3.1. Sample size
The study's goal is to look into the effects of emotional intelligence on investor behaviour in India, a large sample size is recommended. The larger the sample size, the more accurate the results will be. The more representative it can be, the more accurate the results will be (Saunders et al., 2009,) nonetheless, and the sample size is determined by the researchers' available resources, such as time and money. As a result, 625 questionnaires are delivered to individual investors with the hopes of collecting more than 550 responses but 430 respondents are given the response for the questionnaires. Questionnaires are being mailed Telegram App, and through WhatsUp groups. For responders, the first method is convenience sampling, while the second method is snowball sampling because it is the ideal strategy for emailing it to friends, the convenience sampling method was chosen to receive the highest response rate and family to get the highest response rate.
3.2. Investigation Area
The region of research is India context the retail investors who trade in major of the discount brokers of India like [1] Zerodha Broking Limited, RKSV Securities India Private Limited (Upstox), Angel Broking Limited and 5paisa Capital Limited.
3.3. Investigating strategy
The cluster sampling method and purposive sampling framework was utilized in this assessment. It's anything but a non-probability testing framework, which relies upon the features of a general population and the objective of the assessment. The purposive sampling technique is generally called basic analyzing or specific assessing or unique examining. It is furthermore established on the appraisal of the expert.
3.4. Survey
The systematised research was used to collect data from investors. The results are calculated using the Emotional Intelligence Scale and the Investment Decisions Scale. The normalised scale for Emotional Intelligence (Goleman, 2001) is a 50-item, five-point Likert scale that assesses the categories of Emotional Intelligence, Sympathy, Self-Awareness, Motivations, Managing Emotions, and Social Abilities.
3.5. Sources of Data
The fundamental data for this assessment was gathered from retail investors with the assistance of systematized reviews from examiners of institutionalised speculators of securities exchange, and discretionary data was gathered from journals, books, and locales, as well as from the review of composing.
3.6. Research Design
The structure of the data collecting and interpretation process is captured by the current study (Bryman & Bell, 2007, Ghauri & Gronhaug, 2010, p. is experimental or case study design; 2) longitudinal design; and 3) cross-sectional designs are three types of research designs. The experimental design is frequently used to investigate causal relationships between variables, and it includes the use of two distinct groups: the experimental group, which receives treatment/intervention, and the control group, which is used to compare any differences in treatment results between the two groups. The longitudinal design is typically used to analyse changes over time and to incorporate causal elements (Collis & Hussey, 2009,). A case study (Collis & Hussey, 2009,) includes the investigation of a single case. Nonetheless, because this study examines a very small sample size at a certain moment, a cross-sectional approach was adopted. In a cross-sectional design, the investigator evaluates the results and exposures in the research participants at the same time (Setia, 2016; Saunders et al., 2009,), which is exactly what happened in this study. The cross-sectional technique is thus perfect for this study because the primary purpose of this research is to identify a wide trend in investor attitudes about the stock market. The cross-sectional method is best suited for this study since the data was gathered in steps rather than over a set time period. In essence, the cross-sectional design involves the employment of several research methodologies and is suited for this study since it permits the collection of largely quantitative data.
3.7. Data Collection Method
Among the various data collection strategies available, such as unstructured interviews, semi-structured interviews, structured interviews, observation, self-completion questionnaires, group discussions, and so on, the self-completion approach was chosen to collect quantitative data for this analysis. One of the most common forms of data gathering tools in quantitative research is the data collecting tool.
3.8. Design of Measurements and Questionnaire
There are three sections to the questionnaire: The Respondents' Biographical Information is covered in Part-1. The emotional intelligence of an investor is assessed in Part-2. Part-3 examines the investment decisions of Indian investors. Both nominal and ordinal measures are used in Part-1. Nominal scales are used to classify items, while ordinal scales are needed for both categorization and evaluations of objects or observations (Ghauri & Gronhaug, 2010). Table 1 lists the measurement forms that were used for this section.
