This is a research paper on investment portfolio analysis

1. Developing an Investment Decision Making Model with Investors’
Behavioral Biases in relation to their Personality Traits
Presented by –
Joyita Banerji
MBA162604 (13/2/17)
Under the Guidance of:
Kaushik Kundu
1

2. 2

3. Introduction
3

4. Exploratory Review
Stock Market Anomalies
Economic Model of Decision-
Making
Deviations from
Rationality
Bounded
Rationality
Prospect Theory,
Heuristics and
Biases
4

5. Principal Research Statement
“To explore the probable nature of the relationship between
an individual’s behavioral biases and their personality
traits as per the five-factor model, and develop an
investment decision making model on the basis of such
relationship.”
5

6. Research Queries
• Research Query 1: Previously, what attempts have been made to develop a predictive model of investment
decision-making?
• Research Query 2: How have the impact of behavioral biases on investment decision-making been studied in
the past?
• Research Query 3: How have previous studies attempted to investigate the relationship between personality
traits and decision making?
• Research Query 4: What kind of attempts have been made to relate the behavioral biases with the personality
traits of the individuals?
6

7. Prospect Theory, Heuristics and biases
Behavioral Biases in decision-making
Individual differences in decision-
making
Is there any relationship between the
biases?
Can biases be related to individual
differences?
Can these associations be used to
predict investment behavior?
Literature Review
Research Inquiries
7

8. Research Gap
Systematic
deviations from
rationality
• Factors apart from rationality that can affect
the decision-making among individuals.
Relationship
between the biases
•Measurement of biases that affect decision-
making.
Personality traits
affect decisions
•Individual differences affecting decision-
making.
8

9. Objective 1:
To ideate about the factors apart
from rationality that systematically
affect investment decisions and
study their probable associations.
To identify the inadequacies of
traditional models to explain
investment decision-making.
To identify the behavioral biases
associated with their investment
decisions from extant literature.
To propose a categorization of biases
based on review of literature related
to the investment decisions of the
individual.
9

10. Objective 2:
To conceptualise the relationship of
personality with an individual’s
investment decisions.
To identify the suitable model of
personality that may be applicable to
relate to the investment decisions of
the individual.
To explore the possible relationship
between personality variables of the
identified model and behavioral
biases.
To investigate the relationship of the
personality trait variables with
investment decisions of individuals.
10

11. Objective 3:
To construct a framework for the
antecedent variables of the
investment decisions of individuals.
To design a questionnaire that
measure identified behavioral bias
variables of individuals through
survey.
To check the reliability and validity
of the behavioral bias items,
personality items and combined
questionnaire.
To categorise the behavioral biases
and identify the different
personality types depending on
investment decisions.
11

12. Objective 4:
To develop behavioral profiles of
investment decisions based on the
identified relationships of behavioral
biases and personality of the individual.
To explore the link between the
validated models of behavioral biases
and personality among the individual
investors of Kolkata.
To suggest profiles that can predict
investor behavior in the real financial
markets.
12

13. Behavioral
Biases
13

14. Behavi
oral
Biases
Contd.
14

15. Conceptual Model:
15

16. Design of Research:
16

17. Design of research contd.
17

18. Steps of Questionnaire Development
18
Step 1
• Developing
statements
for
behavioral
bias
Step 2
• Developing
statements
for
personality
traits
Step 3
• Selection of
the most
appropriate
statements
based on
interrater
agreement
measured
through Kappa
Step 4
• Coding of
statements for
final
questionnaire.
Step 5
• Scaling
and
jumbling
up of
coded
items in
the final
questionn
aire

