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ACTA UNIVERSITATIS DANUBIUS Vol 21, No 5, 2025

The Impact of Emerging E-Commerce

Platforms on Amazon’s Stock Price

Performance

Nthabiseng Matsepe1, Fabian Moodley2, Sune Ferreira-Schenk3

Abstract: Objective: This study analyzes how emerging e-commerce competitors—Temu, eBay, and

Etsy—influenced Amazon’s stock returns and volatility between 2018 and 2024. Approach: Using

covariance analysis and GARCH-type models, the findings reveal that Amazon’s returns were

significantly affected by market shocks and competitive pressures, with clear evidence of volatility

clustering and asymmetric effects. While eBay and Etsy moved largely in tandem with Amazon,

Temu’s returns displayed an inverse relationship, suggesting its rapid market expansion occurred partly

at Amazon’s expense. Results: These results support the study’s objective by demonstrating that rising

competition from new entrants materially altered investor sentiment and short-term stock performance

in the global e-commerce space. The study also contributes to the intersection of the Efficient Market

Hypothesis (EMH) and Behavioral Finance. It shows that investor reactions to competitive

developments were not always rational, with herding behaviour and sentiment-driven responses

amplifying short-term volatility. Amazon’s heightened sensitivity to firm-specific shocks, relative to

broader market trends, underscores its exposure to competitive and innovation-driven risks in a fast-

evolving digital landscape. Value: From a practical standpoint, the research highlights the importance

for investors to monitor behavioural signals and market narratives, which can influence valuation and

risk beyond traditional fundamentals. For e-commerce firms, the results underscore the need for

continuous innovation, strategic differentiation, and adaptive pricing to retain market share. While

limited by a focused sample and geographic scope, this study lays groundwork for future research

1 Student, Risk management, North-West University, Vanderbijlpark, South Africa, Address: 21

Hendrik Van Eck Blvd., Vanderbijlpark, Gauteng, South Africa, E-mail: [email protected]. 2 Ph.D., Risk management, North-West University, Vanderbijlpark, South Africa, Address: 21 Hendrik

Van Eck Blvd., Vanderbijlpark, Gauteng, South Africa, Corresponding author:

[email protected]. 3 Professor, Risk management, North-West University, Vanderbijlpark, South Africa, Address: 21

Hendrik Van Eck Blvd., Vanderbijlpark, Gauteng, South Africa, E-mail: [email protected].

AUDOE Vol. 21, No. 6/2025, pp. 50-72

Open access publication under the terms and conditions of the

Creative Commons Attribution-NonCommercial (CC BY NC) license

(https://creativecommons.org/licenses/by-nc/4.0/)

ISSN: 2065-0175 ŒCONOMICA

incorporating broader datasets, cross-country analysis, and sentiment indices to better understand the

interplay of competition and investor psychology in digital markets.

Keywords: Behavioural finance; Volatility; GARCH

JEL Classification: G2; G4

1. Introduction

Over the last decade the electronic commerce sector popularly known as e-commerce

has experienced remarkable transformation, propelled by technological

advancements and shifting consumer behaviour (Shahbaz, 2024). This

transformation has significantly reshaped the global business environment, reducing

the reliance in traditional retail models by offering flexibility, adaptability and

convenience (Simakov, 2020) According to Hao (2019) in particular countries like

China have witnessed dramatic rise in online shopping, fuelled by the widespread

use of computers and the internet. The world at large has evolved to online

participation such as online banking that we use daily. The convenience of 24/7

access, lower operating costs, and minimal inventory pressures have made online

platforms attractive to both consumer and merchant. As a result, e-commerce

ushered in a new era of digital commerce by removing the barriers to traditional

business operations and opening up new business prospects (Hao & Choi, 2019).

E-commerce has a rich history, from primitive electronic data transactions in the

1960s and the first online retail transactions in 1994, to the modern popularity of e-

commerce giants such as Amazon with a revenue of 280.5 billion USD (Agyeman

et al., 2022). In 2024 Ahmar and Shahbaz conducted a comparative analysis of the

innovative strategies employed by three prominent e-commerce companies:

Amazon, Alibaba, and eBay. Through a comprehensive examination of their

business models, technological advancements, customer engagement strategies, and

market expansion efforts, where their focus was providing insights into the key

factors driving success in the highly competitive e-commerce landscape (Moodley

et al., 2025). For consumers, the convenience of accessing products over the Internet

anytime and anywhere, often at lower prices, gives online shopping high advantages.

On the other hand, merchants benefit from the ability to sell products without the

need to physically stock them, which significantly reduces operational costs,

inventory pressures, and many of the risks typically associated with traditional

business models (Hao & Choi, 2019).

However, the landscape is shifting as new platforms such as Alibaba, ASOS, Boohoo

Group and Temu enter and become popular in the industry and course disruption in

the market dynamic. Major e-commerce companies like Amazon who are known for

their well-built wide range consumer confidence through technological innovation,

it provides secure payment gateways and robust logistics systems which help

ACTA UNIVERSITATIS DANUBIUS Vol 21, No 5, 2025

overcome consumers initial scepticism (Ahmar & Shahbaz, 2024). Amazon faces

intense competition which could lead to fragmentation of the e-commerce industry

and threatens its performance knowing this allows us to reflect upon the problem.

The increasing popularity of emerging e-commerce platforms such Alibaba, ASOS,

Boohoo Group and Temu due to the COVID-19 pandemic has introduced notable

disruptions in the global e-commerce landscape posing a growing challenge to the

market dominance of established giants like Amazon (Moodley, 2025b). While

Amazon has long maintained a strong brand reputation and global customer base

(Ahmar & Shahbaz, 2024). Its stock price has experienced notable volatility in recent

years, raising questions about the impact of new e-commerce entrants on investor

sentiment and market performance.

Previous studies have explored various dimensions of the e-commerce sector,

including the innovative strategies employed by major players, their operational

efficiencies, and the surge in revenue during the COVID-19 pandemic (Agyeman et

al., 2022; den Ouden, 2021; Hao & Choi, 2024). Other scholars examined the

entrepreneurial evolution of e-commerce platforms, focusing on their adaptability

and competitiveness (Simakov, 2020). However, there remains a gap in literature

regarding the direct impact of emerging platforms on the stock performance of

established giants. Fewer studies have investigated how consumers immigrate

towards new online platforms, influenced by behavioural factors such as herding

behavior, may be affecting the volatility and performance of stocks like Amazon’s.