Table 1: Shows the different types of measurements were used in Section A of the questionnaire.
Part-1: Respondents Biographical Information
|
Types of Measurements
|
Classifying: Gender, Marital status and Occupation
|
Nominal scale
|
Order of Age, Educational level, Years of Investing, Income Range
|
Ordinal scale
|
Part-2; of the questionnaire analyzes Indian investors' emotional intelligence using Goleman's four dimensions (1998) and _______ (add here one more author should be added) measured on a 5-point Likert scale, they're all rated. The questionnaires of part-3; analyses Indian investors' investment habits. The Prospect theory, heuristic theory, and other theories on the influence of behavioural aspects on investor decision making, as listed by Waweru et al. (2008,) and various other authors in the area.
3.9. Analyze and Process of Data
The data is processed and analysed using IBM SPSS 23 and IBM SPSS AMOS. The data is first cleaned by removing low-quality questionnaire features like skewed rankings, too many missing values, and outlier observations. The statistical approaches, which include Descriptive Statistics, ANOVA, Cronbach Alpha Reliability Tests, and Structural Equation Model, are then discussed. Descriptive statistics: Descriptive statistics are used to characterize the respondents' personal information (biographical information).
Table 2: Reliability and Validity Test for Emotional Intelligence and investment decisions of retail investors
Item-Total Statistics
|
Emotional Intelligence and investment decisions of retail investors
|
Scale Mean if Item Deleted
|
Scale Variance if Item Deleted
|
Corrected Item-Total Correlation
|
Cronbach's Alpha if Item Deleted
|
SCS
|
88.2047
|
490.037
|
0.905
|
0.967
|
ROE
|
92.7070
|
543.942
|
0.820
|
0.973
|
UOE
|
92.8744
|
566.525
|
0.741
|
0.977
|
SAS
|
88.2209
|
489.660
|
0.902
|
0.968
|
LOA
|
87.7558
|
458.936
|
0.958
|
0.965
|
SQO
|
92.1372
|
497.130
|
0.956
|
0.965
|
EOB
|
92.0070
|
511.457
|
0.968
|
0.965
|
ROA
|
88.4442
|
463.916
|
0.956
|
0.965
|
The above table 2 shows, reliability items result of Cronbach's Alpha (if Item deleted). By applying ALPHA method in SPSS, proved that there is an internal consistency between the items of 8 items in questionnaire related to the Emotional Intelligence and investment decisions of retail investors. The Instrument is analyzed based on the coefficient alpha as a measure of reliability of measurement instruments.
Table 3: Frequency Distribution of Gender of Respondents
Gender
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
Valid
|
Male
|
217
|
50.5
|
50.5
|
50.5
|
Female
|
213
|
49.5
|
49.5
|
100.0
|
Transgender
|
-
|
-
|
-
|
-
|
Total
|
430
|
100.0
|
100.0
|
|
Source: Primary data
The above table 3 shows that 50.5 per cent of the respondents are male and 49.5 per cent of the respondents are female.
Table 4: Frequency Distribution of Age of Respondents
Age
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
Valid
|
11 to 15 years
|
143
|
33.3
|
33.3
|
33.3
|
16 to 22 years
|
47
|
10.9
|
10.9
|
44.2
|
23 to 35 years
|
81
|
18.8
|
18.8
|
63.0
|
36 to 50 years
|
118
|
27.4
|
27.4
|
90.5
|
Above 50 years
|
41
|
9.5
|
9.5
|
100.0
|
Total
|
430
|
100.0
|
100.0
|
|
Source: Primary data
The above table 4 indicates that 33.s3 per cent of the respondents belong to age group between 11 to 15 years, 10.9 per cent of the respondents belong to age group between 16 years and 22 years, 18.8 per cent of the respondents belong to age group of 23 years and 35 years, 27.4 per cent of the respondents belong to age group between 36 years and 50 years and 9.5 per cent of the respondents belong to age group above 50 years.
