I need a data set based on the attached thesis instruction for the data set attached
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NAME: APURVAA DINESH
KALAMKAR
STUDENT NO.: 23250522
EMAIL ID.:
APURVAA.KALAMKAR.2024@mumai
l.ie
THESIS PROPOSAL
“HERD BEHAVIOUR IN CAPITAL
MARKET”
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Introduction: –
• Brief background of the topic:
In terms of the capital markets, “herd behaviour” describes the propensity of
individuals or investors to follow the lead instead than individually forming
judgments based on information or basic analysis. (Hirshleifer and Hong,2003) This
phenomenon has its origins in the study of behavioural finance, which studies
the ways in which psychological variables impact market results and financial
decisions. Academics, financial institutions, regulators, and investors all need to
understand herd behaviour. It illuminates the behavioural biases that impact
market dynamics and offers explanations for how and why markets can depart
from reasonable expectations. (Hirshleifer and Hong,2003) This area of study
advances our knowledge of capital markets and helps us create risk
management plans for herd behaviour.
• Rationale for the study:
Many stakeholders can profit from the results of research on herd behaviour in
capital markets, which is important for a number of reasons. Market Dynamics,
Investor Decision-Making, Behavioural Finance Insights, Policy Implications,
Financial Stability, Investors, Financial Institutions, Regulators, Policy
Advocates, Herd behaviour in capital markets affects investor decision-making,
market dynamics, financial stability, and regulatory policies, research on this
topic is crucial (Chan, Y. C.,1988). The results could be advantageous to many
different parties since they offer information that helps people behave in the
financial markets in a more knowledgeable and sensible manner.
• Objective and scope:
To explore and evaluate herd behaviour in capital markets in a comprehensive
manner is our main goal. The key goals include:
1. Understanding Herd Behaviour Dynamics
2. Impact on Market Dynamics
3. Identification of Herd Behaviour Indicators
4. Regulatory Implications
5. Investor Decision-Making
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Scope of the research will focus on the following key aspects:
1. Time Frame
2. Asset Classes
3. Geographic Scope
4. Methodologies
Research Questions/ Hypotheses:-
• Main research question:
Research Question:
1.”How does the presence of herd behaviour influence the price dynamics of
financial assets in capital markets, and to what extent do psychological factors
contribute to the manifestation of herd behaviour among investors?”
Hypothesis 1: Impact of Herd Behaviour on Asset Prices:
H0:β1 = 0
H1: β1 ≠ 0
In a regression model evaluating the influence of herd behaviour on asset prices,
the coefficient of herd behaviour is denoted by β1. The alternative hypothesis,
H1, contends that herd behaviour has a major influence on asset values, contrary
to the null hypothesis, H0, which states that there is no meaningful association.
Hypothesis 2: Psychological Drivers of Herd Behaviour:
H0: γ1 = 0
H1: γ1 ≠ 0
In a regression model that analyses their impact on the expression of herd
behaviour, γ1 stands for the coefficient of psychological components (such
social influence and FOMO). The alternative hypothesis, H1, contends that
psychological variables have a major impact on herd behaviour, whereas the
null hypothesis, H0, assumes no substantial association.
Hypothesis 3: Information Cascades and Herd Behaviour:
H0: δ1 = 0
H1: δ1 ≠ 0
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where the coefficient δ1 indicates how information cascades affect herd
behaviour. The alternative hypothesis, H1, suggests a substantial correlation
between information cascades and the prevalence of herd behaviour, whereas
the null hypothesis, H0, states that there is no meaningful relationship.
These hypotheses offer a quantitative framework for examining the connections
among psychological drives, asset values, and herd behaviour. They are
expressed as equations. In order to verify these theories and determine the kind
and degree of herd behaviour in financial markets, the research will employ
statistical analysis(Hirshleifer and Hong,2003).
Literature Review:-
• Key theories and models:
1. Information-based theories: This theory highlights the significance of
Reputational concerns, Social learning, Imitation(Guillermo A And Enrique
G,1997).
2. Psychological theories: This theory highlights the significance of Loss
aversion, Biased attention, Overconfidence.