This study aims to address this gap by exploring the shift in consumer preference

from dominate platforms like Amazon to new entrants like Aliababa, ASOS, Boohoo

Group and Temu. It will examine how these shifts, driven by low pricing,

convenience, fast shipping and aggressive discount strategies, are reflected in stock

price movement, volatility and whether the Efficient Market Hypothesis still holds

in these emerging e-commerce contexts. By analysing the e-commerce industry in

comparison to the market, this paper seeks to understand the extent to which

emerging e-commerce platforms influence the financial health and investor

confidence in industry giants, with knowing this we can investigate our

goals/objectives.

The primary objective of this research study is to analyse the impact of new e-

commerce entrants on the stock price volatility of industry giant Amazon, and factors

that lead to the shift on consumer behaviour.

To facilitate the achievement of the primary objective, the empirical portion of the

study consists of the following objectives: To analyse the effect of emerging e-

commerce competitors on the stock returns of Amazon using the covariance analysis.

To investigate the extent to which the presence of new e-commerce competitors

influences Amazon’s stock price volatility. To analyse the volatility of Amazon’s

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stock price index in relation to the market.

2. Literature Review

2.1. Theoretical Justification

The rapid growth of e-commerce has significantly transformed consumer behaviour

and market dynamics, necessitating a deeper understanding of its impact on firm

performance and investor responses. This study addresses the need to explore the

interplay between consumer impulsivity, investor psychology, and the strategic

challenges faced by e-commerce companies within an evolving competitive

landscape. The literature review begins by examining foundational theories such as

the Efficient Market Hypothesis and Behavioural Finance, and which provide

theoretical justification for understanding market efficiency and investor behaviour

in digital markets. Following this, an empirical review highlights key findings on

consumer impulse buying, social media influence, stock market reactions, and the

effects of international expansion on e-commerce firms. Together, these perspectives

frame the complex environment in which e-commerce firms operate and outline the

basis for this study’s investigation.

2.1.1. Efficient Market Hypothesis

The most well-known theory that is the backbone of financial theories is the Efficient

Market Hypothesis theory (EMH) developed by Fama (1970). The Efficient Market

Hypothesis (EMH) theory states that financial markets, especially stock markets, are

efficient and that prices reflect all available information. Fama (1970) argues prices

adjust rapidly to the arrival of new information either public or private information,

and that it is impossible to “beat the market” and achieve above average returns in

the market. The EMH theory is divided into three forms, the Weak form, Semi-

Strong form and the Strong for each based on the type of information reflected in

prices. The weak form asserts that prices reflect all information historical prices and

volume information, and that the use of technical analysis strategies cannot be used

to yield abnormal returns. The Semi-strong form claims that current stock prices

reflect not only historical information but all publicly available information about

the companies’ securities. Lastly the Strong from suggests that stock price reflects

all available information both public and private known by any market participant

about the company.

The Efficient Market Hypothesis further points out that new information regarding

securities enter the market randomly an independently through the “Random Walk

theory”, and that investors respond quickly and rationally to the arrival of this new

information leading to unbiased price adjustment. The EMH assumes that investors

act rationally and that they make decisions of investing based on available

ACTA UNIVERSITATIS DANUBIUS Vol 21, No 5, 2025

information, and that investors possesses/share homogenous expectation leading to

the same valuation and actions. The Efficient Market Hypothesis proposes that the

arrival and popularity of new e-commerce establishments/companies is quickly and

accurately reflected in the Amazon’s stock price. All publicly available information

regarding the competitors of Amazon, including earnings reports, market trends,

news about new entrants or innovations, and regulatory developments, is

immediately reflected in stock prices.

Thus, fundamental and technical analysis cannot consistently generate excess

returns. According to the EMH the stock price movement of Amazon is a fair

representation of the collective knowledge of investors and the ever-evolving e-

commerce landscape including risk and growth prospects. Additionally the EMH

suggests that investors act rationally and independently but in reality we know that

investors are irrational beings and that investor behaviour is driven by cognitive

biases, emotions and social influence especially during periods of market disruption

(Mehwish &Tariq, 2015).The Efficient Market Hypothesis provides foundation for

market and investors reactions to new information such as the launch and growth of

competing e-commerce companies. EMH also assists in assessing market efficiency

in e-commerce sector.

The EMH theory has faced substantial criticism over the past decades theoretically

and empirically, leading to the emergence of other theories such as Behavioural

finance that explain the behaviour of investors and factors that influence them such

as overreactions, biases and herding mentality.

2.1.2. Behavioural Finance

Behavioural finance theories emerged at the start of the 21st century as an alternative

framework for understanding the financial market through the lens of psychology

and sociology. Unlike traditional economics financial theories that assume

rationality among investors, behavioural finance acknowledges that irrationality,

cognitive biases and emotion reaction frequently drive market and investor

behaviour (Mehwish & Tariq, 2025). Behavioural finance is composed of key areas

and theories which can be used to explain cognitive biases and heuristics, these

include but are not limited to herding behaviour and impulse behaviour.

2.1.2.1. Herding Behaviour

Herding Behaviour, a critical concept in behavioural finance, was extensively

developed and formalised by scholars such as Banerjee (1992), Bikhchandani et al.

(1992), Shiller (2000), Lee et al. (2015) and Zhang et al. (2018). Herding Behaviour

theory contests that individuals tend to mimic the actions of a larger group or follow

the majority even when such behaviour may not be justified by personal analysis

(Ding & Li, 2018). Herd bias is triggered by psychological tendencies such as the

desire to conform, fear of missing out (FOMO), or believing that others may have

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superior knowledge. Herding behaviour among investors can be driven by irrational

motives, which can lead to market stress by pushing asset prices away from their

fundamental value, creating asset bubbles and driving up market volatility (Blasco

et al., 2012).

The theory states that when investors are faced with ambiguous or incomplete

information, they may rationally infer that the crowd is better informed, leading them

to disregard their own information and join the majority. Herding is an obvious intent

by investors to ignore their personal analysis and copy the actions of our investors,

leading them to trade the same securities in the same direction. Unlike traditional

finance theories which assume investors are rational and markets are efficient, the

herding theory suggests that collective psychological biases can result in systemic,

predictable deviations from market efficiency (Moodley, 2025a). Herding behaviour

explains how investors might react to new and popular e-commerce platforms and

collectively act the same challenging the dominance of Amazon. Investors may

perceive consumer migration towards these new platforms like Alibaba, Temu,

ASOS as signal of declining competitiveness of Amazon even before fundamentals

fully reflect the shift. Investors might also herd towards these other platforms simply

because of the influence of other investors and the increasing traffic of attention that

they receive. Instead of individually assessing each new company’s competitive

threat, investors may follow prevailing market sentiment or the actions of

institutional investors, amplifying Amazon’s stock price movements beyond what

traditional fundamental analysis would predict.