Table 5: Frequency Distribution of Marital Status of Respondents
Marital Status
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
Valid
|
Married
|
178
|
41.4
|
41.4
|
41.4
|
Un Married
|
194
|
45.1
|
45.1
|
86.5
|
Divorced
|
23
|
5.3
|
5.3
|
91.9
|
Widow
|
1
|
0.2
|
0.2
|
92.1
|
Separated
|
17
|
4.0
|
4.0
|
96.0
|
Widower
|
17
|
4.0
|
4.0
|
100.0
|
Total
|
430
|
100.0
|
100.0
|
|
Source: Primary data
From the above table 5, it is clear that 41.4 per cent of the respondents are married, 45.1 per cent of the respondents are unmarried, 5.3 per cent of the respondents are Divorced, 0.2 per cent of the respondents are Widow, 4.0 per cent of the respondents are Separated and 4.0 per cent of them are Widower.
Table 6: Frequency Distribution of Education of Respondents
Education
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
Valid
|
Up to Metric
|
-
|
-
|
-
|
-
|
Pre-University
|
35
|
8.1
|
8.1
|
8.1
|
Graduation
|
134
|
31.2
|
31.2
|
39.3
|
Post-Graduation
|
242
|
56.3
|
56.3
|
95.6
|
Ph.D.
|
19
|
4.4
|
4.4
|
100.0
|
Uneducated
|
-
|
-
|
-
|
-
|
Total
|
430
|
100.0
|
100.0
|
|
Source: Primary data
The above table 6 proves that 8.1 per cent of the respondents have education of Pre-University, 31.2 per cent of the respondents are have Graduation, 56.3 per cent of the respondents have Post-Graduation and 4.4 per cent of the respondents have Ph.D., and none are uneducated.
Ho1: There is no correlation between emotional intelligence and investment decisions of retail investors
Table 7: Inter Correlation Matrix on the Dimensions of Emotional Intelligence and investment decisions of retail investors
|
SCS
|
ROE
|
UOE
|
SAS
|
LOA
|
SQO
|
EOB
|
ROA
|
SCS
|
1
|
0.828**
|
0.724**
|
0.822**
|
0.877**
|
0.859**
|
0.879**
|
0.872**
|
ROE
|
-
|
1
|
0.766**
|
0.750**
|
0.769**
|
0.782**
|
0.761**
|
0.749**
|
UOE
|
-
|
-
|
1
|
0.772**
|
0.767**
|
0.761**
|
0.876**
|
0.963**
|
SAS
|
-
|
-
|
-
|
1
|
0.863**
|
0.859**
|
0.881**
|
0.896**
|
LOA
|
-
|
-
|
-
|
-
|
1
|
0.986**
|
0.983**
|
0.963**
|
SQO
|
-
|
-
|
-
|
-
|
-
|
1
|
0.975**
|
0.959**
|
EOB
|
-
|
-
|
-
|
-
|
-
|
-
|
-
|
0.984**
|
ROA
|
-
|
-
|
-
|
-
|
-
|
-
|
-
|
1
|
**. Correlation is significant at the 0.01 level (2-tailed)
From table 7, based on the outcomes of correlation, it is clear that the outcome variables are highly and positively correlated with the other variables. Self-Care (SCS) positively influences Endowment (EOB) highly. Regulation of Emotion (ROE) are highly positively related with Status Quo (SQO). Use of Emotion or Motivation (UOE) positively influences Regret Aversion (ROA) highly. Social Aptitudes (SAS) positively focus on Regret Aversion (ROA). Loss Aversion (LOA) positively and highly impacts on Status Quo (SQO). Endowment (EOB) highly influences Regret Aversion (ROA) positively.
