Research work on behavioral finance presented at National conference at Dyal Singh College,University of Delhi

1. “Behavioral Finance”
At
National Conference, NCBME 2019,
Dyal Singh College, University of Delhi
Thursday, April 25th, 2019
By
Saurabh Agarwal
Ph D (FMS, Delhi University), M. Phil. (DSE, DU), M. Com. (DSE, DU),
B. Com. (H) (SRCC), UGC (NET), AMT (AIMA)
Professor of Accounting & Finance, IIF
Member, ESIC, MOLE, Government of India
Member, Governing Body, CBWE, MOLE, Government of India
Member, Managing Committee, ASSOCHAM

2. 2
Why Behavioral Finance?
•The Royal Swedish Academy of Sciences in Stockholm
announced that Richard H. Thaler, who teaches at the Booth
School of Business at the University of Chicago, had won the
2017 prize in economics “for his contributions to behavioral
economics.”
•Thaler “has incorporated psychologically realistic
assumptions into analyses of economic decision-making. By
exploring the consequences of limited rationality, social
preferences, and lack of self-control, he has shown how these
human traits systematically affect individual decisions as well
as market outcomes.”

3. 3

4. 4
Why Behavioral Finance?
•Thaler is perhaps best-known for his popular book about
choices: Nudge: Improving Decisions about Health, Wealth,
and Happiness — how we make them and what we can do to
improve how we make them.
•His “nudge” theory is also credited with inspiring former UK
prime minister David Cameron’s Behavioural Insights Team
(BIT) — or “Nudge Unit.”
•Former US president Barack Obama also officially adopted
the “nudge” approach when he created the Social and
Behavioral Science Team (SBST), which sought to integrate
behavioral science research into policy making.

5. 5
Why Behavioral Finance?
•Cowen also noted that “perhaps unknown to many,
Thaler’s most heavily cited piece is on whether the stock
market overreacts for psychological reasons.”
•In his prescient conclusion to the 1999 piece, “The End
of Behavioral Finance,” he wrote:
“Behavioral finance is no longer as controversial a subject as it once was. As
financial economists become accustomed to thinking about the role of
human behavior in driving stock prices, people will look back at the articles
published in the past 15 years and wonder what the fuss was about. I
predict that in the not-too-distant future, the term ‘behavioral finance’ will
be correctly viewed as a redundant phrase. What other kind of finance is
there? In their enlightenment, economists will routinely incorporate as much
‘behavior’ into their models as they observe in the real world. After all, to do
otherwise would be irrational.”

6. 6
Why Behavioral Finance?
•In “Theoretical Foundations I,” published in June 2001, Thaler explored
the equity risk premium. According to the article’s abstract:
•“One of the puzzles about the equity risk premium is that in the U.S.
market, the premium has historically been much greater than standard
finance theory would predict. The cause may lie in the mismatch between
the actual asset allocation decisions of investors and their forecasts for the
equity risk premium. In this review of the theoretical explanations for this
puzzle, two questions are paramount: (1) How well does the explanatory
theory explain the data? (2) Are the behavioral assumptions consistent with
experimental and other evidence about actual behavior? The answers to
both questions support the theory of ‘myopic loss aversion’ — in which
investors are excessively concerned about short-term losses and exhibit
willingness to bear risk based on their most recent market experiences.”

7. 7
Why Behavioral Finance?
•Nobel Lecture: Richard Thaler, The Sveriges
Riksbank Prize in Economic Sciences
•https://www.youtube.com/watch?v=ej6cygeB2X0

8. I. Important Themes of Portfolio Selection
Goal Based
Portfolio
Creation
Equity Market
Analysis
Equity
Characteristics
Theories and
Models
The
Behavioural
Underpinnings
The
Mechanics of
Portfolio
Selection
Questionnaires
for
understanding
investors and
experts
Understanding
Portfolio
Selection
Equity
Portfolio
Creation
Portfolio
Selection
8

9. I. Approach for Understanding This Thesis
Empirical Observations :
Analysis and
Interpretation of
Questionnaire for Retail
Investor
Questionnaire for
Expert Opinion
Empirical Results
Factor Analysis
Hypotheses Testing
Investment Portfolios
using Goal Programming
9