While EMH posits that stock prices fully reflect all available information and

investors are rational, herding behaviour highlights how psychological and social

factors can cause systematic deviations from this rationality and market efficiency.

Consumers can also be influenced by other consumers, leading them to engage more

with these new companies neglecting establishes like Amazon because the products

are offered elsewhere at a much lower cost.

2.2. Empirical Literature

The recent evolution of e-commerce has dramatically accelerated spontaneous and

emotionally driven purchases, which often bypass deliberate and rational decision-

making processes. In the context of e-commerce, such impulsive behaviour is further

amplified by factors like website design, ease of access, and digital marketing cues

especially social media (Lashari, 2025). Building on this, Verhagen and Dolen

(2011) extended on this conceptualisation by applying Cognitive Emotion Theory

(CET) to online settings, illustrating how online store features such as merchandise

attractiveness, website enjoyment, and communication style help shape consumer

emotions and trigger unplanned, impulsive purchases. Their empirical findings

ACTA UNIVERSITATIS DANUBIUS Vol 21, No 5, 2025

confirmed that these online store beliefs mediate through emotional responses like

excitement or inspiration, ultimately leading to impulse buying. This is particularly

relevant for hedonic products such as fashion and beauty items, where emotional

engagement is high (Corbishley et al., 2022).

In a similar study Corbishley et al. (2022) investigates whether consumer personal

involvement with the COVID-19 pandemic led to hedonic or utilitarian buying

motives, and how these buying motives might have encouraged impulse or planned

buying behaviour in a developed country (Germany) and developing country (South

Africa). The authors found that respondents with high levels of involvement with

COVID-19 also show high levels of hedonic motivation, whereas utilitarian

motivation appeared less important and not linked to a greater involvement with

COVID-19.Futhermore the study found that high levels of hedonic motivation is

associated with impulsive buying and that there is no significance difference between

the buying behaviour of consumers in a developing country and a developed country.

While the consumer side of impulse buying is well documented, its spillover effects

on financial markets lacks attention. Increases in the popularity of other e-commerce

companies might lead to observable shifts in investor sentiment and behaviour

(Kumar, 2024). Scholars like Singh (2025) examine the role of social media

sentiment as a predictor for stock returns by employing the ARIMA, GARCH and

machine learning algorithms including LSTM and XGBoost to analyse the

correlation between sentiment and stock returns. Findings reveal that social media

sentiment significantly correlates with stock market returns beyond traditional

financial indicators argue that investors are not immune to psychological biases and

that they can be influenced by consumer sentiment and media narratives. For

example, during periods of high consumer spending or viral sales events, like black

Friday consumer irrational behaviour may be triggered which leads investors to

overvalue e-commerce firms and increase speculative trading in associated stocks in

the short-term (Ganesh, 2022). This relationship between consumer impulses and

investor psychology becomes very significant, especially when analysing stock

performance of dominant players like Amazon. Investor expectations often reflect

perceived consumer behaviour. When platforms adopt new marketing strategies such

as flash sales that encourage impulsive buying, investor confidence might increase

possibly driving share prices above their fundamental values. This indicates that

consumer impulsivity and investor sentiment support one another leading to short-

term volatility in the stock market.

Extending beyond individual buyer or investor psychology, recent work has focused

attention on group driven dynamics such as herding behaviour, especially in markets

driven by news about consumer trends and disruptive marketing techniques. Kenneth

et al. (2023) differentiates genuine herding active imitation from false herding

resulting from related public information, determining that false herding is more

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common among worldwide investors. This is particularly prominent in e-commerce,

where information, memes, or influencer promotions trigger joint investor response.

Singh (2025) further argues that the speed and contagion of social media trends

(think Temu, Shein, Alibaba) enable viral marketing to induce herding, amplifying

both consumer excitement and investor momentum. This can lead to a surge in

related stocks or a shift away from established players like Amazon without

fundamental justification. Such sentiment-driven shifts are amplified by social media

platforms, where rapid dissemination of opinions and shopping trends fuels

speculative behaviour (Singh, 2025). Furthermore, Yoon and Oh (2022), examining

the Korean stock market, reveal that retail investors’ herding is significantly

influenced by abnormal information creation activity (AICA) on social media,

whereas institutional and foreign investors approach this information distinctly,

often reducing their herding tendencies. Notably, negative bullish sentiment

increases herding across all investor classes, while the rise of retail participation

post-COVID-19 further intensifies these patterns in e-commerce stocks, potentially

pushing investor flows away from established giants like Amazon.

E-commerce rapid growth has been driven by its implementation or engagement in

cross-border sales programs, which have allowed not only foreign individuals to

access their products but also foreign companies (Zahra et al., 2000).When people

travel for either business, work, school, or even leisure, they are introduced to

various cross-border goods and brands that are locally unavailable or expensive in

their domestic areas, encouraging them to use transboundary e-commerce to

purchase those goods (Agyeman et al., 2022). Acheampong (2022) adds that nations

capitalizing on e-commerce see outsized gains in the global digital economy,

whereas those that fall behind risk being sidelined.

Furthermore, researchers like Barua et al. (2001) examine the factors that drive e-

business excellence and find that achieving excellence in e-business operations leads

to improved financial performance. This research addresses the question of which

operational factors drive firms to achieve excellence in e-business and generate

financial returns. The results indicate that firms with a high score on operational

measures of e-business achieve higher revenue, gross profit, return on assets and

return on investment. The impact of competitive entry and international expansion

on stock markets are curial to understand as they might affect the dynamics of e-

commerce firms like Amazon. Competitive entry interjects new market pressures

that can affect incumbent firms’ market share, pricing power, and ultimately stock

price volatility. Zhu and Kraemer (2005) established that the business value of e-

commerce is contingent upon both technological readiness and local market

structures, which moderate the competitive pressures experienced by e-commerce

companies.

ACTA UNIVERSITATIS DANUBIUS Vol 21, No 5, 2025

Expanding to international markets allows e-commerce firms to diversify their

revenue streams (Ouden, 2021). However, it also brings uncertainty that may

increase short-term stock volatility due to perceived risks and challenges in

adaptation. Simakov (2020) highlights how the role of e-commerce platforms is

breaking down international trade barriers, allowing firms to reach broader markets

effectively but it requires strategic adaptation. This internationalization influences

stock market responses not only through profit potential but also by changing

investor expectations regarding firm resilience and growth in the context of global

competition. Simakov (2020) discovers that venturing into global markets can

promote diversification yet brings adaptation difficulties and investor uncertainty,

frequently increasing short-term stock volatility. Modern examples such as Shein’s

disruptiveness affecting firms like ASOS, or Temu influencing Alibaba’s and

Amazon’s investor sentiment underscore both the short-term overreaction and the

longer-term need for innovation among incumbents.