• Gaps in existing research:
While several studies have examined the Rational Herding in Financial
Economics, they have primarily focused on introducing the concept of rational
herding and investigating the conditions under which such behaviour can be
rational and contribute to the efficiency of information aggregation in financial
markets (Devenow, A., & Welch, I.,1996).Research gaps have the potential to greatly
improve our knowledge of capital market herd behaviour and there is a need to
investigate the influence of its wider effects on investor behaviour, market
dynamics, and financial stability. Researchers can help develop more successful
methods for risk management, market regulation, and investor education by
bridging these gaps in the literature.
• Justification for the study:
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In the capital markets, herd behaviour is a common occurrence that has
significant ramifications for individual investors, market dynamics, and
financial stability. In order to effectively offset the negative impacts of herd
behaviour and promote a more resilient and efficient financial system, it is
imperative to comprehend the causes, mechanisms, and repercussions of this
behaviour. Although previous studies on herd behaviour have yielded important
insights, there are still large gaps that require attention.
Data and Methodology:-
• Research design:
Herd behaviour in the capital market and its effects on investor returns and
market dynamics can be methodically studied with the use of this study
approach(Cipriani and Guarino,2014). Scholars can enhance comprehension of
financial markets and get important insights into this intricate phenomenon by
meticulously gathering, evaluating, and interpreting data. And this research
adopts a quantitative approach.
• Variable choice:
1.Individual variables: Information sources, Risk tolerance and Experience
2. Market variables: Technological factors, News events and Market volatility.
3. Institutional variables: Fundamental analysis, Reputational concerns and
Benchmark pressure.
• Data collection methods:
To gather important information that can shed light on the phenomena of herd
behaviour in the capital market, one must conduct a data collection study. We
can get data’s Bloomberg, financial databases. And available reports and
publications from financial institutions, central banks, and government
agencies. Access data from stock exchanges and trading platforms.
• Sampling technique:
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The sampling strategy selected should be in line with the goals, limitations, and
design of the study. When choosing a sample technique, researchers may need
to take into account variables including investor types, market segmentation,
and pertinent financial instruments in the context of investigating herd
behaviour in the capital market. The choice of sampling technique for
quantitative analysis may also be influenced by the accessibility of past
financial data.
• Tools and software to be used:
We can use R language or python for data analysis. And also, we can use
spreadsheet software for data organization.
Expected Results:-
• Anticipated findings:
Herd behaviour is a complex phenomenon with multiple causes. There are a
number of potential strategies to mitigate the negative effects of herd behaviour.
Factors related to technology might aggravate herd behaviour. Positive and
negative outcomes can result from herd behaviour. In order to disseminate
knowledge and bring prices closer to underlying values, herd behaviour can
result in more efficient markets. Yet, because it can magnify price fluctuations
and prompt hasty judgements from investors, herd behaviour can also result in
market bubbles and crashes.
• Contributions to the field:
Research on herd behaviour has made significant contributions to the field of
finance, providing a deeper understanding of investor behaviour, developing
better measures of herd behaviour, and identifying strategies to mitigate its
negative effects. This research has also enhanced the integration of behavioural
finance into mainstream finance theory and has had policy implications for
market regulation and investor protection Researcher contributions to a more
effective, steady, and resilient financial system can be increased by pursuing
herd behaviour research further.
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References:-
1.Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion,
custom, and opinion-following as informational cascades. Journal of Political
Economy, 100(5), 992-1026.
2.Devenow, A., & Welch, I. (1996). “Rational Herding in Financial Economics.”
3.Chan, Y. C. (1988). Herd behaviour in commodity futures trading. Journal of
International Money and Finance, 7(4), 403-418.
4.De Bondt, W. F. M., & Thaler, R. H. (1990). Greater than reason: The effect of
market sentiment on the allocation of investment. Journal of Finance, 45(3), 793-
805.
5.Hirshleifer, D. (2001). Investor psychology and financial markets. Oxford University
Press.
6.Marco Cipriani, Antonio Guarino (2014). Estimating a Structural Model of Herd
Behaviour in Financial Markets.
7.Kahneman, D., & Riepe, M. (1998). Valuation by similarity: The effects of similar
preceding prices on valuation judgments. Journal of Economic Psychology, 19(2),
317-339.
8.Andrei Shleifer, Lawrence H. Summers (1990).”Herd on the Street: Informational
Inefficiencies in a Market with Short-Term Speculation” The Journal of Finance.