19. Sampling Design
The study is focused on the
investment decisions of individuals.
The population are the individual
investors of India.
SEBI reports provide state-wise break-
up of the individual investors in India.
The Economic Survey gives the age
demographic of the population.
Maximum working-age population is
concentrated in Uttar Pradesh, Bihar,
West Bengal, Maharashtra (above 50
Mn each in 2021).
Kolkata is the largest investment
Centre in the Eastern Region and the
Central Province of India, as well as
having highest foundational literacy
and is thus chosen as the location for
the survey.
Kolkata has approx. 50 lac direct
investors on BSE, making it the 5th
highest number of registered
individual investors. Final sampling
frame chosen for the study belongs to
the urban, financially aware graduates,
mostly between the ages of 18-30
years.
Systematic random sampling was
chosen as the one with least chance of
bias.
The major educational institutions
based on student strength was chosen
from the largest university in the state,
University of Calcutta. Among the
affiliated institutions, St. Xavier's
College, Rashbehari Shiksha Prangan,
University of Calcutta were chosen for
the study.
Cochran's formula gives the sample size
required as 384 for the population
A random number generator was used
to generate the interval. The
researcher held one session each with
the selected educational institutions.
The total no. of respondents added up
to 550. The responses were collected
by the researcher through group-
administered survey on 3 different
days to the identified respondents
across the mentioned educational
institutions.
Survey responses were then encoded
on the SPSS software for further
analysis.
19

20. Respondent Profile
381
165
4
0
50
100
150
200
250
300
350
400
450
Graduate Post Graduate Professional
Education levels of Respondents
481
66
3
0
100
200
300
400
500
600
18-30 years 31-45 years 46-60 years
Age of Respondents
20

21. 115
435
0
50
100
150
200
250
300
350
400
450
500
F M
Gender of Respondents
Male Female
Total No. of respondents 435 115
Undergraduate 373 8
Postgraduate 59 106
Professional 3 1
18-30 years 427 54
31-45 years 6 60
46-60 years 2 1
Respondent Profile contd.
21

22. Reliability, Adequacy and EFA results of biases
Cronbach's
Alpha
Cronbach's
Alpha Based
on
Standardized
Items
N of
Items
.703 .710 22
Comp
onent
Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulati
ve % Total
% of
Variance
Cumulat
ive % Total
% of
Variance
Cumulat
ive %
1 3.615 24.101 24.101 3.615 24.101 24.101 3.573 23.822 23.822
2 3.191 21.274 45.376 3.191 21.274 45.376 3.214 21.425 45.247
3 2.045 13.631 59.007 2.045 13.631 59.007 2.064 13.760 59.007
4 .847 5.644 64.651
5 .744 4.961 69.612
6 .718 4.784 74.396
7 .641 4.276 78.672
8 .544 3.626 82.298
9 .535 3.564 85.862
10 .446 2.976 88.838
11 .419 2.795 91.633
12 .378 2.520 94.153
13 .336 2.243 96.396
14 .305 2.033 98.430
15 .236 1.570 100.000
22

23. Component
1 2 3
OC3r My other friends are far superior to me when it comes to taking a
decision in a complex situation
.853
OC1 Most of the time my decisions are far superior to others .822
R3 The perfect outcome of any event can only be predicted when the latest
information is used, ignoring past history
.774
R1r Past history is important to predict the future, thus the old information is
very important to make the right decisions.
.766
AN2 It is always better to rely on some information as a base to take future
decisions
.735
CB2 Information that does not support my expectations always irritates me .653
SQ2 I dislike any kind of changes in my lifestyle, irrespective of the benefits .821
RA3 I utterly dislike to get involved in any situation with the slightest
chance of danger or risk
.816
RA1r I enjoy the challenges of dealing with uncertain and risky situations .805
EE3 I am unwilling to sell off my family heirlooms even if I face financial
crises
.797
LA1 When I make decisions, I always worry more about losses .739
GF2 There is no true randomness in real life all events are interrelated .805
HM2 I feel more confident about my choice in risky situations if I have been
successful in my previous trials
.700
HD1 I always accept group decisions even when I realize they are wrong
HN1 Outcomes of any decisions always seem obvious and predictable after
have occurred
.673
.670
SQ3 I always stick to the existing conditions even when a change would
have more benefits
LA2 I do not like decisions that have even a minimal chance of making
losses
EE2 I find it difficult to dispose of items that have sentimental value for me
CB1 I only search for information that ultimately supports my ideas
AN1 While making decisions, I always set clear expectations of what the
outcomes should be
GF3 If it did not rain for 3 consecutive days during the monsoon I would
always
HN3r Correct prediction of any complex event is always a chance of luck
23