In support of this, Agyeman et al. (2021) utilized a case study approach to analyse

firms like Amazon, JD.com, Alibaba, and Sunning.com, emphasizing their revenue

growth during the COVID-19 pandemic and the rising adoption of offline-to-online

business models. Their research indicated a significant movement of trading towards

digital platforms, forecasting that e-commerce would represent 22% of total global

transactions by 2023, with a projected 95% prevalence by 2040. Complementing

this, Wanghao Li (2024) examined the effects of the COVID-19 pandemic on

Alibaba’s stock price, assessing changes in its market behaviour during and

following the outbreak through ARIMA models. These studies together demonstrate

how e-commerce expansion is driven by the pandemic and changes in consumer

behaviour intensify competitive dynamics and influence changing stock market

reactions in the industry.

2.3. Research Gap

It is evident from the theoretical underpinnings reviewed that while the Efficient

Market Hypothesis (EMH) assumes that stock prices, including those of dominant

firms like Amazon, fully and rationally reflect all available information, this view

has been substantially challenged by Behavioural Finance. Behavioural Finance

emphasizes the role of investor irrationality, cognitive biases, emotions, and herding

behaviour, especially during periods of market disruption and intense competition.

These behavioural factors are particularly relevant in the rapidly evolving e-

commerce sector, where consumer impulses and investor sentiment can drive stock

price volatility beyond what traditional financial models might predict.

Empirically, the literature has largely focused on developed markets, with limited

research exploring the dynamics within emerging e-commerce environments and

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their effects on incumbent firms such as Amazon. A significant research gap exists

regarding how the entry and growing popularity of new e-commerce platforms

impact Amazon’s market dominance and stock price performance. This gap includes

understanding the role of investor sentiment and herding behaviour towards these

new entrants, which may influence perceptual shifts and valuation changes in

platform markets. This study seeks to fill this gap by analysing how emerging e-

commerce competitors affect Amazon’s stock price through the lens of Behavioural

Finance and investor sentiment. By doing so, it contributes to a better understanding

of how market competition shapes firm valuations and investor perceptions in digital

platform markets. The insights gained can help investors make more informed

decisions by recognizing how behavioural responses to competitive dynamics

influence stock performance in the e-commerce sector.

3. Methodology

3.1. Design, Data Collection and Sample Description

The main aim of the study was to analyse the effect of emerging and evolving e-

commerce competitors on Amazon’s stock price returns and the extent to which

these competitors’ presence influences Amazon’s stock price volatility. By doing so,

the study adopted a quantitative research design, with a sample period consisting of

daily data from the period July 2018 to December 2024. The sample period accounts

for historical financial markets events such as the 2019-2022 COVID-19 pandemic.

The selection of the data frequency and sample period was dictated by the

availability of data, specifically the one independent variable, Temu’s returns. The

choice of the sample period and data frequency followed that of previous literature,

Li (2024) and Singh et al. (2024). The dependent variable is Amazon’s stock price

returns, and the independent variable consist of Amazon’s competitors which are

eBay Inc, Etsy Inc, and Temu. The data were collected from the IRESS database and

EViews was the preferred econometric analysis program.

The selection on the dependent variable and independent variables was motivated by

the objective of the study to analyse the impact of other e-commerce on the

performance of Amazon’s. Table 1 shows each of the included variables and a

description of each and the database from which they were sourced. In addition to

the above-mentioned variables, as by table 1, two control variables were included.

The inflation rate is the rate at which the average level of prices for goods and

services increase, resulting a decline in the purchasing power (Vipond, 2021). The

inflation rate is selected to control for the macroeconomic effect of changing price

level, which can impact Amazon’s stock returns independently and its competitor’s

performance (Chiang & Chen, 2023). This ensures that the estimated impact of

competitors on Amazon’s returns is not biased by inflation-driven market

ACTA UNIVERSITATIS DANUBIUS Vol 21, No 5, 2025

fluctuations. Another control variable includes Money supply (M2), there are several

categories in which money supply is separated including M1, M2 and M3 (Mofokeng

& Moodley, 2025). However, a wide money supply M2 is used as a control variable

because it reflects total amount of money available in the economy, which influences

interest rates, inflation, and overall economic activity (Vo & Mai, 2023). By

controlling for M2, the study accounts for the macroeconomic liquidity conditions

that can affect investment decisions and stock returns. Nasdaq100 index is employed

as the market proxy to compare and analyze Amazon’s stock returns volatility. Since

Amazon is one of the largest and most influential technology companies globally, it

is a key constituent of the Nasdaq 100, which comprises the 100 largest non-financial

companies listed on the Nasdaq Stock Market. This index effectively represents the

overall performance and volatility of the leading technology sector companies.

There is no widely accepted, dedicated market proxy specifically for e-commerce

companies. Therefore, the Nasdaq 100 serves as the best available proxy due to its

broad coverage of the tech sector, which includes major internet and e-commerce

firms alongside Amazon. Using the Nasdaq 100 as a market benchmark allows for

meaningful comparison of Amazon’s stock volatility relative to the broader market

context in which it operates. The dependent and independent data was obtained from

IRESS and Sant Louis Federal Reserve. Table 1 provides a summary of the variables

used in the study.

Table 1. Summary of Variables

Variables Description Database

AMZN Amazon’s stock returns, which was founded in

1994 in the United States and is listed by

NASDAQ.AMZN is the dependent variable.

IRESS

TEMU PDD Holdings stock price returns which, is a

parent company of TEMU since TEMU on its

own is not listed. PDD is also listed by

NASDAQ.

IRESS

EBAY Ebay Inc stock price returns IRESS

ETSY Etsy Inc stock price returns IRESS

M2 Money supply (M2) control variable 1. Sant Louis Federal

Reserve

INF Inflation rate of the United States which is

measured using the Consumer Price Index

(CPI). control variable 2.

Sant Louis Federal

Reserve

Nas100 It is the market proxy for e-commerce market. IRESS Authors’ own construction (2025)

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3.2. Model Specification

The study will conduct a covariance analysis, to measure how the returns of Amazon

and its competitors move together over time. By examining covariance, the study

can capture the direction and strength of the relationship between Amazon’s returns

and those of its rivals, offering insight into whether competitors’ stock returns are

positively or negatively associated with Amazon’s.

A Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is

used to assess the volatility of Amazon’s stock return in response to the rise and the

popularity of emerging e-commerce companies. The GARCH model is widely used

in the finance to analyse the time-vary volatility by identifying patterns in periods of

high and low volatility which reflects the nature of market behaviour (Asteriou &

Hall, 2021).

The mean equation for all GARCH type models used in this study remains consistent

and is specified as follows:

𝜎𝑡 2 = 𝑤 + 𝐵𝑖𝜎𝑡

2 + 𝑎𝑖𝜀𝑡−1 (1)

Where 𝜎𝑡 2 is the conditional variance (volatility) of the return at time t. 𝑤 is the

constant term that represents the long-term average variance level. 𝐵𝑖 represents the

GRACH coefficient, that measures the effect of the past term variance on current

variance. 𝑎𝑖 is the arch effect while 𝜀𝑡 indicates the past squared residuals.

The study further explores the Exponential GARCH(EGARCH) and GJR GARCH

to test for asymmetric volatility effects. The EGARCH allows us to determine

whether the effect of the volatility shock is asymmetrical, which means that negative

and positive shock have different effect on volatility (Nelson, 1991). The variance

equation for EGARCH model allows for the exponential effect which enables the

model to capture asymmetries without imposing non-negativity on parameters.

𝑙𝑛(𝜎𝑡 2) = 𝑤 + 𝑎𝑖 (

⌊𝜀𝑡−1⌋

𝜎𝑡−1 − √

𝜋 ) + 𝛾𝑖

𝜀𝑡−1

𝜎𝑡−1 + 𝐵𝑖ln ( 𝜎𝑡−1

2 ) (2)

Where, 𝛾𝑖 captures the asymmetric effect (leverage effect). If 𝛾𝑖 < 0, negative

shocks increase volatility more than positive ones. The ln form ensures that 𝜎𝑡 2 > 0

automatically (no non-negativity constraints).

Lastly the study estimates the GJR GARCH model that accounts for the leverage

effect by introducing the dummy variable to differentiate between the effect of

positive and negative shocks with the aim to measure whether bad news has a

disproportional impact of volatility (Glosten et al., 1993).The model will be used to

examine the rapid market expansion, whether it will result in heightened volatility

doe Amazon’s stock compared to positive development.

ACTA UNIVERSITATIS DANUBIUS Vol 21, No 5, 2025

𝜎𝑡 2 = 𝑤 + 𝑎𝑖𝜖𝑡−1

2 + 𝛾𝑖 𝐼𝑡−1𝜖𝑡−1 2 + 𝐵𝑖 𝜎𝑡−1

2 (3)

Where, 𝐼𝑡−1 = 1if 𝜖𝑡−1 2 < 0 (bad news), and 0 otherwise. 𝛾𝑖 measures the asymmetry

— the additional impact of negative shocks and 𝛾𝑖 > 0, negative shocks have a

stronger effect on volatility (leverage effect).

4. Empirical Results

4.1. Preliminary Tests

4.1.1. Graphical Representation

Figure 1. Returns of e-commerce companies and the market proxy

Source: The authors’ own estimation (2025)

ISSN: 2065-0175 ŒCONOMICA

Figure 1 presents the graphical representation of Amazon and its chosen competitors

and Nasdaq 100 which serves as the market proxy for amazon since there is no

widely used market proxy for tech companies. The graphs show evidence that the

returns of these e-commerce companies’ variance are not constant overtime in fact

it shows an autoregressive pattern, thus resulting in volatility clustering for all the

companies. The visual graphs demonstrate that certain periods appear to be more

volatile than others, hence the volatility of returns during these periods. The

significant events that coincide with the volatile periods is the COVID-19 pandemic

in 2020. The visual graphs also confirm the stationarity of the variables. Temu is the

most volatile company which the highest change in return of 54% and the lowest

change of 33%.

4.1.2. Descriptive Statistics

Table 2 provides the descriptive statistics, unit root test, and the ARCH tests for the

various e-commerce companies, market proxy and macroeconomic variables. In part

A, Amazon’s mean return is lower than that of some competitors eBay, Etsy, and

significantly lower than that of Temu, reflecting the competitive pressures in the e-

commerce market. This suggests that emerging e-commerce companies, and other

platforms are gaining popularity and capturing growth opportunities, consequently

impacting Amazon’s returns. The findings are supported by Ologunebi (2024) who

emphasized how shifts in consumer behaviour and competitive dynamics in e-

commerce influence firm performance and sales outcome.

Amazon’s minimum return is notably extreme, and the high kurtosis indicate

exposure to downside risk and sharp negative shocks, which can be linked to industry

disruptions, regulatory changes, and competitive threats from agile newcomers

offering differentiated pricing and product strategies (such as Temu’s aggressive

pricing). The negative skewness of Amazon suggests that investors are more likely

to face substantial negative returns rather than positive spikes. This reflects market

sensitivity to adverse competitive events or operational challenges. Similar market

behavior is detailed by Berman (2020), who studied e-commerce platforms’

performance and found volatility and downside risk driven by market sentiment and

rapid changes in consumer preferences.

ACTA UNIVERSITATIS DANUBIUS Vol 21, No 5, 2025

Table 2. Descriptive statistics, unit root test, and the ARCH tests result

Notes: 1 *** indicate a statistical significance level of 1%, and 5%

Source: Authors’ own estimation (2025)

In contrast, Temu’s mean return is the highest among the e-commerce platforms in

your dataset, significantly above Amazon, eBay, and Etsy. This suggests that Temu

has been experiencing strong positive stock price performance, likely driven by

investor optimism about its rapid growth and market penetration in the highly

competitive e-commerce sector. This aligns with market analyses noting Temu’s

aggressive pricing and expansion strategies as key drivers for its valuation gains in

the market. The standard deviation for Temu is also the highest among all e-

commerce companies listed, indicating that while Temu has strong growth potential

reflected in its higher mean return, it also experiences high volatility and risk. This

