I need a data set based on the attached thesis instruction for the data set attached  1 NAME: APURVAA DINESH KALAMKAR STUDENT NO.:

I need a data set based on the attached thesis instruction for the data set attached 




STUDENT NO.: 23250522







mailto:[email protected]
mailto:[email protected]


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


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


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


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:


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:


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.



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-

5.Hirshleifer, D. (2001). Investor psychology and financial markets. Oxford University

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),

8.Andrei Shleifer, Lawrence H. Summers (1990).”Herd on the Street: Informational
Inefficiencies in a Market with Short-Term Speculation” The Journal of Finance.

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