24. Data analysis for personality traits
Total Variance Explained
Compo
nent
Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulat
ive % Total
% of
Variance
Cumulat
ive % Total
% of
Variance
Cumulat
ive %
1 2.621 29.117 29.117 2.621 29.117 29.117 2.619 29.105 29.105
2 2.158 23.983 53.101 2.158 23.983 53.101 2.129 23.657 52.762
3 1.494 16.600 69.700 1.494 16.600 69.700 1.524 16.938 69.700
4 .604 6.716 76.417
5 .530 5.891 82.308
6 .518 5.755 88.063
7 .455 5.056 93.119
8 .357 3.970 97.089
9 .262 2.911 100.000
Extraction Method: Principal Component Analysis.
24

25. Component
1 2 3
Agree1 I am a very compassionate type of person .850
Cons3 I lead a highly disciplined life and maintain a rigorous daily routine .807
Open3 I am always enthusiastic about new, experimental ideas .795
Neuro3 I often become agitated for no apparent reason .780
Neuro1 I always feel nervous before starting a new job .900
Extra1 I like to spend my leisure time in company of other people .847
Open2r I cannot stand complex intellectual decisions .764
Cons1 I cannot tolerate casual persons who do their jobs shabbily .862
Extra3 I am a fun loving happy go lucky type person .858
Extraction Method: Principal Component Analysis.
Data analysis for personality traits contd.
25

26. Bias profiles identified and named from EFA
26
Behavioural
biases
Presumptuous
justifier
Emotional
misoneist
Nostalgic
collectivist
Personality
traits
Solicitous
disciplinarian
High-strung
sociable
Industrious
convivial

27. Presumptuous
justifier
Overconfidence
My other friends are far
superior to me when it
comes to taking a decision
in a complex situation.
Most of the time my
decisions are far superior
to others.
Recency
The perfect outcome of any
event can only be
predicted when the latest
information is used,
ignoring past history.
Past history is important to
predict the future, thus the
old information is very
important to make the
right decisions.
Anchoring
It is always better to rely on
some information as a base
to take future decisions.
Confirmation
Information that does not
support my expectations
always irritates me.
Bias profile
27

28. Emotional
misoneist
Status Quo
I dislike any kind of
changes in my lifestyle,
irrespective of the
benefits.
Risk Aversion
I utterly dislike to get
involved in any situation
with the slightest chance
of danger or risk.
I enjoy the challenges of
dealing with uncertain
and risky assets.
Endowment Effect
I am unwilling to sell off
my family heirlooms even
if I face financial crises.
Loss Aversion
When I make decisions, I
always worry more about
losses.
Bias profile
28

29. Nostalgic
collectivist
Gambler’s Fallacy
There is no true
randomness in real life all
events are interrelated.
House Money
I feel more confident about
my choice in risky
situations if I have been
successful in my previous
trials.
Herding
I always accept group
decisions even when I
realise they are wrong.
Hindsight
Outcomes of any decisions
always seem obvious and
predictable after they have
occurred.
Bias profile
29

30. Solicitous
disciplinarian
Agreeableness
I am a very compassionate
type of person.
Conscientiousness
I lead a highly disciplined
life and maintain a rigorous
daily routine.
Openness to
Experience
I am always enthusiastic
about new, experimental
ideas.
Neuroticism
I often become agitated for
no apparent reason.
Personality profile
30

31. High-strung sociable
Neuroticism
I always feel nervous before starting
a new job.
Extraversion
I like to spend my leisure time in
company of other people.
Less open to Experience
I cannot stand complex intellectual
decisions.
Personality profile
31

32. Industrious
convivial
Conscientiousness
I cannot tolerate casual persons who do their jobs
shabbily.
Extraversion
I am a fun-loving happy go lucky type of person.
Personality profile
32