Ho21: There is no impact of Self-Care bias on accounting information of retail investors
Table 8: ANOVA for significant difference between Self-Care bias on accounting information of retail investors
ANOVA
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
SCS1
|
Between Groups
|
0.570
|
2
|
0.285
|
0.195
|
0.822
|
Within Groups
|
622.827
|
427
|
1.459
|
|
|
Total
|
623.398
|
429
|
|
|
|
SCS2
|
Between Groups
|
0.603
|
2
|
0.301
|
0.295
|
0.745
|
Within Groups
|
436.783
|
427
|
1.023
|
|
|
Total
|
437.386
|
429
|
|
|
|
SCS3
|
Between Groups
|
6.034
|
2
|
3.017
|
2.574
|
0.077
|
Within Groups
|
500.534
|
427
|
1.172
|
|
|
Total
|
506.567
|
429
|
|
|
|
SCS4
|
Between Groups
|
3.071
|
2
|
1.536
|
1.379
|
0.253
|
Within Groups
|
475.403
|
427
|
1.113
|
|
|
Total
|
478.474
|
429
|
|
|
|
Source: Statistically analyzed data
Since P value is greater than 0.05, the null hypothesis is accepted and thus there is no significant difference between Self-Care bias on accounting information of retail investors.
Since P value is less than 0.01, the null hypothesis is rejected at 1 percent level of significance. Hence there is significant difference between Self-Care bias on accounting information of retail investors.
H022: There is no impact of Self-Care bias on personal needs of retail investors
Table 9: ANOVA for significant difference between Self-Care bias on personal needs of retail investors
ANOVA
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
SCS1
|
Between Groups
|
0.322
|
2
|
0.161
|
0.110
|
0.896
|
Within Groups
|
623.076
|
427
|
1.459
|
|
|
Total
|
623.398
|
429
|
|
|
|
SCS2
|
Between Groups
|
3.394
|
2
|
1.697
|
1.670
|
0.190
|
Within Groups
|
433.992
|
427
|
1.016
|
|
|
Total
|
437.386
|
429
|
|
|
|
SCS3
|
Between Groups
|
30.527
|
2
|
15.264
|
13.691
|
0.000**
|
Within Groups
|
476.040
|
427
|
1.115
|
|
|
Total
|
506.567
|
429
|
|
|
|
SCS4
|
Between Groups
|
0.155
|
2
|
0.077
|
0.069
|
0.933
|
Within Groups
|
478.320
|
427
|
1.120
|
|
|
Total
|
478.474
|
429
|
|
|
|
Source: Statistically analyzed data
Note: **Denotes significance at 1 % level
Since P value is greater than 0.05, the null hypothesis is accepted and thus there is no significant difference between Self-Care bias based on SCS1, SCS2 and SCS4 on personal needs of retail investors.
Since P value is less than 0.05, the null hypothesis is rejected at 5 percent level of significance. Hence there is significant difference between Self-Care bias based on SCS3 on personal needs of retail investors.
H023: There is no impact of Self-Care bias on neutral information of retail investors
Table 10: ANOVA for significant difference between Self-Care bias on neutral information of retail investors
ANOVA
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
SCS1
|
Between Groups
|
3.724
|
2
|
1.862
|
1.283
|
0.278
|
Within Groups
|
619.674
|
427
|
1.451
|
|
|
Total
|
623.398
|
429
|
|
|
|
SCS2
|
Between Groups
|
4.194
|
2
|
2.097
|
2.067
|
0.128
|
Within Groups
|
433.192
|
427
|
1.015
|
|
|
Total
|
437.386
|
429
|
|
|
|
SCS3
|
Between Groups
|
5.897
|
2
|
2.948
|
2.515
|
0.082
|
Within Groups
|
500.671
|
427
|
1.173
|
|
|
Total
|
506.567
|
429
|
|
|
|
SCS4
|
Between Groups
|
4.055
|
2
|
2.028
|
1.825
|
0.162
|
Within Groups
|
474.419
|
427
|
1.111
|
|
|
Total
|
478.474
|
429
|
|
|
|
Source: Statistically analyzed data
Since P value is greater than 0.05, the null hypothesis is accepted and thus there is no significant difference between Self-Care bias based on SCS1, SCS2, SCS3 and SCS4 on neutral information of retail investors.