Part A: Descriptive Statistics

AMAZON EBAY ETSY TEMU INF M2 NAS100

Mean 0.507408 1.208461 1.848552 4.051636 0.315063 0.548184 1.591980

Median 1.648491 0.696518 -0.069381 0.715368 0.310989 0.364918 2.295343

Maximum 27.05960 32.50166 68.75650 54.26762 1.373608 6.327452 15.19178

Minimum -95.58230 -16.58736 -33.50631 -32.86421 -0.668694 -1.455309 -13.36854

Std. Dev. 11.15520 7.508250 14.38013 16.37111 0.338529 1.005743 4.756247

Skewness -3.670757 0.591943 0.773266 0.672120 0.335888 3.150616 -0.373916

Kurtosis 26.91832 3.663058 4.519846 2.861735 3.472249 16.09479 2.822454

Jarque-Bera 61189.94 179.9806 459.5906 178.5010 65.91313 20642.75 57.74832

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

Sum 1190.378 2835.049 4336.703 9505.137 739.1372 1286.040 3734.785

Sum Sq. 292412.2 135622.6 492935.0 667002.1 501.6156 3077.001 58994.03

Sum Sq. Dev. 291808.2 132196.6 484918.4 628490.7 268.7410 2372.013 53048.33

Observations 2346 2346 2346 2346 2346 2346 2346

Part B: Unit root and stationarity tests

ADF

-9.528378

***

-9.426075

***

8.971669

***

8.985077

***

6.085688

***

-5.124413

***

-9.429321

***

-5.428590

***

-5.519059

***

5.232888

***

5.341379

***

3.761984

***

-2.975304

***

-5.566577

***

Order of

integration I (0) I (0) I (0) I (0) I (0) I (0) I (0)

Part C: Variance Inflation Factor

VIF 2.268 2.029 1.125361 1.277453 1.416334 1.997071

Part D: Heteroskedasticity ARCH test

ARCH LM

0.0000*** 0.0000***

0.0000***

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elevated volatility can be explained by Temu being a newer entrant, exposed to

uncertainties such as market acceptance, operational scaling, regulatory changes, and

competitive reactions from established players like Amazon.

Part B of Table 2 presents the unit root and stationarity tests. The for the ADF test,

the t-statistics value is more negative than the associated critical values and all the

variables are significant at the 1% significance level. The null hypothesis that the

variables contain unit root is rejected, concluding that the variables are stationary

(accepting the alternative hypothesis). The findings are further supported by the PP

test statistics, as the study rejects the null hypothesis that the variables contain unit

roots at all significant levels. The t-statistics of the PP test are also more negative

than the associated critical values. The findings for the ADF and PP tests suggest

that the variables are integrated at order 0.

Having found the variables to be stationary, the next step entails determining if the

variables have ARCH effects. This condition must be met to estimate the GARCH

models. Part C in Table 2 provides the ARCH LM test. The null hypothesis that the

variables don’t contain ARCH effects (the variables are homoscedastic) is rejected

at a 1% significance level. Given these test results, the study will use GARCH

models to estimate the extent to which the presence of new e-commerce competitors

influences Amazon’s stock price volatility and the volatility of Amazon’s stock price

index in relation to the market.

4.2. Empirical Model Results

4.2.1. Univariate GARCH Model Selection

Table 3 provides Amazon’s univariate GARCH model specification and the market’s

univariate GARCH model specification. This study uses Schwarz’s information

criteria (SIC) to determine the best-fitted model for the univariate specification as

the number of observations exceed 130. Moreover, this study considers the normal,

Student’s T, and generalized error distribution (GED) estimation techniques. For the

influence of other e-commerce companies on Amazon the suggest GARCH model

is the GARCH (1,1) model for the univariate model specification. For Amazon’s

volatility in relation the market, the suggested model is also the GARCH (1,1) model.

Table 3. Univariate GARCH model specification

GARCH GJR GARCH EGARCH

Norm

Student’

s t

GED Norm

Student’

s t

GED Norm

Student’

s t

GED

Amazo

0.848

-1.3452 -

1.050

0.801

-1.0808

0.986

0.763

-1.1156 0.959

ACTA UNIVERSITATIS DANUBIUS Vol 21, No 5, 2025

Nas10

1.421

-1.7169 -

1.450

1.433

1.21444

1.389

1.224

-1.4617 -

1.315

Note: Table indicates the superior model specifications based on SIC. 2

Source: Authors’ own estimation (2025)

4.2.2. Univariate GARCH Model Results

Table 4 shows the univariate GARCH estimation based on the model specification.

The mean equation is provided in Panel A, and the variance equation is presented in

Panel B. In the mean equation the intercept (µ) provides the average return of

Amazon when the returns of Amazon’s competitors are zero. The returns Φ and ᵹ

are positive and significant, indicating that positive movements in these returns are

associated with positive changes in Amazon’s returns. The Ҏ is also significant

however, it is negative suggesting an inverse relationship between Temu’s returns

and Amazon’s returns. These findings further confirms that analysis made from the

descriptive stats that Temu returns may come at Amazon’s expense, consistent with

the competitive threat Temu poses as a disruptive e-commerce entrant.

α and β represent the AR and MA term respectively, both terms are positive and

highly significant indicating that past values and errors influence current returns of

Amazon however In Panel B, the variance equation intercept (ϕ) is greater than one

but is insignificant, this implies that the volatility on Amazons returns are not

explained by the interpret but by past shocks (ARCH term) and past variance

(GARCH term).

Table 4 also presents the variance equations the univariate GARCH model that

explains the volatility of Amazon’s returns in relation to the market, where

Amazon’s returns are regressed against the market proxy (Nasdaq 100), and vice

versa where Nasdaq 100 returns are regressed against Amazon. These models were

estimated to compare which series exhibits greater volatility and to study how

volatility patterns differ between the individual firm and the broader market. Both

Amazon and Nasdaq 100 show high ARCH coefficient, indicating strong immediate

shock impact on volatility. The GARCH term for Nasdaq is much higher and

significant, indicating that past volatility plays a more persistent role in market

volatility compared to Amazon. The high GARCH term shows that Nasdaq’s

volatility appears more persistent which means that the market volatility clustering

is strongly influenced by past volatility. While Amazon’s volatility is more

influenced by immediate shocks but less by the persistence of past volatility. Thus,

Amazon is more reactive.