10. I. Raison d'être of the study
 Behaviour, priorities and needs of individual investors
Investment Portfolios
Multiple Objective
Optimisation
Understanding Experts
and Investors
10

11.  Research work analysing the effect of demographics
like age, family responsibility, occupation,
qualification and annual income on investment goals
often give contradictory results and are based on small
sample size
I. Problem Statement
11

12. I. Research Objectives
 To identify the multiple goals pursued by investors
Sub-Objectives
 To understand and investigate the relationship
between portfolio goals, portfolio constraints,
macroeconomic factors, market capitalisation &
individual investor’s demographics
 To collect and analyse the opinions of practitioners
and renowned academic experts on select portfolio
management issues
12

13. Hypothesis 1 (H1): The hypothesis focuses on the relationship
between the professional level of the investor and gain sought
from portfolio
H0: Gain sought from portfolio is independent of
professional level
HA: Gain sought from portfolio is not independent of
professional level
I. Research Hypotheses
13

14. Hypothesis 2 (H2): The hypothesis focuses on the relationship between the
portfolio goals of the investor and demographics (age, annual income and
family responsibility)
Sub-Hypothesis A. Portfolio Goals and Age
H0: Portfolio goals are independent of investor’s age
HA: Portfolio goals are not independent of investor’s age
Sub-Hypothesis B. Portfolio Goals and Annual Income
H0: Portfolio goals are independent of investor’s annual
income
HA: Portfolio goals are not independent of investor’s annual
income
Sub-Hypothesis C Portfolio Goals and Family Responsibility
H0: Portfolio goals are independent of the investor’s
family responsibility
HA: Portfolio goals are not independent of the investor’s
family responsibility
I. Research Hypotheses
14

15. Hypothesis 3 (H3): The hypothesis focuses on the relationship between the
portfolio constraints of the investor and demographics (occupation, annual income
and family responsibility)
Sub-Hypothesis A. Portfolio Constraints and Occupation
H0: Portfolio constraints are independent of investor’s occupation
HA: Portfolio constraints are not independent of investor’s
occupation
Sub-Hypothesis B. Portfolio Constraints and Annual Income
H0: Portfolio constraints are independent of investor’s annual
income
HA: Portfolio constraints are not independent of investor’s annual
income
Sub-Hypothesis C. Portfolio Constraints and Family Responsibility
H0: Portfolio constraints are independent of investor’s family
responsibility
HA: Portfolio constraints are not independent of investor’s family
responsibility
I. Research Hypotheses
15

16. Hypothesis 4 (H4): The hypothesis focuses on the relationship between
the qualification of the investor and macroeconomic factor considered
for timing selection of equities for inclusion in portfolio
H0: Macroeconomic factor considered for timing selection of
equities in portfolio is independent of investor’s qualification
HA: Macroeconomic factor considered for timing selection of
equities in portfolio is not independent of investor’s qualification
Hypothesis 5 (H5): The hypothesis focuses on the relationship between
the market capitalisation and annual income of the investor
H0: Market capitalisation of companies included in portfolio is
independent of the annual income of the investor
HA: Market capitalisation of companies included in portfolio is
not independent of the annual income of the investor
I. Research Hypotheses
16

17. I. Sources of Data
Primary Data
Questionnaire for
Retail Investors
512 Respondents
Questionnaire for
Expert Opinion
5 Respondents
3 Industry Experts
2 Academic Experts17

18. Covered more than 150 research papers and 20 Doctoral
Dissertations
 Thematic Overview
 Review of International Studies
 Review of Indian Studies
III. Review of Existing Literature
18

19. IV. Research Methodology
Questionnaire for Retail Investor
Sampling Design
Responses analysed using
a. Percentage and mean-standard
deviation
b. Factor Analysis
c. Contingency analysis [Chi-Square Test
( ) of Independence]
Questionnaire for Expert
Opinion
Qualitatively Analysed
2

19

20. IV. Questionnaire for Retail Investor (QRI)
 The study is based on the primary data collected from
the retail investors above the age of 18 years and
having demat account with a portfolio of equities
 Total Sample size: 512 (309-203) Respondents
 From 668 (354-314) questionnaires collected, we
have used 512 questionnaires for the purpose of
analysis in our research work
20

