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.”
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
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
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
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