ISSN: 2065-0175 ŒCONOMICA

Table 4. GARCH Results

Amazon

(Amazon

Nasdaq 100

(Market Proxy) Amazon vs Nasdaq 100

Model GARCH GARCH GARCH

Panel A: Mean Equation

µ - Intercept 0.434118*** 3.818878*** -

Φ – ebay 0.359727*** - -

ᵹ - etsy 0.071564*** - -

Ҏ – temu -0.015465*** - -

α – ar (1) 0.996678*** 0.989136*** -

β – ma (1) 0.642459*** 0.687268*** -

Panel B: Variance Equation

ϕ – intercept 6.46E-11# 1.03E-10# 0.034539***

ᴡ - ARCH(-1) 0.815204*** 0.997774*** 0.988536***

Ʋ - GARCH(-1) 0.357067*** 0.322573*** 0.032419#

Panel C: Diagnostic test

ARCH-LM

0.9832

- -

Note: 1*** indicates a statistical significance level of 1%

Source: Authors’ own estimation (2025)

4.2.3. Correlation Analysis

Table 5 presents the Covariance matrix which shows the correlation coefficients

between Amazon and its competitors eBay, Etsy and Temu. eBay and Etsy have a

positive and statistically significant correlation with Amazon which means that their

stock return tend to move in the same direction, while the correlation between

Amazon and Temu is negative, suggesting a weak or insignificant relationship

between them. The negative correlation between Amazon and the control variable

inflation posits that as inflation raises Amazon’s return tend to decrease, controlling

for macroeconomic factors. The positive correlation means that when the

competitors returns (eBay and Etsy) increase, Amazon’s tend to increase as well.

Suggesting that market-wide factors and investor sentiments in the e-commerce

sector affecting all players similarly. This essentially means that the positive

correlations between Amazon and some of its competitors is because of they both

get affected by economic conditions similarly and that the competitor’s growth does

not directly harm Amazon’s returns.

ACTA UNIVERSITATIS DANUBIUS Vol 21, No 5, 2025

While the weak correlation between Temu and Amazon may simply mean that

because Temu is a relatively new platforms that it just emerged, its influence on the

border-ecommerce market particularly Amazon may not yet be fully reflected in its

stock price and return pattern. Furthermore, Temu might be targeting different

consumer segments, or employing a business model that does not overlap with

Amazon’s operations. Therefore, because they respond to somewhat different

consumer bases, market factors their stock returns may not move closely together.

Table 5. Covariance Matrix

Correlation

Probability AMAZON EBAY ETSY INF M2 TEMU

AMAZON 1.000000

-----

EBAY 0.451543 1.000000

0.0000 -----

ETSY 0.328350 0.644708 1.000000

0.0000 0.0000 -----

INF -0.212471 -0.182694 -0.020953 1.000000

0.0000 0.0000 0.3104 -----

M2 0.252808 0.386138 0.398043 -0.282971 1.000000

0.0000 0.0000 0.0000 0.0000 -----

TEMU 0.018311 0.101550 0.115258 -0.128893 0.278138 1.000000

0.3754 0.0000 0.0000 0.0000 0.0000 ----- Source: Authors’ own estimations (2025)

4.3. Discussion of Results

This study’s results reveal distinct in how Amazon’s returns react to emerging

competition and broader market dynamics, providing key insights for both investors

and industry participants. The findings from the descriptive statistics and correlation

analysis indicate that Amazon’s mean return is lower than some of its major

competitors, notably Temu, which has demonstrated the highest mean return and the

most pronounced volatility among all e-commerce platforms. This suggests that

newer and more aggressive competitors are capturing growth opportunities and

reshaping the competitive landscape. These results align with earlier research by

Mutlu and Bish, 2018 that indicated that newer and more aggressive online players

have managed to capture significant market share by offering innovative solutions

and responding swiftly to changing consumer preferences. The positive correlation

between Amazon, eBay, and Etsy indicates that these firms are often influenced

similarly by market and industry-wide forces, reflecting a sector where investor

sentiment and macroeconomic trends drive collective performance.

ISSN: 2065-0175 ŒCONOMICA

In contrast, the weak and insignificant correlation between Amazon and Temu

implies that Temu’s impact is not yet fully integrated into Amazon’s return

dynamics. This could be due to Temu’s recent entry into the market, differences in

business models, or consumer segmentation, resulting in limited simultaneous

movement between the two. Empirical results from the GARCH model demonstrate

that Amazon’s return volatility is highly sensitive to recent shocks, while the Nasdaq

100 market proxy exhibits greater volatility persistence, indicating sustained patterns

of volatility clustering in the broader market. Yadav (2018), empirical findings also

found that Amazon squared daily returns display volatility clustering where high

volatility tends to be followed by high volatility and low volatility by low volatility.

Amazon is more reactive meaning it responds quickly and sharply to new

information or shocks, such as competitive disruption or regulatory events, whereas

the market’s volatility endures over longer periods as a result of prevailing

macroeconomic conditions. This distinction underlines Amazon’s heightened

exposure to firm-specific risk drivers, especially those stemming from competition,

compared to the broader market.

Collectively, these results illustrate that Amazon operates in an environment where

sector-wide conditions and new entrant pressures can meaningfully affect stock

returns and risk. The company’s relatively lower mean return, greater exposure to

downside shocks (high negative skewness and kurtosis), and more pronounced

volatility response to immediate events highlight the challenges of maintaining

dominance amidst intensifying competition and shifting consumer behaviour. For

investors, this underscores the importance of monitoring emerging competitors and

recognizing the potential for increased return variability and risk in Amazon’s stock,

compared to a more persistent market volatility backdrop.

The study’s findings suggest that established e-commerce leaders like Amazon are

now contending with a new era of heightened risk and competitive flux, driven by

innovative entrants such as Temu. The findings align with Olugubeni et al. (2025)

research that Temu’s aggressive promotional strategies and focus on appeal

especially for cost-conscious and young shoppers’ contrasts Amazon’s brand trust

and extensive product offering. This dynamic compels both investors and corporate

strategists to adjust their risk assessments and growth expectations.

5. Conclusion

This study sets out to analyse how emerging e-commerce competitors such as Temu,

eBay, and Etsy influenced Amazon’s stock returns and volatility between 2018 and

2024. Using covariance and GARCH-type models, the research found that Amazon’s

returns were significantly affected by market shocks and competitive pressures,

revealing a strong presence of volatility clustering and asymmetric effects. The

ACTA UNIVERSITATIS DANUBIUS Vol 21, No 5, 2025

findings indicated that while eBay and Etsy moved in conjunction with Amazon,

Temu’s returns displayed an inverse relationship, suggesting that its rapid market

expansion occurred partly at Amazon’s expense. These outcomes validated the

study’s objective by demonstrating that intensified competition from new entrants

altered investor sentiment and short-term stock performance in the global e-

commerce landscape.

The results also bridged the gap between the Efficient Market Hypothesis (EMH)

and Behavioral Finance by showing that investor reactions to competitive

information were not always rational. And that herding tendencies and sentiment-

driven responses appeared to amplify short-term volatility, implying that

psychological and informational biases shape price movements in technology-driven

markets. Amazon’s sensitivity to immediate shocks, contrasted with the broader

market’s persistence of volatility, highlighted its heightened exposure to firm-

specific risks and competitive dynamics. From a practical perspective, these findings

carry several implications. Investors should monitor behavioral trends and market

narratives surrounding emerging platforms, as these can influence valuation and risk

beyond fundamental indicators. For e-commerce firms, the results emphasized the

necessity of innovation, differentiation, and adaptive pricing strategies to maintain

market share in a rapidly evolving industry.