H031: There is no impact of use of emotions bias on accounting information of retail investors
Table 11: ANOVA for significant difference between use of emotions bias on accounting information of retail investors
ANOVA
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
UOE1
|
Between Groups
|
3.715
|
2
|
1.858
|
1.153
|
0.317
|
Within Groups
|
687.962
|
427
|
1.611
|
|
|
Total
|
691.677
|
429
|
|
|
|
UOE2
|
Between Groups
|
6.859
|
2
|
3.429
|
2.805
|
0.062
|
Within Groups
|
522.127
|
427
|
1.223
|
|
|
Total
|
528.986
|
429
|
|
|
|
UOE3
|
Between Groups
|
8.529
|
2
|
4.264
|
3.899
|
0.021*
|
Within Groups
|
467.015
|
427
|
1.094
|
|
|
Total
|
475.544
|
429
|
|
|
|
Source: Statistically analyzed data
Note: *Denotes significance at 5 % level
Since P value is greater than 0.05, the null hypothesis is accepted and thus there is no significant difference between use of emotions bias of UOE1 and UOE2 on accounting information of retail investors.
Since P value is less than 0.05, the null hypothesis is rejected at 5 percent level of significance. Hence there is significant difference between use of emotions bias based on UOE3 on accounting information of retail investors.
H032: There is no impact of use of emotions bias on personal needs of retail investors
Table 12: ANOVA for significant difference between use of emotions bias on personal needs of retail investors
ANOVA
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
UOE1
|
Between Groups
|
2.413
|
2
|
1.207
|
0.748
|
0.474
|
Within Groups
|
689.263
|
427
|
1.614
|
|
|
Total
|
691.677
|
429
|
|
|
|
UOE2
|
Between Groups
|
7.076
|
2
|
3.538
|
2.895
|
0.056
|
Within Groups
|
521.910
|
427
|
1.222
|
|
|
Total
|
528.986
|
429
|
|
|
|
UOE3
|
Between Groups
|
8.296
|
2
|
4.148
|
3.791
|
0.023*
|
Within Groups
|
467.248
|
427
|
1.094
|
|
|
Total
|
475.544
|
429
|
|
|
|
Source: Statistically analyzed data
Note: *Denotes significance at 5 % level
Since P value is greater than 0.05, the null hypothesis is accepted and thus there is no significant difference between use of emotions bias of UOE1 and UOE2 on personal needs of retail investors.
Since P value is less than 0.05, the null hypothesis is rejected at 5 percent level of significance. Hence there is significant difference between use of emotions bias based on UOE3 on personal needs of retail investors.
H033: There is no impact of use of emotions bias on neutral information of retail investors
Table 13: ANOVA for significant difference between use of emotions bias on neutral information of retail investors
ANOVA
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
UOE1
|
Between Groups
|
2.604
|
2
|
1.302
|
0.807
|
0.447
|
Within Groups
|
689.073
|
427
|
1.614
|
|
|
Total
|
691.677
|
429
|
|
|
|
UOE2
|
Between Groups
|
15.553
|
2
|
7.776
|
6.467
|
0.002**
|
Within Groups
|
513.433
|
427
|
1.202
|
|
|
Total
|
528.986
|
429
|
|
|
|
UOE3
|
Between Groups
|
12.611
|
2
|
6.306
|
5.816
|
0.003**
|
Within Groups
|
462.933
|
427
|
1.084
|
|
|
Total
|
475.544
|
429
|
|
|
|
Source: Statistically analyzed data
Note: **Denotes significance at 1 % level
Since P value is greater than 0.05, the null hypothesis is accepted and thus there is no significant difference between use of emotions bias of UOE1 on neutral information of retail investors.
