72 | 0.95 | 139 |
Pearson correlation is used to see relation among variables and the range of scale of Pearson correlation is from -1.0 to +1.0, the values should be fall between these ranges. The negative (-) values shows the opposite directions of the variables and positive (+) values shows that variables are moving in a same direction. If the value is zero (0) then it means there is no correlation exists between variables.
Table 7 Correlations
| Correlations | |||||
| HB | RP | IDM | FE | ||
| HB | Pearson Correlation | 1 | .573** | .622** | .579** |
| Sig. (2-tailed) | .000 | .000 | .000 | ||
| N | 139 | 139 | 139 | 139 | |
| RP | Pearson Correlation | .573** | 1 | .536** | .627** |
| Sig. (2-tailed) | .000 | .000 | .000 | ||
| N | 139 | 139 | 139 | 139 | |
| IDM | Pearson Correlation | .622** | .536** | 1 | .447** |
| Sig. (2-tailed) | .000 | .000 | .000 | ||
| N | 139 | 139 | 139 | 139 | |
| FE | Pearson Correlation | .579** | .627** | .447** | 1 |
| Sig. (2-tailed) | .000 | .000 | .000 | ||
| N | 139 | 139 | 139 | 139 | |
| **. Correlation is significant at the 0.01 level (2-tailed). | |||||
Pearson correlation analysis is used to authenticate the influence of HB, RP and FE on IDM .As well as table shows that Pearson correlation research is used to indorse the influences in the middle of the dependent variable. Correlation study proves a vital link (at p<0.01 range) in the middle of all variables. The values .778**, .627** & .447** gives a significant association in the middle of Decision Making of investor with independent variables (Heuristic Behavior, Role of personality and Feeling and emotion
Regression Analysis is statistical process used to estimate and predict relationship between the variables. When to estimate relationship of one dependent variable with one or more independent variables then regression analysis is used and it give the detail estimates of variables and basically, we will find out regression and regression will show that how strength has in our dependent variable and independent variable. If results will be in between 0.05 then it means that our variable has strong relationship and has significant relationship.
Table 8 Model Summary
| Model Summaryb | |||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Durbin-Watson |
| 1 | .772a | .597 | .588 | .53933 | 1.509 |
| a. Predictors: (Constant), FE, RP, HB | |||||
| b. Dependent Variable: IDM | |||||
The model summary table explains the value of independent variables influence of Heuristic Behavior, Role of personality and Feeling and emotion on Decision Making of investor. R square value is .772 which means 77 percent variance shows in dependent variable in all independent variables. The value forecasts strong bond between variables.
Table 9 ANOVA
| ANOVAb | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 58.071 | 3 | 19.357 | 66.548 | .000a |
| Residual | 39.268 | 135 | .291 | |||
| Total | 97.339 | 138 | ||||
| a. Predictors: (Constant), FE, RP, HB | ||||||
| b. Dependent Variable: IDM | ||||||
Anova provides statistical to see that the means of the variable are identical or not. Anova is very useful in matching three or more means for statistical consequence. This table shows that df value is 135, value of F is 66.548 and Sig is .000 for the data. Anova analysis indicates that Heuristic Behavior, Role of personality and Feeling and emotion effects the Decision Making of investor
Table 10 Coefficients
| Coefficientsa | ||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
| B | Std. Error | Beta | ||||
| 1 | (Constant) | .612 | .231 | 2.653 | .009 | |
| HB | .391 | .092 | .400 | 4.250 | .000 | |
| RP | .373 | .045 | .630 | 8.309 | .000 | |
| FE | -.228 | .078 | -.259 | -2.904 | .004 | |
| a. Dependent Variable: IDM | ||||||
Regression table shows that unstandardized beta value for Heuristic Behavior is .391, Role of personality .373 unstandardized beta value and .228 is the unstandardized beta value for Feeling and emotion; Heuristic Behavior, Role of personality and Feeling and emotion have positive values which indicates the positive relationship between the dependent and independent variables. On the basis of unstandardized beta values it shows that there is a substantial impact of Heuristic Behavior, Role of personality and Feeling and emotion on Decision Making of investor;
Value of significant has great importance in above table and according to the table Decision Making of investor which is our dependent variable has sig value of 0.00. Heuristic Behavior, has significant value of 0.00. 0.00 is the value of Role of personality and Feeling and emotion has 0.00 significant value. Significant value range which consider as acceptable is below 0.05. In this study stats shows that Heuristic Behavior has sig value below 0.05 and also Role of personality value is below than 0.05 which accepts the hypothesis and also Feeling and emotion has lower value than 0.05 and in this case is accepted
Chapter-5 Conclusion and Discussion
Introduction
This chapter consist of conclusion and discussion as well as after discussion, suggestion and recommendation would be part of this chapter, then after suggestion and discussion, limitation of this research are given along with future research.
Conclusion and Recommendation
Primary aim, of conducting to research, to check effect of Heuristic Behavior, Role of personality and Feeling and emotion on Decision making of investors. In research, IV’s are euristic Behavior, Role of personality and Feeling and emotion and our DV is Decision Making of investor, it was find out that either Decision Making of investor will be influence by Heuristic Behavior, Role of personality and Feeling and emotion or not. Data was obtain from primary source and it is run on SPSS.
Our study contains three independent variable and one dependent variable. First we did reliability test to check the reliability of data for further proceed. It was founded that our data is reliability because all the values are greater than 0.70 then our study shows that there is significant influence of Heuristic Behavior, Role of personality and Feeling and emotion on Decision Making of investor
Value of significant has great importance in above table and according to the table Decision Making of investor which is our dependent variable has sig value of 0.01. Heuristic Behavior, has significant value of 0.00. 0.00 is the value of Role of personality and Feeling and emotion has 0.00 significant value. Significant value range which consider as acceptable is below 0.05. In this study stats shows that Heuristic Behavior has sig value below 0.05 and also Role of personality value is below than 0.05 which accepts the hypothesis and also Feeling and emotion has lower value than 0.05 and in this case is accepted
Limitation
Despite having 139 respondents complete the survey, the researcher feels that additional people are required for a more thorough analysis. Chapter 3 “Research Methodology” explains how time constraints cut down on the number of respondents. The sample strategy also contributed to the above findings. Convenience sampling was used to pick survey respondents, which is not a statistically valid approach.The principal results of the investigation cannot be generalized to investors in other parts of the world owing to variances in investment and lifestyle philosophies across societies.
Future Research
Future studies may investigate other biases that were not analyzed here. Investors in mutual funds may also see how their activities affect the market as a whole
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