33. Profile attributes
33

34. Model NPAR CMIN DF P CMIN/DF
Default
model
33 446.604 102 0 4.378
Saturated
model
135 0 0
Independen
ce model
15 3102.494 120 0 25.854
NFI RFI IFI TLI
Delta1 rho1 Delta2 rho2
Default
model
0.876 0.831 0.885 0.864 0.884
Saturated
model
1 1 1
Independen
ce model
0 0 0 0 0
Model PRATIO PNFI PCFI
Default
model
0.85 0.728 0.752
Saturated
model
0 0 0
Independen
ce model
1 0 0
Model RMSEA LO 90 HI 90 PCLOSE
Default
model
0.078 0.071 0.086 0
Independen
ce model
0.213 0.206 0.219 0
CMIN
Baseline Comparisons
Model CFI
Parsimony-Adjusted Measures
RMSEA
Validity test results of behavioral biases
34
Construct Variables Items
Std.
Loadings AVE CR
Sq. Correlation of Constructs
Factor 1 Factor 2 Factor 3
Presumptuous Justifier OC3r 0.78 0.524 0.869 1 .483 .498
OC1 0.71
R1r 0.72
R3 0.74
CB2 0.7
AN2 0.69
Emotional misoneist SQ2 0.76 0.542 0.855 .483 1 .493
EE3 0.76
RA1r 0.69
RA3 0.73
LA1 0.74
Nostalgic collectivist HM2 0.58 0.512 0.801 .498 .493 1
HN1 0.6
HD1 0.56
GF2 0.69

35. Validated model of
behavioral biases
35

36. Model NPAR CMIN DF P CMIN/DF
Default
model
21 91.341 33 0 2.768
Saturated
model
54 0 0
Independen
ce model
9 1542.98 45 0 34.288
NFI RFI IFI TLI
Delta1 rho1 Delta2 rho2
Default
model
0.941 0.919 0.961 0.947 0.961
Saturated
model
1 1 1
Independen
ce model
0 0 0 0 0
Model PRATIO PNFI PCFI
Default
model
0.733 0.69 0.705
Saturated
model
0 0 0
Independen
ce model
1 0 0
Model RMSEA LO 90 HI 90 PCLOSE
Default
model
0.057 0.043 0.071 0.198
Independen
ce model
0.246 0.236 0.257 0
CMIN
Baseline Comparisons
Model CFI
Parsimony-Adjusted Measures
RMSEA
Factor A Factor B Factor C
Industrious
convivial
Extra3 0.75 0.501 0.665 1 0.45 .44
Cons1 0.66
High-strung
sociable
Neuro 1 0.85 0.604 0.819 0.45 1 0.44
Extra1 0.8
Open2r 0.67
Solicitous
disciplinari
an
Agree1 0.82 0.548 0.828 0.44 .44 1
Cons3 0.77
Neuro3 0.67
Open3 0.69
Construct
Variables
Items
Std.
Loadings
AVE CR
Sq.Correlation of Constructs
Validity test results of personality traits
36

37. Validated model of
personality traits
37

38. SEM: Interrelationship
between behavioral
biases and personality
38

39. Model NPAR CMIN DF P
CMIN/D
F
Default
model
34 1178.43 291 0 4.05
Saturated
model
325 0 0
Independen
ce model
25 6643.93 300 0 22.146
NFI RFI IFI TLI
Delta1 rho1 Delta2 rho2
Default
model
0.923 0.917 0.96 0.956 0.96
Saturated
model
1 1 1
Independen
ce model
0 0 0 0 0
Model PRATIO PNFI PCFI
Default
model
0.97 0.898 0.934
Saturated
model
0 0 0
Independen
ce model
1 0 0
Model RMSEA LO 90 HI 90 PCLOSE
Default
model
0.075 0.07 0.079 0
Independen
ce model
0.196 0.192 0.2 0
CMIN
Baseline Comparisons
Model CFI
Parsimony-Adjusted Measures
RMSEA
39
Fit indices
of the
interrelatio
nship
between
behavioral
biases and
personality
traits