Since P value is less than 0.01, the null hypothesis is rejected at 1 percent level of significance. Hence there is significant difference between use of emotions bias based on UOE2 and UOE3 on neutral information of retail investors.
H041: There is no impact of Loss Aversion Behavior bias on accounting information of retail investors
Table 14: ANOVA for significant difference between Loss Aversion Behavior bias on accounting information of retail investors
ANOVA
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
LOA1
|
Between Groups
|
0.018
|
2
|
0.009
|
0.009
|
0.992
|
Within Groups
|
447.368
|
427
|
1.048
|
|
|
Total
|
447.386
|
429
|
|
|
|
LOA2
|
Between Groups
|
0.074
|
2
|
0.037
|
0.034
|
0.967
|
Within Groups
|
470.868
|
427
|
1.103
|
|
|
Total
|
470.942
|
429
|
|
|
|
LOA3
|
Between Groups
|
1.854
|
2
|
0.927
|
0.496
|
0.609
|
Within Groups
|
798.053
|
427
|
1.869
|
|
|
Total
|
799.907
|
429
|
|
|
|
LOA4
|
Between Groups
|
0.074
|
2
|
0.037
|
0.034
|
0.967
|
Within Groups
|
470.868
|
427
|
1.103
|
|
|
Total
|
470.942
|
429
|
|
|
|
Source: Statistically analyzed data
Since P value is greater than 0.05, the null hypothesis is accepted and thus there is no significant difference between Loss Aversion Behavior bias of LOA1, LOA2, LOA3 and LOA4 on accounting information of retail investors.
H042: There is no impact of Loss Aversion Behavior bias on personal needs of retail investors
Table 15: ANOVA for significant difference between Loss Aversion Behavior bias on personal needs of retail investors
ANOVA
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
LOA1
|
Between Groups
|
2.505
|
2
|
1.252
|
1.202
|
0.302
|
Within Groups
|
444.881
|
427
|
1.042
|
|
|
Total
|
447.386
|
429
|
|
|
|
LOA2
|
Between Groups
|
1.783
|
2
|
0.891
|
0.811
|
0.445
|
Within Groups
|
469.159
|
427
|
1.099
|
|
|
Total
|
470.942
|
429
|
|
|
|
LOA3
|
Between Groups
|
4.624
|
2
|
2.312
|
1.241
|
0.290
|
Within Groups
|
795.283
|
427
|
1.862
|
|
|
Total
|
799.907
|
429
|
|
|
|
LOA4
|
Between Groups
|
1.783
|
2
|
0.891
|
0.811
|
0.445
|
Within Groups
|
469.159
|
427
|
1.099
|
|
|
Total
|
470.942
|
429
|
|
|
|
Source: Statistically analyzed data
Since P value is greater than 0.05, the null hypothesis is accepted and thus there is no significant difference between Loss Aversion Behavior bias of LOA1, LOA2, LOA3 and LOA4 on accounting information of retail investors.
H043: There is no impact of Loss Aversion Behavior bias on neutral information of retail investors
Table 16: ANOVA for significant difference between Loss Aversion Behavior bias on neutral information of retail investors
ANOVA
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
LOA1
|
Between Groups
|
2.203
|
2
|
1.102
|
1.057
|
0.349
|
Within Groups
|
445.183
|
427
|
1.043
|
|
|
Total
|
447.386
|
429
|
|
|
|
LOA2
|
Between Groups
|
2.063
|
2
|
1.031
|
0.939
|
0.392
|
Within Groups
|
468.879
|
427
|
1.098
|
|
|
Total
|
470.942
|
429
|
|
|
|
LOA3
|
Between Groups
|
5.017
|
2
|
2.509
|
1.348
|
0.261
|
Within Groups
|
794.890
|
427
|
1.862
|
|
|
Total
|
799.907
|
429
|
|
|
|
LOA4
|
Between Groups
|
2.063
|
2
|
1.031
|
0.939
|
0.392
|
Within Groups
|
468.879
|
427
|
1.098
|
|
|
Total
|
470.942
|
429
|
|
|
|
Source: Statistically analyzed data
Since P value is greater than 0.05, the null hypothesis is accepted and thus there is no significant difference between Loss Aversion Behavior bias of LOA1, LOA2, LOA3 and LOA4 on neutral information of retail investors.