21. IV. Questionnaire for Expert Opinion (QEO)
 Thirteen Open Ended Questions
 Responses collected from prominent experts in the field of
wealth management
 Responses Analysed Qualitatively
21

22. IV. Factor Analysis
 This multivariate statistical technique has been used for the purpose of data
reduction and summarisation in question number 9 of section II of the
questionnaire for retail investor
 We have used “R” factor analysis
 Kaiser-Meyer-Olkin (KMO) and Bartlett’s test has been used for checking
the suitability of factor analysis for this question
 As the KMO value is more than 0.5, so it is appropriate to run factor
analysis and it should yield distinct and reliable factors
 Bartlett’s Test of Sphericity which tests the null hypothesis that the 8
variables in the correlation matrix are uncorrelated. The observed
significance level is 0.000. Hence, we reject the null hypothesis. It may be
concluded that the strength of the relationship among variables is strong
22

23. IV. Contingency Analysis: Chi-Square
Test ( ) of Independence
 Most often used by researchers in this area of behavioural
finance
 Contingency analysis has been undertaken to analyse the
frequencies of two variables with multiple categories and for
commenting on the independence of the two variables
 Chi-Square Test of Independence is represented by
=
where:
df = (r-1)(c-1)
r = number of rows
c = number of columns
2

2
( )fo fe
fe


2

23

24. Profile of the Questionnaire Respondents
The respondents to this questionnaire were mostly
 males,
 married,
 between the age group of 25-40 years,
 post-graduates,
 middle level executives,
 employed with private company and
 having 2-5 members in their family
V. Empirical Observations: Questionnaire
for Retail Investor
24

25. V. Portfolio Allocation
Where do Savings go?
Allocation of Investment
 Equities
 Mutual funds
 Real estate and fixed deposit
 Low preference for gold and
silver
25

26. V. Portfolio Goals
Investors Objectives
 Minimization of Risk
 High Long Term Return
Tax Savings
Liquidity
Very few investor believe equity to be a
suitable asset for financing
future contingencies and
consumption need
26

27. V. Portfolio
Constraints
Constraints
 Budget
 Share price
 Profit booking
 Inflation
 Income
 Brokerage Fees
 Stop Loss
 Volume
27

28. V. Mutual Funds and Key Demographic Factor
MF Preference
 Diversified Equity Mutual Funds (Most)
 Index Based Mutual Funds (Least )
Demographic Factor
 Risk Bearing Capacity
28

29. V. Portfolio and Macroeconomic Factors
Macro economic Factor
 Growth potential of the industry
 Political stability
 Buy and sell activity of the FIIs
 Monetary policy
 Exchange rates
 Bulk deals
 Crude oil prices
 Bullion rates
NotImportant
Important
Need for change in
Investor Awareness
Programs
29

30. V. Equity Selection Factors
Company Factor
 Valuation of the company
 Price to Earnings ratio (P/E ratio)
 Sales/Net Profit and EPS of the equity
 Share Price
 Percentage of Pledged Shares
 Interest Obligation of the Company
 Public Announcements
 Application of circuit filters
Important
Not
Important
30

31. SocialInvesting
21
23
18
38
33
37
36
45
43
15
30
42
39
45
55
32
21
15
20
23
45
41
39
38
48
22
18
26
39
46
42
46
31
27
23
14
33
56
34
37
37
31
22
22
22
83
30
30
23
29
34
23
21
21
14
41
29
32
18
24
46
54
36
38
23
33
24
29
26
41
48
32
44
29
17
18
18
18
19
38
93
EmployeeWelfare
DonationforSpecialCauses
SpreadingEducation
AidinNationalDistress
FamilyPlanningandHealth
EmploymentofMinorities
Worker'sParticipationinManagement
PollutionControl
ProductInnovationandSafety
Disclosures
DistributionofCumulativeFrequency
1
2
3
4
5
6
7
8
9
V. Impact Investing
31

32. V. Derivatives
32

33. Resolution to attainment of Multiple Goals
 Balanced Approach
 Some of the experts advised to create a matrix and undertake
portfolio allocation based on priority of the clients
 Academic experts recommended to either use multi-objective
optimisation algorithm or linear programming
Goals pursued by an investor are identified by practitioners by
undertaking investor profiling
 Investor profile is interpreted from age, time span for
investment, location, family background, tax consideration,
liquidity requirements, preferences, income level, asset
position and ethical beliefs
V. Empirical Observations: Questionnaire for
Expert Opinion
33