40. Final Behavioral Profile
40
Easy-going
presumptuous justifier
• Seeks supportive
information
• Reliant on arbitrary
anchors
• Overweighs recent
information
• Dislikes casual approach
to work
• Fun-loving
• Self-assured
Nervous fixated
sentimentalist
• Avoidant of change
• Dislikes challenges
• Sentimental attachment
to owned items
• Frets about losses
• Obstinate
• Social
• Skittish
Thrill-seeking agitated
gambler
• Harbours creeping
determinism
• Adherent
• Influenced by past,
unrelated successes
• Believes in universal
connection
• Compassionate
• Regimented
• Novelty-seeking
• Tremulous

41. Research objectives Research findings
Objective 1:
To ideate about the factors apart from rationality that systematically relate
to investment decisions and study their probable associations.
Sub-objective:
i. To identify the inadequacies of traditional models to explain investment
decision-making.
ii. To identify the behavioral biases of the individuals associated with their
investment decisions from extant literature.
iii. To propose a categorization of biases based on review of literature
related to the investment decisions of the individual.
i. The development of traditional economic theories and their drawbacks
were traced through literature review.
ii. The various behavioral biases were studied from the point of their
origin.
iii. The conceptual framework regarding the interrelationships between
biases was proposed.
Objective 2:
To conceptualise the relationship of personality with individual’s investment
decisions.
Sub-objective:
i. To identify the suitable model of personality that may be applicable to
relate to the investment decisions of the individual.
ii. To explore the possible relationship between personality variables of the
identified model and behavioral biases.
iii. To investigate the relationship of the personality trait variables with
investment decisions of individuals.
i. Literature from the field of personality psychology was reviewed to
comprehend how personality shapes decisions.
ii. Major models of personality based on the self-assessment were studied
and the five-factor model was found to be most appropriate as a
measure of personality.
iii. A conceptual model for the possible link between behavioral biases and
personality traits was proposed.
41

42. Research objectives Research findings
Objective 3:
To construct a framework for the antecedent variables of the investment
decisions of individuals.
Sub-objective:
i. To design a questionnaire to measure identified behavioral bias variables
of individuals through survey.
ii. To check the reliability and validity of the behavioral bias items,
personality items and combined questionnaire.
iii. To categorise the behavioral biases and identify the different personality
types depending on investment decisions.
i. The important features of each of the biases and personality traits were
traced from the literature and each item of the instrument was
developed from the same.
ii. Pilot study was carried out and the Cronbach’s alpha was used to identify
items that have reliability above cut-off criteria.
iii. Exploratory factor analysis was used to group the biases and identify the
personalities of the individuals. These factors were validated through
path analysis.
Objective 4:
To develop behavioral profiles of investment decisions based on identified
relationships of behavioral biases and personality of an individual.
Sub-objective:
i. To explore the link between validated models of behavioral biases and
personality among the individual investors of Kolkata.
ii. To suggest profiles that can predict investor behavior in the real financial
markets.
i. Correlation analysis was used to ascertain the association between the
biases and the traits of an individual. SEM was used to validate the links
between the behavioral biases.
ii. Based on the results of the SEM, profiles inclusive of behavioral biases
and personality traits of the investor were proposed.
42

43. Contribution to Academic Body
43
• The study provides a new framework for classifying biases
• It measures the influence of biases on investment decisions
• It links an individual’s personality with biases
• It measures the impact of personality on investment
decisions

44. Practical implications
44
• Increased self-awareness among individual investors
regarding potential investors
• Help the financial advisors in educating their clients about
potential mistakes
• Profiles may be used to provide advice that can counteract
the probable mistakes clients may make
• Professional investors may also learn about how their
personality affects investment decisions.

45. Future Scope of Study
• Replication in different urban areas
• Experiments on financial behavior
• Inclusion of other variables such as age, gender, experience
etc.
45

46. Thank You
46