H051: There is no impact of Status Quo Behavior bias on accounting information of retail investors
Table 17: ANOVA for significant difference between Status Quo Behavior bias on accounting information of retail investors
ANOVA
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
SQO1
|
Between Groups
|
1.854
|
2
|
.927
|
0.496
|
0.609
|
Within Groups
|
798.053
|
427
|
1.869
|
|
|
Total
|
799.907
|
429
|
|
|
|
SQO2
|
Between Groups
|
0.369
|
2
|
0.185
|
0.158
|
0.854
|
Within Groups
|
498.617
|
427
|
1.168
|
|
|
Total
|
498.986
|
429
|
|
|
|
SQO3
|
Between Groups
|
0.330
|
2
|
0.165
|
0.131
|
0.877
|
Within Groups
|
536.917
|
427
|
1.257
|
|
|
Total
|
537.247
|
429
|
|
|
|
Source: Statistically analyzed data
Since P value is greater than 0.05, the null hypothesis is accepted and thus there is no significant difference between Status Quo Behavior bias of SQO1, SQO2 and SQO3 on accounting information of retail investors.
H052: There is no impact of Status Quo Behavior bias on personal needs of retail investors
Table 18: ANOVA for significant difference between Status Quo Behavior bias on personal needs of retail investors
ANOVA
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
SQO1
|
Between Groups
|
4.624
|
2
|
2.312
|
1.241
|
0.290
|
Within Groups
|
795.283
|
427
|
1.862
|
|
|
Total
|
799.907
|
429
|
|
|
|
SQO2
|
Between Groups
|
3.252
|
2
|
1.626
|
1.401
|
0.248
|
Within Groups
|
495.734
|
427
|
1.161
|
|
|
Total
|
498.986
|
429
|
|
|
|
SQO3
|
Between Groups
|
3.597
|
2
|
1.799
|
1.439
|
0.238
|
Within Groups
|
533.649
|
427
|
1.250
|
|
|
Total
|
537.247
|
429
|
|
|
|
Source: Statistically analyzed data
Since P value is greater than 0.05, the null hypothesis is accepted and thus there is no significant difference between Status Quo Behavior bias of SQO1, SQO2 and SQO3 on personal needs of retail investors.
H053: There is no impact of Status Quo Behavior bias on neutral information of retail investors
Table 19: ANOVA for significant difference between Status Quo Behavior bias on neutral information of retail investors
ANOVA
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
SQO1
|
Between Groups
|
5.017
|
2
|
2.509
|
1.348
|
0.261
|
Within Groups
|
794.890
|
427
|
1.862
|
|
|
Total
|
799.907
|
429
|
|
|
|
SQO2
|
Between Groups
|
1.619
|
2
|
0.809
|
0.695
|
0.500
|
Within Groups
|
497.367
|
427
|
1.165
|
|
|
Total
|
498.986
|
429
|
|
|
|
SQO3
|
Between Groups
|
5.633
|
2
|
2.817
|
2.262
|
0.105
|
Within Groups
|
531.613
|
427
|
1.245
|
|
|
Total
|
537.247
|
429
|
|
|
|
Source: Statistically analyzed data
Since P value is greater than 0.05, the null hypothesis is accepted and thus there is no significant difference between Status Quo Behavior bias of SQO1, SQO2 and SQO3 on neutral information of retail investors.