Despite its contributions, the study was limited to a selected sample of competitors

and a single geographic context, excluding other relevant factors such as exchange-

rate dynamics and global political risks. Future research could expand the analysis

by incorporating additional e-commerce firms, investor sentiment indices, or cross-

country comparisons to deepen understanding of how competition and investor

psychology interact in digital platform markets. Overall, the study demonstrated that

emerging e-commerce platforms have materially reshaped the risk profile and stock

performance of dominant players like Amazon, reinforcing the view that behavioural

forces and competitive innovation jointly determine firm valuation in the modern

digital economy.

Data Availability Statement

The data is available on reasonable request from the corresponding author.

References

Agyeman, F. O., Zhiqang, M. A., Li, M., Sampene, A. K., Dapaah, M. F., & Brenya, R. (2022).

Compherending the Globalization and Performance of E-commerce Business: Evidence from E-

commerce Companies. Asian Journal of Advances in Research, 14(1), 13-28.

ISSN: 2065-0175 ŒCONOMICA

Baker, M., & Nofsinger, J. (2002). Psychological biases of investors. Financial Analysts Journal, 58(4),

43-56.

Banerjee. A. V. (1992). A Simple Model of Herd Behaviour. The Quarterly Journal Economics, 107(3),

797-817.

Barua, A., Konana, P., Whinston, A. B., & Yin, F. (2001). Driving e-business Excellence. MIT Sloan

Management Review, 43(2), 36-44.

Bikhchandani, S., & Sharma, S. (2001). Herd behaviour in financial markets: A review. IMF Staff

Papers, 47(3), 279-310.

Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A Theory of Fads, Fashion, Custom and Cultural

Change as Informational Cascades. Journal of Political Economy, 100(5), 992-1026.

Blasco, M., Corredor, P., & Ferreuela, S. (2012). Market Sentiment: a key factor of Investors’ Imitative

behviour. Accounting & Finance, 53(3), 663-689.

Brown, S. J., & Cliff, M. T. (2005). Investor sentiment and asset valuation. The Journal of Business,

78(2), 405-440.

Coranalysebishley, K., Mason, R. B., & Pobbelstein, T. (2022). COVID-19 Involvement, Shopping

motives and buying behaviour: A German/ South African Comparison. Expert Journal of Marketing.

10(1), 43-61.

Ding, A. W., & Li, S. (2019). Herding In the Consumption and purchase of digital goods and moderators

of the herding bias. Journal of the Academy of Marketing Science, 47(1), 460-478.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of

Finance, 25(2), 383-417.

Feizollahia, S., Shirmohammadib, A., Kahrehc, Z. S., & Kaherhd, M. S. (2014). Investigating the Effect

of Internet Technology on Performance of service Organizations with E-commerce Orientation.

Procedia Social Behavior Sciences, 109(1), 605-609.

Jahanshahi, A. A., Razaei, M., Nawarser, K., Ranjibar, V., & Pitamber, B. K. (2012). Analyzing the

effect of Electronic Commerce on Organizational Performance: Evidence from Small and Medium

Enterprise. African Journal Business Management, 6(1), 6486-6496.

Jovanovic, J. S., Vujadinovic, R., Mitreva, E., Fragassa, C., & Vujovic, A. (2020). The Relationship

between E-commerce and Firm Performance: The Mediating Role of Internet Sales Channels.

Sustainability,12(2), 1-17.

Lashari, M. A. (2025). Exploring the impact of Instagram Influencers: and Influencer-product

congruence on perceived value and purchase Intentions for hedonic Cosmetic products. Master’s

Thesis, Vilniadus Universities.

Lee, I., & Lee, K. (2015). The Internet of Things (IOT): applications, Investment and Challenges for

enterprices. Business Horizons, 58(4), 431-440.

Mehwish, M., & Tariq, Y. B. (2015). The Efficient Market Hypothesis: A Critical Review of the

literature. The IUP Journal of Financial Risk Management, 12(4), 48-63.

Moodley, F. (2025a). The Co-Movement of JSE Size-Based Indices: Evidence from a Time–Frequency

Domain. Journal of Risk and Financial Management, 18(11), 633.

Moodley, F. (2025b). The impact of changing market conditions on esg quality and firm performance

within South Africa. EUREKA: Social and Humanities, 1(5), 3-21.

ACTA UNIVERSITATIS DANUBIUS Vol 21, No 5, 2025

Moodley, F., Lawrence, B., & Tabash, M. I. (2025). Are Macroeconomic Variables a Determinant of

ETF Flow in South Africa Under Different Economic Conditions? Economies, 13(9), 260.

Rook, D. W. (1987). The buying impulse. Journal of Consumer Research, 1(42), 189-199.

Shiller, R. J. (2010). Measuring bubble Expectations and Investor Confidence. Journal of Psychology

and Financial Markets, 1(1), 49-60.

Simakov, V. (2020). History of Formation of E-Commerce Enterprise as Subjects as Innovative

Entrepreneurship. Three Seas Economic Journal, 1(1), 84-90.

Singh, R. (2025). The Role of Social Media Sentiment in Predicting Stock Return: An Econometric and

Text analysis approach. International Journal for Multidisciplinary Research (IJMR), 7(1), 1-20.

Verhagen, T., & van Dolen, W. (2011). The influence of online store beliefs on consumer impulse

buying: A theoretical and empirical investigation. Information & Management, 48(8), 368-378.

Vingineni, M., & Rao, M. B. (2017). Contemporary Developments in Behavioural finance.

International Journal of Economics and Financial Issues, 7(1), 448-459.

Xia, Y., & Zhang, P. G. (2010). The impact of the Online Channel on Retailers Performance: An

Empirical Evaluation. Decision Science, 41(1), 517-546.

Yoon, S. M., & Oh, J. (2022). Investors herding behavior in social media sentiment. Frontiers in

Physics, 10(1), 88-102.

Zahra, S. A., Ireland, R. D., & Hitt, M. A. (2000). International Expansion by New Venture Firms:

International diversity, mode of market entry, technological learning, and performance. Academy of

Management Journal, 43(5), 925-950.

Zhu, K., & Kraemer, K. L. (2005). Post-adoption Variation in usage and Value of e-business by

Organizations: Cross-country Evidence from the Retail Industry. Information System Research, 16(1),

61-84.

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