34. Equity Selection
Quantitative factors : beta, price/book value, dividend yield, ratios
concerning profitability, liquidity, valuations, cash flows, operating
and financial leverage, asset utilization and operational efficiency,
expected return, uncertainty of returns and covariance with other
assets
Qualitative factors : quality and prior record of senior management,
corporate governance, timely disclosures, level and extent of
competition in the sector, government policies affecting the
company and its sector, product/service nature and industry
characteristics and life cycle and sensitivity to the business cycle.
One of the industry experts focussed on performance in terms of 4Ps
i.e. Profit, Promoter, Product and Price for the purpose of equity
selection
V. Empirical Observations: Questionnaire for
Expert Opinion
Table V.18 Practitioner’s Solutions to Portfolio Selection Issues
34

35. VI. Empirical Results: Questionnaire Survey
Factor Analysis performed using SPSS 16
Since none of the significance values is greater than 0.05 and
correlation coefficients greater than 0.9, hence, there is no problem of
singularity in the data
The determinant value is 0.094 which is greater than 0.00001
representing no problem of multi-co linearity in the data
From the correlation matrix it can be said that the eight variables are
related at 0.275 levels or above
35

36. VI. Empirical Results: Questionnaire Survey
Single factor extraction on the basis of
1. Eigen values over 1
2. Scree Plot
Hence, Kaiser’s rule has been applied for extraction of factors
The criterion is suitable as we have 8 variable under study (should be
less than 30) and communalities after extraction are greater than 0.7
(except for Time Span)
Also, the sample size is greater than 250 and the average communality
is 0.749 which is greater than 0.6
Based on applicability of both the rules, Kaiser’s criterion is best
suited
36

37. VI. Empirical Results: Questionnaire Survey
Varimax Rotation performed to derive more meaningful results
Rotated Component Matrix
represents 4 Factors
1. Timing of Portfolio
2. Security from Portfolio
3. Knowledge of
Portfolio selection
4. Life Cycle Portfolio
37

38. VI. Contingency Analysis: Chi-Square Test ( )
of Independence
2

S. No. of
Hypothesis
Portfolio Variable Demographic Decision
( for α = 0.05)
Null Hypothesis
1 Gain Sought
Professional
level Dependent Reject
2 A Goals Age Dependent Reject
2 B Goals Income Independent Accept
2 C Goals Responsibility Dependent Reject
3 A Constraints Occupation Independent Accept
3 B Constraints Income Independent Accept
3 C Constraints Responsibility Independent Accept
4
Macro Economic
Factors Qualification
Independent*
(α = 0.05) Accept
5
Market
Capitalisation Income Independent Accept38

39. Critical Insights into the dynamics of investor’s behaviour
Analysed the manner in which experts resolve portfolio
management issues
Identified four main factors affecting portfolio goals
Results of our hypothesis testing assist in uncovering investor
biasness
VII. Summary & Conclusions
39

40. Thank You
for
your Patience
40

41. Select References
Agarwal, J.D., (2000), Security Analysis, IIF Publications, Delhi, India,
pp. 423.
Das, Binay Bhanu, “A study of Multi-Objective Decision Making
Approach in Business & Industries”, Ph. D. Thesis, Faculty of Management
Studies (FMS), University of Delhi, 2006.
Kumar, P. C., George C. Philippatos and John R. Ezzell, (1978), “Goal
Programming and the Selection of Portfolios by Dual-Purpose Funds”, The
Journal of Finance, Vol. 33, No. 1, pp. 303-310.
Lee, Sang M. and A. J. Lerro, (1973), “Optimizing the Portfolio Selection
for Mutual Funds”, The Journal of Finance, Vol. 28, No. 5, pp. 1087-1101.
Vij, Madhu, (2010), Multinational Financial Management, Excel Books,
Delhi, India.
https://blogs.cfainstitute.org/investor/2017/10/09/nobel-laureate-
richard-h-thaler-on-the-end-of-behavioral-finance/
41