H061: There is no impact of Regret Aversion bias on accounting information of retail investors
Table 20: ANOVA for significant difference between Regret Aversion bias on accounting information of retail investors
ANOVA
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
ROA1
|
Between Groups
|
0.085
|
2
|
0.043
|
0.042
|
0.959
|
Within Groups
|
434.959
|
427
|
1.019
|
|
|
Total
|
435.044
|
429
|
|
|
|
ROA2
|
Between Groups
|
0.594
|
2
|
0.297
|
0.382
|
0.683
|
Within Groups
|
331.834
|
427
|
0.777
|
|
|
Total
|
332.428
|
429
|
|
|
|
ROA3
|
Between Groups
|
2.400
|
2
|
1.200
|
0.583
|
0.559
|
Within Groups
|
878.542
|
427
|
2.057
|
|
|
Total
|
880.942
|
429
|
|
|
|
ROA4
|
Between Groups
|
0.388
|
2
|
0.194
|
0.158
|
0.854
|
Within Groups
|
525.789
|
427
|
1.231
|
|
|
Total
|
526.177
|
429
|
|
|
|
Source: Statistically analyzed data
Since P value is greater than 0.05, the null hypothesis is accepted and thus there is no significant difference between Loss Aversion Behavior bias of ROA1, ROA2, ROA3 and ROA4 on accounting information of retail investors.
Ho62: There is no impact of Regret Aversion bias on personal needs of retail investors
Table 21: ANOVA for significant difference between Regret Aversion bias on personal needs of retail investors
ANOVA
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
ROA1
|
Between Groups
|
2.264
|
2
|
1.132
|
1.117
|
0.328
|
Within Groups
|
432.780
|
427
|
1.014
|
|
|
Total
|
435.044
|
429
|
|
|
|
ROA2
|
Between Groups
|
0.269
|
2
|
0.134
|
0.173
|
0.842
|
Within Groups
|
332.159
|
427
|
0.778
|
|
|
Total
|
332.428
|
429
|
|
|
|
ROA3
|
Between Groups
|
3.528
|
2
|
1.764
|
0.858
|
0.425
|
Within Groups
|
877.414
|
427
|
2.055
|
|
|
Total
|
880.942
|
429
|
|
|
|
ROA4
|
Between Groups
|
1.969
|
2
|
0.984
|
0.802
|
0.449
|
Within Groups
|
524.208
|
427
|
1.228
|
|
|
Total
|
526.177
|
429
|
|
|
|
Source: Statistically analyzed data
Since P value is greater than 0.05, the null hypothesis is accepted and thus there is no significant difference between Loss Aversion Behavior bias of ROA1, ROA2, ROA3 and ROA4 on personal needs of retail investors.
H063: There is no impact of Regret Aversion bias on neutral information of retail investors.
Table 22: ANOVA for significant difference between Regret Aversion bias on neutral information of retail investors
ANOVA
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
ROA1
|
Between Groups
|
3.586
|
2
|
1.793
|
1.775
|
0.171
|
Within Groups
|
431.458
|
427
|
1.010
|
|
|
Total
|
435.044
|
429
|
|
|
|
ROA2
|
Between Groups
|
1.532
|
2
|
0.766
|
0.989
|
0.373
|
Within Groups
|
330.896
|
427
|
0.775
|
|
|
Total
|
332.428
|
429
|
|
|
|
ROA3
|
Between Groups
|
6.374
|
2
|
3.187
|
1.556
|
0.212
|
Within Groups
|
874.568
|
427
|
2.048
|
|
|
Total
|
880.942
|
429
|
|
|
|
ROA4
|
Between Groups
|
1.396
|
2
|
0.698
|
0.568
|
0.567
|
Within Groups
|
524.781
|
427
|
1.229
|
|
|
Total
|
526.177
|
429
|
|
|
|
Source: Statistically analyzed data
Since P value is greater than 0.05, the null hypothesis is accepted and thus there is no significant difference between Loss Aversion Behavior bias of ROA1, ROA2, ROA3 and ROA4 on neutral information of retail investors.
[1] https://tradebrains.in/biggest-stockbrokers-india-with-highest-clients/
https://cxl.com/blog/survey-response-scales/